Precast concrete steel box girder tensioning operation method

Abstract

The application discloses a prefabricated concrete steel box girder tensioning operation method and relates to the technical field of prestressed simply-supported box girder maintenance. A comprehensive digital twin is formed by integrating and mapping a BIM model, a tensioning equipment parameterized model and multi-source environmental data, so that the box girder structure, equipment state and environmental factors are more accurately and timely mastered. In the traditional operation, equipment performance degradation is often detected after data anomaly occurs, and it is difficult to quantify the specific influence of environmental factors on degradation.

Description

Technical Field

[0001] This invention relates to the field of prestressed simply supported box girder manufacturing technology, and in particular to a method for tensioning precast concrete steel box girders. Background Technology

[0002] The tensioning process for prestressed concrete simply supported box girders is a crucial step in the construction of this structure, forming the basis for achieving the designed load-bearing capacity and structural durability. Traditional tensioning techniques are continuously being integrated with modern construction equipment and information technology, gradually developing towards intelligent and digital methods to improve construction efficiency and quality assurance.

[0003] Currently, the tensioning of prestressed concrete simply supported box girders typically employs a combination of tensioning equipment and sensors. Prestress is applied using tensioning jacks, while sensors deployed on-site collect real-time data on tension force, strain, displacement, and other parameters. During construction, operators adjust the tensioning plan based on the collected data to ensure the prestress meets design requirements. Some advanced construction sites have also introduced BIM (Building Information Modeling) technology to assist in construction planning and schedule management; however, its real-time responsiveness to changes in the site environment and equipment status remains relatively limited.

[0004] However, environmental factors such as high and low temperatures, strong winds, vibrations, and poor communication can easily lead to performance degradation of tensioning equipment and sensors, thereby affecting the accuracy and reliability of data acquisition. At the same time, the traditional single acquisition mode lacks effective fault tolerance and adaptive processing mechanisms when faced with the loss or anomaly of critical data, making it difficult to guarantee the continuity and safety of tensioning operations. Summary of the Invention

[0005] The main objective of this invention is to propose a method for tensioning precast concrete steel box girders. This method aims to address the technical problems of existing technologies, which are prone to performance degradation of tensioning equipment and sensors due to factors such as high and low temperatures, strong winds, vibrations, and poor communication. This degradation affects the accuracy and reliability of data acquisition. Furthermore, the traditional single acquisition mode lacks effective fault tolerance and adaptive processing mechanisms when faced with the loss or anomalies of critical data, making it difficult to ensure the continuity and safety of tensioning operations.

[0006] To achieve the above objectives, in a first aspect, the present invention proposes a method for tensioning precast concrete steel box girders, characterized in that the method is applied to a box girder prestressing tensioning scenario under a preset environment, wherein the preset environment includes at least one of a high temperature environment, a low temperature environment, a strong wind environment, a strong vibration environment, and a communication-restricted environment. The method for tensioning precast concrete steel box girders includes the following steps: The established building information model of the precast concrete steel box girder, the parametric model of the tensioning equipment, and the multi-source environmental data are integrated and mapped to form a target digital twin model; wherein, the parametric model of the tensioning equipment has built-in degradation parameters of each device, and the multi-source environmental data includes temperature data, vibration data, wind speed and direction data, and communication signal strength data; Based on the degradation prediction model embedded in the digital twin model, the expected degradation mode and degradation magnitude of each data acquisition channel in the future within a preset time period are predicted according to the current environmental parameter vector, and shadow data segments of the corresponding acquisition period are generated to obtain benchmark reference data. A multi-mode redundant acquisition method is used to collect measured data during the tensioning process, and high-reliability data protection is implemented for the measured data at the edge of the precast concrete steel box girder; wherein, the edge refers to the corner area and edge area of ​​the precast concrete steel box girder. The measured data is input into the digital twin model and verified with the shadow data segment from the same period to obtain the current verification result; The integrity of the measured data is classified according to the current verification result, and the corresponding adaptive handling strategy is executed according to the classification result; wherein, the integrity classification includes three levels: complete, suspicious and unreliable; The precast concrete steel box girder is tensioned according to the corresponding adaptive handling strategy.

[0007] The technical solution of this invention integrates and maps BIM models, parametric models of tensioning equipment, and multi-source environmental data to form a comprehensive digital twin, enabling more precise and real-time understanding of box girder structures, equipment status, and environmental factors. In traditional operations, equipment performance degradation is often only detected after data anomalies occur, and it is difficult to quantify the specific impact of environmental factors on degradation. For example, in the low-temperature environment of location A, sensors may experience zero drift, but traditional methods struggle to predict this drift before data acquisition. This method can predict the expected degradation mode and magnitude of each data acquisition channel within a preset time period based on the current environmental parameter vector, and generate shadow data segments as a benchmark. This forward-looking predictive capability allows operators to understand potential data deviations in advance and provides a more environmentally adaptable and ideal reference for subsequent data verification. The multi-mode redundant acquisition method and highly reliable data protection mechanism significantly improve the reliability and integrity of data acquisition. In the case of unstable communication signals at location A, traditional single communication modes are prone to data loss or transmission interruption. This method employs multi-modal redundant acquisition and high-reliability data protection at the edge for measured data, such as using a ring buffer combined with a segmented hash tree for storage. Even under extreme communication constraints, it maximizes the integrity and traceability of critical data, avoiding operational interruptions or quality risks due to data loss. Its intelligent level is demonstrated by classifying the measured data for integrity and implementing adaptive handling strategies. Traditional methods typically only perform simple pass / fail judgments, with relatively simple handling strategies. For example, when data is abnormal, the only option might be to suspend the operation. This method verifies the measured data against shadow data segments and classifies the data into three levels—complete, questionable, and unreliable—based on the verification results, implementing differentiated adaptive handling strategies for each level. For example, in the low-temperature environment of location A, when data is determined to be unreliable, the system can use shadow data as a transitional value and automatically adjust the tensioning rate and extend the holding time, thereby maintaining operational continuity while ensuring operational quality. This refined judgment and adaptive adjustment capability effectively solves the problem of insufficient or over-responding response of traditional methods in complex environments, improving the efficiency and safety of tensioning operations. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0009] Figure 1A flowchart of the tensioning operation method for precast concrete steel box girders provided by the present invention.

[0010] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0012] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.

[0013] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text includes three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0014] Please see Figure 1 This invention proposes a method for tensioning precast concrete steel box girders, characterized in that the method is applied to a box girder prestressing tensioning scenario under a preset environment, the preset environment including at least one of a high temperature environment, a low temperature environment, a strong wind environment, a strong vibration environment, and a communication-restricted environment; The method for tensioning precast concrete steel box girders includes the following steps: S100. Integrate and map the established building information model of the precast concrete steel box girder, the parametric model of the tensioning equipment, and the multi-source environmental data to form a target digital twin model; wherein, the parametric model of the tensioning equipment has built-in degradation parameters of each device, and the multi-source environmental data includes temperature data, vibration data, wind speed and direction data, and communication signal strength data. S200. Based on the degradation prediction model embedded in the digital twin model, predict the expected degradation mode and degradation magnitude of each data acquisition channel in a future preset period according to the current environmental parameter vector, and generate shadow data segments for the corresponding acquisition period to obtain benchmark reference data. S300. A multi-mode redundant acquisition method is used to collect measured data during the tensioning process, and high-reliability data protection is implemented on the measured data at the edge of the precast concrete steel box girder; wherein, the edge refers to the corner area and edge area of ​​the precast concrete steel box girder. S400. Input the measured data into the digital twin model and verify it with the shadow data segment of the same period to obtain the current verification result; S500. Based on the current verification result, the measured data is classified into three levels: complete, suspicious, and unreliable. S600. Tensioning operation is performed on the precast concrete steel box girder according to the corresponding adaptive handling strategy.

[0015] Specifically, Building Information Modeling (BIM) models first integrate and map the existing BIM model of the precast concrete steel box girder, the parametric model of the tensioning equipment, and multi-source environmental data to form a target digital twin model. In traditional construction management, BIM models are mainly used for design and construction planning, while tensioning equipment information and environmental data are often managed independently or only simply correlated. For example, the BIM model can provide the geometric dimensions and duct locations of the box girder, while tensioning equipment information may be limited to equipment model and basic performance parameters. Multi-source environmental data may be collected through independent sensors, but lacks deep integration with the BIM model and equipment model. This separate management approach makes it difficult to comprehensively and in real-time grasp the overall status and potential risks of the tensioning operation.

[0016] The parametric model of tensioning equipment incorporates degradation parameters for each component. This multi-source environmental data includes temperature, vibration, wind speed and direction, and communication signal strength data. In existing technologies, tensioning equipment is typically used according to factory calibration parameters, with little consideration given to performance degradation under actual operating conditions. For example, sensors may experience measurement drift in high or low temperature environments, but this drift is usually not quantified in real time and incorporated into data processing. While environmental data may be collected, its correlation with equipment performance degradation is often insufficient, leading to an inability to accurately assess the impact of the environment on the reliability of data acquisition.

[0017] Based on the degradation prediction model embedded in this digital twin model, the expected degradation mode and magnitude of each data acquisition channel within a preset future time period are predicted according to the current environmental parameter vector. This generates shadow data segments for the corresponding acquisition cycle, obtaining benchmark reference data. In traditional tensioning operations, data anomalies are usually only discovered after they occur, and it is difficult to trace their root cause to determine whether it is related to equipment degradation or environmental impact. For example, when sensor readings deviate, manual calibration or replacement may be required, interrupting operations. The lack of prediction of future degradation trends prevents operators from taking preventative measures in advance and from obtaining ideal data that considers environmental impact as a reference.

[0018] Subsequently, a multi-mode redundant acquisition method was used to collect measured data during the tensioning process, and high-reliability data protection was implemented at the edge of the precast concrete steel box girder. In traditional tensioning operations, data acquisition typically relies on a single sensor or a single communication link. When a sensor fails or communication is interrupted, critical data may be lost, causing the tensioning process to be unable to continue or data integrity to be compromised. For example, in environments with strong vibrations, sensor connectors may loosen, leading to unstable data transmission; in areas with limited communication, data may not be uploaded in a timely manner. Traditional local storage methods may not be effective in addressing the risk of data loss in extreme environments.

[0019] The measured data is input into the digital twin model and verified against the corresponding shadow data segment to obtain the current verification result. In existing technologies, the verification of measured data is typically based on preset fixed thresholds or historical experience values. For example, tension or elongation exceeding a certain range is marked as abnormal. However, this verification method fails to fully consider the impact of the current environment on equipment performance and cannot distinguish between genuine anomalies and normal deviations caused by the environment. The lack of comparison with the "shadow data segment" limits the accuracy and guidance of the verification results.

[0020] Based on the current verification result, the measured data is classified into three integrity levels: complete, questionable, and unreliable. In traditional tensioning operations, data quality assessment is often binary (normal or abnormal), lacking detailed classification. When data is deemed abnormal, the usual handling strategies are also relatively simple, such as suspending operations or manual intervention. This crude assessment and handling approach may lead to overreaction or underreaction, affecting the continuity and efficiency of the operation.

[0021] The precast concrete steel box girder is tensioned according to the corresponding adaptive handling strategy. Without such a strategy, when data anomalies or environmental changes occur during tensioning, operators may spend considerable time analyzing and making decisions, and might even take inappropriate measures. For example, in low-temperature environments, increased hydraulic oil viscosity may cause a delay in displacement response; if loading continues at the conventional rate, it may lead to overshoot or undershoot of the tension force. This method, through a preset adaptive strategy, can automatically adjust the tensioning parameters based on data integrity levels and environmental factors, ensuring a smooth operation.

[0022] In this embodiment, the BIM model, the parametric model of the tensioning equipment, and multi-source environmental data are integrated and mapped to form a comprehensive digital twin, enabling a more precise and real-time understanding of the box girder structure, equipment status, and environmental factors. In traditional operations, equipment performance degradation is often only detected after data anomalies occur, and it is difficult to quantify the specific impact of environmental factors on degradation. For example, in the low-temperature environment of location A, sensors may experience zero drift, but traditional methods struggle to predict this drift before data acquisition. This method can predict the expected degradation mode and magnitude of each data acquisition channel within a preset time period based on the current environmental parameter vector, and generate shadow data segments as a benchmark reference. This forward-looking predictive capability allows operators to understand potential data deviations in advance and provides a more environmentally adaptable and ideal reference for subsequent data verification. The multi-mode redundant acquisition method and highly reliable data protection mechanism significantly improve the reliability and integrity of data acquisition. In the case of unstable communication signals at location A, traditional single communication modes are prone to data loss or transmission interruption. This method employs multi-modal redundant acquisition and high-reliability data protection at the edge for measured data, such as using a ring buffer combined with a segmented hash tree for storage. Even under extreme communication constraints, it maximizes the integrity and traceability of critical data, avoiding operational interruptions or quality risks due to data loss. Its intelligent level is demonstrated by classifying the measured data for integrity and implementing adaptive handling strategies. Traditional methods typically only perform simple pass / fail judgments, with relatively simple handling strategies. For example, when data is abnormal, the only option might be to suspend the operation. This method verifies the measured data against shadow data segments and classifies the data into three levels—complete, questionable, and unreliable—based on the verification results, implementing differentiated adaptive handling strategies for each level. For example, in the low-temperature environment of location A, when data is determined to be unreliable, the system can use shadow data as a transitional value and automatically adjust the tensioning rate and extend the holding time, thereby maintaining operational continuity while ensuring operational quality. This refined judgment and adaptive adjustment capability effectively solves the problem of insufficient or over-responding response of traditional methods in complex environments, improving the efficiency and safety of tensioning operations.

[0023] In one embodiment, the step of integrating and mapping the established building information model of the precast concrete steel box girder, the parametric model of the tensioning equipment, and multi-source environmental data to form a target digital twin model includes: A box girder model of the precast concrete steel box girder is established based on the BIM platform; wherein, the box girder model includes the coordinates of the ducts, the spatial positioning coordinates of the steel strands, the position coordinates of the anchors, the layout coordinates of the jacks, and the coordinates of the sensor pre-embedded points; The tensioning equipment is integrated into the box girder model in the form of a parametric family or components to form a digital twin sub-model of the equipment; wherein, the degradation parameters include temperature drift coefficient, vibration sensitivity and calibration curve; Real-time access to multi-source environmental data from the site; wherein, the multi-source environmental data also includes surface and internal temperatures of multiple points on the beam, triaxial vibration acceleration, and extreme weather forecast data obtained through BeiDou or meteorological stations; A lightweight rendering engine is used to achieve synchronous mapping between the physical world and the digital twin, forming the three-field coupled digital twin.

[0024] Specifically, a box girder model of the precast concrete steel box girder is established based on a BIM platform. This aims to accurately digitally represent the geometry and internal structure of the precast concrete steel box girder, providing a high-precision structural foundation for the digital twin. The various coordinate information included is crucial for achieving accurate mapping between the physical world and the digital twin. These coordinates define the precise spatial positions of the prestressing system (duct coordinates, steel strand spatial positioning coordinates, anchorage position coordinates), tensioning equipment (jack placement coordinates), and monitoring system (sensor embedded point coordinates) within the box girder. This box girder model can be created using specialized BIM software. Engineers input the box girder's geometric dimensions and material properties based on design drawings and accurately annotate the three-dimensional coordinates of key components. Alternatively, actual three-dimensional point cloud data of the box girder can be obtained through technologies such as laser scanning or photogrammetry. This data can then be converted into a BIM model using point cloud processing software, and key coordinates can be extracted and annotated on this basis.

[0025] Integrating tensioning equipment into the box girder model as parametric families or components creates digital twin sub-models. This means that these devices not only have a geometric representation in the digital twin but also adjustable parameters and behavioral logic, enabling them to simulate the actual working state and performance of the equipment. Degradation parameters (temperature drift coefficient, vibration sensitivity, calibration curves) are quantitative indicators of equipment performance changes with environment or time. They are the basis for predicting performance degradation under specific environments and are crucial for ensuring the accuracy and safety of tensioning operations. In the BIM platform, parametric family files can be created for various tensioning devices. These family files not only contain the geometric model of the equipment but also embed its working parameters, performance curves, and degradation models under different environmental conditions. Alternatively, detailed 3D models and behavioral models of the equipment can be created using independent equipment modeling software, then exported to a format recognizable by the BIM platform, and integrated with the box girder model.

[0026] Real-time access to multi-source environmental data from the site aims to obtain the actual environmental conditions at the tensioning operation site. This data serves as input for the digital twin to perform environmental adaptability analysis and degradation prediction. Multi-point surface and internal temperatures of the beam reflect the uneven heating and thermal stress distribution of the box girder; triaxial vibration acceleration monitors the dynamic response of the box girder during tensioning or under external environmental influences; extreme weather forecast data provides early warnings of future environmental changes, enabling the digital twin to conduct risk assessments and adjust strategies in advance. Various sensor networks can be deployed, including thermocouples or infrared thermometers to collect surface and internal beam temperatures, and accelerometers to collect triaxial vibration data, which are transmitted in real-time to edge computing gateways or data acquisition servers via wired or wireless means. Furthermore, regional extreme weather forecast data can be obtained from external data sources through API interfaces or data sharing protocols and integrated into the digital twin platform.

[0027] A lightweight rendering engine is used to synchronously map the physical world to the digital twin, forming a three-field coupled digital twin. Its function is to efficiently and synchronously present the real-time states of the box girder, tensioning equipment, and environment in the physical world in a visual manner within the digital twin. Forming a "three-field coupled digital twin" means deeply integrating and interacting the three key elements—structural field (box girder model), equipment field (tensioning equipment sub-model), and environmental field (multi-source environmental data)—within the digital twin, enabling it to comprehensively and dynamically reflect the complex behavior of the physical entity. Rendering engines based on technologies such as WebGL or OpenGL ES can be used to visualize the BIM model, equipment sub-model, and real-time environmental data. Real-time data collected by sensors and equipment states are mapped onto the digital twin model through data interfaces, and rendered in real-time using color changes, animations, or numerical labels. Alternatively, a game engine can be used for custom development, leveraging its powerful rendering capabilities and physics engine to achieve more realistic and interactive digital twin visualization.

[0028] In one embodiment, the degradation prediction model includes a sensor zero drift and hysteresis model set for high temperature environment, a hydraulic oil viscosity-displacement response delay model set for low temperature environment, a connector loosening probability and cable fatigue cumulative damage model set for strong vibration environment, and a packet loss rate and bit error rate model set for weak signal or electromagnetic interference environment. The shadow data segment is a five-dimensional data segment containing force, elongation, oil pressure, displacement and time. The acquisition period is 1 to 5 seconds, and the preset time period is the next 5 to 30 minutes.

[0029] Specifically, the sensor zero-drift and hysteresis model designed for high-temperature environments aims to quantify and predict the shift (zero drift) of the sensor's output signal and the lag in response speed under high-temperature conditions. Zero drift refers to the phenomenon that the sensor's output value deviates from the calibrated zero point or reference value as the temperature rises when there is no input or a constant input. Hysteresis refers to the phenomenon that the sensor's output response lags behind the actual temperature change, or that the output curves do not coincide during heating and cooling. This model can be implemented by establishing a mathematical relationship between temperature, zero drift, and hysteresis time, for example, using polynomial fitting, neural network models, or lookup tables based on experimental data. Through this model, the output deviation of the sensor under specific high-temperature environments can be predicted, thereby compensating for or correcting the collected data.

[0030] A hydraulic oil viscosity-displacement response delay model designed for low-temperature environments is primarily used to describe the impact of increased hydraulic oil viscosity on the displacement response speed and accuracy of hydraulic systems (such as jacks) under low-temperature conditions. Low temperatures reduce the fluidity of hydraulic oil, increasing system resistance and thus slowing the extension and retraction speed of the jack, resulting in a delay in displacement response. This model, based on fluid mechanics principles and incorporating the viscosity-temperature characteristic curve of hydraulic oil, establishes the relationship between oil temperature, viscosity, and displacement response delay time. For example, the functional relationship between viscosity and temperature can be fitted using experimental data, and then viscosity can be used as a parameter input into the dynamic model of the hydraulic system to predict its displacement response delay at low temperatures.

[0031] This study establishes a connector loosening probability model and a cable fatigue cumulative damage model specifically designed for high-vibration environments. These models assess and predict the probability of electrical connectors (such as connectors between sensors and data acquisition equipment) becoming loose and causing poor contact under sustained high vibration, as well as the cumulative degree of fatigue damage and eventual failure of connecting cables due to repeated bending or stretching. The connector loosening probability model can be established using statistical methods or reliability engineering models based on factors such as vibration intensity, vibration frequency, vibration duration, and connector type. The cable fatigue cumulative damage model can employ fatigue cumulative damage theories such as Miner's law, combined with the cable material's SN curve and actual vibration stress spectrum, to predict the cable's remaining life or degree of damage.

[0032] This model, designed for environments with weak signals or electromagnetic interference, aims to predict the probability of packet loss (packet loss rate) and bit error rate (bit error rate) during data transmission in harsh communication environments with weak signal strength or strong electromagnetic interference. The model can be established based on a wireless communication channel model, combined with parameters such as signal strength, noise level, interference source characteristics, and modulation / demodulation methods, through theoretical calculations or statistical analysis of measured data. For example, the data transmission quality under specific communication environments can be predicted based on the relationship curve between signal-to-noise ratio (SNR) and bit error rate (BER).

[0033] In this embodiment, the data integrity and reliability of precast concrete steel box girder tensioning operations under extreme environments are improved. By introducing refined degradation prediction models for different extreme environments, the digital twin model can more accurately simulate and predict the performance degradation of sensors, hydraulic systems, connectors, and communication links under specific environments, thereby generating a more realistic five-dimensional shadow data segment. This five-dimensional shadow data segment, which includes force, elongation, hydraulic pressure, displacement, and time, is generated with a collection cycle of 1-5 seconds and a preset time period of 5-30 minutes, providing a high-precision, multi-dimensional, and timely benchmark reference for the verification of measured data. This enables the system to more accurately identify the real anomalies in measured data when facing complex environments such as high temperature, low temperature, strong vibration, weak signal, or electromagnetic interference, rather than misjudging normal deviations caused by environmental factors as data errors. This improves the accuracy of data integrity classification and provides a more reliable basis for the execution of subsequent adaptive handling strategies, effectively ensuring the quality and safety of precast concrete steel box girder tensioning operations.

[0034] In one embodiment, the step of acquiring measured data of the tensioning process using a multi-mode redundant acquisition method and performing high-reliability data protection on the measured data at the edge of the precast concrete steel box girder includes: Deploy industrial-grade wide-temperature vibration-resistant sensors and edge computing gateways, and implement an adaptive multi-mode communication switching strategy based on 5G signal transmission. Specifically, industrial-grade wide-temperature and vibration-resistant sensors are measurement devices specifically designed for stable operation in harsh industrial environments. Their "industrial-grade" characteristics mean they possess high reliability, long lifespan, and resistance to harsh environments; "wide-temperature" refers to their ability to maintain measurement accuracy and stability over a wide temperature range, unaffected by extreme high or low temperatures; and "vibration-resistant" means their internal structure and packaging design effectively resist mechanical vibration and shock, preventing measurement errors or equipment damage caused by vibration. These sensors ensure continuous and accurate acquisition of key physical quantities during the tensioning process under preset extreme environments such as high temperatures, low temperatures, and strong vibrations, providing high-quality measured data input for digital twin models. For example, sensors manufactured using MEMS (Micro-Electro-Mechanical Systems) technology, combined with special packaging materials and structural designs, can improve their performance under wide-temperature and high-vibration conditions; alternatively, sensors based on fiber optic sensing principles, such as fiber Bragg grating (FBG) sensors, can be selected. These are inherently unaffected by electromagnetic interference, and through special fiber coatings and packaging designs, wide-temperature and vibration-resistant performance can be achieved.

[0035] An edge computing gateway is an intelligent device deployed near the data source (i.e., at the "edge"), integrating functions such as data acquisition, preprocessing, local storage, protocol conversion, and network communication. It can perform preliminary calculations and analysis where the data is generated, reducing reliance on cloud servers and lowering network bandwidth requirements and communication latency. At the edge of a precast concrete steel box girder, the edge computing gateway receives data from industrial-grade wide-temperature vibration sensors, performs real-time data preprocessing, local storage, and high-reliability data protection, and uploads the processed data according to the appropriate communication mode based on the communication strategy. It provides the first line of defense for data processing and protection at the data source. For example, an ARM-based embedded system can be used as the hardware platform, running a Linux operating system and integrating multiple communication interfaces, with data processing logic and communication protocol stack implemented in software; or an FPGA (Field-Programmable Gate Array) or DSP (Digital Signal Processor) can be used as the core processor to achieve more efficient and low-latency data processing capabilities, combined with industrial-grade Ethernet switches and wireless communication modules to build a gateway with edge computing capabilities.

[0036] The adaptive multi-mode communication switching strategy based on 5G signal transmission is an intelligent communication management mechanism. It uses 5G communication as the primary or preferred transmission mode, but can automatically and dynamically switch between multiple communication modes based on the real-time communication environment to ensure the continuity, reliability, and efficiency of data transmission. This strategy addresses the problem of interrupted or degraded data transmission during tensioning operations in communication-constrained environments. By prioritizing the high bandwidth and low latency characteristics of 5G for data transmission, and intelligently switching to other more reliable but lower bandwidth communication modes when 5G signal is weak, it ensures the continuous uploading of critical data and the effectiveness of highly reliable data protection. For example, a communication management module can be integrated into the edge computing gateway. This module continuously monitors the current 5G signal strength, signal-to-noise ratio, and other indicators, and presets the switching thresholds and priorities for different communication modes. When the 5G signal quality is detected to be lower than the preset threshold, the switching logic is automatically triggered to select the suboptimal communication mode for data transmission. Alternatively, software-defined radio (SDR) technology can be used to implement a programmable communication module in the edge computing gateway. Through software configuration and algorithm optimization, communication parameters and modes can be dynamically adjusted. Combined with machine learning algorithms, based on historical communication data and environmental characteristics, the communication quality change trend can be predicted, and communication mode pre-switching can be performed in advance.

[0037] In this embodiment, by deploying industrial-grade wide-temperature vibration-resistant sensors and an edge computing gateway at the edge of the precast concrete steel box girder, and implementing an adaptive multi-mode communication switching strategy based on 5G signal transmission, the aim is to improve the reliability and stability of the measured data acquisition and transmission during the tensioning process from the source. The industrial-grade wide-temperature vibration-resistant sensors are configured to directly acquire high-precision tensioning data in extreme environments. Their design ensures that measurement accuracy remains even under conditions of drastic temperature changes or strong vibrations, thus providing high-quality raw input for subsequent data processing. The data acquired by these sensors is transmitted in real time to the edge computing gateway adjacent to the data source. The edge computing gateway not only performs preliminary preprocessing and local storage of this raw data, but more importantly, it integrates a multi-mode communication module and runs an adaptive communication switching strategy. This strategy prioritizes 5G communication, leveraging its high bandwidth and low latency advantages to achieve rapid and full data upload. However, considering the communication uncertainties in extreme environments, when 5G signal strength is insufficient or interfered with, the edge computing gateway can intelligently switch to other backup communication modes, such as LoRa or BeiDou short message service, to ensure the continuous transmission of critical data. Even under bandwidth constraints, it can upload compressed feature values ​​or data summaries, thereby maintaining the connection with the digital twin model and the integrity of the data stream. This deployment method enables data to be collected, pre-processed, and protected with high reliability from the moment it is generated, and ensures the continuity of data transmission through intelligent communication strategies. It effectively overcomes the risks of sensor failure and communication interruption in extreme environments, providing stable and reliable measured data input for the digital twin model, thereby supporting accurate decision-making and safe implementation of tensioning operations.

[0038] In one embodiment, the step of deploying an industrial-grade wide-temperature vibration-resistant sensor and an edge computing gateway, and implementing an adaptive multi-mode communication switching strategy based on 5G signal transmission, includes: When the 5G signal strength is greater than or equal to the preset signal threshold, the 5G communication mode is used to perform real-time full data upload. When the 5G signal strength is lower than the preset signal threshold, switch to LoRa communication mode and upload compressed feature value and hash root; When there is no ground communication signal, switch to BeiDou short message communication mode to upload data block summary, alarm information and key features.

[0039] Specifically, 5G signal strength refers to the received signal strength of the 5G wireless communication network, typically measured by metrics such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), or Signal-to-Interference-plus-Noise Ratio (SINR). Its level directly impacts data transmission rate and reliability. A preset signal threshold is a pre-defined critical value used to determine whether the 5G signal strength meets the requirements for real-time full data upload. This threshold can be dynamically or statically configured based on the actual application scenario, data transmission volume, real-time requirements, and network service quality (QoS) standards. For example, it can be set to an RSRP higher than -90dBm or an SINR higher than 10dB. 5G communication mode refers to the method of data transmission using fifth-generation mobile communication technology, characterized by high bandwidth, low latency, and wide connectivity, suitable for real-time, large-capacity full data upload. Real-time full upload means that after data collection, all raw or uncompressed data is immediately uploaded to the cloud or central server via the 5G network to ensure data integrity and real-time performance. LoRa communication is a low-power wide-area network (LPWAN) communication technology characterized by long transmission distance, low power consumption, and strong anti-interference capabilities. However, its bandwidth is relatively low, making it suitable for transmitting small data packets, such as compressed feature values ​​and hash roots. Compressed feature values ​​refer to key data points or statistics extracted from raw measured data, which have been compressed or simplified. They represent the core information of the original data, but the data volume is much smaller than the original data. For example, they can be the extraction and encoding of peak values, average values, and rates of change of data such as force, elongation, oil pressure, and displacement. A hash root is a data structure that obtains a unique hash value by performing hash operations on data blocks and aggregating them layer by layer. Hash roots can be used to quickly verify the integrity and consistency of data blocks without transmitting all the original data. The absence of terrestrial communication signals refers to situations in certain extreme or remote areas where communication networks relying on terrestrial base stations, such as 5G and LoRa, are unavailable or have extremely low signal strength, making effective communication impossible. BeiDou short message communication mode refers to a data transmission method that utilizes the short message communication function of the BeiDou satellite navigation system. It does not rely on ground base stations and features wide coverage and strong disaster resistance, but its transmission rate and data volume are limited. A data block digest is a fixed-length hash value obtained by hashing a data block, used to verify the integrity of the data block. Alarm information refers to a brief description of abnormalities or potential risks detected during tensioning, such as overload, exceeding limits, or equipment failure. Key features refer to the most critical parameters that best reflect the tensioning status and safety, selected from measured data to ensure the transmission of basic information in extreme situations without ground communication signals. For example, only the current tension force, elongation, and whether the target value has been reached can be uploaded.

[0040] In this embodiment, an adaptive multi-mode communication switching strategy is constructed to ensure that critical data can be effectively transmitted during the tensioning of precast concrete steel box girders, regardless of changes in the communication environment. Specifically, the system continuously monitors the 5G signal strength. When the 5G signal strength meets a preset threshold, the high bandwidth and low latency characteristics of the 5G network are utilized to achieve real-time full uploading of the measured data during the tensioning process. This ensures that the digital twin model can obtain the most detailed and real-time physical world data for accurate verification and analysis. Once the 5G signal strength drops below the preset threshold, the system intelligently switches to LoRa communication mode. In this mode, considering the bandwidth limitations of LoRa, the system no longer uploads the full data, but instead uploads compressed feature values ​​and hash roots used for data integrity verification. This approach effectively reduces the data volume while ensuring the transmission of critical information, adapting to the transmission needs in weak signal environments. Furthermore, in extreme cases, i.e., when there is no ground communication signal at all, the system will switch to BeiDou short message communication mode. Although BeiDou short message communication has limitations in transmission rate and data volume, its satellite communication characteristics allow it to operate without the constraints of ground base stations, providing basic communication guarantees. In this case, the system prioritizes uploading data block summaries, alarm information, and the most critical tensioning characteristic data, ensuring timely transmission of core status and safety warning information even under the worst communication conditions. This hierarchical, adaptive communication strategy, combined with a highly reliable data protection mechanism at the edge, constitutes a crucial link in ensuring the integrity and reliability of tensioning data in complex environments. In this way, even in communication-constrained environments, the digital twin model can continuously receive data of different granularities, maintaining its mapping and predictive capabilities of the physical world, thus providing data support for the integrity classification and adaptive handling strategies of tensioning operations.

[0041] In one embodiment, the step of acquiring measured data of the tensioning process using a multi-mode redundant acquisition method and performing high-reliability data protection on the measured data at the edge end of the precast concrete steel box girder further includes: The measured data corresponding to the edge end is protected by using a ring buffer combined with a segmented hash tree and industrial-grade solid-state storage. The storage capacity of the ring buffer combined with the segmented hash tree is at least enough to cover the data volume of 2 to 3 complete tensioning processes. An independent data block is generated every 30 to 60 seconds, and a hash root is calculated for each data block and they are chained together. Forced write protection is applied to the data blocks corresponding to critical abnormal events; wherein, the critical abnormal events include out-of-tolerance events, alarm events, and communication interruption events, thus completing the data protection operation.

[0042] Specifically, a circular buffer is a data structure characterized by new data overwriting the oldest data when the storage space is full. Its function is to provide a continuous and efficient temporary storage area for temporarily storing real-time acquired test data, ensuring smooth data stream processing, and effectively preventing data overflow or loss during temporary communication interruptions or processing delays. For example, a fixed-size memory array or a circular queue based on a linked list can be used to construct a circular buffer. A segmented hash tree is a data structure that segments data, calculates the hash value of each segment, and then builds these hash values ​​upwards into a tree structure, ultimately obtaining a unique hash root. Its function is to provide an efficient and tamper-proof data integrity verification mechanism, capable of quickly detecting any subtle changes to data blocks, ensuring the authenticity and reliability of the test data. For example, cryptographic hash algorithms such as SHA-256 can be used to hash data segments and construct a binary hash tree. Industrial-grade solid-state storage refers to high-reliability, high-durability storage devices specifically designed for harsh industrial environments. Its function is to provide long-term, stable, non-volatile storage for measured data collected at the edge, capable of withstanding harsh environmental conditions such as wide temperature range, high vibration, and shock, ensuring that data is not lost in the event of power failure or system failure. For example, SLC (single-cell) NAND flash memory chips can be used in conjunction with a professional firmware controller, or embedded storage modules with wide temperature range and vibration-resistant design can be used. Data protection refers to ensuring the integrity, availability, and confidentiality of data through a series of technical means. In this context, data protection aims to prevent the loss, damage, or unauthorized modification of measured data during acquisition, transmission, and storage, thereby ensuring the safety and reliability of tensioning operations. The storage capacity of the ring buffer combined with the segmented hash tree covers at least the data volume of 2-3 complete tensioning processes. This feature is designed to ensure that, even in extreme cases, if the main communication link is interrupted for a long time, the edge can still locally store a sufficient amount of critical tensioning data. Its function is to provide sufficient data redundancy and backtracking capabilities, so that data can be retransmitted after communication is restored or detailed analysis can be performed locally, thereby minimizing the impact of data loss on the integrity of tensioning operations. Generating independent data blocks involves dividing continuously collected test data into independent data units at preset time intervals (e.g., 30-60 seconds). This divides massive amounts of real-time data into manageable and processable logical units, facilitating hash calculations, storage management, and transmission. Calculating the hash root involves using a cryptographic hash algorithm to calculate a unique hash root for each independently generated data block. This provides a digital fingerprint for each data block; any modification to the data block content will result in a change to the hash root, enabling rapid verification of data integrity. Chaining associates the hash root of the current data block with the hash root of the previous data block, forming an immutable chain.Its function is to establish the temporal order and logical relationship between data blocks, further enhancing the data's anti-tampering capability. Once any data block in the chain is tampered with, the subsequent link relationship will become invalid, thus exposing the tampering behavior. For example, the hash root of the previous data block can be used as one of the inputs for the hash calculation of the current data block. Critical anomalies refer to special situations that occur during tensioning operations and may have a significant impact on structural safety, construction quality, or equipment operation. Its function is to identify and mark data that requires special attention and permanent preservation, ensuring that this critical information is not accidentally overwritten or deleted. Forced write-protected storage refers to imposing an immutable attribute on specific data blocks, making them impossible to erase, modify, or overwrite after storage. Its function is to ensure the originality and non-repudiation of data related to critical anomalies, providing reliable original evidence for subsequent accident analysis, accountability, and quality assessment. For example, this can be achieved through the read-only attribute of a file system, the physical write protection mechanism of the storage medium, or blockchain technology. The critical anomalies include out-of-tolerance events, alarm events, and communication interruption events. Among them, out-of-tolerance events refer to events where key parameters such as tension, elongation, hydraulic pressure, and displacement exceed preset safety ranges or specification limits. Alarm events are events automatically detected and triggered by the system, indicating equipment failure, operational errors, or other potential risks. Communication interruption events refer to events where the communication link between the edge device and the central system fails, resulting in a temporary or prolonged interruption of data transmission.

[0043] In this embodiment, a highly reliable data protection mechanism is deployed at the edge of the precast concrete steel box girder, effectively addressing the integrity and reliability issues that may arise from measured data in harsh environments. Specifically, after the measured data during the tensioning process is collected, it is first temporarily stored in a circular buffer, ensuring the continuity of the data stream and the ability to handle instantaneous data surges. Simultaneously, to guarantee data integrity and tamper-proofness, this data is organized into independent data blocks at preset time intervals. For each data block, the system calculates its unique hash root and links these hash roots together in a chain-like manner, forming an immutable data chain. This mechanism allows any tampering with historical data to be quickly detected. Furthermore, all data processed by the circular buffer and hash tree is ultimately written to an industrial-grade solid-state storage device, which is capable of stable operation in harsh environments such as wide temperature ranges and high vibration, ensuring long-term reliable data preservation. In particular, for critical abnormal events such as out-of-tolerance events, alarm events, and communication interruption events, the system performs forced write-protected storage on the corresponding data blocks, further guaranteeing the originality and non-repudiation of key evidence. Through this multi-layered and multi-method data protection strategy, this application achieves highly reliable protection of measured data at the edge. Even in the event of communication restrictions or interruptions, it can ensure the integrity and traceability of the data, providing a solid data foundation for subsequent digital twin verification and adaptive handling strategies, and greatly improving the reliability and safety of the entire tensioning operation.

[0044] In one embodiment, the step of inputting the measured data into the digital twin model and verifying it with the concurrent shadow data segment to obtain the current verification result includes: Perform basic range and format checks on the measured data to remove hard out-of-limit values; The similarity score is obtained by calculating the similarity between the verified measured data and the shadow data segment of the same period. The degradation contribution of the similarity scores is analyzed to determine the environmental factors that cause the bias and their proportions. The integrity level is determined based on the similarity score, and the current verification result is obtained.

[0045] In this embodiment, the current verification result is obtained by inputting measured data into the digital twin model and verifying it with the concurrent shadow data segment. Specifically, firstly, basic range and format verification is performed on the measured data collected from the edge of the precast concrete steel box girder. This preliminary screening can effectively eliminate hard out-of-limit values ​​caused by sensor failure, transmission errors, or extreme interference, ensuring that the data for subsequent analysis has basic validity. Subsequently, the similarity between the pre-verified measured data and the concurrent shadow data segment generated by the digital twin model based on the current environmental parameter vector and the built-in degradation prediction model is calculated to obtain a quantitative similarity score. The shadow data segment is generated considering the expected degradation mode and degradation magnitude of each device in the current environment. Therefore, the similarity between the measured data and the shadow data segment can more accurately reflect whether the measured data conforms to the expected behavior under specific environmental conditions. When the similarity score is low, the system will further analyze the degradation contribution of the score. By reverse analysis of the degradation parameters and environmental factors in the digital twin model, the main environmental factors causing the deviation between the measured data and the shadow data segment and their respective contribution ratios are accurately determined. This analysis process enables the system not only to recognize data biases but also to understand their root causes. Finally, based on the similarity score and the results of degradation contribution analysis, the measured data is classified into complete, questionable, or unreliable levels to determine the current verification result. This series of steps forms a closed-loop data verification and diagnostic mechanism that fully leverages the simulation and prediction capabilities of digital twin models for complex physical world behaviors. Especially when considering equipment degradation and environmental impacts, it makes the reliability assessment of measured data more accurate and in-depth. In this way, this solution effectively addresses the challenges of data acquisition under pre-defined conditions, providing a solid data foundation for subsequent adaptive handling strategies and significantly improving the safety and reliability of tensioning operations.

[0046] In one embodiment, the step of performing integrity classification determination on the measured data based on the current verification result, and executing the corresponding adaptive handling strategy based on the determination result, includes: When the similarity score is greater than or equal to the first threshold, it is determined to be of the complete level; When the similarity score is less than the first threshold and greater than or equal to the second threshold, it is determined to be of a suspicious level; When the similarity score is less than the second threshold, it is determined to be of an untrustworthy level.

[0047] Specifically, the similarity score is a quantitative indicator measuring the degree of matching between the measured data and the corresponding shadow data segment. It reflects the reliability of the measured data and its consistency with expected behavior under the current environment. This score can be obtained through statistical methods such as calculating the Euclidean distance, cosine similarity, or Pearson correlation coefficient between the two data sequences. Alternatively, it can be evaluated using dynamic time warping (DTW) algorithms or machine learning-based pattern matching algorithms, resulting in a value between 0 and 1, with higher scores indicating higher similarity. The first threshold is a preset value used to distinguish between highly reliable data and generally reliable data; it is a key boundary for determining integrity grading. This threshold can be determined based on historical data analysis, expert experience, or simulation experiments, for example, set to 0.95, or dynamically adjusted using statistical process control (SPC) methods, combined with the allowable fluctuation range of the tensioning process and sensor accuracy. The second threshold is another preset value used to distinguish between generally reliable data and unreliable data. It is lower than the first threshold and serves as the lower limit for determining whether the data requires further intervention or replacement. This threshold can be determined based on engineering safety margins, data fault tolerance, or risk assessment results. For example, it can be set to 0.80, or it can be adaptively set by modeling sensor degradation patterns under different environmental conditions and combining the impact of data on the safety of tensioning operations. The "complete" level indicates that the measured data is highly consistent with the expected behavior, indicating high data quality and direct applicability to tensioning operation decisions. When the similarity score reaches or exceeds the first threshold, the system marks the data as "complete," indicating extremely high data reliability and requiring no special processing. The "suspicious" level indicates that the measured data deviates somewhat from the expected behavior, indicating moderate data quality and requiring further verification or cautious measures. When the similarity score is below the first threshold but still above or equal to the second threshold, the system marks the data as "suspicious," indicating potential anomalies and requiring attention. The "unreliable" level indicates that the measured data deviates significantly from the expected behavior, indicating poor data quality and unsuitability for direct use in tensioning operation decisions, potentially requiring replacement or suspension of operations. When the similarity score is below the second threshold, the system marks the data as "unreliable," indicating extremely low data reliability and requiring strong intervention measures.

[0048] In this embodiment, a tiered judgment mechanism is introduced to transform continuous similarity scores into discrete data integrity levels with clear operational guidance. Specifically, the system first obtains the similarity score between the measured data and the concurrent shadow data segment. This score quantifies the reliability of the measured data in the current environment. To transform this quantification result into an actionable decision-making basis, this scheme sets two key thresholds: a first threshold and a second threshold. When the similarity score reaches or exceeds the first threshold, it indicates that the measured data highly matches the expected behavior predicted by the digital twin model, and the data quality is extremely high, thus being judged as complete. This means that the data can be completely trusted and directly used for subsequent tensioning operation decisions. If the similarity score is lower than the first threshold but still higher than or equal to the second threshold, it indicates that there is a certain degree of deviation between the measured data and the expected behavior, but it has not yet reached a completely unacceptable level. At this time, the data is judged as suspicious, prompting the system or operators to further review the data or adopt a more cautious strategy. When the similarity score falls below the second threshold, it indicates a significant discrepancy between the measured data and the expected behavior, signifying extremely low data reliability. This suggests the data may have been severely affected by environmental factors or sensor malfunction. In this case, the data is classified as unreliable, and the system will trigger stricter handling strategies, such as activating backup data or suspending operations, to avoid safety risks caused by erroneous data. Through this tiered judgment mechanism, this solution simplifies the complex real-time data verification results into clear integrity levels, providing explicit input for subsequent adaptive handling strategies. This allows the system to intelligently adjust the tensioning operation process based on the actual reliability of the data, thereby ensuring the safety and accuracy of tensioning operations even in extreme environments. This tiered approach not only improves decision-making efficiency but also enhances the robustness of the entire tensioning operation method.

[0049] In one embodiment, the step of performing integrity classification determination on the measured data based on the current verification result and executing the corresponding adaptive handling strategy based on the determination result further includes: When the system is determined to be of the complete level, the measured data, along with the timestamp and hash root, will be directly stored in the certificate. When the data is determined to be of the aforementioned suspicious level, the measured data is marked with a verification pending flag, and a retest or resend instruction is sent to the site. When the level of untrust is determined, the shadow data segment of the same period is used as the engineering transition value, and the information related to the cause of the deviation is recorded. When the integrity classification result remains at the suspicious or unreliable level, an environmentally adaptive tensioning strategy is invoked from the strategy library. The environmentally adaptive tensioning strategy includes at least one of the following: reducing the graded loading rate, extending the pressure holding time, increasing the number of oil return and tensioning cycles, and pausing the current steel strand tensioning while prioritizing the holding of already completed steel strands.

[0050] Specifically, storing the measured data directly in the database along with its timestamp and hash root means that when the measured data is determined to be of integrity level, the system immediately and securely stores the data, along with its timestamp and hash root. The timestamp is precise time information recording the moment the data was generated or collected, ensuring the data's timeliness, traceability, and preventing data backtracking attacks. It can be automatically generated UTC time or time synchronized with the local time zone. The hash root is the root hash value of a cryptographic hash function tree, used to efficiently verify the integrity and consistency of large amounts of data. Through the hash root, it is possible to quickly verify whether a data block has been tampered with without transmitting all the data. This can be achieved by performing hierarchical hash calculations on the data blocks to obtain a unique root hash value. Storing the data in the database means securely storing the verified measured data and its associated metadata (such as timestamps and hash roots) in a persistent storage system as a formal, immutable record. This can be done by writing to a distributed ledger system, a blockchain database, or storing it in an industrial-grade database using encryption and digital signature technologies.

[0051] Marking the measured data with a "pending review" identifier and sending a retest or retransmission command to the field means that when measured data is determined to be suspicious, the system marks it with a "pending review" identifier and sends a retest or retransmission command to the field operators or relevant equipment. The "pending review" identifier is a status marker in the data management system, used to indicate that the data requires further review, confirmation, or processing by humans or the system. This identifier can be a Boolean value, an enumeration type, or a specific status code, stored in the data's metadata. Sending a retest or retransmission command to the field means sending a notification that data needs to be remeasured or retransmitted to the field operators or relevant equipment through automated or semi-automated methods. This can be done through pop-up prompts on the field human-machine interface (HMI), mobile application notifications, SMS messages, emails, or by directly sending commands to sensors or edge computing devices to trigger data re-acquisition or retransmission.

[0052] Using the shadow data segment as a transitional value and recording the correlation information of deviation causes means that when the measured data is determined to be unreliable, the system automatically switches to using the shadow data segment, which is generated by the digital twin model based on the current environmental parameters and corresponds to the time period of the measured data, to replace the measured data, in order to maintain the continuity of the tensioning process and the stability of control. The shadow data segment is ideal or expected data based on model prediction, and its function is to provide a reliable reference benchmark. Recording the correlation information of deviation causes means recording and storing relevant data such as specific environmental factors, equipment status, sensor fault information, and the time of occurrence that led to the unreliability of the measured data. This information may include, but is not limited to, ambient temperature, vibration intensity, communication signal strength, sensor ID, tensioning equipment operating parameters, etc., for subsequent fault diagnosis, cause analysis, and system optimization.

[0053] Retrieving environmentally adaptive tensioning strategies from the strategy library refers to the system invoking corresponding environmentally adaptive tensioning strategies from a pre-defined strategy library when the data integrity classification result remains at a suspicious or untrustworthy level. The strategy library is a collection of predefined and stored adjustment schemes for tensioning operations to cope with different environmental conditions and data anomalies. This strategy library can be stored on an edge computing gateway, local server, or cloud platform, and can be dynamically updated and expanded according to actual needs. Environmentally adaptive tensioning strategies are adjustment measures taken to ensure the safety and quality of tensioning operations under harsh environments or continuous data anomalies. These strategies aim to adapt to current adverse conditions by changing tensioning parameters or processes.

[0054] The environmentally adaptable tensioning strategy includes at least one of the following: reducing the staged loading rate, extending the holding time, increasing the number of back-oiling and supplementary tensioning cycles, and pausing the current tendon tensioning while prioritizing the holding of already completed tendons. Reducing the staged loading rate means slowing down the application of tension force, for example, by extending the interval between each loading stage or reducing the increment of force per loading stage. This helps provide the system with longer response and adjustment time in uncertain environments, reducing sudden stress. Extending the holding time means increasing the time spent holding the target tension force after it has been reached. This helps the prestress be more fully transferred and stabilized in the concrete, especially in environments with large temperature fluctuations or vibrations. Increasing the number of back-oiling and supplementary tensioning cycles means gradually reaching the final tension force through multiple small-amplitude tensioning-back-oiling-supplementary tensioning cycles. This method can reduce the impact of a single tensioning operation on the structure and equipment, improve the accuracy and safety of tensioning, and is particularly suitable for scenarios where material properties may be affected by the environment. Suspending current tendon tensioning and prioritizing the maintenance of tension in completed tendons refers to immediately halting ongoing tendon tensioning operations in extreme or high-risk situations, and focusing system resources and attention on monitoring and maintaining the prestress state of completed tendons to ensure their safety. This is a risk mitigation strategy to prevent greater losses in situations of extremely high uncertainty.

[0055] In this embodiment, an adaptive handling mechanism based on data integrity classification is established to effectively address the challenges of tensioning precast concrete steel box girders in complex environments. When measured data is verified against the shadow data segment generated by the digital twin model and is determined to be of the integrity level based on the similarity score, the system immediately stores the data along with its timestamp and hash root as evidence. This processing method ensures the authenticity, integrity, and immutability of key data in the tensioning process, providing a solid chain of evidence for subsequent quality traceability and acceptance. When measured data is determined to be of the doubtful level, it indicates that the data has a certain degree of anomaly, but has not yet reached the level of being completely unreliable. At this time, the system will not immediately stop the operation, but will mark the data with a verification mark and push a retest or resend instruction to the on-site operators. This mechanism allows on-site personnel to manually check or re-collect data, thereby confirming the data a second time without interrupting the operation, avoiding resource waste or operation delays caused by misjudgment, and improving data reliability. If the measured data is determined to be of the unreliable level, the system will immediately use the shadow data segment generated by the concurrent digital twin model as the engineering transition value. This means that even if serious problems arise in the measured data, the control logic of the tensioning operation can still continue to operate based on the predicted reliable data, ensuring the continuity of the operation. Simultaneously, the system records detailed information on the causes of deviations leading to unreliable data, providing valuable data for subsequent troubleshooting, environmental impact analysis, and model optimization. Furthermore, when the data integrity classification result remains at a questionable or unreliable level, it indicates that environmental conditions or equipment status may continue to deteriorate, posing a potential risk to the tensioning operation. In this case, the system will invoke environmentally adaptive tensioning strategies from a pre-set strategy library. These strategies, such as reducing the graded loading rate, extending the holding time, increasing the number of return oil replenishment cycles, or pausing the current steel strand tensioning and prioritizing the holding of completed steel strands, can dynamically adjust tensioning parameters and operational procedures according to the actual risk level and environmental characteristics. For example, in strong wind environments, reducing the loading rate can reduce the impact of wind on tensioning accuracy; in low-temperature environments, extending the holding time can ensure sufficient prestress transfer. This multi-layered, intelligent adaptive handling mechanism enables tensioning operations to maintain a high degree of safety, accuracy, and reliability even when facing extreme environments and data uncertainties, significantly improving the quality control level of prestressed tensioning construction in complex environments.

[0056] In one embodiment, after the steps of performing integrity classification determination on the measured data based on the current verification result and executing the corresponding adaptive handling strategy based on the determination result, the method further includes: The measured value, shadow value, and standard limit of the tension five-dimensional curve are rendered in real time in the digital twin. Render a complete cloud map of the entire beam data; during the rendering process, the beam is distinguished by coloring according to segments, steel strands, and ducts; Render anomaly heatmaps and pinpoint the exact location of jacks, sensors, or beam segments; Based on the aforementioned integrity proof chain, an integrity report for prestressed tensioning data of box girders in extreme environments is automatically generated; wherein, the report includes integrity rate statistics, abnormal segment location, and instructions for using shadow data, which serve as an attachment for quality acceptance and lifelong traceability.

[0057] Specifically, the real-time rendering of the comparison view between measured values, shadow values, and standard limits of the tensioning five-dimensional curve refers to the system's ability to instantly generate and display curves showing the changes of key parameters (such as force, elongation, hydraulic pressure, displacement, and time) over time during the tensioning process. Measured values ​​are the actually collected data, shadow values ​​are the expected data generated by the digital twin based on a degradation prediction model, and standard limits are the allowable ranges specified by engineering design or industry standards. By visualizing these three types of curves on the same interface, the consistency between the actual tensioning process and the expected and standard requirements can be intuitively compared. This can be achieved by receiving real-time data pushed from the backend through a front-end visualization framework (e.g., a JavaScript-based charting library such as ECharts or Plotly) and dynamically drawing curves in a web page or desktop application. The data update frequency can be consistent with the data acquisition cycle, for example, updating every 1-5 seconds. In addition, integration development can be carried out using the API interface of professional engineering visualization software to associate tensioning data with the BIM model, realizing the linkage display of the three-dimensional model and the two-dimensional curve, and providing a richer interactive experience.

[0058] Rendering a cloud map of the entire beam's data integrity involves color-coding segments, tendons, and ducts during the rendering process. This utilizes visualization tools to visually display the data integrity status of each component (such as segments, tendons, and ducts) of the precast concrete steel box girder. By color-coding different areas, regions with poor data quality or problems can be quickly identified. This can be achieved on a BIM model or a simplified 3D beam model, assigning different colors (e.g., green for complete, yellow for doubtful, red for unreliable) to each segment, tendon, or duct based on its data integrity classification (e.g., complete, questionable, unreliable). Another approach is to divide the beam into corresponding segment, tendon, and duct regions on a 2D plan view and color-code them according to their data integrity status, while providing legends explaining the meaning of different colors.

[0059] Rendering anomaly heatmaps and precisely locating specific jacks, sensors, or beam segments refers to a visualization method that uses variations in color depth or brightness to represent the degree of anomaly. This heatmap highlights jacks, sensors, or beam segments exhibiting anomalies during tensioning and provides precise location information for rapid problem identification and resolution. This can be achieved by coloring the corresponding jack, sensor, or beam segment area on the beam model or plan view based on the frequency, severity, or duration of anomaly events (e.g., out-of-tolerance, alarms, communication interruptions), with darker or brighter colors indicating a higher degree of anomaly. Alternatively, it can be combined with a BIM model, directly overlaying anomaly information onto the 3D model. When a user clicks or hovers over an anomaly area, detailed information such as the anomaly type, occurrence time, and related data is displayed, along with a view linking to the corresponding sensor or jack.

[0060] Based on the aforementioned integrity proof chain, an integrity report for prestressed tensioning data of box girders in extreme environments is automatically generated. This report includes integrity rate statistics, anomaly segment locations, and instructions for using shadow data. Serving as an appendix for quality acceptance and lifelong traceability, it utilizes an integrity proof chain constructed through data encryption and hash chains (e.g., hash trees) to ensure that data has not been tampered with during collection, transmission, storage, and processing, possessing traceability and non-repudiation. On this basis, the system automatically generates an authoritative and reliable tensioning data quality summary report for project quality acceptance and subsequent lifelong traceability. This report includes data integrity statistics, anomaly locations, and instructions for using shadow data. The system can periodically or automatically summarize the integrity grading results of all collected data after tensioning operations, calculate the overall integrity rate, and automatically generate a list and location of anomaly segments based on anomaly heatmaps and integrity cloud maps. Furthermore, for cases where shadow data was used as transitional values ​​in the project, the report will detail the time, reason, and scope of its use. The report can be generated as a PDF, Word, or other standard format document.

[0061] In this embodiment, based on digital twin modeling, degradation prediction, multi-modal redundant data acquisition, high-reliability data protection, data verification, and integrity grading, a comprehensive visualization feedback and automated reporting mechanism are further provided. After the measured data is verified and integrity grading is completed, the system uses the digital twin environment to render a real-time comparison view of the measured values, shadow values, and specification limits of the tension five-dimensional curve, allowing operators to instantly observe the differences between the actual changes in tension parameters and the expected values ​​and specification requirements. Simultaneously, to provide an overall overview of the structural data quality, the system renders a data integrity cloud map of the entire beam on the digital twin model, visually displaying the data integrity status of each part by coloring different segments, steel strands, and ducts. Furthermore, to accurately identify and locate problems, the system also renders an anomaly heatmap, precisely locating abnormal events (such as out-of-tolerance, alarms, and communication interruptions) during tensioning to specific jacks, sensors, or beam segments. Ultimately, based on the integrity verification chain established during the data protection phase, the system automatically generates an integrity report for the prestressed tensioning data of box girders in extreme environments. This report details the integrity rate statistics, anomaly segment location, and the usage of shadow data, serving as an authoritative appendix for project quality acceptance and lifelong traceability. This series of visualization and reporting functions transforms raw data and judgment results into intuitive, actionable information and verifiable documents, effectively solving the problems of opaque monitoring of the tensioning process, untimely problem location, and difficulties in quality traceability in complex environments.

[0062] The above description is merely an exemplary embodiment of the present invention and does not limit the scope of the present invention. Any equivalent structural transformations made based on the technical concept of the present invention and the contents of the specification and drawings of the present invention, or direct / indirect applications in other related technical fields, are included within the protection scope of the present invention.

Claims

1. A method for tensioning precast concrete steel box girders, characterized in that, The operation method is applied to the prestressing tensioning of box girders under a preset environment, which includes at least one of the following: high temperature environment, low temperature environment, strong wind environment, strong vibration environment, and communication-restricted environment. The method for tensioning precast concrete steel box girders includes the following steps: The established building information model of the precast concrete steel box girder, the parametric model of the tensioning equipment, and the multi-source environmental data are integrated and mapped to form a target digital twin model; wherein, the parametric model of the tensioning equipment has built-in degradation parameters of each device, and the multi-source environmental data includes temperature data, vibration data, wind speed and direction data, and communication signal strength data; Based on the degradation prediction model embedded in the digital twin model, the expected degradation mode and degradation magnitude of each data acquisition channel in the future within a preset time period are predicted according to the current environmental parameter vector, and shadow data segments of the corresponding acquisition period are generated to obtain benchmark reference data. A multi-mode redundant acquisition method is used to collect measured data during the tensioning process, and high-reliability data protection is implemented for the measured data at the edge of the precast concrete steel box girder; wherein, the edge refers to the corner area and edge area of ​​the precast concrete steel box girder. The measured data is input into the digital twin model and verified with the shadow data segment from the same period to obtain the current verification result; The integrity of the measured data is classified according to the current verification result, and the corresponding adaptive handling strategy is executed according to the classification result; wherein, the integrity classification includes three levels: complete, suspicious and unreliable; The precast concrete steel box girder is tensioned according to the corresponding adaptive handling strategy.

2. The method for tensioning precast concrete steel box girders as described in claim 1, characterized in that, The step of integrating and mapping the established building information model of the precast concrete steel box girder, the parametric model of the tensioning equipment, and multi-source environmental data to form a target digital twin model includes: A box girder model of the precast concrete steel box girder is established based on the BIM platform; wherein, the box girder model includes the coordinates of the ducts, the spatial positioning coordinates of the steel strands, the position coordinates of the anchors, the layout coordinates of the jacks, and the coordinates of the sensor pre-embedded points; The tensioning equipment is integrated into the box girder model in the form of a parametric family or components to form a digital twin sub-model of the equipment; wherein, the degradation parameters include temperature drift coefficient, vibration sensitivity and calibration curve; Real-time access to multi-source environmental data from the site; wherein, the multi-source environmental data also includes surface and internal temperatures of multiple points on the beam, triaxial vibration acceleration, and extreme weather forecast data obtained through BeiDou or meteorological stations; A lightweight rendering engine is used to achieve synchronous mapping between the physical world and the digital twin, forming the three-field coupled digital twin.

3. The method for tensioning precast concrete steel box girders as described in claim 2, characterized in that, The degradation prediction model includes a sensor zero drift and hysteresis model for high-temperature environments, a hydraulic oil viscosity-displacement response delay model for low-temperature environments, a connector loosening probability and cable fatigue cumulative damage model for strong vibration environments, and a packet loss rate and bit error rate model for weak signal or electromagnetic interference environments. The shadow data segment is a five-dimensional data segment containing force, elongation, oil pressure, displacement, and time. The acquisition period is 1 to 5 seconds, and the preset time period is 5 to 30 minutes into the future.

4. The method for tensioning precast concrete steel box girders as described in claim 3, characterized in that, The step of acquiring measured data of the tensioning process using a multi-mode redundant acquisition method and performing high-reliability data protection on the measured data at the edge end of the precast concrete steel box girder includes: Deploy industrial-grade wide-temperature vibration-resistant sensors and edge computing gateways, and implement an adaptive multi-mode communication switching strategy based on 5G signal transmission.

5. The method for tensioning precast concrete steel box girders as described in claim 1, characterized in that, The steps of deploying industrial-grade wide-temperature vibration-resistant sensors and edge computing gateways, and implementing an adaptive multi-mode communication switching strategy based on 5G signal transmission, include: When the 5G signal strength is greater than or equal to the preset signal threshold, the 5G communication mode is used to perform real-time full data upload. When the 5G signal strength is lower than the preset signal threshold, switch to LoRa communication mode and upload compressed feature value and hash root; When there is no ground communication signal, switch to BeiDou short message communication mode to upload data block summary, alarm information and key features.

6. The method for tensioning precast concrete steel box girders as described in claim 5, characterized in that, The step of acquiring measured data of the tensioning process using a multi-mode redundant acquisition method and performing high-reliability data protection on the measured data at the edge end of the precast concrete steel box girder further includes: The measured data corresponding to the edge end is protected by using a ring buffer combined with a segmented hash tree and industrial-grade solid-state storage. The storage capacity of the ring buffer combined with the segmented hash tree is at least enough to cover the data volume of 2 to 3 complete tensioning processes. An independent data block is generated every 30 to 60 seconds, and a hash root is calculated for each data block and they are chained together. Forced write protection is applied to the data blocks corresponding to critical abnormal events; wherein, the critical abnormal events include out-of-tolerance events, alarm events, and communication interruption events, thus completing the data protection operation.

7. The method for tensioning precast concrete steel box girders as described in claim 6, characterized in that, The step of inputting the measured data into the digital twin model and verifying it with the shadow data segment from the same period to obtain the current verification result includes: Perform basic range and format checks on the measured data to remove hard out-of-limit values; The similarity score is obtained by calculating the similarity between the verified measured data and the shadow data segment of the same period. The degradation contribution of the similarity scores is analyzed to determine the environmental factors that cause the bias and their proportions. The integrity level is determined based on the similarity score, and the current verification result is obtained.

8. The method for tensioning precast concrete steel box girders as described in claim 7, characterized in that, The step of determining the integrity level of the measured data based on the current verification result and executing the corresponding adaptive handling strategy based on the determination result includes: When the similarity score is greater than or equal to the first threshold, it is determined to be of the complete level; When the similarity score is less than the first threshold and greater than or equal to the second threshold, it is determined to be of a suspicious level; When the similarity score is less than the second threshold, it is determined to be of an untrustworthy level.

9. The method for tensioning precast concrete steel box girders as described in claim 8, characterized in that, The step of determining the integrity level of the measured data based on the current verification result and executing the corresponding adaptive handling strategy based on the determination result further includes: When the system is determined to be of the complete level, the measured data, along with the timestamp and hash root, will be directly stored in the certificate. When the data is determined to be of the aforementioned suspicious level, the measured data is marked with a verification pending flag, and a retest or resend instruction is sent to the site. When the level of untrust is determined, the shadow data segment of the same period is used as the engineering transition value, and the information related to the cause of the deviation is recorded. When the integrity classification result remains at the suspicious or unreliable level, an environmentally adaptive tensioning strategy is invoked from the strategy library. The environmentally adaptive tensioning strategy includes at least one of the following: reducing the graded loading rate, extending the pressure holding time, increasing the number of oil return and tensioning cycles, and pausing the current steel strand tensioning while prioritizing the holding of already completed steel strands.

10. The method for tensioning precast concrete steel box girders as described in claim 1, characterized in that, After the steps of determining the integrity level of the measured data based on the current verification result and executing the corresponding adaptive handling strategy based on the determination result, the method further includes: The measured value, shadow value, and standard limit of the tension five-dimensional curve are rendered in real time in the digital twin. Render a complete cloud map of the entire beam data; during the rendering process, the beam is distinguished by coloring according to segments, steel strands, and ducts; Render anomaly heatmaps and pinpoint the exact location of jacks, sensors, or beam segments; Based on the aforementioned integrity proof chain, an integrity report for prestressed tensioning data of box girders in extreme environments is automatically generated; wherein, the report includes integrity rate statistics, abnormal segment location, and instructions for using shadow data, which serve as an attachment for quality acceptance and lifelong traceability.

Images