Intelligent detection and early warning method and system for bridge collapse

Abstract

The invention belongs to the technical field of bridge monitoring, and particularly relates to an intelligent detection and early warning method and system for bridge collapse, and the method achieves the continuous and synchronous monitoring of a key region of a bridge through the deployment of a composite sensing node with a multi-physical field sensing capability. The time-frequency domain joint analysis and empirical mode decomposition technology is combined to effectively separate environmental interference and structure real response components; constructing a local rigidity evolution trajectory based on the structural response dominant component, and identifying a starting moment when the rigidity is significantly reduced by using a dual-threshold criterion; and multi-scale precursor features in the damage evolution process are further mined through time alignment recombination, space thermodynamic diagram generation and multi-region collaboration analysis. According to the method, the accuracy of small damage identification and the reliability of early warning are remarkably improved, the structure performance degradation process can be stably tracked under the actual service condition with complex environmental interference, and interpretable and traceable intelligent early warning support is provided for the bridge collapse risk.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of bridge monitoring, and particularly relates to an intelligent detection and early warning method and system for bridge collapse. BACKGROUND

[0002] In the prior art, bridge structure health monitoring mainly relies on laying strain gauges, accelerometers and other sensors at key positions to collect response data of the structure under load, and judges whether there is damage through periodic inspection or threshold alarm. Some systems introduce vibration modal analysis or strain trend statistical methods to identify stiffness degradation or abnormal response. However, these methods usually only focus on a single physical quantity or static threshold, and it is difficult to effectively distinguish the response difference caused by environmental interference (such as temperature change, humidity fluctuation) and real structure damage, resulting in that the monitoring result is easily affected by external factors, and false positives or false negatives occur.

[0003] Therefore, the main technical problem existing in the prior art is that there is a lack of deep fusion of multi-source perception data and effective separation of environmental interference, and it is difficult to stably identify the starting point of the micro damage of the bridge bearing member from long-term monitoring data, and further difficult to capture the early precursor features of the overall instability of the structure. SUMMARY

[0004] The purpose of the present application is to provide an intelligent detection and early warning method and system for bridge collapse, which significantly improves the accuracy of micro damage identification and the reliability of early warning to solve the problems raised in the background art.

[0005] To achieve the above purpose, the technical scheme adopted by the present application is as follows: an intelligent detection and early warning method for bridge collapse, comprising: According to the bridge structure drawing and the stress analysis result, composite sensor nodes with strain, vibration, temperature and humidity sensing capabilities are deployed in the key monitoring area of the bearing member, each sensor node is connected through wireless ad hoc network and time synchronization is performed, a periodic data collection and uploading mechanism is set, a long-term continuous monitoring mode is started, and synchronized multi-source perception data are obtained; The strain and vibration signals in the synchronized multi-source perception data are time-aligned and baseline-corrected, the corrected signals are converted into time-frequency distribution graphs by using short-time Fourier transform, an association model of environmental influence factors and low-frequency signals is established in combination with temperature and humidity data, and the structure response dominant component is separated from the original signal by using empirical mode decomposition; The moving root mean square value sequence of each monitoring area is calculated based on the structure response dominant component, the moving root mean square value sequence is normalized, the normalized sequence is analyzed by using sliding window linear regression to extract the local stiffness change slope, and the starting time of significant stiffness decline is identified by setting double threshold criteria; The normalized RMS sequence of each monitoring area is time-aligned and reorganized based on the starting moment, the local growth rate of the damage evolution curve is calculated and a spatial distribution thermodynamic map is generated, the time correlation of the multi-area growth rate sequence is analyzed to identify the coordinated evolution anomaly, and the multi-scale precursor index is defined to comprehensively evaluate the overall instability risk of the structure.

[0006] Preferably, the key monitoring area of the load-bearing member is deployed with a composite sensing node with strain, vibration, temperature and humidity sensing capability, which includes: The sensing nodes are arranged on the main span middle section, bridge tower root, expansion joint, transverse web plate connection and beam above the support; Each sensing node integrates a micro strain gauge, a three-axis accelerometer, a high-precision temperature and humidity sensor, and a low-power wireless communication unit; The sensing unit is fixed to the surface of the concrete or steel member by embedding or surface pasting, and a protective coating and sealing structure are applied; Each node collects static strain and environmental parameters every 10 minutes, and triggers high-frequency vibration sampling every hour for 30 seconds.

[0007] Preferably, the time alignment and baseline correction includes: The GPS or Beidou time signal in the wireless ad hoc network is used to calibrate the millisecond clock of all sensing nodes, ensuring that the data at different positions are strictly aligned on the time axis; For each continuously collected strain signal, the mean value is calculated during the external load-free period and subtracted from the original signal to eliminate the linear drift caused by slow temperature changes.

[0008] Preferably, the correlation model between the environmental influence factor and the low-frequency signal includes: Short-time Fourier transform is applied to the detrended strain signal to observe the slow fluctuation component with energy concentrated below 0.1 Hz; Select the temperature sequence and humidity sequence collected synchronously, and perform correlation analysis on the low-frequency strain signal after 0.1 Hz low-pass filtering; The least squares method is used to fit the comprehensive influence of temperature and humidity on low-frequency signals to obtain the estimated value of environmental interference.

[0009] Preferably, the separation of the dominant component of the structure response includes: The detrended strain signal is adaptively decomposed into a number of intrinsic mode functions and residual terms; The intrinsic mode functions with a correlation coefficient below 0.3 with the environmental influence factor are screened out; The screened intrinsic mode functions are combined into the dominant component of the structure response, and the response anomaly caused by internal damage of the load-bearing member is retained.

[0010] Preferably, the normalization process for the moving root mean square value sequence includes: The mean and standard deviation of the moving root mean square value sequence during the initial stable operation of the system were selected as reference parameters. Subtract the mean from the moving root mean square value at each subsequent time step and divide by the standard deviation to obtain the normalized response intensity in units of the standard deviation from the initial state.

[0011] Preferably, the setting of the dual threshold criterion to identify the starting time of a significant decrease in stiffness includes: When the slope of the local stiffness change is greater than the trend intensity threshold and continues for more than 5 days, it is marked as a potential damage period. When the normalized response intensity increases cumulatively from the initial state to exceed the cumulative deviation threshold, it is confirmed that the damage initiation state has been entered. Minor damage is determined to have occurred in the monitored area only when both conditions are met simultaneously, and the start time of such damage is recorded.

[0012] Preferably, generating the spatial distribution heatmap includes: The relative time is defined as the difference between the current time and the time of damage onset, and the normalized RMS sequence of each monitoring area is rearranged according to the relative time. The central difference method is used to estimate the instantaneous rate of change at each point as the local growth rate; The growth rate at the same relative moment is mapped onto the bridge plan, with the color intensity indicating the rate of growth, forming a spatial distribution heat map that evolves over time.

[0013] Preferably, the definition of the multi-scale precursor index includes: Calculate the average normalized RMS of all damaged areas as the intensity dimension; The percentage of regions whose statistical growth rate exceeds a set threshold is used as the speed dimension; The ratio of the two largest eigenvalues ​​of the correlation coefficient matrix between regions reflects the degree of system synchronization as a cooperative dimension. By weighting and summing the three dimensions, a multi-scale precursor index that monotonically increases over time is obtained and used to comprehensively assess the overall structural instability risk.

[0014] On the other hand, this invention proposes an intelligent detection and early warning system for bridge collapse, comprising: The distributed multiphysics sensing network is used to deploy composite sensing nodes with strain, vibration, temperature and humidity sensing capabilities in key monitoring areas of load-bearing components based on bridge structural drawings and stress analysis results. The sensing nodes are connected through a wireless self-organizing network and time synchronization is performed. A periodic data acquisition and uploading mechanism is set up to start a long-term continuous monitoring mode and output synchronized multi-source sensing data. The time-frequency domain analysis module is used to perform time alignment and baseline correction on the strain and vibration signals in the synchronized multi-source sensing data. It uses short-time Fourier transform to convert the corrected signal into a time-frequency distribution map, combines temperature and humidity data to establish a correlation model between environmental influence factors and low-frequency signals, and uses empirical mode decomposition to separate the dominant structural response components from the original signal. The stiffness evolution tracking module is used to calculate the moving root mean square value sequence of each monitoring area based on the dominant component of the structural response, normalize the moving root mean square value sequence, use sliding window linear regression analysis to extract the slope of local stiffness change, and set a dual threshold criterion to identify the starting time of significant stiffness decrease. The damage path modeling module is used to perform time-aligned recombination of the normalized RMS sequences of each monitoring area based on the starting time, calculate the local growth rate of the damage evolution curve and generate a spatial distribution heat map, analyze the temporal correlation of the growth rate sequences of multiple regions to identify co-evolution anomalies, and define a multi-scale precursor index to comprehensively assess the overall structural instability risk.

[0015] Technical effects and advantages of the present invention: The intelligent detection and early warning method and system for bridge collapse proposed in this invention have the following advantages compared with the prior art: This invention achieves continuous and synchronous monitoring of key areas of bridges by deploying composite sensing nodes with multi-physics sensing capabilities. It effectively separates environmental interference from the actual structural response by combining time-frequency domain joint analysis and empirical mode decomposition techniques. Based on the dominant components of the structural response, it constructs a local stiffness evolution trajectory and uses a dual-threshold criterion to identify the onset of a significant decrease in stiffness. Furthermore, through time-aligned reconstruction, spatial heatmap generation, and multi-regional collaborative analysis, it uncovers multi-scale precursor features in the damage evolution process. This method significantly improves the accuracy of minor damage identification and the reliability of early warning, enabling stable tracking of structural performance degradation under complex environmental interference conditions during actual service, providing interpretable and traceable intelligent early warning support for bridge collapse risks. Attached Figure Description

[0016] Figure 1 This is a flowchart of the intelligent detection and early warning method for bridge collapse according to the present invention; Figure 2 This is a block diagram of the intelligent detection and early warning system for bridge collapse according to the present invention. Detailed Implementation

[0017] 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 some embodiments of the present invention, and not all embodiments. The specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention provides, for example Figure 1 The method shown is an intelligent detection and early warning system for bridge collapse. It aims to achieve highly reliable continuous monitoring of minor damage occurring to bridge load-bearing components during long-term service, and to identify early warning signs of bridge structure collapse before overall collapse. Specifically, it includes: Based on the bridge structural drawings and stress analysis results, composite sensor nodes with strain, vibration, temperature and humidity sensing capabilities are deployed in the key monitoring areas of the load-bearing components. The sensor nodes are connected through a wireless self-organizing network and time synchronization is performed. A periodic data collection and uploading mechanism is set up to start a long-term continuous monitoring mode and obtain synchronized multi-source sensing data. Among them, composite sensing nodes with strain, vibration, temperature and humidity sensing capabilities are deployed in key monitoring areas of load-bearing components. These include: sensing nodes are deployed in the middle section of the main span, at the base of the bridge tower, near the expansion joints, at the connection of the diaphragm, and on the beam above the support. Each sensing node integrates a miniature strain gauge, a triaxial accelerometer, a high-precision temperature and humidity sensor, and a low-power wireless communication unit. The sensing unit is fixed to the surface of the concrete or steel component using an embedded or surface-mount method, and a protective coating and sealing structure are applied. Each node is configured to collect static strain and environmental parameters every 10 minutes and trigger a high-frequency vibration sampling lasting 30 seconds every hour.

[0019] By deploying composite sensor nodes integrating multi-physics sensing capabilities in key monitoring areas of load-bearing components, and employing a time-synchronized wireless self-organizing network architecture and a periodic data acquisition mechanism, high spatiotemporal resolution and long-term stable data acquisition of the bridge structure's mechanical response and environmental status were achieved. This deployment method effectively covers stress concentration, peak bending moment, and vulnerable connection areas, ensuring that local anomalies caused by minor damage are captured in a timely manner. Multi-source synchronous data provides reliable input for subsequent environmental interference separation and structural response reconstruction, significantly improving the spatial representativeness and data reliability of the monitoring system, and laying a solid data foundation for damage initiation identification and evolution tracking.

[0020] Time alignment and baseline correction were performed on strain and vibration signals in synchronized multi-source sensing data. Short-time Fourier transform was used to convert the corrected signals into time-frequency distribution maps. A correlation model between environmental influence factors and low-frequency signals was established by combining temperature and humidity data. Empirical mode decomposition was used to separate the dominant structural response components from the original signals. The process of time alignment and baseline correction includes: millisecond-level clock calibration of all sensor nodes using GPS or BeiDou timing signals in the wireless ad hoc network to ensure strict alignment of data from different locations on the time axis; and calculating the mean of each continuously acquired strain signal during periods without external loads and subtracting the mean from the original signal to eliminate linear drift caused by slow temperature changes. Furthermore, by calculating the mean during periods without external loads and subtracting it from the original signal, the linear drift caused by slow temperature changes is effectively eliminated, ensuring the authenticity and stability of the data.

[0021] Furthermore, a correlation model between environmental impact factors and low-frequency signals was established, including: applying short-time Fourier transform to the detrended strain signal to observe the slow fluctuation components with energy concentrated below 0.1 Hz; selecting synchronously acquired temperature and humidity sequences and performing correlation analysis with the low-frequency strain signal after low-pass filtering at 0.1 Hz; and fitting the combined influence of temperature and humidity on the low-frequency signal using the least squares method to obtain an estimated value of environmental interference. By applying short-time Fourier transform to the detrended strain signal to observe the slow fluctuation components with energy concentrated below 0.1 Hz, and combining temperature and humidity data, a correlation model between low-frequency signals and environmental impact factors was established. By fitting the influence of temperature and humidity on the low-frequency signal using the least squares method, an accurate estimate of environmental interference was obtained, thereby effectively isolating the influence of environmental factors on the structural response and avoiding false alarms and missed alarms.

[0022] Specifically, the dominant components of the structural response are separated, including: adaptively decomposing the detrended strain signal into several intrinsic mode functions (EMFs) and residual terms; selecting EMFs with a correlation coefficient lower than 0.3 with environmental impact factors; and combining the selected EMFs into the dominant components of the structural response, while retaining response anomalies caused by internal damage to load-bearing components. This process preserves response anomalies caused by internal damage to load-bearing components, enhances the sensitivity and accuracy of identifying minor damage, and provides a reliable foundation for subsequent damage initiation point identification and evolution path analysis.

[0023] The moving root mean square (RMS) value sequence of each monitoring area is calculated based on the dominant structural response components. The moving RMS value sequence is normalized, and the normalized sequence is analyzed by sliding window linear regression to extract the slope of local stiffness change. A dual threshold criterion is set to identify the starting moment of significant stiffness decrease. The normalization process for the moving root mean square (RMS) value sequence includes: selecting the mean and standard deviation of the moving RMS value sequence during the initial stable operation of the system as reference parameters; subtracting the mean from the moving RMS value at each subsequent time point and dividing by the standard deviation to obtain the normalized response intensity in units of the standard deviation multiple of the deviation from the initial state.

[0024] Specifically, a dual threshold criterion is set to identify the starting point of a significant decrease in stiffness, including: when the slope of the local stiffness change is greater than the trend intensity threshold and continues for more than 5 days, it is marked as a potential damage period; when the normalized response intensity increases cumulatively from the initial state to exceed the cumulative deviation threshold, it is confirmed that the damage has started; only when both conditions are met simultaneously, it is determined that a minor damage has occurred in the monitoring area, and its starting point is recorded.

[0025] By constructing a moving root mean square (RMS) sequence based on the dominant structural response components, performing normalization and slope analysis, and combining this with a dual-threshold criterion to identify the onset of a significant decrease in stiffness, this invention achieves high-sensitivity, low-false-report early identification of minor damage to bridge load-bearing components. This processing method effectively eliminates data bias caused by differences in sensor sensitivity, installation conditions, and initial stress states across different monitoring areas, making the state changes in each area comparable. The use of sliding window linear regression to extract local stiffness evolution trends enhances the ability to track slow degradation processes. The dual-threshold criterion comprehensively considers both the rate of change and the degree of cumulative deviation, avoiding false responses to instantaneous fluctuations from a single threshold, significantly improving the stability and reliability of damage initiation point determination, and providing an accurate time reference for subsequent damage evolution analysis.

[0026] Based on the initial time, the normalized RMS sequences of each monitoring area are time-aligned and recombined to calculate the local growth rate of the damage evolution curve and generate a spatial distribution heat map. The temporal correlation of the growth rate sequences of multiple regions is analyzed to identify co-evolution anomalies, and a multi-scale precursor index is defined to comprehensively assess the overall structural instability risk.

[0027] The specific steps for generating a spatial distribution heat map include: defining relative time as the difference between the current moment and the moment when the damage started; rearranging the normalized RMS sequence of each monitoring area according to relative time; estimating the instantaneous rate of change of each point as the local growth rate using the central difference method; mapping the growth rate at the same relative moment onto the bridge plan view, with the color intensity indicating the rate of growth, thus forming a spatial distribution heat map that evolves over time.

[0028] In addition, the definition of the multi-scale precursor index specifically includes: calculating the average normalized RMS of all damaged areas as the intensity dimension; the proportion of areas with a statistical growth rate exceeding a set threshold as the velocity dimension; calculating the ratio of the two largest eigenvalues ​​of the correlation coefficient matrix between areas to reflect the system synchronization degree as the coordination dimension; and weighting and summing the three dimensions to obtain a multi-scale precursor index that monotonically increases over time for comprehensive assessment of the overall structural instability risk.

[0029] By reconstructing the normalized RMS sequences of each monitoring area based on the damage initiation time, generating spatial distribution heatmaps, and analyzing the temporal correlation of multi-regional growth rates, this invention achieves a leap from local damage identification to overall structural instability trend assessment. This method effectively reveals the spatial expansion path and evolution rhythm of damage, visualizes hidden collaborative degradation processes, and significantly improves the ability to perceive systematic structural degradation. The spatial heatmap intuitively presents the dynamic migration and concentration trends of weak areas, providing a basis for judging load redistribution and deterioration of key force transmission paths. Through correlation analysis of multi-regional growth rates, non-random synchronous acceleration phenomena can be identified, effectively distinguishing isolated local damage from collective behavior that foreshadows overall instability. The constructed multi-scale precursor index integrates the intensity, speed, and spatial synergy of damage development, forming a quantitative, monotonic, and comparable comprehensive indicator of overall structural instability risk, significantly enhancing the objectivity and early warning judgment, and providing reliable technical support for cross-scale risk assessment of bridges from local damage to overall failure.

[0030] On the other hand, this invention proposes an intelligent detection and early warning system for bridge collapse, such as... Figure 2 As shown, it includes: The distributed multiphysics sensing network is used to deploy composite sensing nodes with strain, vibration, temperature and humidity sensing capabilities in key monitoring areas of load-bearing components based on bridge structural drawings and stress analysis results. The sensing nodes are connected through a wireless self-organizing network and time synchronization is performed. A periodic data acquisition and uploading mechanism is set up to start a long-term continuous monitoring mode and output synchronized multi-source sensing data. The time-frequency domain analysis module is used to perform time alignment and baseline correction on strain and vibration signals in synchronized multi-source sensing data. It uses short-time Fourier transform to convert the corrected signal into a time-frequency distribution map, combines temperature and humidity data to establish a correlation model between environmental influence factors and low-frequency signals, and uses empirical mode decomposition to separate the dominant structural response components from the original signal. The stiffness evolution tracking module is used to calculate the moving root mean square value sequence of each monitoring area based on the dominant components of structural response, normalize the moving root mean square value sequence, use sliding window linear regression analysis to extract the slope of local stiffness change, and set a dual threshold criterion to identify the starting moment of significant stiffness decrease. The damage path modeling module is used to perform time-aligned recombination of the normalized RMS sequences of each monitoring area based on the start time, calculate the local growth rate of the damage evolution curve and generate a spatial distribution heat map, analyze the temporal correlation of the growth rate sequences of multiple regions to identify co-evolution anomalies, and define a multi-scale precursor index to comprehensively assess the overall structural instability risk.

[0031] In addition, the modules in the above system also implement other steps of an intelligent detection and early warning method for bridge collapse, as follows: Step 1: Deploy a distributed multiphysics sensing network to establish a continuous sensing foundation for bridge load-bearing components. To achieve long-term monitoring of minor damage to bridge load-bearing components, it is first necessary to deploy multi-dimensional sensing units in key structural areas, forming a distributed network covering the main beams, piers, supports, and connection nodes. This network not only collects the mechanical response of the structure but also simultaneously acquires environmental excitation and material state information, providing raw data support for subsequent damage identification. The arrangement of the sensing units follows the principle of structural mechanics sensitivity, prioritizing coverage of peak bending moment areas, shear concentration areas, and locations of stress abrupt changes to ensure that local anomalies caused by minor damage can be effectively captured.

[0032] Step 1.1: Based on the bridge structural drawings and stress analysis results, determine the key monitoring areas of the load-bearing components: Before implementing monitoring, it is necessary to retrieve the bridge's design drawings and as-built data, and combine this with structural static and dynamic characteristic analysis to identify areas that bear significant stress or are prone to fatigue accumulation under normal operating loads. These areas include the mid-span of the main span, the base of the bridge towers, the vicinity of expansion joints, diaphragm connections, and the beams above the supports. By reviewing structural finite element simulations or historical inspection records, it is possible to further confirm which areas have previously experienced cracks, corrosion, or deformation, thus identifying them as key areas of focus. This process provides spatial guidance for the subsequent deployment of sensors, ensuring that sensing resources are concentrated on the structural parts most likely to be damaged, avoiding resource waste or monitoring blind spots due to blind deployment. The key monitoring areas identified in this process will become the geographical benchmark for the deployment of the sensor network, and the installation locations of all subsequent sensor units will be planned based on this.

[0033] Step 1.2: Install composite sensor nodes with strain, vibration, temperature, and humidity sensing capabilities in key monitoring areas: After identifying the key monitoring areas, composite sensing nodes integrating multiple sensing functions were deployed in each area. Each node includes a micro strain gauge, a triaxial accelerometer, a high-precision temperature and humidity sensor, and a low-power wireless communication unit. The strain gauge is used to capture minute deformations on the surface of the component, with a resolution of 1με, and can identify local stress redistribution caused by crack initiation or material degradation. The accelerometer records the dynamic response of the structure under vehicle traffic, wind loads, or seismic disturbances, with a sampling frequency of no less than 200Hz to ensure that high-frequency vibration information is not lost. The temperature and humidity sensor monitors changes in environmental conditions in real time to distinguish between spurious damage signals caused by thermal expansion and contraction and real structural damage. All sensing units are fixed to the surface of concrete or steel components using embedded or surface-mount methods, and are protected against wind and rain erosion and electromagnetic interference through protective coatings and sealing structures. The composite sensing system composed of these sensing nodes allows for the simultaneous acquisition of mechanical, environmental, and material state information at a single location, laying the foundation for subsequent multi-source data fusion. The output data of the above nodes will serve as the input source for signal preprocessing in step two.

[0034] Step 1.3: Connect all sensor nodes via a wireless self-organizing network to form a communication architecture with data synchronization and fault tolerance capabilities: To ensure efficient data transmission among numerous sensor nodes, a communication architecture based on ZigBee or LoRa wireless ad hoc networking protocols is adopted. Each node automatically detects neighboring nodes and establishes communication links upon power-up, forming a mesh topology. Data from any node can be transmitted to the central aggregation node via multi-hop transmission. A time synchronization mechanism is implemented in the network, using GPS or BeiDou timing signals to perform millisecond-level clock calibration on all nodes, ensuring strict alignment of data collected from different locations on the timeline and avoiding subsequent analysis errors due to clock drift. Simultaneously, the network possesses link redundancy and fault self-healing capabilities. When a node fails due to power depletion or signal interruption, its neighboring nodes can automatically take over the data forwarding task, maintaining the overall network connectivity. This communication architecture not only reduces wiring complexity but also improves the system's scalability and robustness. The resulting synchronized data stream serves as a prerequisite for multi-channel signal alignment and joint analysis in step two.

[0035] Step 1.4: Set up a periodic data collection and upload mechanism, and start the long-term continuous monitoring mode: After the sensor network is deployed, each node is configured to perform data acquisition and uploading operations at fixed intervals. The acquisition cycle is set according to the bridge type and traffic density, generally collecting static strain and environmental parameters every 10 minutes, and triggering high-frequency vibration sampling for 30 seconds every hour. All data is cached locally and then uploaded to edge computing devices or cloud servers via wireless network for storage and preliminary processing. The system also has an anomaly triggering mechanism: when a node detects a strain mutation exceeding a threshold or an abnormal increase in vibration energy, it immediately initiates high-frequency sampling (e.g., 1kHz) and prioritizes data uploading to ensure that no sudden events are missed. This mechanism ensures low-power operation under normal conditions while enabling rapid response to sudden damage. The long-term continuous monitoring mode established in this way provides a complete time-series and spatially comprehensive data foundation for tracking damage evolution trends in subsequent steps.

[0036] Step 2: Perform joint time-frequency domain analysis on the multi-source sensing data to separate environmental interference and structural response components. After acquiring long-term, continuous, multi-source sensing data, its intrinsic components need to be decomposed and identified to eliminate non-structural interference caused by temperature changes, humidity fluctuations, or traffic randomness, and to extract the response signal that truly reflects the mechanical state of the load-bearing components. Since bridge structures are constantly in a dynamic environment during service, their sensing signals are often a mixture of structural response and external excitation; failure to distinguish between them will lead to misjudgments. Therefore, this step employs a joint time-frequency domain analysis method, utilizing the signal's distribution characteristics in the time and frequency dimensions to effectively separate interference and response, providing a "clean" input signal for subsequent damage feature extraction.

[0037] Step 2.1: Perform time alignment and baseline correction on the synchronously acquired strain and vibration signals: Before proceeding with the analysis, the strain and vibration signals from different sensing nodes are first time-aligned. Due to potential minor transmission delays in the wireless network, the time synchronization information established in step one is used to re-interpolate the data from all channels onto a unified time grid, ensuring consistency of the physical state across channels at the same moment. Baseline correction is then performed to eliminate trend terms caused by sensor zero-point drift. Specifically, for each continuously acquired strain signal... Calculate its mean value during periods without external loads (such as low traffic periods at night). The mean is then subtracted from the original signal to obtain the detrended signal. This operation effectively removes linear drift caused by slow temperature changes, while retaining transient or periodic fluctuations caused by structural stress. The aligned and calibrated signal sequence will serve as the basis for subsequent frequency domain analysis, ensuring the comparability of data from different locations and types of sensors.

[0038] Step 2.2: Convert the time-domain signal into a time-frequency distribution graph using the Short-Time Fourier Transform (STFT): To further reveal the time-varying frequency structure of the signal, a short-time Fourier transform was applied to the destressed strain and vibration signals. This method utilizes a sliding window function on the time axis. t: represents the center moment of the time window, τ is the integration variable, representing the time of the signal; for the signal The mathematical expression for local spectral analysis is as follows: ; in, Center of the time window For frequency variables, A Hanning or Gaussian window is selected to balance time and frequency resolution. Transformation results. This is a two-dimensional matrix, with time on the horizontal axis and frequency on the vertical axis. The color intensity represents the energy intensity at the corresponding time and frequency point. By observing this spectrum, the inherent frequency components of the structure (represented by stable frequency bands), transient excitations caused by vehicle traffic (represented by brief high-frequency energy clusters), and low-frequency drift caused by temperature changes (represented by slowly changing low-frequency components) can be intuitively identified. This spectrum provides a visual basis for subsequent component separation, allowing signals from different physical sources to exhibit distinguishable patterns in the time and frequency domain.

[0039] Step 2.3: Based on temperature and humidity data, establish a correlation model between environmental influencing factors and low-frequency signals: In time-frequency plots, a type of slowly fluctuating signal with energy concentrated below 0.1 Hz can usually be observed, and its variation trend is highly consistent with the temperature and humidity records. To quantify the impact of environmental factors on the structural response, synchronously acquired temperature sequences were selected. With humidity sequence Combine it with low-frequency strain signals (Obtained through a 0.1Hz low-pass filter) Correlation analysis was performed. Environmental impact factors were defined. For a linear combination of the two: ; α represents the weighting factor or influence factor of temperature, and β represents the weighting factor or influence factor of humidity. Fitting by least squares method The relationship is obtained, i.e., the solution is found: ; This model reflects the combined influence of environmental variables on structural deformation. If the goodness of fit... If this is the case, then the low-frequency signal is considered to be mainly driven by environmental changes and can be regarded as interference components and removed. The output of this model... This will be used as an estimate of environmental interference in the next step of the signal separation process.

[0040] Step 2.4: Use Empirical Mode Decomposition (EMD) to separate the structural response-dominated intrinsic mode functions from the original signal: After obtaining the environmental disturbance estimate, in order to further extract the dynamic characteristics of the structure itself, the destressed strain signal is processed. Empirical Mode Decomposition (EMD) is applied. This method adaptively decomposes a signal into several Intrinsic Mode Functions (IMFs), each IMF representing an oscillation mode at a specific time scale. The decomposition process is achieved by repeatedly extracting the mean values ​​of the upper and lower envelopes of the signal until the remaining components become monotonic functions or trend terms. Let the decomposition result be: ; in This represents residual trends. Typically, the first few IMFs correspond to high-frequency vibrations (such as vehicle excitation), the middle IMFs reflect the structure's inherent modes, while the last few IMFs or residual terms may contain slow changes caused by the environment. This is achieved by comparing each IMF with environmental impact factors. Correlation, filter out those with Low correlation (e.g., correlation coefficient) The IMF of the structure is combined into "dominant structural response components": ; The set It includes all IMF indices that are weakly correlated with the environment. This component preserves to the greatest extent possible the response anomalies caused by local stiffness changes due to internal damage to load-bearing components, providing a clean input signal for damage evolution tracking in step three.

[0041] Step 3: Construct local stiffness evolution trajectory based on the dominant components of structural response to identify the initiation point of minor damage. After obtaining the response signal reflecting the actual mechanical behavior of the structure, it needs to be further transformed into quantifiable structural state indicators to identify whether load-bearing components have experienced performance degradation. Since minor damage often initially manifests as a slight decrease in local stiffness, this step focuses on retrieving the stiffness change history of each monitored area from the dominant components of the structural response and locating the damage initiation time through trend analysis. This process relies on the intrinsic relationship between the statistical characteristics and dynamic features of the response signal, and by establishing repeatable state indicators, early detection of damage initiation can be achieved.

[0042] Step 3.1: Calculate the moving root mean square (RMS) sequence of the dominant structural response components for each monitoring area: To quantify the intensity change of the structural response, the dominant components of the structural response obtained in step two are... Calculate its moving root mean square value. Select the sliding window length. (e.g., for 1-hour data), define the moving RMS sequence. for: ; This index reflects the average vibration energy level of the structure over a local time period. When microcracks propagate or material peels off in load-bearing components, their local stiffness decreases, leading to an increase in the response amplitude under the same external excitation, thus causing an increase in the RMS value. Conversely, if the structure is stable, the RMS sequence should exhibit small fluctuations around a certain baseline value. By simultaneously calculating the RMS sequence in all monitoring areas, a set of time-continuous state trajectories can be obtained for subsequent trend analysis. This sequence, as an indirect representation of stiffness changes, provides raw data for the next normalization process.

[0043] Step 3.2: Normalize the RMS sequence to eliminate initial state differences and sensor sensitivity bias. Due to differences in sensor installation conditions, structural geometry, and initial stress states at different locations, the RMS baseline values ​​may vary significantly across regions. To enable cross-regional comparisons, the RMS series needs to be normalized. The average RMS value from the initial stable operation phase of the system (e.g., the first 7 days) is selected. with standard deviation As the first Reference parameters for each monitoring area define the normalized response intensity. for: ; This processing ensures that the response intensity of all regions is expressed in terms of "a multiple of the standard deviation from the initial state," eliminating the influence of dimensions and initial bias. After normalization, if a certain region... A consistently higher value than other regions or a monotonically increasing trend suggests potential performance degradation at that location. This normalized sequence serves as input for subsequent trend fitting, enhancing the comparability of state changes across different locations.

[0044] Step 3.3: Use sliding window linear regression analysis to analyze the normalized sequence and extract the slope of local stiffness changes: To identify the starting point of stiffness degradation, a normalized RMS sequence for each monitoring region was generated. Perform sliding window linear regression. Set the window length. (e.g., 30 days), in time Fit a local trend line: ; Where the slope This indicates the rate of change of the response intensity within that time period. If The continued increase indicates that the structural response energy in this region is constantly increasing, suggesting that the local stiffness is decreasing. The slope sequence was calculated using a daily sliding window. This allows for a dynamic profile of the stiffness evolution trend in each region. This slope sequence serves as a quantitative indicator of damage development speed, providing a basis for setting initial criteria in the next step.

[0045] Step 3.4: Set the dual threshold criterion to identify the starting point when stiffness significantly decreases: To avoid false triggering caused by noise, a dual-threshold method is used to determine the starting point of minor damage. First, a trend intensity threshold is set. (e.g., 0.01 / day), when the slope of a certain area... Furthermore, if the period exceeds 5 consecutive days, it is marked as a "potential damage period"; secondly, a cumulative deviation threshold is set. (e.g., normalized unit 2.0), if the region The cumulative increase since the initial state exceeds If both conditions are met simultaneously, the system confirms that the monitored area has entered the "damage initiation state". Only when both conditions are met simultaneously will the system determine that minor damage has occurred in the monitored area and record its initiation time. This criterion combines the rate of change with the cumulative effect, improving the stability of the identification. The starting point determined in this way will serve as the starting point for damage evolution path modeling in step four, used to track the subsequent development of the damage.

[0046] Step 4: Construct a damage evolution path map and mine multi-scale feature patterns of collapse precursors. After identifying the starting point of minor damage, it is necessary to further analyze its subsequent development patterns and explore whether there are predictable precursors to overall failure. Since bridge collapses are often the result of the accumulation and expansion of localized damage, ultimately triggering a chain reaction, this step involves constructing a damage evolution path map to correlate the degradation processes of multiple monitoring areas in time and space, identifying characteristic patterns with early warning significance, such as abnormal acceleration, cross-regional propagation, or abrupt pattern changes. This map not only reflects the state changes of individual components but also reveals the synergistic response at the structural system level, providing a basis for judging overall stability.

[0047] Step 4.1: Based on the damage initiation time, perform time-aligned reconstruction of the normalized RMS sequences of each monitoring area: To facilitate cross-regional comparison of damage development processes, normalized RMS sequences of all monitoring areas where damage initiation points have been identified were used. According to their respective Perform timeline alignment. Define relative time. Rearrange the original sequence into a sequence with A new sequence of independent variables In this way, the damage evolution process of all regions is displayed starting from "Day 0," facilitating the observation of differences in response at different locations after damage initiation. For example, some regions may experience a rapid increase after the initial damage, while others remain stable; this differentiation may reflect load redistribution within the structure or damage propagation paths. This aligned set of sequences constitutes the original data layer of the damage evolution path map, providing a unified temporal reference framework for subsequent pattern extraction.

[0048] Step 4.2: Calculate the local growth rate of the damage evolution curve in each region and generate a spatial distribution heatmap: To further quantify the damage propagation rate, the aligned The sequence's local growth rate is calculated. The instantaneous rate of change at each point is estimated using the central difference method. ; in Use a time step (e.g., 1 day). Place each region in the same... growth rate at time Mapping this onto the bridge plan generates a spatial distribution heatmap. Darker colors indicate a faster increase in structural response at that location, potentially corresponding to areas of active damage. Observing the heatmap's movement... The evolution of this thermal map can reveal whether damage spreads from a single hotspot to the surrounding area, or whether multiple independent damage zones accelerate synchronously. Such spatial evolution patterns are important clues for determining whether a structure has entered an unstable phase. This thermal map sequence, as a visual representation of the damage propagation path, provides an intuitive basis for further correlation analysis.

[0049] Step 4.3: Analyze the temporal correlation of multi-regional growth rate series to identify co-evolution anomalies: When multiple regions simultaneously show an increase in growth rate, it is necessary to determine whether they are independent events or have an inherent linkage. Calculate the growth rate of any two regions. growth rate series Pearson correlation coefficient within the sliding time window: ; in For time window, This is the mean within the window. If... A significant increase in correlation coefficient (e.g., exceeding 0.7) in the later stages of damage development indicates that the damage processes in the two regions are becoming synchronized, potentially reflecting stress redistribution or crack penetration within the structure. In particular, the sudden emergence of strong correlations in previously low-correlation regions, or the formation of highly correlated networks among multiple regions, suggests a loss of structural integrity and may be a precursor to collapse. This correlation evolution sequence is used to identify system-level anomalies, providing a basis for setting warning levels.

[0050] Step 4.4: Define a multi-scale precursor index to comprehensively assess the overall structural instability risk: To integrate the aforementioned multi-source information into a unified risk criterion, a multi-scale precursor index is defined. Its composition includes three dimensions: Intensity dimension: the average normalized RMS of all damaged areas. ; Speed ​​dimension: Percentage of regions with growth rates exceeding the threshold ; Collaborative Dimension: The ratio of the two largest eigenvalues ​​of the inter-regional correlation coefficient matrix This reflects the degree of system synchronization.

[0051] The composite index is defined as follows: ; in Weights (satisfying) (This can be adjusted according to the type of bridge.) When the index monotonically increases over time and exceeds a set threshold, the system issues a high-level warning. This index integrates local damage intensity, propagation speed, and system synergy, enabling it to capture the entire process from minor damage to overall instability, providing a quantitative basis for subsequent warning decisions.

[0052] Step 5: Establish a multi-level early warning and response mechanism to achieve dynamic assessment and tiered output of risk levels. After completing the mapping of the bridge structural damage evolution path and calculating the multi-scale precursor index, Subsequently, this quantitative indicator needs to be transformed into an actionable early warning signal to support maintenance personnel in taking appropriate intervention measures. Since the development of bridge structures from minor damage to overall collapse typically takes a considerable amount of time, the early warning mechanism should have phased response capabilities to avoid premature alarms leading to a "boy who cried wolf" effect, or delayed alarms resulting in missed opportunities for intervention. This step achieves dynamic classification of the structural safety status by setting multi-level threshold ranges and combining them with time-based persistence criteria. Based on different levels, corresponding information push and response processes are triggered to ensure the rationality and practicality of the early warning.

[0053] Step 5.1: Based on historical evolution patterns, set the three-level threshold range for the multi-scale precursor index: The multi-scale precursor index has been defined in step four. Its numerical range is theoretically... This reflects the cumulative degree of overall structural instability risk. To achieve tiered early warning, it is necessary to statistically analyze long-term monitoring data of the bridge under normal service conditions. Based on the background fluctuation level and the evolutionary trajectory of typical injury cases, three progressive thresholds are set: Level 1 threshold :Pick The 95th percentile of the stable period (such as the first 6 months) is set at 0.25 to 0.35, indicating a slight deviation in the structural state and possible local performance degradation. Secondary threshold The value of 0.55 to 0.65 represents the initial value of the historical accelerated degradation stage, indicating that multiple regions are deteriorating in tandem and the overall structural integrity is beginning to weaken. Level 3 threshold :near The theoretical upper limit is set above 0.85, indicating that the system is highly synchronously unstable and on the verge of failure.

[0054] These three thresholds will The value space is divided into four intervals: "Normal," "Attention," "Warning," and "Emergency." This division method not only relies on the numerical value but also emphasizes the degree of deviation from the structure's historical behavior, thus adapting to the individual differences of different types of bridges. This three-level threshold system will serve as the basic framework for subsequent dynamic judgments, ensuring that the warning signal matches the actual state of the structure.

[0055] Step 5.2: Introduce a time persistence criterion to prevent false alarms caused by transient disturbances: although While integrating multi-dimensional information, temporary spikes can still occur due to extreme weather, short-term passage of heavily loaded vehicles, or momentary sensor malfunctions. Issuing warnings based solely on single-point value exceeding limits would severely compromise the system's reliability. Therefore, a time-duration criterion is introduced, requiring... A status change can only be confirmed if the state remains within a certain threshold range for a specified duration. The specific rules are as follows: when If it continues for more than 7 consecutive days, it will be determined to enter the "watch" status; when If the condition persists for more than 3 consecutive days, it will be considered to enter a "warning" state. when If the situation persists for more than 12 consecutive hours, a state of "emergency" is declared.

[0056] This mechanism mimics the "adaptive filtering" characteristic of biological systems, allowing the system to buffer short-term fluctuations and only confirming abnormal states when they have sufficient persistence. This design significantly improves the robustness of the judgment, preventing high-level responses from being triggered by accidental events. The status results filtered by this criterion will serve as the basis for the next step of information tiered delivery, ensuring that each level of warning has practical significance.

[0057] Step 5.3: Generate differentiated early warning information content and target audience based on the assessment level: Different risk levels correspond to different handling priorities and resource allocations; therefore, the content and recipients of early warning information need to be configured differently. After confirming a status change, the system automatically generates a structured report and distributes it through multiple communication channels. Attention Level: Generates a brief analysis report, including the location of the over-limit area, the RMS increase rate and trend chart, and pushes it to the bridge daily inspection team and the area management platform, prompting them to strengthen visual inspection and data review; Early warning level: Generate a detailed diagnostic report, including a growth rate heatmap, regional correlation network diagram, and precursor index evolution curve, and push it to the technical maintenance center, design unit, and higher-level regulatory department, and recommend the activation of special testing and load limitation plans; Emergency Level: Generate an emergency notice, clearly marking high-risk areas, possible failure modes, and recommended closure areas. Simultaneously notify the emergency command center, traffic control departments, and on-site personnel via SMS, voice calls, and a dedicated alarm system, and initiate temporary traffic control and personnel evacuation procedures.

[0058] This tiered delivery mechanism ensures the accuracy and timeliness of information transmission, enabling different stakeholders to obtain the necessary information at the appropriate time and forming efficient collaboration. The generated report content directly references the graphs and index results constructed in step four, guaranteeing the consistency and traceability of information sources.

[0059] Step 5.4: Establish an early warning status backtracking mechanism to record the basis and context of each judgment: To enhance system transparency and auditability, the complete decision context must be recorded for each state change, including... The system records real-time values, duration of exceeding limits, affected monitoring areas, environmental conditions (temperature, humidity, traffic density), and concurrent external events (such as rainstorms and earthquakes). This information is packaged into "early warning event packages," stored in a separate database, and assigned a unique timestamp and event number. If structural anomalies or accidents occur subsequently, the appropriateness of the early warning system's response can be verified by tracing back through these event packages. This information can also be used to optimize threshold parameters and decision-making logic. This mechanism not only enhances the system's accountability but also provides data support for long-term performance evaluation. The recorded event packages will become an important input source for system self-verification and adaptive adjustments in step six.

[0060] Step Six: Implement system self-verification and environmental adaptation adjustments to maintain the stability of long-term monitoring. Bridges have service lives spanning decades, during which environmental conditions, traffic patterns, and material properties can change significantly. If monitoring system parameters remain fixed for extended periods, they will struggle to adapt to these gradual shifts, leading to criterion failure or decreased sensitivity. Therefore, during long-term operation, it is necessary to periodically self-calibrate each component of the system and dynamically adjust key parameters based on the actual structural response characteristics to ensure its discriminative capabilities consistently match the current state. This step establishes a closed-loop self-maintenance mechanism through periodic benchmark recalibration, parameter relearning, and anomaly detection feedback, extending the system's effective lifespan.

[0061] Step 6.1: Perform baseline state recalibration quarterly and update normalized reference parameters: In step three, the normalization process depends on the mean from the initial runtime. with standard deviation However, after months or years of operation, the structure may enter a new equilibrium state due to maintenance and reinforcement, material aging, or foundation settlement, rendering the original benchmark inapplicable. Therefore, the system is designed to perform a benchmark recalibration quarterly: selecting a period of stable traffic load and normal environmental conditions within the most recent 30 days (such as the low-peak nighttime period with consecutive sunny days), and recalculating the statistical characteristics of the RMS sequence for each monitoring area. and The reference values ​​in the normalization formula are then updated. The update process employs a smooth transition, mixing old and new parameters with time weights. ; in This is a transition period (e.g., 7 days). This marks the start time for recalibration. This gradual update avoids jumps in state indicators due to abrupt parameter changes, ensuring the continuity of trend analysis. The updated normalized sequence will be used for subsequent slope calculations and precursor exponent generation, ensuring fairness in long-term comparisons.

[0062] Step 6.2: Analyze the causes of false alarms and missed alarms using the early warning event package, and optimize the dual threshold criterion: The dual threshold criteria (trend strength and cumulative deviation) set in step three may deviate in specific scenarios. For example, in a certain instance... Not reached However, localized cracks still occurred in the structure (missed reports), or were caused by a strong wind. Short-term exceedance of limits without structural damage (false alarm). Such anomalies can be identified by retrieving the warning event package stored in step five, combined with post-incident inspection records or drone imagery. For missed alarm cases, the alert level should be appropriately reduced. Alternatively, the duration requirement can be shortened; for false alarms, the weighting of environmental interference suppression can be increased or the duration extended. The adjustment process uses incremental modifications, with each change not exceeding 10% of the original value, and the reasons for the adjustment and the expected effect are recorded. This feedback mechanism enables the system to accumulate experience, gradually improving the accuracy of its judgments. The optimized criteria will be applied to the early warning determination in the next cycle.

[0063] Step 6.3: Monitor sensor performance degradation, automatically identify and block failed channels: Prolonged exposure to outdoor environments may lead to decreased sensor sensitivity, increased zero-point drift, or communication interruptions. To prevent poor-quality data from contaminating analysis results, the system periodically assesses the health status of each sensor channel. Assessment metrics include: Data integrity (daily average missing rate < 5%); Noise level (power spectral density variation in the high-frequency band < 3dB); Correlation with other neighboring channels (Pearson correlation coefficient > 0.6).

[0064] If a channel fails to meet any metric for two consecutive weeks, it is marked as "suspicious." The system automatically removes its data from the multi-scale precursor index calculation and sends a replacement reminder to maintenance personnel. Simultaneously, spatial interpolation methods (such as inverse distance weighting) are used to estimate the response value at that location, maintaining the integrity of the spectrum. This shielding mechanism ensures the quality of the analysis input and avoids global misjudgments due to single-point failures. The status information of the shielded channels is included in the system maintenance log for assessing equipment lifespan and deployment strategies.

[0065] Step 6.4: Record all parameter adjustments and channel changes to create a system evolution log: To ensure system manageability and reproducibility, all parameter updates, criterion optimizations, and channel masking operations occurring in this step must be recorded in the "System Evolution Log." This log includes the operation time, reason for execution, values ​​before and after the change, operator (automatically marked as "self-adapt"), and scope of impact. The log file is archived monthly for technical personnel to access at any time and can also be used to generate system health reports. This record not only supports troubleshooting and performance auditing but also provides historical evidence for future system upgrades. The resulting log data will serve as the foundation for multi-source information fusion and decision support in Step Seven.

[0066] Step 7: Integrate manual inspection and external monitoring information to enhance the overall credibility of the judgment results. Although automated monitoring systems provide continuous data, their judgment results still need to be cross-validated with human experience and other information sources to improve the comprehensiveness and reliability of the overall assessment. The complexity of bridge structures means that a single data source cannot cover all risk dimensions. Therefore, this step introduces an external information fusion mechanism, incorporating regular inspection records, drone imagery, weather warnings, and traffic flow data into the analysis framework to form a comprehensive human-machine collaborative judgment model. This fusion does not alter the original automated process but serves as an independent verification layer to confirm or correct the system output.

[0067] Step 7.1: Receive the periodic inspection report and extract the location and description of structural appearance anomalies: Bridge maintenance units typically conduct manual inspections monthly or quarterly, recording visible defects such as cracks, corrosion, and water seepage. The system receives electronic inspection reports through a standardized interface and uses natural language processing (NLP) technology to extract key information, such as "A vertical crack, approximately 80cm long, was found in the south web of the main span, 2.3m from the support." This information is converted into structural coordinates and defect type labels, and then spatially matched with the damage initiation area identified by the automated system. If the two locations have a high degree of overlap (e.g., distance < 1m), it is considered mutually corroborating, enhancing the credibility of the damage assessment for that area. If the automated system does not trigger an alarm but the inspection finds serious defects, a "reverse verification" process is triggered to re-examine historical data for any missed detections in that area. This process achieves complementary human and machine information, compensating for the limitations of the automated system's insufficient sensitivity to surface damage.

[0068] Step 7.2: Integrate UAV inspection images and perform visual feature comparison and analysis: In addition to written reports, drone inspections can provide high-resolution imagery to identify macroscopic damage that is difficult for automated sensors to capture. After receiving the imagery, the system automatically extracts the shooting time, location, and viewing angle, and overlays it with a normalized RMS heatmap from the same period. For example, if the RMS value of a certain area rises significantly and the image shows concrete spalling or exposed rebar at the corresponding location, it is judged as high-confidence damage; if the RMS is stable but new cracks appear in the image, it indicates that the sensor may not cover that area, requiring additional sensors to be deployed. This visual comparison does not rely on complex image recognition but achieves rapid correlation through spatial alignment and manual verification, reducing the risk of misjudgment. The comparison results will be included as supplementary evidence in the early warning event package, enriching the judgment criteria.

[0069] Step 7.3: Introduce meteorological and traffic data to assess the contribution of external loads to the response anomaly: Some structural anomalies may be caused by extreme external conditions rather than internal damage. For example, continuous heavy rain can cause foundation erosion, strong winds can induce vortex-induced vibration, and heavy-duty truck fleets can cause momentary overloading during holidays. The system integrates rainfall, wind speed, and temperature warnings issued by the meteorological bureau, as well as traffic flow and axle load monitoring data from the transportation department, and analyzes them in conjunction with... The temporal correlation of leap events. If If the rise coincides with a once-in-a-century rainstorm, environmental factors should be prioritized; if the rise continues even without external disturbances, internal damage is more likely. This external load assessment is not used to negate automated alarms, but rather to provide interpretive context to help decision-makers determine response priorities. Relevant data will be synchronously stored in the system evolution log to support long-term environmental impact analysis.

[0070] Step 7.4: Generate a comprehensive judgment opinion for decision-makers' reference: After completing the comparison of multi-source information, the system generates a "comprehensive judgment opinion," summarizing automated alarms, inspection findings, video evidence, and external load conditions, presented in tabular or timeline format. For example: "On [Date], the system alarmed..." The inspection lasted for four days; the inspection report showed that cracks in pier #3 were expanding on the same day; drone imagery confirmed concrete spalling; there was no extreme weather during this period; it was recommended to upgrade to the warning level. This recommendation does not replace automated judgment, but rather serves as an auxiliary decision-making tool, allowing managers to make final judgments in complex situations. This integrated process ensures the organic combination of technical judgment and practical experience, enhancing the robustness of the overall early warning system.

[0071] Step 8: Construct a comprehensive view of the structural safety situation to support long-term trend analysis and maintenance planning. Building upon the fusion of multi-source information and system self-verification, it is necessary to further integrate the scattered monitoring events, evolution trajectories, and maintenance records into a unified panoramic view of the structural safety situation. This view not only reflects the current risk status but also reveals the performance degradation patterns of bridge load-bearing components over multi-year timescales, helping managers shift from "passive response" to "proactive planning" and achieve scientific management of the entire bridge lifecycle.

[0072] Step 8.1: Integrate all damage initiation points and early warning events along the timeline to form a timeline of structural health evolution: To clearly demonstrate the dynamic evolution of the bridge's structural condition, the system arranges all key events identified since its commissioning on a continuous timeline in chronological order. These events include: the damage initiation time of each monitored area. (From step three) Multiscale precursor index The following events are considered: durations exceeding various threshold levels (from step five), records of major defects discovered through manual inspection (from step seven), macroscopic damage captured by drone imagery, and all system parameter adjustments and sensor replacement operations (from step six). Each event is labeled with a standardized icon, with colors indicating its nature: blue indicates damage initiation automatically identified by the system, yellow indicates "attention" level warning, orange indicates "warning" level, red indicates "urgent" level, gray indicates anomalies discovered manually, and purple indicates system maintenance operations.

[0073] The timeline supports zooming and scrolling, allowing users to view event density within any time period. For example, if multiple red alerts appear within a year with increasingly shorter intervals, it indicates that the overall structural stability is deteriorating rapidly; if no new events occur for a long period after a reinforcement project, it suggests that the maintenance measures are effective. The system also provides an "event density curve," which statistically analyzes the number of abnormal events each month, generating a trend chart to help determine whether structural degradation exhibits exponential growth characteristics. This timeline is not only used to review history but also serves as a reference for new events—when an area experiences another increase in RMS, its development pace can be compared to the previous damage to predict the possible path of this evolution. This timeline will become the foundational framework for subsequent spatial analysis and pattern recognition, ensuring that all information is aligned on a unified time coordinate.

[0074] Step 8.2: Generate a spatial cumulative damage heat map to show the distribution of weak areas in the structure and the load redistribution path: Building upon the timeline, the system further maps the long-term performance degradation of each monitoring area onto the bridge's three-dimensional geometric model, generating a "spatial cumulative damage heatmap." The construction of this map relies on the normalized RMS sequence for each monitoring point. Perform integration and define the cumulative damage index at that point. for: ; in The baseline response level at that point (usually the average of the previous 3 months), the integration interval Covers the entire system operation process. The larger the value, the more abnormal response energy that location will withstand during long-term service, and the more likely it is to be a structural weak point. (All) The values ​​are interpolated onto the bridge surface according to their spatial coordinates to form a continuous thermal distribution map, with the color gradually changing from blue (low) to red (high).

[0075] This heatmap can overlay historical repair areas (e.g., reinforced beam segments marked with green borders), sensor placement locations (marked with small dots), and design load paths (theoretical stress transfer direction indicated by dashed lines). By comparing the heatmap with the design path, it can be determined whether the actual load transfer has deviated. For example, if the original design intended for the load to be transferred through the main beam, but the heatmap shows severe accumulated damage in the diaphragm area, it indicates that the load path has been redistributed, possibly due to a decrease in the stiffness of the main beam causing the force flow to shift to secondary components. This "unintended force transfer" is a potential cause of cascading failures. Managers can use this information to prioritize in-depth inspections of areas with high accumulated damage or adjust traffic control strategies to reduce their load. This heatmap is updated quarterly, forming a time series for observing the migration trends of vulnerable areas.

[0076] Step 8.3: Analyze the damage evolution pattern and identify typical development paths and structural response types: To improve the ability to predict emerging injuries, the system performs cluster analysis on the evolution curves of all recorded injury events, identifying several typical injury development paths. Specifically, it extracts the evolution curves of each injury event from its initial time... Normalized RMS sequence for the following 180 days This is treated as a time series sample; the similarity between sequences is measured using Dynamic Time Warping (DTW) distance, and a hierarchical clustering algorithm is applied to divide the sample into several clusters. The following typical patterns can usually be identified: Slowly rising type: The RMS value increases at a steady rate without obvious inflection point. This is commonly seen in the gradual degradation caused by concrete carbonation or steel corrosion. Accelerated mutation type: initially stable, then RMS rises sharply after a certain point, often corresponding to crack penetration or loosening of connectors; Cyclical fluctuation type: RMS fluctuates with the seasons or traffic cycles, but the overall trend is upward. This is often seen on bridges in areas with large temperature differences. Self-limiting type: RMS rises briefly and then tends to stabilize or decline, possibly due to the release of stress from local microcracks and the cessation of propagation.

[0077] Each pattern is assigned a unique identifier, and an "evolutionary pattern library" is established. When new damage occurs, the system automatically calculates the similarity between its early curve and each typical pattern, recommends the best-matching category, and predicts the time it will take for the damage to reach the next warning level based on the average development speed of historical cases of that category. For example, if a new event matches the "mutation acceleration type," and historical similar events have an average development speed of [missing information - likely related to a specific pattern or event] within 60 days... The system can then issue an advance warning. This pattern recognition process does not rely on complex training but is based on statistical patterns in historical data, ensuring interpretability and stability. The recognition results will be labeled on a timeline and heatmap, forming "pattern tags" for easy comparison across events.

[0078] Step 8.4: Generate an annual structural health report, providing maintenance recommendations and lifespan predictions: At the end of each year, the system automatically generates an "Annual Health Report for Bridge Structures," which serves as a core document for asset management and decision support. This report includes the following core contents: 1. Overall Assessment Summary: Lists the highest warning level for the year, the cumulative number of abnormal events, system availability (percentage of sensor uptime), and a review of major events; 2. Trend Analysis Chart: Displays multi-scale precursor indices The annual evolution curve, monthly distribution of event density, and spatial cumulative damage heatmap; 3. Detailed description of key areas: [Specifically targeting...] List the 3-5 regions with the highest values, including their damage onset time, evolution pattern, RMS peak value, and associated external factors; 4. Maintenance Recommendations: Based on the damage mode and cumulative extent, specific measures are proposed, such as "It is recommended to carry out crack grouting and surface protection on the south side of the #2 main beam", "Consider lubricating and maintaining the bearings before winter", and "Plan to carry out scour protection and reinforcement of the bridge pier foundation within three years". 5. Lifetime prediction: The RMS of the main damage area is estimated to reach a critical value using linear extrapolation (e.g., ...). The time frame is given, and an interval estimate of the remaining safe service life is provided (e.g., "the main span fatigue life is expected to be 8 to 12 years remaining").

[0079] The report is generated in PDF format and includes an interactive web version, allowing users to click on charts to view raw data. A copy of the report is stored in the bridge archive as a basis for future assessments of maintenance effectiveness and budget planning. This annual reporting mechanism ensures that the monitoring system is not only an "alarm system" but also a "health manager," driving a shift in bridge maintenance from "remedial measures" to "preventative measures."

[0080] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart detection and early warning method for bridge collapse, characterized in that, include: Based on the bridge structural drawings and stress analysis results, composite sensor nodes with strain, vibration, temperature and humidity sensing capabilities are deployed in the key monitoring areas of the load-bearing components. The sensor nodes are connected through a wireless self-organizing network and time synchronization is performed. A periodic data collection and uploading mechanism is set up to start a long-term continuous monitoring mode and obtain synchronized multi-source sensing data. The strain and vibration signals in the synchronized multi-source sensing data are time-aligned and baseline-corrected. The corrected signals are converted into time-frequency distribution maps using short-time Fourier transform. A correlation model between environmental influence factors and low-frequency signals is established by combining temperature and humidity data. The dominant structural response components are separated from the original signals using empirical mode decomposition. Based on the dominant components of the structural response, the moving root mean square value sequence of each monitoring area is calculated. The moving root mean square value sequence is normalized. The normalized sequence is analyzed by sliding window linear regression to extract the slope of local stiffness change. A dual threshold criterion is set to identify the starting time of significant stiffness decrease. Based on the aforementioned starting time, the normalized RMS sequences of each monitoring area are time-aligned and recombined to calculate the local growth rate of the damage evolution curve and generate a spatial distribution heatmap. The temporal correlation of the growth rate sequences in multiple regions is analyzed to identify co-evolution anomalies, and a multi-scale precursor index is defined to comprehensively assess the overall structural instability risk.

2. The intelligent detection and early warning method for bridge collapse according to claim 1, characterized in that, The deployment of composite sensing nodes with strain, vibration, temperature, and humidity sensing capabilities in key monitoring areas of load-bearing components includes: Sensor nodes are installed in the middle section of the main span, at the base of the bridge tower, near the expansion joints, at the connection of the transverse diaphragms, and on the beam above the supports. Each sensing node integrates a miniature strain gauge, a triaxial accelerometer, a high-precision temperature and humidity sensor, and a low-power wireless communication unit. The sensing unit is fixed to the surface of concrete or steel structure by means of embedded or surface bonding, and a protective coating and sealing structure are applied. Configure each node to collect static strain and environmental parameters every 10 minutes, and trigger a high-frequency vibration sampling lasting 30 seconds every hour.

3. The intelligent detection and early warning method for bridge collapse according to claim 1, characterized in that, The time alignment and baseline correction include: Using GPS or BeiDou timing signals in the wireless ad hoc network, all sensor nodes are clocked at the millisecond level to ensure that data from different locations are strictly aligned on the timeline. For each continuously acquired strain signal, its mean value is calculated during periods without external load, and the mean value is subtracted from the original signal to eliminate linear drift caused by slow temperature changes.

4. The intelligent detection and early warning method for bridge collapse according to claim 1, characterized in that, The establishment of the correlation model between environmental impact factors and low-frequency signals includes: Short-time Fourier transform was applied to the detrended strain signal to observe the slow fluctuation components with energy concentrated below 0.1 Hz; The temperature and humidity sequences acquired synchronously were selected, and their correlation with the low-frequency strain signal after a 0.1Hz low-pass filter was analyzed. By fitting the combined influence of temperature and humidity on low-frequency signals using the least squares method, an estimate of environmental interference is obtained.

5. The intelligent detection and early warning method for bridge collapse according to claim 1, characterized in that, The separated dominant components of the structural response include: The destressed strain signal is adaptively decomposed into several intrinsic mode functions and residual terms; Intrinsic mode functions with correlation coefficients lower than 0.3 with environmental impact factors were selected; The selected intrinsic mode functions are combined into the dominant components of the structural response, while retaining the response anomalies caused by internal damage to load-bearing components.

6. The intelligent detection and early warning method for bridge collapse according to claim 1, characterized in that, The normalization process for the moving root mean square value sequence includes: The mean and standard deviation of the moving root mean square value sequence during the initial stable operation of the system were selected as reference parameters. Subtract the mean from the moving root mean square value at each subsequent time step and divide by the standard deviation to obtain the normalized response intensity in units of the standard deviation from the initial state.

7. The intelligent detection and early warning method for bridge collapse according to claim 1, characterized in that, The method for identifying the starting point of a significant decrease in stiffness using the dual-threshold criterion includes: When the slope of the local stiffness change is greater than the trend intensity threshold and continues for more than 5 days, it is marked as a potential damage period. When the normalized response intensity increases cumulatively from the initial state to exceed the cumulative deviation threshold, it is confirmed that the damage initiation state has been entered. Minor damage is determined to have occurred in the monitored area only when both conditions are met simultaneously, and the start time of such damage is recorded.

8. The intelligent detection and early warning method for bridge collapse according to claim 1, characterized in that, The generation of the spatial distribution heatmap includes: The relative time is defined as the difference between the current time and the time of damage onset, and the normalized RMS sequence of each monitoring area is rearranged according to the relative time. The central difference method is used to estimate the instantaneous rate of change at each point as the local growth rate; The growth rate at the same relative moment is mapped onto the bridge plan, with the color intensity indicating the rate of growth, forming a spatial distribution heat map that evolves over time.

9. The intelligent detection and early warning method for bridge collapse according to claim 1, characterized in that, The definition of the multi-scale precursor index includes: Calculate the average normalized RMS of all damaged areas as the intensity dimension; The percentage of regions whose statistical growth rate exceeds a set threshold is used as the speed dimension; The ratio of the two largest eigenvalues ​​of the correlation coefficient matrix between regions reflects the degree of system synchronization as a cooperative dimension. By weighting and summing the three dimensions, a multi-scale precursor index that monotonically increases over time is obtained and used to comprehensively assess the overall structural instability risk.

10. An intelligent detection and early warning system for bridge collapse, used to implement the method as described in any one of claims 1-9, characterized in that, include: The distributed multiphysics sensing network is used to deploy composite sensing nodes with strain, vibration, temperature and humidity sensing capabilities in key monitoring areas of load-bearing components based on bridge structural drawings and stress analysis results. The sensing nodes are connected through a wireless self-organizing network and time synchronization is performed. A periodic data acquisition and uploading mechanism is set up to start a long-term continuous monitoring mode and output synchronized multi-source sensing data. The time-frequency domain analysis module is used to perform time alignment and baseline correction on the strain and vibration signals in the synchronized multi-source sensing data. It uses short-time Fourier transform to convert the corrected signal into a time-frequency distribution map, combines temperature and humidity data to establish a correlation model between environmental influence factors and low-frequency signals, and uses empirical mode decomposition to separate the dominant structural response components from the original signal. The stiffness evolution tracking module is used to calculate the moving root mean square value sequence of each monitoring area based on the dominant component of the structural response, normalize the moving root mean square value sequence, use sliding window linear regression analysis to extract the slope of local stiffness change, and set a dual threshold criterion to identify the starting time of significant stiffness decrease. The damage path modeling module is used to perform time-aligned recombination of the normalized RMS sequences of each monitoring area based on the starting time, calculate the local growth rate of the damage evolution curve and generate a spatial distribution heat map, analyze the temporal correlation of the growth rate sequences of multiple regions to identify co-evolution anomalies, and define a multi-scale precursor index to comprehensively assess the overall structural instability risk.

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