Steel structure full-life-cycle fine detection system and method based on BIM fused with multi-source data

Abstract

The invention relates to the field of constructional engineering, and discloses a steel structure full-life-cycle fine detection system with BIM fused with multi-source data, which comprises a data acquisition module, a sensor network module, a data fusion module, a data analysis module and a decision support module, and also discloses a steel structure full-life-cycle fine detection method with BIM fused with multi-source data. Comprising the following steps that in the steel structure construction stage, geometric dimension data and potential defect data of a steel structure are obtained through total station measurement and ultrasonic flaw detection, and the initial data are imported into a BIM platform so as to establish a preliminary health model of the steel structure, and a foundation is laid for subsequent health monitoring and data analysis. Through BIM technology and multi-source data fusion, real-time monitoring, dynamic evaluation and prediction of the whole life cycle of the steel structure are realized, continuous updating of the health state and intelligent optimization of the maintenance strategy are ensured, and the structure safety and the maintenance efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of construction engineering, in particular to a BIM fusion multi-source data steel structure full life cycle precision inspection system and method. BACKGROUND

[0002] In the modern construction field, steel structures are widely used in various buildings due to their high strength and durability. However, over time and with changes in the use environment, the health of steel structures will change, and if not monitored and maintained in a timely manner, it can easily lead to serious safety hazards. In order to ensure the long-term safety and service life of the structure, regular health assessment of the steel structure must be carried out. Although traditional methods can to some extent find problems, due to the lack of continuity and full life cycle monitoring, potential risks are often not identified in a timely manner.

[0003] In the prior art, the traditional method of steel structure health monitoring mainly relies on periodic manual inspection and static data collection. These methods can provide partial health assessment of the structure, especially in the acceptance stage after construction is completed. Ultrasonic flaw detection and radiographic non-destructive testing technology are widely used in the health inspection of steel structures, which can effectively find some potential defects such as cracks and welding problems. In addition, there are also some steel structure monitoring systems based on environmental changes that can provide some long-term state tracking. However, existing technical means have gradually failed to meet the needs of modern building steel structure health management, especially the lack of real-time monitoring and full life cycle management, which limits its application in actual operation.

[0004] However, there are some deficiencies in the prior art that limit its application in long-term health management of steel structures; first, traditional detection methods usually rely on periodic inspection, and such monitoring methods cannot cover every stage of the steel structure's full life cycle. Low monitoring frequency can easily miss important health changes. Second, existing detection technologies are mostly isolated and cannot effectively integrate different data sources, often only providing partial data, lacking comprehensiveness and systematicness. In addition, existing technologies fail to use real-time monitoring data and historical data for intelligent analysis and prediction, resulting in structure health assessment and maintenance decisions often lagging behind actual conditions. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a BIM fusion multi-source data steel structure full life cycle precision inspection system and method, which solves the problems of lack of continuity, data isolation and insufficient prediction ability in steel structure health monitoring in the prior art.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: a BIM fusion multi-source data steel structure full life cycle precision inspection system, comprising: a data acquisition module for obtaining geometric dimension data and potential defect data of the steel structure during the construction phase by means of total station measurement and ultrasonic flaw detection, and importing the data into the BIM platform to generate a preliminary model of the steel structure; a sensor network module connected with the data acquisition module, for real-time monitoring of stress, displacement and vibration data of the steel structure after it is put into use by means of strain gauges, displacement sensors and accelerometer devices arranged, and uploading the data to the BIM platform; a data fusion module connected with the data acquisition module and the sensor network module, for integrating the initial geometric data and defect data from the construction phase of the steel structure and the real-time monitoring data from the operation phase by means of a fusion algorithm, generating a health status map of the steel structure, and dynamically updating the structure health status in the BIM model on this basis; a data analysis module connected with the data fusion module, for performing performance evaluation of the steel structure based on the integrated data, and predicting the future health trend of the steel structure by means of a prediction model; a decision support module connected with the data analysis module, for automatically generating a maintenance scheme for the steel structure based on the performance evaluation and prediction results, and adjusting and optimizing the maintenance strategy according to real-time data and changes in the health status.

[0007] Preferably, the data acquisition module comprises: a total station measurement unit for measuring the geometric dimensions of the steel structure by means of a total station, obtaining coordinate and dimension data of each component in the steel structure, which are used to generate a preliminary geometric BIM model of the steel structure; an ultrasonic flaw detection unit for detecting defects in the welded joints of the steel structure, and providing type, location and size data of the defects to generate preliminary defect data of the steel structure.

[0008] Preferably, the sensor network module comprises: a strain gauge sensor unit for real-time monitoring of stress distribution and changes at key positions of the steel structure to evaluate the load-carrying capacity and fatigue condition of the steel structure during use; a displacement sensor unit for monitoring displacement data of the steel structure under different loads and evaluating the deformation condition of the steel structure; and an accelerometer sensor unit for monitoring vibration frequency and response of the steel structure and evaluating dynamic characteristics of the steel structure under external loads.

[0009] Preferably, the data analysis module comprises: a performance evaluation unit for evaluating the current health status of the steel structure based on the integrated data, the evaluation items including fatigue condition, stress state and potential damage area of the structure; a trend prediction unit configured to predict future health trends of the steel structure based on historical data and real-time monitoring data using a prediction algorithm, and provide early warning of potential risks to the structure.

[0010] Preferably, the decision support module comprises: a maintenance scheme generation unit configured to automatically generate a maintenance scheme for the steel structure based on the performance evaluation results and the trend prediction results, the maintenance scheme including specific measures of reinforcement, repair and replacement; a maintenance strategy optimization unit configured to automatically adjust and optimize the maintenance strategy for the steel structure according to changes in real-time data and changes in health status of the steel structure.

[0011] Preferably, the data fusion module comprises: a real-time data fusion unit configured to fuse initial geometric data and defect data collected by a total station and ultrasonic flaw detection during the construction phase of the steel structure with real-time stress, displacement and vibration dynamic monitoring data collected by a sensor network during the operation phase of the steel structure.

[0012] a health status atlas generation unit configured to generate a health status atlas of the steel structure based on the fused data, for real-time updating of the health status of the steel structure and dynamic updating of the health status in the BIM model.

[0013] Preferably, the performance evaluation unit further comprises: a fatigue analysis unit configured to analyze the degree of fatigue damage of the steel structure based on real-time stress, displacement and vibration data, in combination with historical load conditions of the steel structure, and evaluate potential fatigue crack areas.

[0014] a damage diagnosis unit configured to automatically diagnose the type and degree of damage present in the structure based on dynamic monitoring data of the steel structure, including stress, displacement and vibration information.

[0015] a stress state analysis unit configured to evaluate the stress state of the steel structure under different loads and identify areas of stress concentration, providing a reference for subsequent maintenance.

[0016] Preferably, the maintenance strategy optimization unit further comprises: a dynamic optimization algorithm unit configured to dynamically optimize the maintenance cycle and repair scheme based on real-time monitoring data, health status atlas and historical maintenance data.

[0017] a maintenance priority evaluation unit configured to prioritize maintenance tasks based on the health status and potential risks of different parts of the steel structure.

[0018] Preferably, the real-time data fusion unit further comprises: A multi-source data integration unit is configured to perform multi-level data fusion on real-time data from different sources, including sensor data, environmental data, and construction data.

[0019] A data preprocessing unit is configured to perform denoising, smoothing, and standardization preprocessing operations on raw monitoring data collected in real time.

[0020] The present application also provides a BIM-fused multi-source data steel structure full life cycle precision inspection method, comprising the following steps: in the steel structure construction stage, the geometric size data and potential defect data of the steel structure are obtained through total station measurement and ultrasonic flaw detection, and these initial data are imported into the BIM platform to establish a preliminary health model of the steel structure, laying a foundation for subsequent health monitoring and data analysis. After the steel structure is put into use, a sensor network is arranged to monitor the stress, displacement and vibration dynamic data of the steel structure in real time, and the collected real-time monitoring data is uploaded to the BIM platform synchronously. Steel structure construction stage data acquisition: in the steel structure construction stage, accurate geometric size measurement is performed by a total station, and ultrasonic flaw detection technology is used to obtain potential defect data of the steel structure, and the above data is imported into the BIM platform to establish a preliminary geometric model and defect information of the steel structure. Real-time monitoring of steel structure after being put into use: after the steel structure is put into use, a sensor network is arranged to collect the stress, displacement and vibration data of the steel structure in real time, and these data are continuously uploaded to the BIM platform. Data fusion and health state updating: through a data fusion algorithm, the initial data from the steel structure construction stage and the real-time monitoring data from the operation stage are effectively fused to generate a real-time updated steel structure health state atlas, and the steel structure health state in the BIM model is dynamically updated to form a visual effect of full-cycle monitoring. Health condition assessment and trend prediction: based on the fused data, a data analysis technique is used to accurately assess the current health condition of the steel structure, and a prediction model is used to predict the future health trend thereof. Maintenance strategy generation and adjustment: based on the results of health condition assessment and trend prediction, a targeted steel structure maintenance strategy is automatically generated, and the maintenance strategy is dynamically adjusted according to the changes in real-time monitoring data.

[0021] The present application provides a BIM-fused multi-source data steel structure full life cycle precision inspection system and method. 1. The steel structure full life cycle monitoring technical scheme of the present application adopts BIM to fuse multi-source data, realizes the full cycle health monitoring of the steel structure by integrating total station measurement, ultrasonic flaw detection and real-time sensor data, and achieves real-time evaluation and dynamic updating of the state of the steel structure. Compared with the scheme in the prior art which only relies on periodic detection, the present application can continuously track the structure health and timely discover potential problems, greatly improving the accuracy and foresight of structure management.

[0022] 2. The present application generates a health state atlas of the steel structure by organically integrating the geometric data and defect data of the construction stage and the real-time monitoring data of the operation stage through a data fusion algorithm, achieving comprehensive integration and accurate analysis of data. Compared with the common data isolated processing scheme in the prior art, the present application effectively solves the problem that multi-source data cannot work together, improving data utilization efficiency and the scientificity of structure evaluation.

[0023] 3. The present application automatically generates and dynamically optimizes the maintenance scheme of the steel structure by introducing an intelligent maintenance decision support module. It achieves real-time adjustment of the maintenance strategy when the structure health changes, ensuring the optimal allocation of resources. Compared with the manual adjustment of the maintenance strategy in the prior art, the present application realizes the automation and optimization of the maintenance strategy, reduces manual intervention, and improves maintenance efficiency and accuracy.

[0024] 4. The present application identifies potential health risks of the steel structure in advance by combining the prediction model of the data analysis module, and provides decision support for subsequent maintenance work. It achieves accurate prediction of future health trends. Compared with the health evaluation relying only on current status data in traditional technology, the present application not only improves the evaluation accuracy of the current state, but also can provide early warning for future problems, significantly reducing the probability of structure failure and maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 is a system construction schematic diagram of the present application; Figure 2 is a data acquisition module framework diagram of the present application; Figure 3 is a sensor network module framework diagram of the present application; Figure 4 is a data analysis module framework diagram of the present application; Figure 5 is a decision support module framework diagram of the present application; Figure 6 is a data fusion module framework diagram of the present application; Figure 7 is a method flowchart of the present application. DETAILED DESCRIPTION

[0026] With reference to the drawings of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0027] Please refer to the drawings of the present application Figure 1 -attached Figure 6 The embodiments of the present application provide a steel structure full life cycle precision inspection system and method based on BIM fusion of multi-source data, which comprises: The data acquisition module is used to obtain the geometric size data and potential defect data of the steel structure through total station measurement and ultrasonic flaw detection means in the steel structure construction stage, and import the data into the BIM platform to generate a preliminary model of the steel structure. The data acquisition module is used to obtain the geometric size data and potential defect data of the steel structure through total station measurement and ultrasonic flaw detection means in the steel structure construction stage, and import the data into the BIM platform to generate a preliminary model of the steel structure. This module is the starting point of the steel structure health monitoring system, and is directly related to the accuracy of subsequent data fusion and structure health assessment. The data acquisition module provides basic data support for subsequent real-time monitoring, health assessment and maintenance decision.

[0028] Generally, the total station measurement unit is used to obtain the geometric size data of each component of the steel structure. The total station can quickly and accurately provide relevant geometric data by accurately measuring the coordinates and sizes of each component of the steel structure, including steel beams, steel columns, connection points, support points, etc. These data are crucial for the establishment of the preliminary BIM model of the steel structure.

[0029] In one possible implementation, the total station sets measurement points and records the coordinates (x i ,y i ,z i ) of each point by using an electronic device, where i is the i-th measurement point, x i ,y i ,z i are the three-dimensional coordinates of the point, respectively. The data measured by the total station can be directly transmitted to the BIM platform through a wireless network to complete data uploading and storage. This process can monitor the construction state of the steel structure in real time and timely discover deviations in design or construction.

[0030] Specifically, the coordinate and size information of each component of the steel structure will be imported into the BIM platform as geometric data to form a preliminary geometric BIM model of the steel structure. This model serves as the basis for subsequent analysis and provides support for data fusion and health monitoring in the later stage.

[0031] In some embodiments, data collection can be performed efficiently and accurately for each important component of the steel structure over a large range by combining the automatic measurement technology of the total station. The preliminary geometric data obtained not only covers the dimensions of each component of the steel structure, but also calibrates the spatial positions of each connection point and support point, further enhancing the accuracy of the BIM model.

[0032] As an option, an ultrasonic flaw detection unit is used to detect potential defects in the steel structure, especially in welded joints, steel plates and other stressed components. The unit emits ultrasonic signals and analyzes defects inside the material, such as cracks, pores and other irregular structures, by receiving reflected waves.

[0033] In some embodiments, the ultrasonic flaw detection unit calculates the type, location and size of the defect based on the detected signal reflection. Specifically, the ultrasonic flaw detection unit uses the following formula to evaluate the defect in the steel structure: where Time is the time of ultrasonic signal reflection, d is the distance from the flaw detection position to the defect, and v is the propagation speed of ultrasonic waves. Through this formula, the location and size of the defect in the steel structure can be calculated, helping to assess whether there is a potential risk in this part of the structure.

[0034] The resulting defect data will include information such as the type of defect (such as cracks, pores, etc.), location coordinates and size of the defect. These data will then be imported into the BIM platform to form a preliminary health model of the steel structure together with the geometric data. Through the BIM platform, all preliminary defect information can be visualized and stored, providing a basis for subsequent health monitoring.

[0035] Specifically, during the data collection phase, all geometric data and defect data obtained through the total station and ultrasonic flaw detection will be uploaded to the BIM platform through wireless data transmission technology. After the data is uploaded, the system automatically integrates these data and generates a preliminary BIM model of the steel structure. This model will include two parts of data: Geometric data part: including the basic information of the dimensions, positions, angles, etc. of each component of the steel structure, reflecting the overall geometric shape of the steel structure.

[0036] Defect data part: reflecting the potential defect area in the steel structure, including the detection results of cracks, pores and other irregular parts that may exist in welded joints, steel plates, etc.

[0037] Through these data, the BIM platform not only can establish a preliminary three-dimensional model of the steel structure, but also can mark the key parts of the model for health monitoring in the subsequent operation stage.

[0038] In some embodiments, the BIM model can also be synchronized with real-time data from the construction site, reflecting any deviations from the design during the steel structure construction process. For example, if there are errors in the dimensions of some components of the steel structure, or problems with the quality of the welded joints, the BIM model can be updated in real time to reflect these changes, helping the construction team to adjust the plan in a timely manner.

[0039] The data acquisition module generates geometric data and defect data through total station measurement and ultrasonic flaw detection technology, providing a basis for real-time monitoring and health assessment in the subsequent operation phase. The subsequent sensor network module will conduct real-time monitoring based on these preliminary models. The geometric information and potential defect information provided by the data acquisition module, combined with real-time monitoring data, generates a comprehensive health status map in the BIM platform, providing a reliable data foundation for subsequent analysis by the data fusion module, data analysis module, and decision support module.

[0040] The sensor network module, connected to the data acquisition module, uses strain gauges, displacement sensors, and accelerometer devices arranged on the steel structure to monitor stress, displacement, and vibration data in real time after the steel structure is put into use, and uploads the data to the BIM platform. In the full life cycle health monitoring system of the steel structure, the sensor network module is the core part that follows the operation of the data acquisition module. The geometric size data and potential defect data obtained by the data acquisition module through total station and ultrasonic flaw detection provide preliminary structural information for real-time monitoring by the sensor network module. After the steel structure is put into use, the sensor network module arranges strain gauges, displacement sensors, and accelerometers, etc. to monitor stress, displacement, and vibration data in real time, and uploads these data to the BIM platform, thereby providing basic data support for subsequent data fusion, health assessment, and maintenance decision-making.

[0041] In the stage after the steel structure is put into use, health monitoring of the structure is particularly important. The sensor network module, through strain gauges, displacement sensors, and accelerometers arranged at key positions of the steel structure, can continuously collect key data related to the operating condition of the steel structure. These data can reflect the stress condition, deformation state, and dynamic response of the structure in actual operation, thereby enabling real-time tracking and evaluation of the health status of the steel structure.

[0042] Generally, the sensor network module is closely connected with the data acquisition module. The data acquisition module provides a preliminary geometric model and potential defect data of the steel structure, while the sensor network module monitors the dynamic changes of the steel structure through real-time data acquisition, ensuring that the real-time health status of the structure is updated and displayed on the BIM platform.

[0043] In some embodiments, strain gauge sensor units are widely applied to key stress points of steel structures. These units are used to monitor the stress distribution and changes of steel structures in real time, especially at important locations such as support points, connection points, beam-column joints, etc. Strain gauge sensors measure the small deformation of materials, calculate the stress, and upload the data to the BIM platform.

[0044] Specifically, strain gauge sensors work by the principle of resistance change, and the change in resistance is proportional to the stress received by the structural component. The formula for strain gauge sensors is: Where σ is the stress, F is the applied force, and A is the force area. The output signal of the strain gauge calculates the stress according to the force and force area, and reflects the stress distribution of the steel structure under different loads in real time. After uploading the data to the BIM platform, it is convenient to combine the structural model for subsequent health assessment.

[0045] In steel structures, displacement sensor units are used to monitor the displacement of the structure under external forces. Displacement sensors are usually installed at key points of the structure and can record the small displacement changes of the steel structure under load. By comparing displacement data at different time points, the deformation of the structure can be evaluated, and whether the structure has overload, deformation, or stability problems can be determined.

[0046] Specifically, displacement sensors measure the displacement change of an object over a certain period of time and use the following formula for calculation: Where δ is the displacement, d is the total displacement, and t is the measurement time. By collecting displacement data in real time, the system can clearly track the development trend of steel structure deformation and timely discover possible deformation or damage problems.

[0047] After uploading the displacement data to the BIM platform, combined with the geometric model, the deformation of the structure can be dynamically displayed, and detailed displacement trend charts can be provided for subsequent health assessment.

[0048] Accelerometer sensor units are used to monitor the vibration frequency and dynamic response of steel structures in real time. This unit is particularly suitable for evaluating the dynamic characteristics of steel structures under external loads or vibrations, such as earthquakes, wind, traffic loads, etc. Accelerometers measure the vibration frequency and acceleration response of steel structures, reflecting the performance of the structure under dynamic loads.

[0049] In some embodiments, the output signal of the accelerometer is proportional to the vibration frequency of the structure, and the vibration frequency is calculated by the following formula: Where ω is the vibration frequency, k is the elastic restoring force, and m is the mass. By measuring the acceleration and vibration frequency, the dynamic performance of the structure can be evaluated, and potential damage or failure of the steel structure due to dynamic loads can be predicted in advance.

[0050] After the data monitored by the accelerometer is uploaded to the BIM platform, a dynamic response diagram of the steel structure can be generated, which intuitively reflects the vibration frequency and dynamic characteristics of the structure. Through the monitoring of these dynamic data, the operational safety of the structure is effectively guaranteed.

[0051] Specifically, in this embodiment, all the data collected by the sensors are uploaded to the BIM platform in real time through a wireless network. Whenever new data is monitored by the sensors, the data will be immediately uploaded to the platform and combined with the existing geometric data and defect data to form the latest health status map of the steel structure. The BIM platform provides accurate input data for subsequent data fusion modules and data analysis modules through the continuous updating of these real-time data.

[0052] These real-time data, combined with the original geometric model and defect data, provide basic information for subsequent health assessment and maintenance decision-making. Through the data fusion module, the health status of the steel structure is assessed and updated in a timely manner, providing accurate assessment data for the decision support module, automatically generating maintenance plans, and adjusting and optimizing them according to real-time data.

[0053] The data acquisition module obtains the preliminary geometric model and defect data of the steel structure through the total station and ultrasonic flaw detection, providing basic data support for the real-time monitoring of the sensor network module. The sensor network module monitors the stress, deformation, and vibration data of the steel structure in real time through strain gauges, displacement sensors, and accelerometers, and uploads these data to the BIM platform, combining them with the preliminary data provided by the data acquisition module. As a result, the health status map of the steel structure in the BIM platform is updated in real time, ensuring the accuracy of subsequent data analysis and decision-making.

[0054] The data fusion module is connected to the data acquisition module and the sensor network module, respectively, and uses fusion algorithms to integrate the initial geometric data and defect data from the construction stage of the steel structure and the real-time monitoring data from the operation stage, generating a health status map of the steel structure and dynamically updating the structure's health status in the BIM model; In this embodiment, the data fusion module plays a key role in connecting with the data acquisition module and the sensor network module. It integrates the initial geometric data and defect data from the steel structure construction phase with the real-time monitoring data from the operation phase by using fusion algorithms. This process not only generates a health status map of the steel structure, but also dynamically updates the structure's health status in the BIM model. The data fusion module provides basic data support for subsequent health assessment, maintenance decision-making, and structure state prediction.

[0055] Generally, the core function of the data fusion module is to effectively integrate monitoring data from different sources. This module uses fusion algorithms to generate a health status map of the steel structure based on preliminary geometric data and potential defect data obtained from the data acquisition module, combined with real-time monitoring data from the sensor network module. The health status in the BIM model is continuously updated dynamically based on new data. This ensures that the health status information on the BIM platform is always up-to-date and accurate, reflecting the current state of the steel structure in real time.

[0056] In one possible implementation, the data fusion module fuses data from different stages through algorithms. Specifically, it receives initial geometric data and defect data from the data acquisition module and combines these data with real-time monitoring data. To achieve this, the system typically uses fusion techniques such as Kalman filtering, weighted averaging, and data assimilation.

[0057] Specifically, the geometric data and potential defect data provided by the data acquisition module mainly include basic geometric information of the steel structure (such as position, size, shape) and potential defect areas obtained by ultrasonic flaw detection. The real-time monitoring data provided by the sensor network module includes dynamic data such as stress, displacement, and vibration. These data have different collection times, sources, and granularities, and direct integration may result in inconsistent or inaccurate data. Therefore, the role of the data fusion algorithm is to integrate these data in terms of time, space, and data quality to generate a more accurate and complete health status map.

[0058] In some embodiments, the data fusion module uses a Kalman filtering algorithm to fuse multi-source data from different stages. The Kalman filtering algorithm dynamically updates the health status of the steel structure based on a series of recursive formulas, combining measurement data and prediction models. The basic form of Kalman filtering is as follows: x k =Ax k-1 +Bu k +w k ; where x k is the state vector at the current time k, A is the force area, B is the control matrix, u kw k is the process noise.

[0059] The measurement equation is: z k = Hx k + v k ; where z k is the measurement at time k, H is the observation matrix, v k is the measurement noise, and x k is the state vector at the current time k.

[0060] By using the Kalman filter algorithm, the system can optimize the fusion of the initial geometric data and defect data (from the data acquisition module) with real-time monitoring data (from the sensor network module), thereby generating a more accurate health status map.

[0061] Specifically, the data fusion module generates a health status map of the steel structure by fusing data from different stages. This map not only shows the current health status of the structure, but also provides a basis for subsequent health assessment and prediction analysis. The generation process of the map can be divided into several steps: Preliminary data integration: First, the initial geometric data and defect data from the data acquisition module are connected with the real-time data from the sensor network module to form a preliminary health status map.

[0062] Dynamic updating: During real-time monitoring, the system continuously updates the health status map through the Kalman filter algorithm. Each time new real-time data is input, the health status map will be updated accordingly to reflect changes in the health status of the steel structure.

[0063] Data mapping: Each node or data point in the map is mapped to the corresponding location in the BIM model, and the health status of the structure can be displayed in real time and intuitively.

[0064] Specifically, the health status map contains information about the health status of each part of the steel structure, such as whether there are damages, fatigue conditions, stress concentration areas, etc. The map continuously changes with the update of data and is displayed to management personnel through the visualization function of the BIM platform, helping them make timely and scientific decisions.

[0065] In a typical implementation, the data fusion module is also responsible for dynamically updating the health status of the structure in the BIM model. According to the changes in the health status map, the health status of each component in the BIM model will be adjusted accordingly. In this way, management personnel can view the real-time health information of the structure on the BIM platform.

[0066] For example, when the data fusion module detects stress concentration or potential damage in a certain part of the steel structure, the model of that part on the BIM platform will automatically be labeled and display the health status change. This automated update process enables managers to understand the health status of the steel structure in real time and take necessary maintenance measures in a timely manner.

[0067] The data fusion module plays a crucial role in the entire system. It receives preliminary geometric data and defect data from the data acquisition module, as well as real-time monitoring data from the sensor network module. By fusing these data, the system can dynamically update the health status of the steel structure and visualize it through the BIM platform. These health status data provide important support for subsequent health assessment, trend prediction, and maintenance decision-making.

[0068] In some embodiments, the data fusion module closely cooperates with the data analysis module and the decision support module to ensure that the health status atlas and model can match the actual situation of the steel structure, thereby continuously providing accurate and reliable information throughout the life cycle of the steel structure.

[0069] The data analysis module, connected to the data fusion module, is used to assess the performance of the steel structure based on the integrated data and predict the future health trend of the steel structure through a prediction model; In the steel structure life cycle precision inspection system, the data analysis module plays a crucial role. This module is connected to the data fusion module and is responsible for assessing the performance of the steel structure based on the integrated data and predicting the future health trend of the steel structure through a prediction model. The data analysis module provides scientific basis for long-term health management of the steel structure through analysis of real-time and historical data. It directly affects the health assessment and maintenance decision of the steel structure, helping to extend the service life of the structure and reduce the occurrence of unexpected failures.

[0070] Generally, the data analysis module uses the comprehensive data provided by the data fusion module to analyze the health status of the steel structure, assess its current performance and predict future trends. The core task of this module is to conduct in-depth analysis of the data in the health status atlas, combining historical data, environmental influences, and real-time monitoring data to provide accurate information for subsequent maintenance planning and decision-making.

[0071] As an option, the data analysis module uses various prediction models to predict the future health trend of the steel structure. This process not only assesses the current health status of the steel structure, but also provides early warning of potential risks, helping managers to take preventive measures in a timely manner to avoid sudden failures.

[0072] In this embodiment, the data analysis module conducts a comprehensive assessment of the steel structure's health condition by integrating data from the data fusion module. The core of the assessment includes the fatigue level, stress state, and potential damage areas of the steel structure. These assessment items help to dynamically monitor the structure and promptly discover and handle potential problems.

[0073] In some embodiments, the data analysis module also predicts the future health trend of the steel structure through a prediction model. This prediction model is usually based on historical data, real-time data, and environmental impact factors, combined with machine learning or statistical models, to predict the health trend of the steel structure. Specifically, the data analysis module uses methods such as long short-term memory network (LSTM), regression analysis, and time series analysis to combine historical data and real-time monitoring data to predict the future health status of the steel structure.

[0074] For example, the LSTM model can be updated by the following formula: i t =σ(W i ·[h t-1 ,x t ]+b i ); where i t is the input gate, h t-1 is the hidden state of the previous moment, x t is the current input data, W i is the weight of the input gate, b i is the bias of the input gate, and σ is the stress.

[0075] This model can not only identify the current health status of the structure, but also capture the pattern of performance degradation, thus predicting the future health status of the steel structure. This is crucial for timely identifying potential risk areas and developing maintenance plans.

[0076] The output of the data analysis module is not only an assessment of the current health status of the steel structure, but also provides guidance for subsequent maintenance decisions. Specifically, the performance evaluation and prediction results can automatically generate maintenance recommendations, such as recommending regular inspection, reinforcement, or replacement when certain parts of the steel structure are found to have high fatigue levels.

[0077] As an option, the data analysis module can generate maintenance priorities for different parts of the steel structure based on the health status assessment results. The damaged or fatigued areas of the steel structure are classified and prioritized for handling based on their impact on the overall structural safety.

[0078] The seamless connection between the data analysis module and the data fusion module ensures the smooth transfer of data. The data fusion module integrates data from different stages, including geometric data, defect data, real-time monitoring data, etc., to generate health status maps, while the data analysis module performs performance evaluation and prediction based on these data maps. The organic combination of the two ensures comprehensive evaluation of the steel structure by the system, providing a scientific basis for subsequent maintenance, reinforcement, replacement, and other decisions.

[0079] Specifically, when the data fusion module generates new health status maps, the data analysis module receives these updated data in real time and evaluates the current state of the steel structure based on the new health status maps. At this time, the prediction model adjusts the future health trend of the steel structure using new real-time data, ensuring that the maintenance plan is always based on the latest and most accurate structure state.

[0080] The decision support module, connected with the data analysis module, automatically generates maintenance plans for the steel structure based on performance evaluation and prediction results, and adjusts and optimizes maintenance strategies according to real-time data and changes in health status.

[0081] The decision support module, connected with the data analysis module, automatically generates maintenance plans for the steel structure based on performance evaluation and prediction results, and adjusts and optimizes maintenance strategies according to real-time data and changes in health status. The decision support module is an indispensable part of the system, which plays a role in generating specific maintenance plans based on health evaluation and prediction data, and can adjust maintenance strategies in real time when the health status of the steel structure changes. This module can ensure that the steel structure maintains the best state throughout its life cycle and effectively reduces maintenance costs and potential risks.

[0082] Generally, the decision support module, through connection with the data analysis module, can obtain health evaluation results and future health trend prediction data of the steel structure. These evaluation and prediction data provide accurate basis for the generation of maintenance strategies. Based on this information, the decision support module automatically generates maintenance plans for the steel structure. As an option, the decision support module also adjusts and optimizes the original maintenance strategy according to real-time monitoring data and changes in health status, ensuring that the maintenance work of the steel structure always adapts to its current health status.

[0083] Specifically, after receiving the performance evaluation results generated by the data analysis module, the decision support module automatically generates maintenance plans and prioritizes the health status of different parts of the steel structure. Maintenance plans include but are not limited to reinforcement, repair, replacement, and other specific measures. With the continuous change of real-time data, the maintenance strategy will also be continuously adjusted to ensure that the maintenance work of the steel structure can be accurately implemented at different stages.

[0084] In this embodiment, the decision support module automatically generates maintenance plans for the steel structure based on the performance evaluation results and future health trends provided by the data analysis module. These maintenance plans are based on a comprehensive assessment of the steel structure's health status. The performance evaluation results typically include the following aspects: Fatigue damage: Whether the steel structure has fatigue damage, especially in areas of stress concentration and potential crack propagation; stress state: Whether the stress level of the steel structure during operation exceeds the safe range; Displacement and deformation: Whether the steel structure has undergone deformation beyond the allowable range, causing stability problems; Potential damage areas: Possible damage areas predicted from monitoring data, such as cracks, corrosion, etc.

[0085] Based on these evaluation results, the decision support module generates targeted maintenance plans for each problem area. For example, if a certain part has high fatigue damage, reinforcement or repair suggestions will be generated; if the stress state of a certain connection point exceeds the design bearing capacity, reinforcement or replacement will be suggested.

[0086] After the maintenance plan is generated, the system will pass it to the maintenance execution module and display it to relevant personnel through the BIM platform. This process ensures that the maintenance measures of the steel structure are timely and accurate.

[0087] In some embodiments, the decision support module can also adjust and optimize the generated maintenance strategy based on real-time data and changes in health status. Since the health status of the steel structure may change over time, with changes in external loads or environmental influences, the maintenance strategy must be flexible.

[0088] For example, after a preliminary repair of a certain part of the steel structure, the system will continue to monitor the health status of that part and update the maintenance plan based on real-time data. If the part fails to fully recover or new problems arise, the system will automatically adjust the maintenance plan, possibly increasing maintenance frequency or replacing some materials.

[0089] In one possible implementation, the decision support module uses a dynamic optimization algorithm to adjust the maintenance strategy, combining real-time monitoring data, historical maintenance data, and steel structure operating environment data. At this time, the optimization algorithm performs real-time optimization through the following formula: R t =f(S t ,A t ,D t ); Where f represents an optimization function, R t is the adjustment amount of the maintenance strategy, S t is the current health status of the steel structure, A tFor the current maintenance measures, D t For changes in environmental and operating conditions. The optimization algorithm automatically adjusts the maintenance strategy based on the health status changes of the steel structure to ensure optimal resource utilization and structural safety.

[0090] In addition, the decision support module also has a maintenance priority assessment function. According to the health status and potential risks of each part of the steel structure, the system can automatically prioritize maintenance tasks. For example, some parts need to be treated first due to high stress concentration or corrosion-induced potential damage. Other possible implementations include regular monitoring, partial structure reinforcement or replacement.

[0091] In one possible implementation, the decision support module dynamically calculates the maintenance priority of each task based on the health assessment results of different parts of the steel structure. The priority is calculated according to the following formula: P i = w1·H i + w2·R i + w3·T i ; Where P i is the maintenance priority of the i-th component, H i is the health status score of the i-th component, R i is the risk assessment result of the i-th component, T i is the potential life of the i-th component, and w1, w2, w3 are weight coefficients. Through this algorithm, the system can scientifically evaluate the maintenance priority of each part, ensuring that the priority order of maintenance work is reasonable and avoiding waste of resources.

[0092] The decision support module is closely linked to the aforementioned modules. The data analysis module provides the health assessment and prediction results of the steel structure to the decision support module, and the real-time monitoring data updates will also be continuously fed back to the decision support module, driving it to dynamically adjust the maintenance strategy. The BIM platform, as an information carrier, visualizes the updates of health status and maintenance strategy, helping managers to understand the status of the steel structure in real time.

[0093] Specifically, after the data analysis module completes the health assessment and future trend prediction of the steel structure, the decision support module will automatically generate and optimize the maintenance plan based on these results. When the health status changes or real-time data is fed back to the system, the maintenance strategy will be dynamically adjusted according to the optimization algorithm.

[0094] The BIM-based multi-source data fusion steel structure full life cycle precision inspection method described below can be mutually corresponding to the BIM-based multi-source data fusion steel structure full life cycle precision inspection system described above.

[0095] Please refer to the attached Figure 7The application also provides a steel structure whole life cycle precision inspection method for BIM fusion of multi-source data, comprising the following steps: S1, in the steel structure construction stage, the geometric size data and potential defect data of the steel structure are obtained through total station measurement and ultrasonic flaw detection, and the initial data are imported into the BIM platform to establish a preliminary health model of the steel structure, laying a foundation for subsequent health monitoring and data analysis; S2, after the steel structure is put into use, a sensor network is arranged to monitor the stress, displacement and vibration dynamic data of the steel structure in real time, and the collected real-time monitoring data are synchronously uploaded to the BIM platform; S3, data collection in the steel structure construction stage: in the steel structure construction stage, accurate geometric size measurement is carried out through a total station, and ultrasonic flaw detection technology is used to obtain potential defect data of the steel structure, and the above data are imported into the BIM platform to establish a preliminary geometric model and defect information of the steel structure; S4, real-time monitoring after the steel structure is put into use: after the steel structure is put into use, a sensor network is arranged to collect the stress, displacement and vibration data of the steel structure in real time, and the data are continuously uploaded to the BIM platform; S5, data fusion and health state updating: through a data fusion algorithm, the initial data from the steel structure construction stage and the real-time monitoring data in the operation stage are effectively fused to generate a real-time updated steel structure health state atlas, and the health state of the steel structure in the BIM model is dynamically updated to form a visual effect of whole cycle monitoring; S6, health condition evaluation and trend prediction: based on the fused data, a data analysis technology is used to accurately evaluate the current health condition of the steel structure, and a prediction model is combined to predict the future health trend of the steel structure; S7, maintenance strategy generation and adjustment: according to the results of health condition evaluation and trend prediction, a targeted steel structure maintenance strategy is automatically generated, and the maintenance strategy is dynamically adjusted according to the changes of real-time monitoring data; The method of the embodiment can be used to execute the system embodiment, and the principles and technical effects are similar, and details are not repeated here.

[0096] Although the embodiments of the application have been shown and described, it can be understood by those of ordinary skill in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the application, and the scope of the application is defined by the appended claims and their equivalents.

Claims

1. The BIM-integrated multi-source data steel structure full life cycle precision inspection system is characterized by: include: The data acquisition module is used to obtain the geometric dimension data and potential defect data of the steel structure through total station measurement and ultrasonic flaw detection during the steel structure construction phase, and import the data into the BIM platform to generate a preliminary model of the steel structure; The sensor network module is connected to the data acquisition module. After the steel structure is put into use, it uses the arranged strain gauges, displacement sensors and accelerometers to monitor the stress, displacement and vibration data of the steel structure in real time and upload the data to the BIM platform; The data fusion module is connected to the data acquisition module and the sensor network module respectively. It uses a fusion algorithm to integrate the initial geometric data and defect data from the steel structure construction phase and the real-time monitoring data from the operation phase to generate a health status map of the steel structure. On this basis, it dynamically updates the structural health status in the BIM model. The data analysis module is connected to the data fusion module and is used to evaluate the performance of the steel structure based on the integrated data and predict the future health trend of the steel structure through the prediction model; The decision support module is connected with the data analysis module to automatically generate maintenance plans for steel structures based on performance evaluation and prediction results, and adjust and optimize maintenance strategies according to real-time data and changes in health status.

2. The BIM-integrated multi-source data steel structure full life cycle precision inspection system according to claim 1 is characterized in that: The data acquisition module includes: A total station measurement unit is used to measure the geometric dimensions of the steel structure using a total station, and obtain coordinate and dimension data of each component in the steel structure. The coordinate and dimension data are used to generate a preliminary geometric BIM model of the steel structure; Ultrasonic flaw detection unit is used to detect defects in welded joints in steel structures and provide data on the type, location and size of the defects to generate preliminary defect data of the steel structure.

3. The BIM-integrated multi-source data steel structure full life cycle precision inspection system according to claim 1 is characterized in that: The sensor network module includes: Strain gauge sensor units are used to monitor stress distribution and changes in key parts of steel structures in real time to assess the load-bearing capacity and fatigue of steel structures during use; Displacement sensor unit, used to monitor the displacement data of the steel structure under different loads and evaluate the deformation of the steel structure; Accelerometer sensor units are used to monitor the vibration frequency and response of steel structures and to evaluate the dynamic characteristics of steel structures under external loads.

4. The BIM-integrated multi-source data steel structure full life cycle precision inspection system according to claim 1 is characterized in that: The data analysis module includes: Performance evaluation unit, used to evaluate the current health status of the steel structure based on the integrated data, including the fatigue condition, stress state and potential damage areas of the structure; The trend prediction unit is used to predict the future health trend of steel structures based on historical data and real-time monitoring data using prediction algorithms, and provide early warning of potential structural risks.

5. The BIM-integrated multi-source data steel structure full life cycle precision inspection system according to claim 1 is characterized in that: The decision support module includes: A maintenance plan generating unit, configured to automatically generate a maintenance plan for the steel structure based on the performance evaluation results and trend prediction results, the maintenance plan including specific measures for reinforcement, repair, and replacement; The maintenance strategy optimization unit is used to automatically adjust and optimize the maintenance strategy of the steel structure according to the real-time data changes and health status changes of the steel structure.

6. The BIM-integrated multi-source data steel structure full life cycle precision inspection system according to claim 1 is characterized in that: The data fusion module includes: A real-time data fusion unit is used to fuse the initial geometric data and defect data collected by total stations and ultrasonic flaw detection during the steel structure construction phase with the real-time stress, displacement and vibration dynamic monitoring data collected by the sensor network during the steel structure operation phase; A health status map generation unit generates a health status map of the steel structure based on the fused data, which is used to update the health status of the steel structure in real time and dynamically update the health status in the BIM model on this basis.

7. The BIM-integrated multi-source data steel structure full life cycle precision inspection system according to claim 4 is characterized in that: The performance evaluation unit further comprises: Fatigue analysis unit, based on real-time stress, displacement and vibration data, combined with the historical load conditions of the steel structure, analyzes the fatigue damage degree of the steel structure and evaluates the potential fatigue crack area; A damage diagnosis unit is used to automatically diagnose the type and extent of damage in the structure based on dynamic monitoring data of the steel structure, wherein the dynamic monitoring data includes stress, displacement and vibration information; The stress state analysis unit is used to evaluate the stress state of steel structures under different loads, identify existing stress concentration areas, and provide a reference for subsequent maintenance.

8. The BIM-integrated multi-source data steel structure full life cycle precision inspection system according to claim 5 is characterized in that: The maintenance strategy optimization unit further includes: Dynamic optimization algorithm unit, which dynamically optimizes maintenance cycles and repair plans based on real-time monitoring data, health status maps, and historical maintenance data; The maintenance priority assessment unit prioritizes each maintenance task based on the health status and potential risks of different parts of the steel structure.

9. The BIM-integrated multi-source data steel structure full life cycle precision inspection system according to claim 1 is characterized in that: The real-time data fusion unit further comprises: A multi-source data integration unit is used to perform multi-level data fusion on real-time data from different sources, including sensor data, environmental data, and construction data; The data preprocessing unit is used to perform denoising, smoothing and standardization preprocessing operations on the raw monitoring data collected in real time.

10. A method for accurate inspection of steel structures throughout their life cycle using BIM and multi-source data, according to any one of claims 1 to 9, characterized in that: The following steps are involved: During the steel structure construction phase, the geometric dimension data and potential defect data of the steel structure are obtained through total station measurement and ultrasonic testing. These initial data are then imported into the BIM platform to establish a preliminary health model of the steel structure, laying the foundation for subsequent health monitoring and data analysis. After the steel structure is put into use, a sensor network is deployed to monitor the stress, displacement and vibration dynamic data of the steel structure in real time, and the collected real-time monitoring data is uploaded to the BIM platform synchronously; Data collection during the steel structure construction phase: During the steel structure construction phase, precise geometric dimension measurements are performed using a total station, and ultrasonic flaw detection technology is used to obtain data on potential defects in the steel structure. This data is then imported into the BIM platform to establish a preliminary geometric model and defect information for the steel structure. Real-time monitoring of steel structures after they are put into use: After the steel structure is put into use, a sensor network is deployed to collect stress, displacement, and vibration data of the steel structure in real time, and this data is continuously uploaded to the BIM platform; Data fusion and health status update: Through the data fusion algorithm, the initial data from the steel structure construction phase and the real-time monitoring data from the operation phase are effectively integrated to generate a real-time updated steel structure health status map. The health status of the steel structure in the BIM model is dynamically updated to form a visualization effect of full-cycle monitoring. Health status assessment and trend prediction: Based on fused data, data analysis technology is used to accurately assess the current health status of the steel structure, and its future health trend is predicted by combining prediction models; Maintenance strategy generation and adjustment: Based on the results of health status assessment and trend prediction, targeted steel structure maintenance strategies are automatically generated, and maintenance strategies are dynamically adjusted according to changes in real-time monitoring data.

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