Bridge beam plate load testing device for road and bridge construction

Abstract

The invention discloses a bridge beam plate load testing device for road and bridge construction, and belongs to the technical field of bridge beam plate detection. The device comprises the following modules: an intelligent multi-point collaborative loading module for realizing multi-point synchronous dynamic loading of a beam plate so as to simulate various traffic load conditions; the multi-modal sensing and data acquisition module is used for acquiring dynamic response data of the beam slab in an omnibearing manner; the central control module is used for coordinating and managing the work of each module, processing dynamic response data in real time and dynamically optimizing a test strategy; the self-adaptive safety protection module monitors the health state of the beam slab in real time, predicts potential risks and takes active prevention measures; the environmental simulation and compensation module simulates the influence of various environmental conditions on the beam slab and compensates the interference of environmental factors on a test result in real time; the artificial intelligence data analysis module evaluates the performance of the beam slab and predicts the service life of the beam slab; and the augmented reality interaction and visualization module provides a visual user interface and visualizes the test process and result in real time.

Description

Technical Field

[0001] The present application relates to the technical field of bridge beam and slab detection, and more specifically, to a bridge beam and slab load testing device for road and bridge construction. Background Art

[0002] With the acceleration of urbanization, bridges, as important transportation infrastructure, face increasingly higher requirements for construction and maintenance. In road and bridge construction, load testing of bridge beams and slabs is a key link in ensuring the safety and durability of bridge structures. Traditional load testing methods mainly rely on manual measurement and fixed sensors, which are not only time-consuming and labor-intensive, but may also lead to inaccurate test results due to improper equipment layout or measurement errors.

[0003] Although some special equipment for bridge load testing has appeared on the market, most of the equipment has the following problems: first, the equipment has poor mobility and is difficult to adapt to complex construction sites; second, existing testing devices usually lack real-time monitoring functions and cannot provide timely feedback on the dynamic changes of the bridge under load; finally, the data processing and analysis process often relies on manual intervention, which is inefficient and difficult to achieve intelligent management.

[0004] In addition, with the development of bridge design and construction technology, the complexity of beam-slab structures and the diversity of load types have increased, and traditional testing methods have been unable to meet the needs of modern bridge engineering. This makes the research and development of new bridge beam-slab load testing devices particularly important.

[0005] In summary, how to develop an efficient, accurate and convenient bridge beam and slab load testing device to achieve real-time monitoring and data analysis of the bridge during construction and use has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] In order to overcome a series of defects existing in the prior art, the purpose of this application is to provide a bridge beam and slab load testing device for road and bridge construction, including the following modules, in view of the above problems.

[0007] The intelligent multi-point collaborative loading module realizes multi-point synchronous dynamic loading of beams and slabs through adaptive impedance control and intelligent load distribution to simulate various traffic load conditions.

[0008] The multi-modal sensing and data acquisition module collects the dynamic response data of the beams and slabs in all directions, and performs preliminary data processing and anomaly detection.

[0009] The central control module, based on digital twin and reinforcement learning technology, coordinates and manages the work of each module, processes dynamic response data in real time and dynamically optimizes the test strategy to achieve intelligent control of the test process.

[0010] The adaptive safety protection module monitors the health status of beams and slabs in real time, predicts potential risks and takes proactive preventive measures to ensure the safety and controllability of the testing process.

[0011] The environmental simulation and compensation module simulates the impact of various environmental conditions on beams and slabs, compensates for the interference of environmental factors on test results in real time, evaluates the performance of beams and slabs in extreme environments, and improves the reliability of test data.

[0012] The artificial intelligence data analysis module uses deep learning and knowledge graph technology to deeply analyze test data, extract key features, evaluate beam and slab performance and predict their service life, providing decision support for bridge design and maintenance strategies.

[0013] The augmented reality interaction and visualization module provides an intuitive user interface through AR technology and dynamic visualization, real-time visualization of test processes and results, and supports interactive data exploration and analysis.

[0014] Furthermore, the intelligent multi-point collaborative loading module includes the following components.

[0015] The multi-point hydraulic actuator array applies variable loads at different positions of the beam and slab through multiple precision-controlled hydraulic actuators, achieving multi-point synchronous loading.

[0016] The adaptive impedance control unit adjusts the impedance characteristics of the actuator in real time to ensure that the dynamic interaction with the beam and slab during loading conforms to the actual traffic load characteristics.

[0017] The intelligent load distribution unit dynamically calculates and distributes the load of each actuator based on the beam-slab structural characteristics and test objectives to simulate different traffic conditions.

[0018] A network of high-precision force sensors is installed at each loading point to enable real-time monitoring of the actual applied load.

[0019] The real-time synchronization controller coordinates the actions of each actuator to ensure accurate synchronization of multi-point loading and maintain the preset load distribution pattern.

[0020] The load pattern generator generates a variety of typical and extreme load timing patterns for testing based on various traffic flow models and historical data.

[0021] Furthermore, the load on the actuator is calculated as follows: F j (t)=∫ 0 L P(x,t)·Ф j (x)dx, where L is the length of the beam; Ф j(x) is the shape function associated with the j-th actuator, which represents the influence range of the j-th actuator on the beam plate; dx is a small position increment used for integration on the beam plate, which represents a small area of ​​the beam plate; F j (t) represents the force that the jth actuator needs to apply at time t; P(x,t) represents the instantaneous load at time t and position x on the beam plate, and the formula is: P(x,t)=∑ i=1 n (m i ·g·δ(xx i (t))), m i represents the mass of the vehicle in the simulated traffic load; g represents the acceleration of gravity; δ(xx i (t)) is used to indicate the specific position x of the actuator on the beam at time t. i The instantaneous load applied at (t).

[0022] Furthermore, the multimodal sensing and data acquisition module includes the following components.

[0023] Multi-type sensor network deploys a variety of sensors to capture the dynamic response characteristics of beams and slabs in all directions.

[0024] High-speed data acquisition unit uses a high-sampling rate data acquisition card to collect multi-channel sensor signals in real time to ensure the time synchronization of the data.

[0025] The signal conditioning and filtering unit pre-processes the raw sensor signal to improve the signal quality and signal-to-noise ratio.

[0026] The real-time data stream processing unit performs real-time stream processing on the collected data to achieve rapid data analysis and preliminary feature extraction.

[0027] Anomaly detection unit detects abnormal patterns in data in real time and identifies potential problems in a timely manner.

[0028] The sensor self-calibration and health monitoring unit performs regular sensor self-inspection and calibration, monitors the sensor health status, and ensures the long-term reliability of data collection.

[0029] Furthermore, the central control module includes the following components.

[0030] The digital twin engine builds and updates the virtual models of beams, slabs and test devices in real time, providing an accurate simulation environment for decision-making and optimization.

[0031] The reinforcement learning optimizer continuously learns and optimizes the test strategy based on the virtual model to achieve adaptive control of the test process.

[0032] The real-time data processing unit is used to process the dynamic response data in real time.

[0033] The module coordination scheduler uniformly manages and coordinates the work of each functional module to ensure the coordinated operation and resource optimization of the entire device.

[0034] The intelligent decision-making unit comprehensively analyzes the processed data and optimization suggestions, makes real-time decisions, and dynamically adjusts test parameters and strategies.

[0035] The status monitoring and fault diagnosis device comprehensively monitors the operating status of the entire device, detects and diagnoses potential problems in a timely manner, and ensures the continuity and reliability of the test.

[0036] Furthermore, based on the virtual model, the test strategy is continuously learned and optimized to achieve adaptive control of the test process, including the following steps.

[0037] Based on the virtual model of the beam and test device, a reinforcement learning framework suitable for the bridge beam test scenario is designed, and the state space, action space and reward function are defined. The state space includes the stress state and deformation degree of the beam, the action space includes adjusting the loading intensity and changing the loading position, and the reward function includes test efficiency and data quality indicators.

[0038] Initialize and pre-train reinforcement learning models using preset test strategies and historical data.

[0039] Continuously train and optimize reinforcement learning models by collecting dynamic response data and test results in real time.

[0040] The reinforcement learning model is continuously trained and optimized by continuously collecting various sensor data and test results to adapt to the dynamic changes in beam and slab states and diverse test conditions.

[0041] Based on the optimized reinforcement learning model, the test strategy is dynamically generated and optimized, including adjusting loading parameters, selecting the best test points and optimizing the test sequence.

[0042] Apply the optimized test strategy to the actual test, monitor the execution effect in real time, and record the key performance indicators during the strategy execution process for the next round of optimization, forming a closed-loop feedback mechanism to achieve continuous optimization and adaptive control of the test process.

[0043] Furthermore, the adaptive security protection module includes the following components.

[0044] The multi-dimensional health status monitoring unit comprehensively analyzes multi-dimensional data including strain, displacement and vibration, and evaluates the overall health status of the beam and slab in real time.

[0045] Intelligent risk prediction engine predicts potential security risks and failure points based on historical data and current status.

[0046] The adaptive safety threshold manager dynamically adjusts the safety threshold of the monitoring indicators according to the real-time status of the beam and slab and environmental conditions.

[0047] The rapid response decision-making unit quickly formulates and implements corresponding safety measures and response strategies when anomalies are detected or risks are predicted.

[0048] The security event recording and analysis unit records all security-related events in detail, supporting post-event analysis and security policy optimization.

[0049] Furthermore, the overall health status of the beam and slab is expressed by the following formula: H = w ε ·ε norm +w u ·u norm +w v ·v norm , where H is the overall health status indicator; w ε 、w u 、w v is the weight coefficient of each indicator, which is used to reflect the relative importance of each indicator in health assessment and satisfies w ε +w u +w v =1; ε norm is the normalized value of strain data, ε norm =(ε-ε min ) / (ε max -ε min ), ε is the actual strain value, ε min and ε max are the minimum and maximum strain values ​​in the historical data; u norm is the normalized value of displacement data, u norm =(uu min ) / (u max -u min ), u is the actual displacement value, u min and u max are the minimum and maximum displacements in the historical data respectively; v norm is the normalized value of the vibration data, v norm =(vv min ) / (v max -v min ), v is the actual displacement value, v min and v max are the minimum and maximum values ​​of vibration in the historical data respectively.

[0050] Furthermore, the safety threshold of the monitoring indicator is expressed by the following formula: T = T base +ΔT ε +ΔT u+ΔT v +E, where T is the dynamically adjusted safety threshold; T base is the initial threshold value set based on historical data and standard design specifications; ΔT ε , ΔT u +, ΔT v They represent the adjustment amount according to the real-time status of strain, displacement and vibration data and environmental conditions, respectively, and are expressed as: ΔT ε =k ε ·(ε current -ε avg ), ΔT u =k u ·(u current -u avg ), ΔT v =k v ·(v current -v avg ), ε current is the current strain value, ε avg is the historical average strain value, k ε is the sensitivity coefficient of strain data to threshold adjustment, u current is the current displacement value, u avg is the historical average displacement value, k u is the sensitivity coefficient of displacement data to threshold adjustment, v current is the current vibration value, v avg is the historical average vibration value, k v is the sensitivity coefficient of vibration data to threshold adjustment; E is the environmental adjustment factor, E=k env ·f(T env ,H env ,W env ), T env is the current ambient temperature, H env is the current ambient humidity, W env is the current wind speed, k env is the sensitivity coefficient of environmental factors to threshold adjustment, f(T env ,H env ,W env ) is a comprehensive function of environmental factors, which is used to comprehensively consider the impact of temperature, humidity and wind speed on the safety threshold of beam and slab monitoring indicators.

[0051] Furthermore, the environment simulation and compensation module includes the following components.

[0052] The multi-parameter environmental simulation unit creates various actual and extreme environmental conditions by simulating multiple environmental factors.

[0053] The environmental monitoring unit monitors various parameters in the test environment in real time, providing an accurate data basis for environmental simulation and compensation.

[0054] Environmental impact analysis engine, used to quantitatively evaluate the impact of various environmental factors on beam and slab performance.

[0055] The real-time data compensator makes real-time corrections to the test data according to the environmental impact analysis results to eliminate the interference of environmental factors.

[0056] Extreme Conditions Test Controller designs and executes test plans in extreme environments to evaluate the performance of beams and slabs under harsh conditions.

[0057] The environment-structure interaction simulator simulates the complex interaction process between environmental changes and beam-slab structures to improve the authenticity of the simulation.

[0058] The test reliability evaluation unit comprehensively analyzes the original data and compensated data to evaluate the reliability and uncertainty of the test results.

[0059] Furthermore, the following steps are used to quantitatively evaluate the impact of various environmental factors on the performance of beams and slabs.

[0060] A beam-slab finite element model including geometric features, material properties and boundary condition information is established to simulate the mechanical behavior of the beam-slab under various environmental conditions.

[0061] The key environmental factors that affect the performance of beams and slabs are identified and parameterized, and a mathematical description is established for each factor, including its temporal and spatial distribution characteristics and variation patterns.

[0062] By systematically changing various environmental parameters, their impact on the key performance indicators of beams and slabs is observed and quantified to identify the most critical environmental factors.

[0063] Based on the constructed beam-slab finite element model, large-scale numerical simulations are carried out to simulate the response of the beam-slab under different combinations of environmental conditions. At the same time, targeted experiments are designed and carried out to test the actual performance of the beam-slab in a controllable environment. The numerical simulation results are compared with the experimental data to verify and optimize the beam-slab finite element model.

[0064] Collect and organize long-term monitoring data, including beam-slab performance indicators and corresponding environmental parameters, and establish a mapping relationship between environmental factors and beam-slab performance to capture the complex interactions ignored by the beam-slab finite element model and provide a more comprehensive impact assessment.

[0065] Furthermore, the artificial intelligence data analysis module includes the following components.

[0066] The deep learning feature extractor uses the convolutional neural network algorithm to automatically extract key features from a large amount of raw test data.

[0067] The bridge knowledge graph unit constructs a knowledge graph including bridge design, materials, environment and loads to provide background knowledge for analysis.

[0068] The multimodal data fusion engine integrates data from different sensors and test phases to form the basis for comprehensive beam and slab performance evaluation.

[0069] Performance evaluation unit, used to comprehensively analyze the current performance of beams and slabs and predict their future service life.

[0070] The abnormal pattern recognition unit uses clustering and anomaly detection algorithms to identify abnormal patterns and potential problems in beam and slab performance data.

[0071] An intelligent decision support engine combines performance evaluation results and expert knowledge to provide recommendations for bridge design optimization and maintenance strategy formulation.

[0072] Explainable AI Analyzer provides visual explanations of data analysis and decision-making processes, enhancing the credibility and understandability of analysis results.

[0073] Furthermore, the future service life of the beam and slab is expressed as: S = (H / D)·((1-R 1 )·(1-R 2 )·...·(1-R N ))·(1+μ), where S is the expected service life of the beam-slab; H is the overall health of the beam-slab; D is the design life of the beam-slab; N is the number of environmental and loading conditions; R 1 , R 2 , ..., R N It indicates the reduction ratio of beam-slab performance due to different environmental or loading conditions; μ is an additional influencing factor, including maintenance measures, repairs, upgrades or accidents, expressed as a percentage.

[0074] Furthermore, the augmented reality interaction and visualization module includes the following components.

[0075] The AR scene construction engine generates and updates augmented reality scenes in real time, superimposing virtual information onto actual bridge beams and slabs.

[0076] Multi-dimensional data visualization renderer converts complex test data into intuitive visualization form.

[0077] The interactive query and filtering unit allows users to interact with the AR environment through gestures or voice to query specific data or filter displayed content.

[0078] Intelligent annotation and labeling unit, automatically identifies and labels key areas and anomalies, and allows adding custom labels.

[0079] Customizable dashboard builder to quickly create personalized data dashboards based on user needs to display key performance indicators and analysis results.

[0080] Compared with the prior art, the present application has the following beneficial effects.

[0081] This application ensures the safety and reliability of the testing process by real-time monitoring and analysis of beam and slab dynamic response data, implementing multi-point synchronous loading and intelligent control. It can also effectively simulate various traffic loads and environmental conditions, thereby providing more accurate decision-making support for bridge design and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 This is a schematic structural diagram of a bridge beam and slab load testing device for road and bridge construction disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present invention, not all of the embodiments.

[0084] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0085] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0086] like Figure 1 As shown, a bridge beam and slab load testing device for road and bridge construction includes the following modules.

[0087] The intelligent multi-point collaborative loading module realizes multi-point synchronous dynamic loading of beams and slabs through adaptive impedance control and intelligent load distribution to simulate various traffic load conditions.

[0088] The multi-modal sensing and data acquisition module collects the dynamic response data of the beams and slabs in all directions, and performs preliminary data processing and anomaly detection.

[0089] The central control module, based on digital twin and reinforcement learning technology, coordinates and manages the work of each module, processes dynamic response data in real time and dynamically optimizes the test strategy to achieve intelligent control of the test process.

[0090] The adaptive safety protection module monitors the health status of beams and slabs in real time, predicts potential risks and takes proactive preventive measures to ensure the safety and controllability of the testing process.

[0091] The environmental simulation and compensation module simulates the impact of various environmental conditions on beams and slabs, compensates for the interference of environmental factors on test results in real time, evaluates the performance of beams and slabs in extreme environments, and improves the reliability of test data.

[0092] The artificial intelligence data analysis module uses deep learning and knowledge graph technology to deeply analyze test data, extract key features, evaluate beam and slab performance and predict their service life, providing decision support for bridge design and maintenance strategies.

[0093] The augmented reality interaction and visualization module provides an intuitive user interface through AR technology and dynamic visualization, real-time visualization of test processes and results, and supports interactive data exploration and analysis.

[0094] In this embodiment, the intelligent multi-point collaborative loading module is one of the core components of the bridge beam and slab load test device, which aims to achieve multi-point synchronous dynamic loading of the beam and slab through adaptive impedance control and intelligent load distribution. This module can flexibly adjust the loading mode according to different traffic load conditions and actual test requirements to simulate the real traffic environment. The adaptive impedance control technology can detect the dynamic response of the beam and slab in real time, and adjust the loading force and position according to real-time feedback to ensure that the load distribution during the test is as close to the actual use as possible. This precise loading method can not only reduce the test error, but also effectively improve the reliability of the data, providing a solid foundation for subsequent analysis and evaluation. In addition, by applying multiple point loads at the same time, complex traffic flow conditions can be simulated, including a combination of various load types such as pedestrians and vehicles, so as to more comprehensively evaluate the performance of the beam and slab under different use conditions. The innovation of this module lies in its efficiency, flexibility and accuracy, which enables the safety and service life of the bridge to be effectively evaluated.

[0095] In this embodiment, the multimodal sensing and data acquisition module ensures the comprehensiveness and accuracy of data acquisition through the combined application of multiple sensors. Through high-precision sensors, the response data of the beam and slab can be recorded in real time during the loading process, capturing its slight changes under various load conditions. At the same time, the preliminary data processing function can quickly screen out valid data and suppress noise, thereby improving the signal-to-noise ratio of the data. In addition, the anomaly detection algorithm can monitor data changes in real time and identify possible faults or abnormal conditions in a timely manner. This is crucial for subsequent performance analysis and safety assessment to ensure the quality and effectiveness of test data. The innovation of this module lies in its diverse sensing methods and efficient data processing capabilities, which provide a reliable foundation for subsequent analysis.

[0096] In this embodiment, the central control module is the "brain" of the entire test device, which coordinates and manages the work of each module based on digital twin and reinforcement learning technology. By processing dynamic response data in real time, the module can dynamically optimize the test strategy to cope with different loading conditions and environmental changes. The application of digital twin technology in this module can build a virtual model of the beam and slab under different conditions, thereby helping to predict its behavior in actual testing. Reinforcement learning enables the test device to self-learn during the test process, and continuously optimize the test plan and parameter adjustment. This intelligent control method not only improves the efficiency of the test, but also ensures the accuracy and safety of the test process. In addition, the real-time feedback capability of the central control module can respond quickly according to the data collected by the sensor, adjust the loading strategy and test conditions, and thus achieve efficient dynamic testing. The innovation of this module lies in its intelligence and adaptability, which makes the entire test process more efficient and reliable.

[0097] In this embodiment, the adaptive safety protection module is an important component to ensure the safety of the test process. It monitors the health status of the beams and slabs in real time, predicts potential risks and takes proactive preventive measures. The module uses advanced monitoring technology to obtain real-time health data of the beams and slabs, including key indicators such as strain, displacement and acceleration. Once an abnormality or potential risk is detected, the module will respond quickly, automatically adjust the loading strategy or issue an alarm to ensure the safety of testers and equipment. This proactive preventive measure can significantly reduce the incidence of safety accidents and provide higher protection for the test process. In addition, the test device can also identify common failure modes through historical data analysis, further improving its predictive ability and response speed. The innovation of the adaptive safety protection module lies in its efficient monitoring and response mechanism, which ensures the safety and controllability of the test.

[0098] In this embodiment, the environmental simulation and compensation module is responsible for simulating the impact of various environmental conditions on the beams and slabs, and compensating in real time for the interference of environmental factors on the test results. Environmental factors such as temperature, humidity, wind speed, etc. may have an important impact on the performance of the beams and slabs. By establishing an environmental model, the module can simulate the performance of the beams and slabs under different environmental conditions, thereby evaluating their performance under extreme weather conditions. In addition, the real-time compensation function can adjust the data in real time during the test process to eliminate test errors caused by environmental changes. The application of this module significantly improves the reliability of the test data, making the test results under various environmental conditions more authentic and reliable. The innovation of the environmental simulation and compensation module lies in its sensitivity to environmental factors and its compensation ability, which provides a more accurate basis for bridge performance evaluation.

[0099] In this embodiment, the artificial intelligence data analysis module uses deep learning and knowledge graph technology to deeply analyze the test data, extract key features, evaluate the performance of the beam and slab, and predict its service life. This module is able to process a large amount of dynamic response data, discover the potential laws and trends in the data through machine learning algorithms, and quickly identify the key factors affecting the performance of the beam and slab. Through knowledge graph technology, information from different sources can be integrated to achieve more comprehensive analysis and decision support. In addition, the service life prediction function of the beam and slab can provide a scientific basis for subsequent maintenance and overhaul, and optimize the design and maintenance strategy of the bridge. The innovation of this module lies in its powerful data analysis capabilities and intelligent decision support, which help engineers make more accurate judgments during the design and maintenance of bridges.

[0100] In this embodiment, the augmented reality interaction and visualization module can present complex data in a visual way, so that engineers and decision makers can understand the test results more intuitively, find problems and conduct further analysis. Through augmented reality technology, the health status and dynamic response of beams and slabs can be viewed in real time on site, which improves the interactivity and convenience of data exploration and analysis. In addition, dynamic visualization also supports user-defined views and analysis dimensions, which enhances the comprehensibility and operability of test results. The innovation of this module lies in its powerful interactivity and visualization capabilities, which facilitates engineers' data analysis and decision-making during the test process.

[0101] In summary, the bridge beam and slab load testing device for road and bridge construction disclosed in this embodiment can achieve efficient, accurate and safe bridge performance testing through the synergy of various modules. Each module plays a unique technical effect, and together they build a comprehensive and intelligent testing system, providing strong support for bridge design, construction and maintenance. Through advanced technical means, this device not only improves the efficiency and safety of bridge testing, but also provides engineers with a more scientific decision-making basis, promoting the intelligent development of bridge engineering.

[0102] Furthermore, the intelligent multi-point collaborative loading module includes the following components.

[0103] The multi-point hydraulic actuator array applies variable loads at different positions of the beam and slab through multiple precision-controlled hydraulic actuators, achieving multi-point synchronous loading.

[0104] The adaptive impedance control unit adjusts the impedance characteristics of the actuator in real time to ensure that the dynamic interaction with the beam and slab during loading conforms to the actual traffic load characteristics.

[0105] The intelligent load distribution unit dynamically calculates and distributes the load of each actuator based on the beam-slab structural characteristics and test objectives to simulate different traffic conditions.

[0106] A network of high-precision force sensors is installed at each loading point to enable real-time monitoring of the actual applied load.

[0107] The real-time synchronization controller coordinates the actions of each actuator to ensure accurate synchronization of multi-point loading and maintain the preset load distribution pattern.

[0108] The load pattern generator generates a variety of typical and extreme load timing patterns for testing based on various traffic flow models and historical data.

[0109] In this embodiment, the multi-point hydraulic actuator array is the basic component of the intelligent multi-point collaborative loading module. It is composed of multiple precision-controlled hydraulic actuators that can apply variable loads at different positions on the beam and slab. The design of this component allows testers to flexibly adjust the force and direction applied by each actuator according to different loading requirements to achieve multi-point synchronous loading. This multi-point loading method can simulate the complex load conditions in actual traffic and help evaluate the dynamic performance of the beam and slab under different working conditions. In addition, the hydraulic actuator has the advantages of fast response and high-precision control, and can adjust the load in a short time to ensure the real-time and accuracy of the test. The innovation of this component lies in its excellent loading capacity and flexibility, which makes the testing of beams and slabs closer to actual application scenarios.

[0110] In this embodiment, the adaptive impedance control unit plays a vital role in the multi-point loading process. It ensures that the dynamic interaction with the beam and slab during the loading process conforms to the actual traffic load characteristics by adjusting the impedance characteristics of the actuator in real time. This unit uses a high-frequency feedback mechanism to monitor the dynamic response of the beam and slab in real time and dynamically adjust the output characteristics of the actuator to achieve the best match between the beam and slab. This adaptive control strategy can not only improve the accuracy of loading, but also effectively reduce the errors caused by uneven load during the test. By simulating the changes in traffic loads, the adaptive impedance control unit can flexibly respond to various working conditions at different test stages, thereby improving the reliability and effectiveness of the test. Its innovation lies in the real-time adjustment and optimization of the loading strategy to ensure the high accuracy of the test data.

[0111] In this embodiment, the intelligent load distribution unit combines engineering structure analysis with machine learning algorithms, which can quickly evaluate the response of the beam and slab under specific load conditions and optimize the loading plan based on the analysis results. Through in-depth research on historical data and traffic flow models, the intelligent load distribution unit can achieve optimal load distribution to ensure that the loads on the beam and slab during the test are as consistent as possible with the actual traffic conditions. This intelligent load distribution capability makes the test process more efficient and accurate, reducing errors caused by human factors. The innovation of this component lies in its intelligence and automation, which improves the flexibility and reliability of the overall test process.

[0112] In this embodiment, the high-precision force sensor network can accurately capture the load data applied by each actuator during the loading process through the collaborative work of multiple high-precision sensors, and feed it back to the central control module. This real-time monitoring capability enables engineers to quickly identify any possible loading anomalies and adjust the loading strategy in a timely manner. Through the application of high-precision sensors, the accuracy and reliability of the data can be greatly improved, helping engineers to gain a clearer perspective during the data analysis process. In addition, the layout design of the force sensor network can also ensure comprehensive monitoring of each key point of the beam and slab, and comprehensively improve the scientific nature of the test results. The innovation of this component lies in its precise monitoring capability, which provides strong support for the validity of the test data.

[0113] In this embodiment, the real-time synchronous controller monitors the working status of each actuator in real time through advanced algorithms and feedback mechanisms to ensure that they apply loads simultaneously according to the set timing and strength. The implementation of real-time synchronous control can effectively avoid data deviations caused by asynchronous loading, thereby improving the effectiveness and credibility of the overall test. In addition, the design of the controller also allows the working status of the actuator to be dynamically adjusted according to real-time feedback, making the testing process more flexible. By ensuring that each loading point can apply the correct load at the optimal time point, the innovation of the real-time synchronous controller lies in its efficiency and accuracy, which provides a guarantee for complex tests of multi-point loading.

[0114] In this embodiment, the load pattern generator is an important component in the intelligent multi-point collaborative loading module. It generates a variety of typical and extreme load timing patterns for testing based on various traffic flow models and historical data. The generator can combine actual traffic conditions to develop a loading pattern that conforms to the actual situation so as to effectively simulate it during the test. Through the analysis of historical data and the construction of models, the load pattern generator can not only generate standard test patterns, but also cope with load changes under extreme working conditions. This flexibility enables the test process to cover a wide range of practical application scenarios and helps to comprehensively evaluate the performance of beams and slabs under various conditions. In addition, the automation capability of the load pattern generator also reduces the workload of testers and improves work efficiency. The innovation of this component lies in its intelligent generation of load patterns, which improves the scientificity and practicality of the entire test process.

[0115] In summary, the intelligent multi-point collaborative loading module forms an efficient, flexible and intelligent loading mechanism through the synergy of key components such as multi-point hydraulic actuator array, adaptive impedance control unit, intelligent load distribution unit, high-precision force sensor network, real-time synchronization controller and load pattern generator. These components each play a unique key role, which not only improves the accuracy and safety of bridge testing, but also provides important decision-making support for engineers in bridge design, construction and maintenance. Through this comprehensive technical solution, the load test of bridge beams and slabs can be carried out in complex environments and variable working conditions, laying a solid foundation for the intelligent development of modern bridge engineering.

[0116] Furthermore, the load on the actuator is calculated as follows: F j (t)=∫ 0 L P(x,t)·Ф j (x)dx, where L is the length of the beam; Ф j (x) is the shape function associated with the j-th actuator, which represents the influence range of the j-th actuator on the beam plate; dx is a small position increment used for integration on the beam plate, which represents a small area of ​​the beam plate; F j (t) represents the force that the jth actuator needs to apply at time t; P(x,t) represents the instantaneous load at time t and position x on the beam plate, and the formula is: P(x,t)=∑ i=1 n (m i ·g·δ(xx i (t))), m i represents the mass of the vehicle in the simulated traffic load; g represents the acceleration of gravity; δ(xx i (t)) is used to indicate the specific position x of the actuator on the beam at time t. i The instantaneous load applied at (t).

[0117] In this embodiment, the innovation of this load calculation method is that it can accurately simulate the impact of real traffic loads on the beam-slab. First, the load calculation process involves dynamically capturing the instantaneous loads at different positions on the beam-slab. This high-resolution real-time monitoring capability can ensure a comprehensive analysis of the stress state of the beam-slab. By summarizing the load on the entire beam-slab in an integral manner, the impact of the loads applied by multiple actuators on the overall performance of the beam-slab can be effectively evaluated.

[0118] In this embodiment, the shape function Φ jThe introduction of (x) makes the calculation more flexible. It provides spatial influence weights for the loads applied by different actuators, allowing different actuators to produce different load distributions at different locations on the beam. This design is crucial for simulating complex traffic conditions. For example, some traffic flow models require a larger load to be applied at a specific location, and the shape function design can reflect these specific requirements. j The reasonable design of (x) can ensure that the action area of ​​each actuator is fully considered in the beam-slab force analysis, thereby improving the accuracy of the test.

[0119] In this embodiment, the calculation of the instantaneous load P(x, t) takes into account the instantaneous change of the traffic load and uses the Dirac function δ(x i (t)) can accurately represent the position of each vehicle on the beam and the load applied within a specific time t. This method not only conforms to the dynamic characteristics of actual traffic conditions, but also makes it possible to analyze the load state of the beam at different time points. Through the real-time calculation of P(x,t), the force that each actuator needs to apply can be updated in real time, further optimizing the loading strategy and ensuring the timeliness and accuracy of the test results.

[0120] In this embodiment, in this load calculation formula, the simulation of traffic load not only considers the mass of the vehicle, but also involves the influence of gravity acceleration. By introducing g, the model can reflect the continuous influence of gravity on the beam and slab, making the overall test more scientific. In addition, the mass of multiple vehicles m i Effective integration through summation can simulate complex traffic flow conditions and improve the data authenticity and practicality during the test. This dynamic, real-time load model makes the test plan more adaptable and can play an important role in various practical applications.

[0121] In summary, by combining precise load calculation with intelligent control strategy, the intelligent multi-point collaborative loading module achieves accurate simulation of the dynamic response of beams and slabs under complex traffic loads. The design of this module not only emphasizes real-time and accuracy, but also effectively improves the flexibility and adaptability of the test, so that beam and slab tests can better meet the needs of modern bridge engineering. Through the accurate calculation of actuator loads, more reliable data support can be provided for subsequent bridge design and maintenance, promoting the development of intelligent bridge technology.

[0122] Furthermore, the multimodal sensing and data acquisition module includes the following components.

[0123] Multi-type sensor network deploys a variety of sensors to capture the dynamic response characteristics of beams and slabs in all directions.

[0124] High-speed data acquisition unit uses a high-sampling rate data acquisition card to collect multi-channel sensor signals in real time to ensure the time synchronization of the data.

[0125] The signal conditioning and filtering unit pre-processes the raw sensor signal to improve the signal quality and signal-to-noise ratio.

[0126] The real-time data stream processing unit performs real-time stream processing on the collected data to achieve rapid data analysis and preliminary feature extraction.

[0127] Anomaly detection unit detects abnormal patterns in data in real time and identifies potential problems in a timely manner.

[0128] The sensor self-calibration and health monitoring unit performs regular sensor self-inspection and calibration, monitors the sensor health status, and ensures the long-term reliability of data collection.

[0129] In this embodiment, a multi-type sensor network is the basis for achieving comprehensive dynamic response capture. By deploying a variety of sensors (such as strain sensors, acceleration sensors, displacement sensors, etc.), the real-time status of the beam and slab can be obtained from different angles and dimensions. This multimodal sensing scheme can effectively capture the stress, displacement and deformation of the beam and slab under different working conditions, thereby providing multi-dimensional information support for subsequent data analysis. For example, strain sensors can monitor the strain changes of materials, acceleration sensors can capture the vibration characteristics of beams and slabs under different loads, and displacement sensors are used to accurately measure the displacement of beams and slabs. This diverse data source provides engineers with rich information, which helps to comprehensively evaluate the health status and performance of beams and slabs.

[0130] In this embodiment, the core of the high-speed data acquisition unit is that it can collect data at a very high frequency, thereby capturing the rapidly changing dynamic response characteristics. Time synchronization is a key factor in data acquisition, especially in the scenario of multi-channel signal acquisition, any delay may cause data distortion. The high-speed data acquisition unit ensures that all sensor data are highly consistent in time through precise time marking, so that subsequent data processing and analysis can accurately reflect the dynamic behavior of the beam and slab. This is crucial for conducting real dynamic response tests under actual traffic load conditions.

[0131] In this embodiment, the signal conditioning and filtering unit is responsible for preprocessing the original sensor signal to improve the signal quality and signal-to-noise ratio. The data collected by the sensor is usually subject to various interferences, such as environmental noise and electrical interference, which may cause the signal quality to deteriorate. Through signal conditioning (including processing such as amplification, attenuation and conversion) and filtering (such as low-pass filtering and high-pass filtering), the unit can effectively remove noise and interference, retain important signal features, and thus improve the reliability and accuracy of the data. This signal processing method ensures the effectiveness of subsequent data analysis and can accurately reflect the true response of the beam under different loads.

[0132] In this embodiment, the real-time data stream processing unit can perform instant analysis while collecting data through a high-speed data processing algorithm, and provide timely feedback on the dynamic state of the beam and slab. This real-time processing capability allows engineers to quickly identify and evaluate the response of the beam and slab under various load conditions and provide necessary decision support. For example, when it is detected that the response of the beam and slab exceeds the preset threshold, an alarm can be issued in time to remind relevant personnel to conduct further inspection and maintenance. By realizing real-time data stream processing, the response speed and effectiveness of the entire test process can be significantly improved.

[0133] In this embodiment, the abnormality detection unit uses an advanced pattern recognition algorithm to analyze the collected signals, and can effectively identify signal features that deviate from the normal working state, such as sudden vibration fluctuations or strain anomalies. When an abnormal pattern is detected, an alarm will be immediately issued to remind the operator to check the health of the beam and slab. This abnormality detection mechanism can not only improve the safety of the test process, but also provide important data basis for subsequent maintenance and management.

[0134] In this embodiment, the sensor self-calibration and health monitoring unit monitors the working status of the sensor, promptly detects and corrects the drift and failure of the sensor, and ensures that the collected data is always accurate and reliable. Regular calibration and health checks are the key to ensuring the effectiveness of long-term monitoring, and are essential for reducing maintenance costs and extending the service life of the equipment. In addition, the introduction of this unit enables the test device to have stronger automation capabilities, reduces manual intervention, and improves the efficiency and reliability of the overall test.

[0135] In summary, through the comprehensive application of multimodal sensing and data acquisition modules, the bridge beam load test device can achieve efficient and comprehensive dynamic response monitoring. This module ensures the accuracy and reliability of test data through a number of advanced technologies such as multi-type sensor networks, high-speed data acquisition, signal processing, real-time data stream analysis, anomaly detection, and sensor self-calibration and health monitoring, providing a solid data foundation for the design, monitoring and maintenance of bridge projects. This systematic design approach not only improves the level of intelligence in testing, but also lays a good foundation for future bridge health monitoring technology.

[0136] Furthermore, the central control module includes the following components.

[0137] The digital twin engine builds and updates the virtual models of beams, slabs and test devices in real time, providing an accurate simulation environment for decision-making and optimization.

[0138] The reinforcement learning optimizer continuously learns and optimizes the test strategy based on the virtual model to achieve adaptive control of the test process.

[0139] The real-time data processing unit is used to process the dynamic response data in real time.

[0140] The module coordination scheduler uniformly manages and coordinates the work of each functional module to ensure the coordinated operation and resource optimization of the entire device.

[0141] The intelligent decision-making unit comprehensively analyzes the processed data and optimization suggestions, makes real-time decisions, and dynamically adjusts test parameters and strategies.

[0142] The status monitoring and fault diagnosis device comprehensively monitors the operating status of the entire device, detects and diagnoses potential problems in a timely manner, and ensures the continuity and reliability of the test.

[0143] In this embodiment, the digital twin engine is the core component of the central control module. It provides an accurate simulation environment for decision-making and optimization by building and updating the virtual model of the beam and test device in real time. This virtual model can reflect the dynamic state of the beam and slab and its response under different load conditions in real time, and simulate various scenarios that may occur in the actual test of the beam and slab. Digital twin technology allows engineers to test and evaluate multiple solutions in a virtual environment, thereby optimizing test strategies and reducing risks in actual testing. With this engine, engineers can foresee potential problems and make adjustments before actual construction, improving the effectiveness and safety of the entire testing process.

[0144] In this embodiment, the reinforcement learning optimizer continuously learns and optimizes the test strategy based on the digital twin model to achieve adaptive control of the test process. By continuously analyzing historical test data and real-time dynamic response, the optimizer can automatically adjust the test parameters and strategies to achieve the best test results. For example, in actual testing, if it is found that a certain loading mode fails to effectively simulate the actual traffic load, the loading mode and intensity can be automatically adjusted to better match the actual situation. This intelligent adaptive control capability not only improves the accuracy of the test, but also significantly reduces the need for manual intervention and improves the efficiency of the test.

[0145] In this embodiment, the main function of the real-time data processing unit is to process the dynamic response data in real time to ensure rapid feedback and analysis of information during the test. The unit uses an efficient algorithm that can process data while it is being collected, enabling rapid analysis and real-time visualization of the data. This capability allows engineers to instantly view the status of the beams and slabs during the test, and to promptly identify and address potential problems. For example, when it is detected that the deformation or strain of the beams and slabs exceeds a preset range, an alarm can be quickly issued to help operators take timely measures. This real-time processing capability greatly improves the response speed and safety of the test, and provides a reliable basis for subsequent data analysis and decision-making.

[0146] In this embodiment, the module coordination scheduler can ensure the collaborative work between modules and maximize the utilization of resources. For example, when performing multi-point collaborative loading, the working order and intensity of each actuator can be dynamically adjusted according to real-time data to ensure the smooth progress of the test process. In addition, the module coordination scheduler can also adjust the test plan in a timely manner according to the test progress and feedback information to deal with emergencies and ensure the continuity and efficiency of the test.

[0147] In this embodiment, the intelligent decision-making unit analyzes historical data and real-time feedback through deep learning algorithms, and can quickly identify important features in the data to provide a basis for decision-making. For example, during the beam and slab test, if an abnormal state is detected at a certain moment, the intelligent decision-making unit can immediately adjust the test parameters, change the load mode, or suspend the test to ensure the safety and reliability of the test. This intelligent decision-making capability greatly improves the flexibility and response speed of the entire test process.

[0148] In this embodiment, the status monitoring and fault diagnosis device can quickly identify faults or abnormal conditions in the device, such as sensor failure, actuator failure, etc., by monitoring the working status of each module in real time. Through timely fault diagnosis, early warning can be given before the problem occurs, reducing the impact on the test process and ensuring the stability of the test. In addition, the unit can also provide maintenance suggestions and optimization plans for the device, helping operation and maintenance personnel to perform maintenance and upgrades in a timely manner and improve the overall reliability of the device.

[0149] In summary, the central control module has built an efficient and intelligent bridge beam and slab load test device by integrating the digital twin engine, reinforcement learning optimizer, real-time data processing unit, module coordination scheduler, intelligent decision-making unit and status monitoring and fault diagnosis device. The design of this module not only improves the accuracy and safety of the test process, but also provides strong technical support for subsequent decision support and maintenance. Through real-time monitoring, intelligent analysis and optimized control, the central control module has created good conditions for the health monitoring and management of bridge projects and promoted the further development of testing technology.

[0150] Furthermore, based on the virtual model, the test strategy is continuously learned and optimized to achieve adaptive control of the test process, including the following steps.

[0151] Based on the virtual model of the beam and test device, a reinforcement learning framework suitable for the bridge beam test scenario is designed, and the state space, action space and reward function are defined. The state space includes the stress state and deformation degree of the beam, the action space includes adjusting the loading intensity and changing the loading position, and the reward function includes test efficiency and data quality indicators.

[0152] Initialize and pre-train reinforcement learning models using preset test strategies and historical data.

[0153] Continuously train and optimize reinforcement learning models by collecting dynamic response data and test results in real time.

[0154] The reinforcement learning model is continuously trained and optimized by continuously collecting various sensor data and test results to adapt to the dynamic changes in beam and slab states and diverse test conditions.

[0155] Based on the optimized reinforcement learning model, the test strategy is dynamically generated and optimized, including adjusting loading parameters, selecting the best test points and optimizing the test sequence.

[0156] Apply the optimized test strategy to the actual test, monitor the execution effect in real time, and record the key performance indicators during the strategy execution process for the next round of optimization, forming a closed-loop feedback mechanism to achieve continuous optimization and adaptive control of the test process.

[0157] In this embodiment, when designing a reinforcement learning framework suitable for bridge beam and slab test scenarios, it is first necessary to establish an environment based on a virtual model. This environment should accurately reflect the beam and slab and its stress state. Specifically, the state space includes the stress state and deformation degree of the beam and slab, such as parameters such as stress, strain and displacement. These state information is the basis for decision-making. The action space includes adjustable loading parameters, such as adjusting the loading intensity and changing the loading position. By defining the action space, it is possible to flexibly respond to the needs under different test conditions. The reward function is an important basis for evaluating the decision-making effect, including test efficiency and data quality indicators. A well-designed reward function can guide the reinforcement learning algorithm to learn in the direction of optimization and improve the test effect.

[0158] In this embodiment, in the initial stage, the reinforcement learning model is initialized and pre-trained using the preset test strategy and historical data. This step is crucial because it lays the foundation for subsequent adaptive learning. By analyzing historical data, effective loading modes and operation strategies in beam and slab tests can be identified. Using this information, the model can converge quickly, thereby reducing uncertainty in the exploration process in actual testing and improving the test efficiency in the initial stage. The pre-training process usually requires simulating multiple tests in a virtual model to obtain sufficient sample data.

[0159] In this embodiment, as the test progresses, the reinforcement learning model is continuously trained and optimized by collecting dynamic response data and test results in real time. This process enables the model to continuously adapt to changes in the state of the beam and slab. For example, if it is found during the test that the response of the beam and slab under a certain loading condition is not as expected, the loading strategy can be adjusted immediately and new data can be recorded to update the model. This real-time feedback mechanism helps to improve the adaptive ability of the test device and ensure that the test strategy is consistent with the actual situation.

[0160] In this embodiment, the optimization of the reinforcement learning model also needs to rely on the continuous collection of various sensor data and test results. These data provide a deep understanding of the state and performance of the beam and slab, enabling the model to maintain good adaptability under a variety of test conditions. By analyzing the test data under different conditions, the model can not only adjust the existing loading strategy, but also learn new operating modes to cope with possible test scenarios in the future. For example, for different traffic load types and extreme environmental conditions, the model can flexibly adjust the loading scheme to ensure the comprehensiveness and scientificity of the test.

[0161] In this embodiment, based on the optimized reinforcement learning model, the test strategy can be dynamically generated and optimized. This includes the adjustment of loading parameters, the selection of optimal test points, and the optimization of test sequences. For example, when the model recognizes that a certain loading position performs well when simulating a specific traffic load, it will automatically adjust and prioritize that position for subsequent testing. This dynamically generated strategy can significantly improve the effectiveness of the test and reduce the number of unnecessary tests, thereby saving time and resources.

[0162] In this embodiment, the optimized test strategy will be applied in the actual test, the execution effect will be monitored in real time, and the key performance indicators during the strategy execution process will be recorded. These indicators include the accuracy of the test results, the stability of the loading mode, etc. By analyzing these key performance indicators, the effectiveness of the current test strategy can be evaluated and a basis can be provided for the next round of optimization. The implementation of the closed-loop feedback mechanism ensures that each test result can be fed back to the reinforcement learning model, thereby continuously improving and enhancing the adaptive control capability.

[0163] In summary, the adaptive control based on the virtual model fully utilizes the real-time feedback and optimization capabilities in the test process through the design and application of the reinforcement learning framework. By defining a reasonable state space, action space and reward function, combined with dynamic data collection and analysis, the test strategy can be continuously adjusted and optimized, realizing the intelligent management of bridge beam and slab testing. This process not only improves the accuracy and efficiency of the test, but also provides important data support for future bridge design and maintenance, and promotes technological progress and innovation in the field of bridge engineering.

[0164] Furthermore, the adaptive security protection module includes the following components.

[0165] The multi-dimensional health status monitoring unit comprehensively analyzes multi-dimensional data including strain, displacement and vibration, and evaluates the overall health status of the beam and slab in real time.

[0166] Intelligent risk prediction engine predicts potential security risks and failure points based on historical data and current status.

[0167] The adaptive safety threshold manager dynamically adjusts the safety threshold of the monitoring indicators according to the real-time status of the beam and slab and environmental conditions.

[0168] The rapid response decision-making unit quickly formulates and implements corresponding safety measures and response strategies when anomalies are detected or risks are predicted.

[0169] The security event recording and analysis unit records all security-related events in detail, supporting post-event analysis and security policy optimization.

[0170] In this embodiment, the multi-dimensional health status monitoring unit is the core part of the adaptive safety protection module, which is responsible for comprehensive analysis of multi-dimensional data including strain, displacement and vibration. By deploying a variety of sensors, the unit can capture the health status information of the beam and slab in real time. The strain sensor is used to monitor the deformation of the beam and slab under load, the displacement sensor tracks the displacement change of the beam and slab, and the vibration sensor provides instant feedback of the dynamic response. Through real-time analysis of these data, the overall health status of the beam and slab can be evaluated and potential structural problems can be identified. This unit not only improves the monitoring capability of the beam and slab safety, but also provides accurate data support for subsequent risk assessment.

[0171] In this embodiment, the intelligent risk prediction engine uses historical data and current status information to predict potential safety risks and failure points. By analyzing past test data and the dynamic response of beams and slabs, the prediction engine can identify risk factors associated with specific load conditions. For example, if a specific load pattern has caused damage to beams and slabs in historical tests, the pattern will be automatically marked and a risk alert will be issued. Combined with real-time monitoring data, the prediction engine can effectively predict the safety risks of beams and slabs in future tests and ensure active safety management during the test process.

[0172] In this embodiment, the adaptive safety threshold manager is responsible for dynamically adjusting the safety threshold of the monitoring indicators according to the real-time status of the beam and slab and the environmental conditions. Traditional safety thresholds are often static and cannot adapt to changes under different test conditions. The adaptive threshold manager can flexibly adjust the safety standards by analyzing data in real time. For example, under extreme climatic conditions, the material properties of the beam and slab may be affected. At this time, certain safety thresholds can be automatically lowered to reflect the actual safety risks. In this way, accurate monitoring of the beam and slab status can be maintained and changes in safety risks can be reflected in a timely manner.

[0173] In this embodiment, the rapid response decision unit is responsible for quickly formulating and implementing corresponding safety measures and response strategies when an abnormality is detected or a risk is predicted. The response speed of this unit is crucial because any delay during the test process may lead to serious consequences. By working in collaboration with the multi-dimensional health status monitoring unit and the intelligent risk prediction engine, the rapid response decision unit can immediately implement preset safety measures, such as reducing the test load, suspending the test, or conducting emergency testing. This flexible response mechanism ensures safety during the test and reduces the risk of accidents.

[0174] In this embodiment, the security event recording and analysis unit records all security-related events in detail to support post-analysis and optimization of security policies. All detected anomalies, risk predictions, and implemented countermeasures are recorded to form a complete security log. Through regular analysis of these events, weak links in security management can be identified and corresponding improvement measures can be formulated. This process not only improves the safety management level of the test, but also provides valuable data support for subsequent bridge design and maintenance, and promotes the continuous advancement of safety technology.

[0175] In summary, the adaptive safety protection module provides comprehensive safety monitoring and management capabilities through the collaborative work of its various components. In the bridge beam and slab load test, real-time health status monitoring, intelligent risk prediction, adaptive safety threshold management, rapid response decision-making and systematic event recording and analysis form a closed-loop safety management mechanism. This highly integrated safety protection mechanism not only improves the safety and reliability of the test process, but also provides a solid guarantee for the long-term use and maintenance of the bridge structure.

[0176] Furthermore, the overall health status of the beam and slab is expressed by the following formula: H = w ε ·ε norm +w u ·u norm +w v ·v norm , where H is the overall health status indicator; w ε 、w u 、w v is the weight coefficient of each indicator, which is used to reflect the relative importance of each indicator in health assessment and satisfies w ε +w u +w v =1; ε norm is the normalized value of strain data, ε norm =(ε-ε min ) / (ε max -ε min ), ε is the actual strain value, ε min and ε max are the minimum and maximum strain values ​​in the historical data; u norm is the normalized value of displacement data, u norm =(uu min ) / (u max -u min ), u is the actual displacement value, u min and u max are the minimum and maximum displacements in the historical data respectively; v norm is the normalized value of the vibration data, v norm =(vvmin ) / (v max -v min ), v is the actual displacement value, v min and v max are the minimum and maximum values ​​of vibration in the historical data, respectively. The design concept of this health status indicator is to provide a comprehensive health assessment through multi-dimensional monitoring data. Through the flexible adjustment of the weight coefficient, personalized evaluation of different types of beams and slabs can be achieved, thereby improving the pertinence and effectiveness of monitoring. In addition, the normalization process ensures the numerical consistency of various indicators, which facilitates comprehensive evaluation. The overall health status index H can not only reflect the actual health status of the beams and slabs in a timely manner, but also provide an important basis for subsequent maintenance decisions and structural optimization. Through this health assessment mechanism, potential safety hazards can be more effectively identified during testing to ensure the safety and reliability of the bridge.

[0177] Furthermore, the safety threshold of the monitoring indicator is expressed by the following formula: T = T base +ΔT ε +ΔT u +ΔT v +E, where T is the dynamically adjusted safety threshold; T base is the initial threshold value set based on historical data and standard design specifications; ΔT ε , ΔT u +, ΔT v They represent the adjustment amount according to the real-time status of strain, displacement and vibration data and environmental conditions, respectively, and are expressed as: ΔT ε =k ε ·(ε current -ε avg ), ΔT u =k u ·(u current -u avg ), ΔT v =k v ·(v current -v avg ), ε current is the current strain value, ε avg is the historical average strain value, k ε is the sensitivity coefficient of strain data to threshold adjustment, u current is the current displacement value, u avg is the historical average displacement value, k u is the sensitivity coefficient of displacement data to threshold adjustment, v current is the current vibration value, v avg is the historical average vibration value, k v is the sensitivity coefficient of vibration data to threshold adjustment; E is the environmental adjustment factor, E=k env·f(T env ,H env ,W env ), T env is the current ambient temperature, H env is the current ambient humidity, W env is the current wind speed, k env is the sensitivity coefficient of environmental factors to threshold adjustment, f(T env ,H env ,W env ) is a comprehensive function of environmental factors, which is used to comprehensively consider the impact of temperature, humidity and wind speed on the safety threshold of beam and slab monitoring indicators. Through this dynamic threshold adjustment formula, the physical state of the beam and slab and changes in environmental conditions can be responded to in real time, so as to accurately adjust the safety threshold. This method can effectively improve the sensitivity and adaptability of monitoring, help to timely discover potential problems and make preventive safety decisions.

[0178] Furthermore, the environment simulation and compensation module includes the following components.

[0179] The multi-parameter environmental simulation unit creates various actual and extreme environmental conditions by simulating multiple environmental factors.

[0180] The environmental monitoring unit monitors various parameters in the test environment in real time, providing an accurate data basis for environmental simulation and compensation.

[0181] Environmental impact analysis engine, used to quantitatively evaluate the impact of various environmental factors on beam and slab performance.

[0182] The real-time data compensator makes real-time corrections to the test data according to the environmental impact analysis results to eliminate the interference of environmental factors.

[0183] Extreme Conditions Test Controller designs and executes test plans in extreme environments to evaluate the performance of beams and slabs under harsh conditions.

[0184] The environment-structure interaction simulator simulates the complex interaction process between environmental changes and beam-slab structures to improve the authenticity of the simulation.

[0185] The test reliability evaluation unit comprehensively analyzes the original data and compensated data to evaluate the reliability and uncertainty of the test results.

[0186] In this embodiment, the multi-parameter environmental simulation unit plays a vital role in the environmental simulation and compensation module. By creating various actual and extreme environmental conditions, this unit can provide rich experimental scenarios for the performance test of beams and slabs. For example, by simulating extreme climates such as high temperature, high humidity, low temperature and strong wind, the dynamic response characteristics of beams and slabs under different environmental conditions can be evaluated. The innovation of this unit is that it can comprehensively cover various environmental factors that may affect the performance of beams and slabs, making the test results more representative. In addition, based on physical models and computational fluid dynamics algorithms, this unit can accurately control the changes in environmental parameters to ensure the repeatability and reliability of the test. This multi-parameter simulation capability not only improves the scientific nature of the experiment, but also helps to identify potential design defects, thereby providing a basis for subsequent engineering improvements.

[0187] In this embodiment, the environmental monitoring unit is equipped with a variety of sensors that can capture data such as temperature, humidity, air pressure, wind speed, etc. in real time. These real-time data are crucial to the accuracy of environmental simulation, because only under real environmental conditions can the test results truly reflect the performance of the beams and slabs. Through close integration with the environmental simulation unit, the monitoring unit can achieve dynamic data feedback to ensure timely adjustments to changes in environmental conditions during the experiment. For example, when a sudden increase in temperature is detected, the simulation unit can quickly adjust the environmental parameters to ensure the consistency of the experimental conditions. The innovation of this unit is that it can provide a more realistic background for the test, thereby improving the credibility and accuracy of the test data and providing a more comprehensive reference for the performance evaluation of the beams and slabs.

[0188] In this embodiment, the core function of the environmental impact analysis engine is to quantitatively evaluate the impact of various environmental factors on the performance of beams and slabs. This engine uses advanced technologies such as data mining and machine learning, and can process a large amount of historical data and real-time monitoring data, so as to identify and analyze the specific impact of different environmental factors on the performance of beams and slabs. By building a model, the engine can predict the strain, displacement, vibration and other reactions that may occur in beams and slabs under specific environmental conditions. This process can not only help engineers understand the impact of various environmental factors on structural safety, but also provide an important basis for subsequent data compensation. For example, the analysis engine can quantify the thermal expansion effect caused by temperature changes, thereby providing the necessary correction data for the real-time data compensator. The innovation of this unit is that it optimizes the design and construction of beams and slabs through scientific evaluation methods, and improves the safety and durability of the structure.

[0189] In this embodiment, the innovation of the real-time data compensator is that the test data is corrected in real time according to the results of the environmental impact analysis to eliminate the interference of environmental factors. By combining the test data collected in real time with the environmental monitoring data, the compensator can dynamically adjust the test results to ensure that it reflects the performance of the beam and slab under ideal conditions. This process is crucial to improving the accuracy and reliability of the test results, because many environmental factors (such as temperature and humidity) directly affect the readings of the sensor. The compensator can achieve dynamic correction of data through a variety of algorithms (such as weighted averaging and Kalman filtering) to ensure that the final result is not affected by environmental changes. The real-time data compensator not only improves the accuracy of the test, but also enhances the trust in the test results, allowing engineers to make judgments based on more reliable data in subsequent analysis and decision-making.

[0190] In this embodiment, the extreme condition test controller is responsible for designing and executing test plans under harsh environmental conditions to evaluate the performance of the beams and slabs under these conditions. This controller can formulate special test plans for different extreme environments (such as high temperature, high humidity, high wind speed, etc.) to ensure that effective performance data can be obtained under extreme conditions. This process not only helps to evaluate the ultimate bearing capacity of the beams and slabs, but also reveals structural defects that may occur under adverse environmental influences. For example, tests conducted under high temperature conditions can help engineers identify changes in the thermal properties of materials, thereby optimizing the design and construction plans. The innovation of this controller is that it can provide strong data support for the extreme use environment of the beams and slabs, improve the comprehensiveness and reliability of the structural design, and provide a response strategy for extreme conditions that may be faced in the future.

[0191] In this embodiment, through in-depth analysis of environmental factors (such as temperature, humidity and wind speed) and structural responses (such as strain, displacement and vibration), the environment-structure interaction simulator can more realistically reflect the performance of beams and slabs under dynamic environmental conditions. This process not only provides a more realistic background for the experiment, but also can predict the behavior of beams and slabs in complex environments in advance, so as to make targeted design improvements. The innovation of this component is to enhance the accuracy and reliability of the test, making the final experimental results more credible and providing an effective scientific basis for the long-term service life of beams and slabs.

[0192] In this embodiment, by statistically analyzing the data and validating the model, the test reliability assessment unit can provide a detailed report on the quality of the test data, helping engineers identify potential problems and sources of uncertainty. This assessment process ensures that when making decisions, judgments can be made based on fully verified data. For example, through the evaluation results, it is possible to identify which test conditions may cause data deviations, so that they can be avoided in future tests. The innovation of this component is that it enhances the transparency and traceability of the entire test process, providing solid data support for subsequent design and construction.

[0193] In summary, the design and implementation of the environmental simulation and compensation module, through the combination of multiple advanced technologies, comprehensively improves the scientificity and reliability of beam-slab performance testing. The synergy between the various components enables the module to accurately evaluate performance in a dynamic environment and ensure the credibility of the test data. This not only provides a solid basis for the design and construction of beams and slabs, but also provides effective protection for their safety under extreme environmental conditions that may be encountered in the future.

[0194] Furthermore, the following steps are used to quantitatively evaluate the impact of various environmental factors on the performance of beams and slabs.

[0195] A beam-slab finite element model including geometric features, material properties and boundary condition information is established to simulate the mechanical behavior of the beam-slab under various environmental conditions.

[0196] The key environmental factors that affect the performance of beams and slabs are identified and parameterized, and a mathematical description is established for each factor, including its temporal and spatial distribution characteristics and variation patterns.

[0197] By systematically changing various environmental parameters, their impact on the key performance indicators of beams and slabs is observed and quantified to identify the most critical environmental factors.

[0198] Based on the constructed beam-slab finite element model, large-scale numerical simulations are carried out to simulate the response of the beam-slab under different combinations of environmental conditions. At the same time, targeted experiments are designed and carried out to test the actual performance of the beam-slab in a controllable environment. The numerical simulation results are compared with the experimental data to verify and optimize the beam-slab finite element model.

[0199] Collect and organize long-term monitoring data, including beam-slab performance indicators and corresponding environmental parameters, and establish a mapping relationship between environmental factors and beam-slab performance to capture the complex interactions ignored by the beam-slab finite element model and provide a more comprehensive impact assessment.

[0200] In this embodiment, establishing a finite element model of the beam and slab that includes information on geometric features, material properties, and boundary conditions is the first step in quantitatively evaluating the impact of various environmental factors on the performance of the beam and slab. This process involves a detailed definition of the geometry, size, material properties (such as elastic modulus, Poisson's ratio, yield strength, etc.) of the beam and slab, as well as actual working conditions (such as fixed ends or free ends). By using finite element analysis (FEA) software, a highly accurate model can be created to simulate the mechanical behavior of the beam and slab under different environmental conditions. At this stage, the accuracy of the model is critical because inaccurate input parameters may lead to unreliable simulation results. The constructed finite element model can reflect the actual stress conditions of the beam and slab, and capture the response characteristics of the beam and slab under various environmental conditions, including strain, displacement, and vibration, which lays the foundation for the subsequent assessment of the impact of environmental factors.

[0201] In this embodiment, identifying and parameterizing the key environmental factors that affect the performance of the beams and slabs is a key step in conducting a comprehensive evaluation. These environmental factors may include temperature, humidity, wind speed, seismic loads, etc., which will change in different times and spaces, thereby affecting the performance of the beams and slabs. At this stage, each key environmental factor needs to be described in detail to describe their spatiotemporal distribution characteristics and changing patterns. For example, a function or distribution model can be used to represent the change of temperature over time, or to consider the seasonal changes of humidity under different climatic conditions. In this way, it is possible to clearly understand how each environmental factor affects the performance of the beams and slabs, providing the necessary basic data for subsequent numerical simulations and experiments.

[0202] In this embodiment, it is an important step to identify the most critical environmental factors by systematically changing various environmental parameters, observing and quantifying their influence on the key performance indicators of the beam and slab. At this stage, the model can be parametrically analyzed, that is, one or more environmental parameters are gradually adjusted in the finite element model, and then the changes in the corresponding key performance indicators such as strain, displacement, and vibration are recorded. This process can quantify the influence of various environmental factors and identify the factors that have the most significant impact on the performance of the beam and slab. For example, sensitivity analysis is used to evaluate the impact of temperature changes on the strain of the beam and slab, so as to determine whether temperature is one of the key environmental factors affecting the performance of the beam and slab. The results of this stage will provide a clear direction for subsequent numerical simulations and experiments, ensuring that the most important environmental factors are focused on when conducting large-scale simulations.

[0203] In this embodiment, when a large-scale numerical simulation is performed based on the constructed beam-slab finite element model, the response of the beam-slab under different combinations of environmental conditions can be simulated. At this stage, the performance of the beam-slab under the combined action of multiple environmental factors can be comprehensively evaluated. At the same time, in order to verify the reliability of the numerical simulation, it is necessary to design and perform targeted experiments to test the actual performance of the beam-slab in a controllable environment. By comparing the numerical simulation results with the experimental data, the accuracy of the finite element model can be evaluated and necessary optimization can be performed. This process can not only help identify potential errors in the model, but also enhance the confidence in the performance of the beam-slab in the actual environment. The verified model will provide an important basis for subsequent design optimization, so that the challenges brought about by environmental changes can be better addressed in future engineering practices.

[0204] In this embodiment, collecting and organizing long-term monitoring data is the last step in quantitatively evaluating the impact of environmental factors on beam-slab performance. At this stage, it is necessary to record the performance indicators of the beam-slab (such as strain, displacement, vibration, etc.) and the corresponding environmental parameters (such as temperature, humidity, wind speed, etc.) for a long time. By analyzing these data, a mapping relationship between environmental factors and beam-slab performance can be established, thereby capturing the complex interactions that may be ignored by the beam-slab finite element model. This mapping relationship can not only help to more comprehensively understand the performance of the beam-slab under different environmental conditions, but also provide data support for future design and construction. In addition, the analysis combined with long-term monitoring data can help predict the performance of the beam-slab under future environmental changes and improve the safety and reliability of the beam-slab.

[0205] In summary, through the above steps, the quantitative evaluation of the impact of various environmental factors on the performance of beams and slabs has achieved comprehensive coverage from theoretical modeling to practical verification. This process not only emphasizes the importance of finite element models, but also highlights the necessity of parameterization, sensitivity analysis and long-term monitoring of environmental factors. Finally, by combining numerical simulation with experimental data, an in-depth understanding of the performance of beams and slabs can be obtained, providing a solid basis for their design optimization and practical application. Overall, this evaluation process has improved the understanding of the performance of beams and slabs under different environmental conditions, laying a good foundation for future engineering practice.

[0206] Furthermore, the artificial intelligence data analysis module includes the following components.

[0207] The deep learning feature extractor uses the convolutional neural network algorithm to automatically extract key features from a large amount of raw test data.

[0208] The bridge knowledge graph unit constructs a knowledge graph including bridge design, materials, environment and loads to provide background knowledge for analysis.

[0209] The multimodal data fusion engine integrates data from different sensors and test phases to form the basis for comprehensive beam and slab performance evaluation.

[0210] Performance evaluation unit, used to comprehensively analyze the current performance of beams and slabs and predict their future service life.

[0211] The abnormal pattern recognition unit uses clustering and anomaly detection algorithms to identify abnormal patterns and potential problems in beam and slab performance data.

[0212] An intelligent decision support engine combines performance evaluation results and expert knowledge to provide recommendations for bridge design optimization and maintenance strategy formulation.

[0213] Explainable AI Analyzer provides visual explanations of data analysis and decision-making processes, enhancing the credibility and understandability of analysis results.

[0214] In this embodiment, the deep learning feature extractor is one of the core components of the artificial intelligence data analysis module, which uses the convolutional neural network (CNN) algorithm to automatically extract key features from a large amount of raw test data. This process extracts high-order features of the data layer by layer through multiple convolutional layers, greatly reducing the workload of manual feature selection. The feature extractor can automatically identify complex patterns and relationships related to the performance of beams and slabs, improving the efficiency and accuracy of data processing. This automated feature extraction capability is crucial for processing high-dimensional, large-scale sensor data, and can more comprehensively reflect the actual performance of beams and slabs under different working conditions. In addition, the adaptability of the deep learning model enables it to be continuously optimized under different test phases and environmental conditions, enhancing the dynamic monitoring and evaluation capabilities of bridge structures. Through the extracted key features, subsequent performance analysis and prediction will be more accurate, which will help improve the safety and reliability of bridges.

[0215] In this embodiment, the bridge knowledge graph unit constructs a knowledge graph containing information such as bridge design, materials, environment and load, providing background knowledge for data analysis. This unit forms a multi-level and multi-dimensional knowledge system by integrating historical data, design specifications and knowledge of domain experts. In practical applications, the knowledge graph can help the data analysis module better understand the characteristics of the bridge structure and its performance under different environmental conditions. In addition, the dynamic update capability of the knowledge graph enables it to reflect the latest research results and technological advances, providing the latest theoretical support for performance analysis. By combining the knowledge graph with a deep learning feature extractor, domain knowledge can be introduced into the analysis process, thereby improving the accuracy and reliability of decision-making. The construction of the knowledge graph not only provides a solid foundation for subsequent analysis, but also effectively reduces the risk of misjudgment.

[0216] In this embodiment, the multimodal data fusion engine is a key component for integrating data from different sensors and test phases. It can process data from different sources such as structural health monitoring systems (SHM), temperature sensors, displacement sensors, vibration sensors, etc., to form a comprehensive basis for beam and slab performance evaluation. Through data fusion technology, multiple types of data can be effectively integrated to eliminate the noise and errors that may exist in single sensor data and improve the accuracy of the overall analysis. The engine can not only optimize the utilization of information during the data fusion process, but also identify the correlation between different data sources and reveal potential structural health problems. The fused data provides a solid foundation for subsequent performance evaluation, anomaly detection and decision support, making the overall analysis process more comprehensive and detailed.

[0217] In this embodiment, the performance evaluation unit is responsible for comprehensively analyzing the current performance of the beam and slab and predicting its future service life. By combining the features extracted by deep learning, the data of multimodal fusion, and the background information in the knowledge graph, the unit can achieve a multi-dimensional evaluation of the performance of the beam and slab. It uses advanced machine learning algorithms, such as regression analysis and time series prediction, to accurately evaluate the performance of the beam and slab under different working conditions. This process can not only identify the current state of the beam and slab, but also predict the future service life based on historical data and current monitoring data. This predictive capability provides an important basis for bridge maintenance and management, enabling managers to formulate more scientific maintenance strategies based on the performance evaluation results. In addition, the output results of the performance evaluation unit can help engineers make better optimizations during the design phase and extend the service life of the bridge.

[0218] In this embodiment, the abnormal pattern recognition unit can automatically identify patterns that deviate from normal performance by analyzing historical data and current monitoring data, prompting engineers to pay attention to potential risks. By using clustering algorithms, performance data can be classified and abnormal data points in different categories can be identified, while the anomaly detection algorithm can quickly locate possible faults in real-time monitoring. This capability is crucial for bridge safety monitoring, and can detect problems at an early stage, thereby avoiding more serious structural damage. By identifying abnormal patterns in a timely manner, intervention measures can be taken more effectively to ensure the safe operation of the bridge.

[0219] In this embodiment, the intelligent decision support engine uses machine learning models to analyze the advantages and disadvantages of different design solutions, and proposes optimization suggestions based on the current environment and bridge usage. By combining real-time monitoring data with historical data, the intelligent decision support engine can quickly respond to changes in bridge status and provide personalized maintenance and improvement plans. This intelligent decision support can not only improve the work efficiency of engineers, but also optimize resource allocation and reduce maintenance costs. In addition, the decision support engine can also continuously optimize its own suggestions through a feedback mechanism to improve the scientificity and rationality of decision-making.

[0220] In this embodiment, the explainable AI analyzer provides a visual explanation of the data analysis and decision-making process, enhancing the credibility and comprehensibility of the analysis results. With the increasing application of artificial intelligence in bridge monitoring, it is particularly important to ensure the transparency and interpretability of the AI ​​decision-making process. The explainable AI analyzer can convert complex model outputs into easy-to-understand results, helping engineers to sort out analysis ideas and understand how the model draws conclusions. This process not only improves the credibility of the analysis results, but also provides a basis for reflection and improvement. At the same time, clear explanations can enhance users' trust in AI systems and promote the widespread application of artificial intelligence technology in bridge engineering.

[0221] In summary, the AI ​​data analysis module provides a comprehensive solution for monitoring and evaluating beam and slab performance by integrating multiple advanced technologies and components. From deep learning feature extractors to explainable AI analyzers, the various components complement each other and jointly improve the safety and reliability of bridge structures. This module not only enhances the real-time monitoring capability of bridge performance, but also provides a scientific basis for long-term maintenance and optimized design, ensuring the sustainable development of bridge engineering. By continuously improving and optimizing these components, future bridge monitoring and management will become more intelligent and efficient.

[0222] Furthermore, the future service life of the beam and slab is expressed as: S = (H / D)·((1-R 1 )·(1-R 2 )·...·(1-R N ))·(1+μ), where S is the expected service life of the beam-slab; H is the overall health of the beam-slab; D is the design life of the beam-slab; N is the number of environmental and loading conditions; R 1 , R 2 , ..., R N It indicates the reduction ratio of beam-slab performance due to different environmental or loading conditions; μ is an additional influencing factor, including maintenance measures, repairs, upgrades or accidents, expressed as a percentage.

[0223] In this embodiment, the overall health status H of the beam and slab is one of the key parameters for evaluating the service life of the beam and slab. This value is quantified by comprehensively considering the material properties, structural integrity, monitoring data and historical performance of the beam and slab. The assessment of health status usually relies on the structural health monitoring system (SHM), combined with the data collected by the sensors, and processed through deep learning algorithms and data analysis models. If the value of H is high, it means that the beam and slab are in a good health state and can effectively withstand the design load, thereby extending its expected service life.

[0224] In this embodiment, the design life is the maximum service life that the beam and slab can theoretically withstand. This parameter is usually determined by the design standard and material properties, reflecting the expected service life of the beam and slab under ideal conditions without major damage and environmental interference. The rationality of the design life directly affects the calculation result of the service life. If the design life is set too low, the actual use potential of the beam and slab may be underestimated; conversely, it may lead to misjudgment in maintenance and management.

[0225] In this embodiment, the environment and load conditions R 1 ,R 2 ,...,R N It represents the reduction ratio of different factors on the performance of beams and slabs. In practical applications, these factors are determined through historical data analysis, experimental tests and simulations. The impact of these conditions on the performance of beams and slabs is dynamic, so when evaluating, each environmental factor needs to be systematically analyzed to identify the most influential conditions, so as to accurately calculate the service life of beams and slabs.

[0226] In this embodiment, the additional influencing factor μ is the impact brought about by the introduction of maintenance measures, repairs, upgrades or unexpected events. This factor is expressed in the form of a percentage, reflecting the potential extension of the service life of the beam and slab through reasonable maintenance and management strategies. For example, regular maintenance and timely repairs can significantly improve the health of the beam and slab, reduce the risk of damage, and thus effectively extend its service life. In addition, unexpected events such as natural disasters or sudden accidents can also lead to performance degradation, and these uncertainties need to be considered in the service life assessment.

[0227] In summary, through comprehensive consideration of the above factors, the formula S=(H / D)·((1-R 1 )·(1-R 2 )·...·(1-R N))·(1+μ) can provide a comprehensive assessment of the expected service life of beams and slabs. This systematic assessment method can not only capture the complex interactions of multiple influencing factors, but also provide a scientific basis for bridge management and maintenance to ensure the safety and reliability of the structure. Through the quantitative analysis of health status, design life, environmental conditions and maintenance measures, more efficient management and maintenance strategies can be achieved to ensure its long-term service performance.

[0228] Furthermore, the augmented reality interaction and visualization module includes the following components.

[0229] The AR scene construction engine generates and updates augmented reality scenes in real time, superimposing virtual information onto actual bridge beams and slabs.

[0230] Multi-dimensional data visualization renderer, transforming complex test data into intuitive visualization form.

[0231] The interactive query and filtering unit allows users to interact with the AR environment through gestures or voice to query specific data or filter displayed content.

[0232] Intelligent annotation and labeling unit, automatically identifies and labels key areas and anomalies, and allows adding custom labels.

[0233] Customizable dashboard builder to quickly create personalized data dashboards based on user needs to display key performance indicators and analysis results.

[0234] In this embodiment, the AR scene construction engine uses the data collected by sensors, combined with computer vision technology, to scan and model the real environment in real time. In this way, the superposition of virtual information, including key data such as health status indicators, strain, displacement, etc., can be observed in the real physical environment. This not only makes professionals more efficient during on-site inspections, but also enhances the comprehensibility of data. The ability to update in real time ensures that users always have access to the latest data, facilitating the formation of instant decisions.

[0235] In this embodiment, the multidimensional data visualization renderer is responsible for converting complex test data into an intuitive visualization form. This component uses a variety of visualization techniques, such as charts, heat maps, and three-dimensional models, to present abstract data in an image-based way. Users can intuitively see the relationship between different data dimensions, making it easier to understand the health status and potential problems of beams and slabs. This visual presentation not only improves the readability of the data, but also helps users better identify abnormal patterns and trends, supporting scientific decision-making.

[0236] In this embodiment, the interactive query and filtering unit allows the user to interact with the AR environment through gestures or voice to query specific data or filter displayed content. The design of this component improves the interactivity between the user and the AR system, making the data query process more intuitive and convenient. For example, a user can zoom in on a specific area with simple gestures, or request specific monitoring data through voice commands. This interactive method not only improves the convenience of operation, but also reduces the cumbersomeness of traditional query methods, allowing users to obtain required information more quickly and improve work efficiency.

[0237] In this embodiment, the intelligent annotation and marking unit automatically identifies and marks key areas and anomalies, and allows custom tags to be added. This component uses computer vision and machine learning technology to analyze real-time data, automatically detect anomalies in the health of the structure, and issue timely alarms. Through automatic marking, users can quickly locate areas of concern when viewing AR scenes, reducing the time of manual search. In addition, users can also add custom tags and annotations as needed to record specific observations or suggestions, thereby improving the efficiency of teamwork.

[0238] In this embodiment, the customizable dashboard generator quickly creates personalized data dashboards based on user needs to display key performance indicators and analysis results. This component allows users to freely select and arrange the data modules to be displayed, providing a flexible data display solution. Users can integrate monitoring data, historical trends, and predictive analysis of different dimensions on a dashboard to facilitate comprehensive monitoring of the health status of beams and slabs. This flexibility and customizability ensures that each user can obtain information that meets their specific needs, thereby improving the accuracy of decision-making.

[0239] In summary, the augmented reality interaction and visualization module significantly improves the display and interaction of beam-slab monitoring data by integrating multiple efficient components. The AR scene construction engine, visualization renderer, interactive query unit, intelligent annotation and tagging unit, and customizable dashboard generator together build an intuitive, interactive, and user-friendly data environment. This module not only improves the work efficiency of professionals, but also promotes a deeper understanding of the health status of beams and slabs, providing strong support for bridge safety management.

[0240] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A bridge beam and slab load testing device for road and bridge construction, characterized in that: Includes the following modules: Intelligent multi-point collaborative loading module, which realizes multi-point synchronous dynamic loading of beams and slabs through adaptive impedance control and intelligent load distribution to simulate various traffic load conditions; Multi-modal sensing and data acquisition module, which collects dynamic response data of beams and slabs in all directions, and performs preliminary data processing and anomaly detection; The central control module, based on digital twin and reinforcement learning technology, coordinates and manages the work of each module, processes dynamic response data in real time and dynamically optimizes the test strategy to achieve intelligent control of the test process; Adaptive safety protection module monitors the health status of beams and slabs in real time, predicts potential risks and takes proactive preventive measures to ensure the safety and controllability of the testing process; Environmental simulation and compensation module simulates the impact of various environmental conditions on beams and slabs, compensates for the interference of environmental factors on test results in real time, evaluates the performance of beams and slabs under extreme environments, and improves the reliability of test data; The artificial intelligence data analysis module uses deep learning and knowledge graph technology to deeply analyze test data, extract key features, evaluate beam and slab performance and predict their service life, providing decision support for bridge design and maintenance strategies; The augmented reality interaction and visualization module provides an intuitive user interface through AR technology and dynamic visualization, real-time visualization of test processes and results, and supports interactive data exploration and analysis.

2. A bridge beam and slab load testing device for road and bridge construction according to claim 1, characterized in that: The intelligent multi-point collaborative loading module includes the following components: Multi-point hydraulic actuator array, through multiple precision-controlled hydraulic actuators, variable loads are applied at different positions of the beam and slab to achieve multi-point synchronous loading; Adaptive impedance control unit adjusts the impedance characteristics of the actuator in real time to ensure that the dynamic interaction with the beam and slab during loading conforms to the actual traffic load characteristics; Intelligent load distribution unit, based on the beam-slab structural characteristics and test objectives, dynamically calculates and distributes the load of each actuator to simulate different traffic conditions; A network of high-precision force sensors installed at each loading point to enable real-time monitoring of the actual applied load; Real-time synchronization controller coordinates the actions of each actuator to ensure accurate synchronization of multi-point loading and maintain the preset load distribution pattern; The load pattern generator generates a variety of typical and extreme load timing patterns for testing based on various traffic flow models and historical data.

3. A bridge beam and slab load testing device for road and bridge construction according to claim 2, characterized in that: The load on the actuator is calculated as follows: F j (t)=∫0 L P(x,t)·Ф j (x)dx, where L is the length of the beam; Ф j (x) is the shape function associated with the j-th actuator, which represents the influence range of the j-th actuator on the beam plate; dx is a small position increment used for integration on the beam plate, which represents a small area of ​​the beam plate; F j (t) represents the force that the jth actuator needs to apply at time t; P(x,t) represents the instantaneous load at time t and position x on the beam plate, and the formula is: P(x,t)=∑ i=1 n (m i ·g·δ(xx i (t))), m i represents the mass of the vehicle in the simulated traffic load; g represents the acceleration of gravity; δ(xx i (t)) is used to indicate the specific position x of the actuator on the beam at time t. i The instantaneous load applied at (t).

4. A bridge beam and slab load testing device for road and bridge construction according to claim 2, characterized in that: The multimodal sensing and data acquisition module includes the following components: Multi-type sensor network, which deploys multiple sensors to capture the dynamic response characteristics of beams and slabs in all directions; High-speed data acquisition unit, using a high-sampling rate data acquisition card, collects multi-channel sensor signals in real time to ensure the time synchronization of data; Signal conditioning and filtering unit, which pre-processes the raw sensor signal to improve signal quality and signal-to-noise ratio; Real-time data stream processing unit, which performs real-time stream processing on the collected data to achieve rapid data analysis and preliminary feature extraction; Anomaly detection unit, which detects abnormal patterns in data in real time and identifies potential problems in a timely manner; The sensor self-calibration and health monitoring unit performs regular sensor self-inspection and calibration, monitors the sensor health status, and ensures the long-term reliability of data collection.

5. A bridge beam and slab load testing device for road and bridge construction according to claim 1, characterized in that: The central control module includes the following components: Digital twin engine, which builds and updates virtual models of beams, slabs and test devices in real time, providing an accurate simulation environment for decision-making and optimization; Reinforcement learning optimizer, based on virtual models, continuously learns and optimizes test strategies to achieve adaptive control of the test process; A real-time data processing unit, used for processing dynamic response data in real time; The module coordination scheduler uniformly manages and coordinates the work of each functional module to ensure the coordinated operation and resource optimization of the entire device; Intelligent decision-making unit, which comprehensively analyzes processed data and optimization suggestions, makes real-time decisions, and dynamically adjusts test parameters and strategies; The status monitoring and fault diagnosis device comprehensively monitors the operating status of the entire device, detects and diagnoses potential problems in a timely manner, and ensures the continuity and reliability of the test.

6. A bridge beam and slab load testing device for road and bridge construction according to claim 5, characterized in that: Based on the virtual model, the test strategy is continuously learned and optimized to achieve adaptive control of the test process, including the following steps: Based on the virtual model of the beam and test device, a reinforcement learning framework suitable for bridge beam test scenarios is designed, and the state space, action space and reward function are defined. The state space includes the stress state and deformation degree of the beam, the action space includes adjusting the loading force and changing the loading position, and the reward function includes test efficiency and data quality indicators. Initialize and pre-train the reinforcement learning model using a preset test strategy and historical data; Continuously train and optimize reinforcement learning models by collecting dynamic response data and test results in real time; The reinforcement learning model is continuously trained and optimized by continuously collecting various sensor data and test results to adapt to the dynamic changes of beam and slab states and diverse test conditions; Based on the optimized reinforcement learning model, dynamically generate and optimize test strategies, including adjusting loading parameters, selecting the best test points, and optimizing test sequences; Apply the optimized test strategy to the actual test, monitor the execution effect in real time, and record the key performance indicators during the strategy execution process for the next round of optimization, forming a closed-loop feedback mechanism to achieve continuous optimization and adaptive control of the test process.

7. A bridge beam and slab load testing device for road and bridge construction according to claim 1, characterized in that: The adaptive security protection module includes the following components: Multi-dimensional health status monitoring unit, which comprehensively analyzes multi-dimensional data including strain, displacement and vibration, and evaluates the overall health status of beams and slabs in real time; Intelligent risk prediction engine, which predicts potential security risks and failure points based on historical data and current status; Adaptive safety threshold manager, dynamically adjusts the safety threshold of monitoring indicators according to the real-time status of beams and slabs and environmental conditions; Rapid response decision-making unit, when abnormalities are detected or risks are predicted, quickly formulate and implement corresponding safety measures and response strategies; The security event recording and analysis unit records all security-related events in detail, supporting post-event analysis and security policy optimization.

8. A bridge beam and slab load testing device for road and bridge construction according to claim 6, characterized in that: The overall health of the beam and slab is expressed by the following formula: H = w ε ·ε norm +w u ·u norm +w v ·v norm , where H is the overall health status indicator; w ε 、w u 、w v is the weight coefficient of each indicator, which is used to reflect the relative importance of each indicator in health assessment and satisfies w ε +w u +w v =1; ε norm is the normalized value of strain data, ε norm =(ε-ε min ) / (ε max -ε min ), ε is the actual strain value, ε min and ε max are the minimum and maximum strain values ​​in the historical data; u norm is the normalized value of displacement data, u norm =(uu min ) / (u max -u min ), u is the actual displacement value, u min and u max are the minimum and maximum displacements in the historical data respectively; v norm is the normalized value of the vibration data, v norm =(vv min ) / (v max -v min ), v is the actual displacement value, v min and v max are the minimum and maximum values ​​of vibration in the historical data respectively.

9. A bridge beam and slab load testing device for road and bridge construction according to claim 6, characterized in that: The safety threshold of the monitoring indicator is expressed by the following formula: T = T base +ΔT ε +ΔT u +ΔT v +E, where T is the dynamically adjusted safety threshold; T base is the initial threshold value set based on historical data and standard design specifications; ΔT ε , ΔT u +, ΔT v They represent the adjustment amount according to the real-time status of strain, displacement and vibration data and environmental conditions, respectively, and are expressed as: ΔT ε =k ε ·(ε current -ε avg ), ΔT u =k u ·(u current -u avg ), ΔT v =k v ·(v current -v avg ), ε current is the current strain value, ε avg is the historical average strain value, k ε is the sensitivity coefficient of strain data to threshold adjustment, u current is the current displacement value, u avg is the historical average displacement value, k u is the sensitivity coefficient of displacement data to threshold adjustment, v current is the current vibration value, v avg is the historical average vibration value, k v is the sensitivity coefficient of vibration data to threshold adjustment; E is the environmental adjustment factor, E=k env ·f(T env ,H env ,W env ), T env is the current ambient temperature, H env is the current ambient humidity, W env is the current wind speed, k env is the sensitivity coefficient of environmental factors to threshold adjustment, f(T env ,H env ,W env ) is a comprehensive function of environmental factors, which is used to comprehensively consider the impact of temperature, humidity and wind speed on the safety threshold of beam and slab monitoring indicators.

10. A bridge beam and slab load testing device for road and bridge construction according to claim 1, characterized in that: The environmental simulation and compensation module includes the following components: Multi-parameter environmental simulation unit, which creates various actual and extreme environmental conditions by simulating multiple environmental factors; Environmental monitoring unit, which monitors various parameters in the test environment in real time and provides accurate data basis for environmental simulation and compensation; Environmental impact analysis engine, used to quantitatively evaluate the impact of various environmental factors on beam and slab performance; Real-time data compensator, based on the results of environmental impact analysis, makes real-time corrections to test data to eliminate interference from environmental factors; Extreme condition test controller, design and execute test plans in extreme environments to evaluate the performance of beams and slabs under harsh conditions; Environment-structure interaction simulator, which simulates the complex interaction between environmental changes and beam-slab structures to improve the authenticity of the simulation; The test reliability evaluation unit comprehensively analyzes the original data and compensated data to evaluate the reliability and uncertainty of the test results.

11. A bridge beam and slab load testing device for road and bridge construction according to claim 9, characterized in that: The following steps are used to quantitatively evaluate the impact of various environmental factors on beam-slab performance: Establish a finite element model of beams and slabs that includes geometric features, material properties, and boundary conditions to simulate the mechanical behavior of beams and slabs under various environmental conditions; Identify and parameterize the key environmental factors that affect the performance of beams and slabs, and establish a mathematical description of each factor, including its temporal and spatial distribution characteristics and change laws; By systematically changing various environmental parameters, observing and quantifying their impact on the key performance indicators of beams and slabs, the most critical environmental factors can be identified; Based on the constructed beam-slab finite element model, large-scale numerical simulations are carried out to simulate the response of the beam-slab under different combinations of environmental conditions. Meanwhile, targeted experiments are designed and performed to test the actual performance of the beam-slab in a controlled environment. The numerical simulation results are compared with the experimental data to verify and optimize the beam-slab finite element model. Collect and organize long-term monitoring data, including beam-slab performance indicators and corresponding environmental parameters, and establish a mapping relationship between environmental factors and beam-slab performance to capture the complex interactions ignored by the beam-slab finite element model and provide a more comprehensive impact assessment.

12. A bridge beam and slab load testing device for road and bridge construction according to claim 1, characterized in that: The AI ​​data analysis module includes the following components: Deep learning feature extractor, which uses convolutional neural network algorithm to automatically extract key features from a large amount of raw test data; The bridge knowledge graph unit builds a knowledge graph including bridge design, materials, environment and loads to provide background knowledge for analysis; Multimodal data fusion engine, integrating data from different sensors and test phases to form a comprehensive basis for beam and slab performance assessment; Performance evaluation unit, used to comprehensively analyze the current performance of beams and slabs and predict their future service life; Anomaly pattern recognition unit, which uses clustering and anomaly detection algorithms to identify abnormal patterns and potential problems in beam and slab performance data; Intelligent decision support engine, combining performance evaluation results and expert knowledge to provide recommendations for bridge design optimization and maintenance strategy formulation; Explainable AI Analyzer provides visual explanations of data analysis and decision-making processes, enhancing the credibility and understandability of analysis results.

13. A bridge beam and slab load testing device for road and bridge construction according to claim 11, characterized in that: The future service life of the beam and slab is expressed as: S = (H / D)·((1-R1)·(1-R2)·...·(1-R N ))·(1+μ), where S is the expected service life of the beam-slab; H is the overall health status of the beam-slab; D is the design life of the beam-slab; N is the number of environmental and loading conditions; R1, R2, ..., R N It indicates the reduction ratio of beam-slab performance due to different environmental or loading conditions; μ is an additional influencing factor, including maintenance measures, repairs, upgrades or accidents, expressed as a percentage.

14. A bridge beam and slab load testing device for road and bridge construction according to claim 1, characterized in that: The augmented reality interaction and visualization module includes the following components: AR scene construction engine, which generates and updates augmented reality scenes in real time, superimposing virtual information on actual bridge beams and slabs; Multi-dimensional data visualization renderer, which converts complex test data into intuitive visualization form; Interactive query and filtering unit, which allows users to interact with the AR environment through gestures or voice to query specific data or filter displayed content; Intelligent annotation and marking unit, which automatically identifies and marks key areas and outliers, and allows adding custom marks; Customizable dashboard builder to quickly create personalized data dashboards based on user needs to display key performance indicators and analysis results.

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