Highway bridge intelligent maintenance decision-making system and decision-making method

Abstract

The invention provides a highway bridge intelligent maintenance decision-making system and decision-making method. The system comprises a data acquisition module, a processing and storage module, a bridge condition evaluation module, a maintenance decision making module, an implementation and management module, and a monitoring and feedback module. The data acquisition module collects various types of data of bridges, environments, traffic and the like through multiple means such as fiber bragg gratings and vibration sensors. And the processing and storage module cleans the data by using a DBSCAN algorithm, processes the classified data by one-hot coding, and constructs distributed database storage based on a block chain. And the evaluation module adopts an Isomap algorithm to evaluate technical conditions and predict diseases. And the decision module generates a scheme by using a particle swarm optimization-simulated annealing algorithm, and analyzes and optimizes by using an analytic hierarchy process-grey clustering. And maintenance implementation is managed by means of a BIM model. The monitoring module collects data in real time and feeds back an adjustment strategy. The system can acquire data in real time through multiple sensors, accurately process data, accurately evaluate diseases, optimize maintenance decisions, dynamically adjust strategies, guarantee bridge safety and prolong the service life.

Description

Technical Field

[0001] The present invention relates to the field of intelligent bridge maintenance, and in particular to an intelligent highway bridge maintenance decision-making system and a decision-making method. Background Art

[0002] As a key component of transportation infrastructure, the structural safety and performance of highway bridges directly affect the smoothness and safety of transportation. However, with the passage of time and the continuous increase in traffic volume, highway bridges will inevitably suffer from various diseases, such as cracks, structural aging, and material performance degradation. These diseases will not only reduce the bearing capacity and service life of the bridge, but may also cause serious safety accidents. Traditional bridge maintenance methods mainly rely on manual regular inspections and empirical judgments. There are problems such as long inspection cycles, strong subjectivity, and difficulty in real-time monitoring of bridge status. It is impossible to detect and deal with bridge diseases in a timely manner, and it is difficult to meet the needs of modern highway bridge maintenance.

[0003] Although some related methods have been applied to the field of bridge maintenance, there are still some problems. For example, the fusion and processing of multi-sensor data are not efficient enough, and the accuracy and reliability of the data need to be improved; the adaptability and generalization ability of the maintenance decision model are insufficient, and it is difficult to cope with bridges of different types and working conditions; the adjustment of the maintenance strategy lacks real-time and pertinence, and cannot be optimized in time according to the dynamic changes of the bridge status. Therefore, it is of great practical significance to develop an intelligent maintenance decision system and decision-making method that can realize real-time monitoring, accurate evaluation, decision-making and dynamic adjustment of highway bridges. Summary of the invention

[0004] The main purpose of the present invention is to provide a highway bridge intelligent maintenance decision-making system and decision-making method to solve the problem that the traditional bridge maintenance method mainly relies on manual periodic inspection and experience judgment, has the problems of long inspection cycle, strong subjectivity, difficulty in real-time monitoring of bridge status, inability to timely detect and deal with bridge diseases, and difficulty in meeting the needs of modern highway bridge maintenance.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a highway bridge intelligent maintenance decision system, the decision system includes a data acquisition module, a data processing and storage module, a bridge condition assessment module, a maintenance decision-making module, a maintenance implementation and management module and a maintenance monitoring and feedback module; The bridge condition assessment module includes the bridge technical condition module and the disease development trend prediction module; The maintenance implementation and management module includes a BIM bridge 3D model module; The monitoring and feedback module is set on the bridge. The monitoring and feedback module includes a fiber grating sensor, a vibration sensor and a high-definition camera. The data monitored by the fiber grating sensor, the vibration sensor and the high-definition camera are transmitted to the data processing and storage module. The data in the data processing and storage module is input into the bridge condition assessment module to assess the technical condition of the bridge and predict the development trend of the disease.

[0006] In the preferred solution, the acquisition module includes fiber grating sensors, vibration sensors, high-definition cameras, drone detection equipment, and manual inspection methods; The collection module collects status data, environmental data, traffic data and historical maintenance record data from the highway intranet and the Internet.

[0007] In the preferred solution, the data processing and storage module uses the DBSCAN algorithm to clean the collected data, uses the one-hot encoding method to encode the classified data, and builds a distributed database based on blockchain technology to store the processed data.

[0008] In the preferred solution, the technical condition module and the block disease development trend prediction module in the bridge condition assessment module both use the Isomap algorithm to assess the technical condition of the bridge.

[0009] In a preferred embodiment, the method comprises: S1. Collect various data of bridges using fiber grating sensors, vibration sensors, high-definition cameras, drone detection equipment and manual inspections. At the same time, collect bridge condition data, environmental data, traffic data and historical maintenance record data through the highway intranet and the Internet; S2, the data processing and storage module uses the DBSCAN algorithm to clean the data collected in step S1, encodes the classified data using the one-hot encoding method, and stores the processed data in a distributed database based on blockchain technology; S3, the bridge condition assessment module extracts the data stored in the database, uses the Isomap algorithm to assess the technical condition of the bridge, predicts the development of the disease, and determines the health level and maintenance priority of the bridge; S4. Evaluate the technical conditions obtained in step S3, predict the disease development data, generate multiple maintenance plans using a hybrid algorithm of particle swarm optimization and simulated annealing, evaluate and optimize the plans using a combination of analytic hierarchy process and grey clustering analysis, and select the optimal maintenance plan; S5. Based on the selected maintenance plan, use BIM technology to organize construction in accordance with construction specifications and safety standards, and manage construction progress and quality; S6, the data collected by the fiber Bragg grating sensor, vibration sensor and high-definition camera in the monitoring and feedback module monitors the bridge status in real time; The monitoring data is then transmitted to the bridge condition assessment module in S3, which regularly re-evaluates the maintenance effectiveness and provides feedback to adjust the maintenance strategy.

[0010] In the preferred solution, in step S1, the fiber grating sensor is installed at the mid-span of the bridge, around the support, and at the connection between the pier and the beam; Apply special glue to the installation position in the middle of the bridge span and around the support, and stick the fiber grating sensor on the bridge surface; The connection between the pier and the beam is embedded, a groove is chiseled out at the connection between the pier and the beam, the fiber grating sensor is placed in the groove, and the groove is sealed with concrete slurry; Fiber Bragg grating sensors monitor the strain and temperature of bridge structures; High-definition cameras and drone inspection equipment obtain images of the bridge's exterior; Manual inspections were conducted to record the detailed location and morphology of the disease.

[0011] In the preferred solution, the vibration sensor analyzes the vibration information of the vehicle passing through the bridge, and the specific steps include: A1. First, use the time-frequency analysis algorithm to reveal the frequency characteristics of the signal at different times, including using short-time Fourier transform or wavelet transform to expand the signal in two dimensions of time and frequency to reveal the frequency characteristics of the signal at different times. Use the multi-resolution analysis characteristics of wavelet transform to automatically adjust the window size according to the signal frequency to capture the transient changes in the bridge vibration signal; A2. Then use the wavelet packet decomposition algorithm to achieve fine signal decomposition to obtain sub-signals in different frequency bands. By analyzing the energy distribution and amplitude change characteristics of the sub-signals, and comparing the characteristic differences of the sub-signals under normal and abnormal conditions, it is possible to determine whether the bridge structure is damaged or abnormal. A3. Use the empirical mode decomposition algorithm to adaptively process nonlinear and non-stationary signals. Use the empirical mode decomposition algorithm to decompose the bridge vibration signal into multiple intrinsic mode functions, extract the main characteristic components related to the vibration of the bridge structure, remove noise and interference components, calculate the energy and frequency parameters of the intrinsic mode function components and observe their changing trends over time, and then evaluate the stability and health of the bridge structure; A4. Finally, the support vector machine algorithm is used to identify abnormal vibration patterns. The support vector machine algorithm is used to identify abnormal vibration patterns. The amplitude, frequency, and energy characteristic parameters are extracted from the vibration signal as the input of the support vector machine. A large amount of bridge vibration data under normal and abnormal conditions is used to train the support vector machine so that it can learn the characteristic differences between normal and abnormal vibration patterns. During actual monitoring, the vibration signal features collected in real time are input into the trained support vector machine model to determine whether the current vibration state is normal and the type and severity of the abnormality.

[0012] In the preferred solution, the steps of the bridge condition assessment method using the Isomap algorithm are as follows: S31. Extract bridge data from a distributed database based on blockchain, including bridge structure strain, displacement, crack size, traffic data, environmental parameters, and past maintenance records. Use multiple filling methods to process missing data and use the local outlier factor algorithm to detect outliers. S32. Use the ReliefF algorithm for feature selection and use the selected features as dimensions to construct a high-dimensional data set, providing a comprehensive information basis for bridge technical condition assessment and disease development prediction and capturing complex relationships; S33, using Mahalanobis distance to measure the distance between data points, using kd tree to determine the k nearest neighbor points of each data point, constructing a neighborhood graph, reflecting the local similarity of data points and providing a structural basis for subsequent calculations; S34. Use BellmanFord algorithm to calculate the shortest path between any two points in the neighborhood graph as the geodesic distance, which reflects the real structural relationship of the bridge samples and is used for low-dimensional embedding and feature extraction, providing key information for evaluation and prediction; S35. Using the kernel Isomap method, the Laplace kernel function is introduced to perform eigendecomposition after centering the kernel matrix, and the data is projected into a low-dimensional space. The spectral clustering algorithm is used to cluster the bridge samples in the low-dimensional space, and the technical condition scores are assigned to the bridge samples based on the clustering results and related factors. S36, using long short-term memory network, taking low-dimensional spatial feature sequence as input, and predicting the disease development trend based on historical data by training the network; S37. Use fuzzy comprehensive evaluation method to divide bridge health level according to technical condition score and disease development prediction results. Combined with bridge importance factors, use data envelopment analysis method to determine maintenance priority.

[0013] In the preferred solution, in step S4, multiple curing methods are generated based on the particle swarm optimization simulated annealing hybrid algorithm: S41. Obtain the bridge technical condition assessment and disease development prediction data from step S3, clarify the maintenance objectives, determine the decision variables, including the type of maintenance measures, maintenance time nodes, maintenance resource allocation, and set the upper limit of the maintenance cost. , Maintenance time limit Constraints; Setting the particle swarm size , randomly initialize the position vector of each particle and the velocity vector ,in is the number of decision variables; Define the fitness function as ,in , , is the weight coefficient and , Indicates the maintenance effect. represents the maintenance cost, Indicates maintenance time; In each iteration, the fitness value of each particle is calculated , update the particle's own historical optimal position and the global optimal position of the entire particle swarm , through the formula and Update particle velocity and position, where is the inertia weight, and is the learning factor, and is a random number uniformly distributed in the interval [0, 1]. Indicates the current iteration number; like , accept the new position; otherwise, with probability Accept new position where For the current temperature, press Cooling, is the temperature reduction coefficient; After each update of the particle position, check whether the constraint conditions are met. If not, correct the particle position to return to the feasible solution space; When the preset maximum number of iterations is reached Or the fitness value changes less than the set threshold in several consecutive iterations When , the iteration is stopped, and the optimal particle position is recorded to generate multiple maintenance plans; The evaluation and optimization steps using the combined method of analytic hierarchy process and grey clustering analysis include: S42. Construct a hierarchical model, including selecting the optimal maintenance plan, maintenance effect, cost-effectiveness, implementation difficulty, environmental impact and the multiple maintenance plans generated above; construct a judgment matrix And assign values; calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector , normalize to get the weight vector, and calculate the consistency index and random consistency ratio ,when When the judgment matrix is ​​considered to have satisfactory consistency, otherwise it will be readjusted; in, represents the order of the judgment matrix, RI represents the random consistency index; The weight of each criterion at the criterion level and the weight of each solution at the solution level under the corresponding criterion are weighted and summed to obtain the comprehensive weight of each solution; Grey cluster analysis is used to cluster the maintenance schemes for evaluation: the cluster evaluation of the maintenance schemes includes the improvement rate of maintenance effect, the rate of cost reduction, the rate of shortening the construction period and the whitening weight function; For each maintenance plan And each clustering index , according to the indicator value Calculate its belonging to each gray class The clustering coefficient ,in is the number of clustering indicators, For indicators weight; divide each maintenance plan into corresponding gray categories according to the clustering coefficient, and rank the maintenance plans based on the comprehensive weight obtained by the hierarchical analysis method; Determine the best solution by combining expert experience with actual conditions: Further evaluate the top-ranked solutions based on expert experience and actual project conditions, and ultimately select the best bridge maintenance solution.

[0014] In the preferred solution, BIM technology manages the construction progress and quality by building a three-dimensional model of the bridge and associating it with the construction progress information.

[0015] In the preferred solution, the solution in S6 includes: The data collected by S61, fiber grating sensor, vibration sensor and high-definition camera are transmitted to the data processing center in real time by wireless transmission technology and data encryption and retransmission mechanism, and stored in the distributed time series database; the sensor error correction formula is used to correct the data. Perform error correction on sensor data, where is the original measurement value, is the true value, and are the coefficients obtained by least squares fitting; Data preprocessing uses linear interpolation formula Fill in missing data values, where is the time point where the missing value is located, and For the adjacent time points before and after it, and is the corresponding measured value; The method based on local outlier factor is used to detect and remove outliers; the maximum and minimum normalization is used to normalize the sensor data of different types; S62: The pre-processed data is transmitted to the bridge condition assessment module in S3, and the current state of the bridge is assessed based on the Isomap algorithm model and the relevant assessment index system; the change value of the bridge health status score is calculated. and crack growth rate ; in is the current rating, is the pre-maintenance score, where is the current bridge damage expansion rate, is the bridge damage expansion rate before maintenance; Constructing comprehensive evaluation indicators for maintenance effectiveness ,in , , is the corresponding weight and , is the actual maintenance cost, is the budgeted maintenance cost; S63. Comprehensive evaluation index based on maintenance effectiveness With the high threshold set and low threshold Make comparative judgments; like , maintain the current maintenance strategy; like , re-analyze the causes and development trends of bridge diseases, use the particle swarm optimization simulated annealing hybrid algorithm to generate new maintenance plans, and use the method of combining hierarchical analysis and grey clustering analysis to evaluate and optimize the new plans to select the optimal strategy; like , and make appropriate fine-tuning to the frequency of implementation of maintenance measures and the proportion of maintenance resource allocation.

[0016] The present invention provides a highway bridge intelligent maintenance decision system and decision method. The highway bridge intelligent maintenance decision system and decision method can realize real-time acquisition of bridge status data by multiple sensors, efficiently integrate and process multi-source data, improve data accuracy and reliability, and provide solid support for bridge condition assessment and disease prediction. The system can accurately capture bridge diseases, discover potential problems in a timely manner, effectively reduce the probability of safety accidents, and ensure smooth and safe transportation. Based on advanced data analysis and evaluation models, combined with high-dimensional data space construction, neighborhood graph construction and geodesic distance calculation, the complex relationship of data can be analyzed more accurately, and accurate assessment of the technical status of the bridge and reliable prediction of the development of diseases can be achieved. The method of combining particle swarm optimization simulated annealing hybrid algorithm and hierarchical analysis method gray clustering analysis can search for the optimal maintenance decision combination under the constraints of maintenance cost, time, etc., evaluate and optimize the maintenance plan, improve the utilization efficiency of maintenance resources, and reduce maintenance costs. The maintenance strategy can be adjusted in real time according to the dynamic changes of the bridge state, enhance the adaptability and pertinence of the decision, ensure that the bridge is always in a good use state, and extend the service life of the bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 is a flow chart of the maintenance decision-making system of the present invention; Figure 2 It is a block diagram of the data acquisition module of the present invention; Figure 3 It is a block diagram of the data processing and storage module of the present invention; Figure 4 is a block diagram of a bridge condition assessment module of the present invention; Figure 5 It is a block diagram of the maintenance decision-making module of the present invention; Figure 6 It is a block diagram of the maintenance implementation and management module of the present invention; Figure 7 It is a block diagram of the maintenance monitoring and feedback module of the present invention. DETAILED DESCRIPTION

[0018] Example 1 like Figure 1-7 As shown, a highway bridge intelligent maintenance decision system includes a data acquisition module, a data processing and storage module, a bridge condition assessment module, a maintenance decision-making module, a maintenance implementation and management module, and a maintenance monitoring and feedback module; The bridge condition assessment module includes the bridge technical condition module and the disease development trend prediction module; The maintenance implementation and management module includes a BIM bridge 3D model module; The monitoring and feedback module is set on the bridge. The monitoring and feedback module includes a fiber grating sensor, a vibration sensor and a high-definition camera. The data monitored by the fiber grating sensor, the vibration sensor and the high-definition camera are transmitted to the data processing and storage module. The data in the data processing and storage module is input into the bridge condition assessment module to assess the technical condition of the bridge and predict the development trend of the disease.

[0019] The data acquisition module collects bridge-related information through a variety of means, including using fiber grating sensors, vibration sensors, and high-definition cameras to monitor the bridge status in real time, and using drone detection equipment and manual inspections to obtain more comprehensive data. At the same time, it collects data such as bridge conditions, environment, traffic, and historical maintenance records from the highway intranet and the Internet.

[0020] The data processing and storage module processes the collected data, uses the DBSCAN algorithm to clean the data to remove noise and outliers, uses the unique hot encoding method to convert the classified data into numerical form for subsequent processing, and then builds a distributed database based on blockchain technology to store data, ensuring the security and non-tamperability of the data as well as data backup and synchronization between different nodes.

[0021] The bridge condition assessment module includes a technical condition module and a disease development trend prediction module. By receiving data from the data processing and storage module, the Isomap algorithm is used to assess the technical condition of the bridge, predict the development of diseases, and then determine the health level of the bridge and the priority of maintenance needs.

[0022] The maintenance decision-making module generates a variety of maintenance plans based on the bridge condition assessment results using a particle swarm optimization simulated annealing hybrid algorithm, and evaluates and optimizes the plans through a combination of analytic hierarchy process and grey clustering analysis, ultimately selecting the optimal maintenance plan.

[0023] The maintenance implementation and management module uses BIM to build a three-dimensional bridge model module, organizes construction according to construction specifications and safety standards, and effectively manages construction progress and quality.

[0024] The maintenance monitoring and feedback module is installed on the bridge. Its fiber grating sensors, vibration sensors and high-definition cameras collect data in real time and transmit it to the data processing and storage module. The bridge condition assessment module then regularly evaluates the maintenance results and adjusts the maintenance strategy based on the feedback results, forming a complete intelligent maintenance closed-loop management system.

[0025] In the preferred solution, the acquisition module includes fiber grating sensors, vibration sensors, high-definition cameras, drone detection equipment, and manual inspection methods; The collection module collects status data, environmental data, traffic data and historical maintenance record data from the highway intranet and the Internet.

[0026] The data collection module of the highway bridge intelligent maintenance decision-making system uses a variety of methods and equipment to collect multiple types of data. The objects collected include bridge condition data, environmental data, traffic data and historical maintenance record data on the highway intranet and the Internet.

[0027] In terms of specific data collection methods, fiber grating sensors are installed at key stress-bearing parts of the bridge, such as the mid-span, around the support, and at the connection between the pier and the beam. In the mid-span and around the support, they are installed by applying special glue to ensure that the sensor fits tightly to the bridge surface and can effectively monitor the strain and temperature of the structure; in the connection between the pier and the beam, embedded installation is adopted, first chiseling out a groove to place the sensor, and then sealing it with concrete slurry. This method can ensure that the sensor works stably in complex stress areas and obtains accurate data.

[0028] High-definition cameras and drone inspection equipment play their respective advantages to obtain images of the bridge's appearance, providing an intuitive basis for evaluating the bridge's appearance and helping to discover surface defects such as cracks and spalling. Manual inspections, as an important supplementary means, rely on the experience and careful observation of professionals to record the detailed location and morphology of the defects and capture some subtle defects that are difficult to detect directly through instruments.

[0029] Vibration sensors are specifically used to analyze vibration information when vehicles pass through bridges. This information is crucial for evaluating the dynamic response and stability of bridge structures. Through in-depth analysis of vibration information, we can understand the working status of bridges under different traffic loads and discover potential structural problems in a timely manner. Multiple collection methods work together to provide comprehensive and accurate data support for subsequent bridge condition assessments and maintenance decision-making.

[0030] In the preferred solution, the data processing and storage module uses the DBSCAN algorithm to clean the collected data, uses the one-hot encoding method to encode the classified data, and builds a distributed database based on blockchain technology to store the processed data.

[0031] The data processing and storage module undertakes the key tasks of data processing and storage in the highway bridge intelligent maintenance decision-making system. It first uses the DBSCAN algorithm to clean the collected data.

[0032] The DBSCAN algorithm is a density-based spatial clustering algorithm that can automatically identify and remove noise points and outliers in the data, effectively improving the quality and reliability of the data and ensuring that subsequent analysis is based on more accurate data. Next, the unique hot encoding method is used to process the categorical data.

[0033] For categorical data such as bridge type and disease type, unique hot encoding will map each category into a unique binary vector, converting non-numeric categorical data into numerical form suitable for computer processing, which is convenient for subsequent data analysis and model training. Finally, a distributed database is built based on blockchain technology to store the processed data. Blockchain technology has the characteristics of decentralization, immutability, and traceability, making data more secure and reliable during storage and transmission. The distributed storage method ensures that data is backed up on multiple nodes to prevent data loss. At the same time, data synchronization between different nodes can also ensure data consistency, providing stable data support for the entire smart maintenance decision-making system.

[0034] In the preferred solution, the technical condition module and the block disease development trend prediction module in the bridge condition assessment module both use the Isomap algorithm to assess the technical condition of the bridge.

[0035] In the bridge condition assessment module, the technical condition module and the disease development trend prediction module both use the Isomap algorithm to assess the technical condition of the bridge. The Isomap algorithm is an isometric mapping algorithm that can construct a high-dimensional data space based on the structural characteristics of the bridge and multi-source monitoring data. By calculating the geodesic distance between data points, the high-dimensional data is mapped to a low-dimensional space, thereby effectively extracting the inherent change characteristics of the bridge structure.

[0036] Using this algorithm, we can accurately evaluate the current technical condition of the bridge and gain insight into the performance of the bridge structure from multi-dimensional data such as strain, displacement, and cracks. We can also predict the development trend of defects and analyze their future development direction, thereby providing key basis for determining the health level of the bridge and the priority of maintenance needs, and helping to make scientific and reasonable maintenance decisions in the future.

[0037] Example 2 Further illustrate with reference to Example 1, Figure 1-7 As shown, the method includes: S1, using fiber grating sensors, vibration sensors, high-definition cameras, drone detection equipment and manual inspection methods to collect various types of bridge data, and at the same time collecting bridge condition data, environmental data, traffic data and historical maintenance record data through the highway intranet and the Internet; S2, the data processing and storage module uses the DBSCAN algorithm to clean the data collected in step S1, encodes the classified data using the one-hot encoding method, and stores the processed data in a distributed database based on blockchain technology; S3, the bridge condition assessment module extracts the data stored in the database, uses the Isomap algorithm to assess the technical condition of the bridge, predicts the development of the disease, and determines the health level and maintenance priority of the bridge; S4. Evaluate the technical conditions obtained in step S3, predict the disease development data, generate multiple maintenance plans using a hybrid algorithm of particle swarm optimization and simulated annealing, evaluate and optimize the plans using a combination of analytic hierarchy process and grey clustering analysis, and select the optimal maintenance plan; S5. Based on the selected maintenance plan, use BIM technology to organize construction in accordance with construction specifications and safety standards, and manage construction progress and quality; S6, the data collected by the fiber Bragg grating sensor, vibration sensor and high-definition camera in the monitoring and feedback module monitors the bridge status in real time; The monitoring data is then transmitted to the bridge condition assessment module in S3, which regularly re-evaluates the maintenance effectiveness and provides feedback to adjust the maintenance strategy.

[0038] In the preferred solution, in step S1, the fiber grating sensor is installed at the mid-span of the bridge, around the support, and at the connection between the pier and the beam; Apply special glue to the installation position in the middle of the bridge span and around the support, and stick the fiber grating sensor on the bridge surface; The connection between the pier and the beam is embedded, a groove is chiseled out at the connection between the pier and the beam, the fiber grating sensor is placed in the groove, and the groove is sealed with concrete slurry; Fiber Bragg grating sensors monitor the strain and temperature of bridge structures; High-definition cameras and drone inspection equipment obtain images of the bridge's exterior; Manual inspections were conducted to record the detailed location and morphology of the disease.

[0039] In the preferred solution, BIM technology manages the construction progress and quality by building a three-dimensional model of the bridge and associating it with the construction progress information.

[0040] Example 3 In combination with Example 2, the vibration sensor analyzes the vibration information of a vehicle passing through the bridge, and the specific steps include: A1. First, use the time-frequency analysis algorithm to reveal the frequency characteristics of the signal at different times, including using short-time Fourier transform or wavelet transform to expand the signal in two dimensions of time and frequency to reveal the frequency characteristics of the signal at different times. Use the multi-resolution analysis characteristics of wavelet transform to automatically adjust the window size according to the signal frequency to capture the transient changes in the bridge vibration signal; The vibration signal of the bridge passing by collected by the vibration sensor Perform denoising to remove possible DC components and high-frequency noise. The moving average filter method can be used, and the filter window size is set to , then the filtered signal for: ; Here we choose wavelet transform for time-frequency analysis. Wavelet transform uses a basic wavelet function The signal is analyzed by stretching and translation. , its continuous wavelet transform Defined as: ; in is the scale parameter, which controls the expansion and contraction of the wavelet function; is the translation parameter, which controls the translation of the wavelet function; yes The complex conjugate of .

[0041] The multi-resolution analysis characteristics of wavelet transform are used to automatically adjust the window size according to the signal frequency. As the scale increases, the wavelet function becomes wider and the resolution of low-frequency signals increases. The wavelet function becomes narrower and the resolution of high-frequency signals is improved by reducing the wavelet coefficients of different scales and positions. The frequency characteristics of the signal at different times are revealed, and the transient changes in the bridge vibration signal are captured.

[0042] A2. Then use the wavelet packet decomposition algorithm to achieve fine signal decomposition to obtain sub-signals in different frequency bands. By analyzing the energy distribution and amplitude change characteristics of the sub-signals, and comparing the characteristic differences of the sub-signals under normal and abnormal conditions, it is possible to determine whether the bridge structure is damaged or abnormal. After time-frequency analysis, the signal Perform wavelet packet decomposition. Wavelet packet decomposition is based on wavelet decomposition, and further decomposes the high-frequency sub-band to decompose the signal into finer frequency bands. In the Tier The wavelet packet coefficient of each node is , then the recursive formula of wavelet packet decomposition is: ; ; in and are the low-pass and high-pass filter coefficients respectively.

[0043] Calculate the energy of each sub-signal and amplitude The energy of the sub-signal is defined as: ; The amplitude of the sub-signal can take the maximum value of its absolute value, that is, .

[0044] Establish a database of energy and amplitude characteristics of each sub-signal under normal conditions of the bridge. During actual monitoring, compare the energy and amplitude of each current sub-signal with the normal characteristics in the database and calculate the characteristic difference index. and : ; ; in and In normal state, Tier The energy and amplitude of the node sub-signal. or When the set threshold is exceeded, it is judged that the bridge structure may be damaged or abnormal.

[0045] A3. Use the empirical mode decomposition algorithm to adaptively process nonlinear and non-stationary signals. Use the empirical mode decomposition algorithm to decompose the bridge vibration signal into multiple intrinsic mode functions, extract the main characteristic components related to the vibration of the bridge structure, remove noise and interference components, calculate the energy and frequency parameters of the intrinsic mode function components and observe their changing trends over time, and then evaluate the stability and health of the bridge structure; Perform empirical mode decomposition on the signal after wavelet packet decomposition. Decomposition into multiple intrinsic mode functions (IMFs) and a residual component ,Right now The specific steps of EMD are as follows: Determine the signal All local maximum and minimum points of are fitted with cubic spline curves to obtain the upper envelope and lower envelope .

[0046] Calculate the average of the upper and lower envelopes .

[0047] Subtract the envelope mean from the signal to get a new signal .

[0048] judge Whether the IMF condition is met (i.e. the number of extreme points and zero crossing points of the signal is equal or differs by at most one, and the upper and lower envelopes of the signal are locally symmetric about the time axis). If not, Repeat the above steps as a new signal until the conditions are met and the first IMF is obtained. .

[0049] Subtract from the original signal , and get the remaining signal ,Will Repeat the above steps as a new signal and get , until the residual component becomes a monotonic function or a constant.

[0050] Analyze the correlation between each IMF component and bridge structure vibration, and select the main IMF components related to bridge structure vibration The correlation coefficient between the IMF component and the original signal can be calculated by To determine relevance: ; in and They are and The IMF component with a larger correlation coefficient is selected as the main characteristic component.

[0051] Calculate the energy of the main IMF components and frequency The energy calculation formula is: , the frequency can be calculated by Fourier transform. Observe the changing trend of energy and frequency parameters over time. When energy or frequency changes abnormally, it indicates that the stability and health of the bridge structure may change.

[0052] A4. Finally, the support vector machine algorithm is used to identify abnormal vibration patterns. The support vector machine algorithm is used to identify abnormal vibration patterns. The amplitude, frequency, and energy characteristic parameters are extracted from the vibration signal as the input of the support vector machine. A large amount of bridge vibration data under normal and abnormal conditions is used to train the support vector machine so that it can learn the characteristic differences between normal and abnormal vibration patterns. During actual monitoring, the vibration signal features collected in real time are input into the trained support vector machine model to determine whether the current vibration state is normal and the type and severity of the abnormality.

[0053] From the main IMF components after empirical mode decomposition Extract characteristic parameters such as amplitude, frequency, and energy from the ,in is the amplitude, is the frequency, It's energy.

[0054] Collect a large number of bridge vibration signals under normal and abnormal conditions, extract feature parameters according to the above method, and construct a training data set ,in It is The feature vector of the samples, is the label of the sample, Indicates an abnormal state. Indicates normal status.

[0055] Choose a suitable kernel function (such as radial basis function ), use the training data set to train the support vector machine. The goal of the support vector machine is to find an optimal hyperplane , so that samples of different categories can be separated to the greatest extent. The training process can be achieved by solving the following optimization problem: ; ; in is the penalty factor, which controls the balance between classification error and interval size; is a slack variable that allows some samples to violate the classification boundary.

[0056] In actual monitoring, the vibration signal collected in real time is extracted according to the above steps to obtain the characteristic parameters and obtain the characteristic vector , input it into the trained support vector machine model, and according to the output of the model Determine whether the current vibration state is normal. , it is judged as an abnormal state, and the type and severity of the abnormality are further determined based on the decision boundary of the model and the position of the eigenvector.

[0057] Example 4 Further described in conjunction with Example 2, the steps of the bridge condition assessment method using the Isomap algorithm are as follows: S31. Extract bridge data from a distributed database based on blockchain, including bridge structure strain, displacement, crack size, traffic data, environmental parameters, and past maintenance records. Use multiple filling methods to process missing data and use the local outlier factor algorithm to detect outliers. Data such as bridge structure strain, displacement, crack size, traffic data, environmental parameters, and past maintenance records are extracted from the distributed database based on blockchain. These data are stored in different nodes, and the consistency and integrity of the data are ensured through the consensus mechanism of blockchain. Use database query statements to filter out the required data based on the time range of the data, bridge number, and other conditions.

[0058] For data columns with missing values, multiple imputation is used. First, the missing values ​​are predicted using a regression model. For each missing value, a regression equation is established based on other related variables. The missing values ​​of Variables with high correlation Building a linear regression model , the regression coefficients are estimated by the least squares method Then, this process is repeated multiple times to generate multiple imputed data sets, which are finally merged to reduce the imputed error.

[0059] Calculate the local outlier factor for each data point. For each point in the data set , first determine its Neighbor Point Set , calculation point to The average distance between neighboring points , defining point The local reachable density ,in Yes To point Finally, calculate the Euclidean distance of the point The local outlier factor .when When it is greater than the set threshold, the point is considered are outliers and are removed from the dataset.

[0060] S32. Use the ReliefF algorithm for feature selection and use the selected features as dimensions to construct a high-dimensional data set, providing a comprehensive information basis for bridge technical condition assessment and disease development prediction and capturing complex relationships; Initialize the weights of each feature ,in Represents the feature. For each sample in the dataset , find its nearest neighbor sample from the same class (recent hit sample), found from different classes Nearest neighbor samples (recently missed samples). For each feature , update its weight : ; in It is a sample and In Features The difference in is the number of samples in the data set. Repeat the above process multiple times, and finally sort according to the feature weights and select features with higher weights.

[0061] The filtered features are used as dimensions to construct a high-dimensional data set. Each sample corresponds to a point in the high-dimensional space, and its coordinates are determined by the value of the selected feature.

[0062] S33, using Mahalanobis distance to measure the distance between data points, using kd tree to determine the k nearest neighbor points of each data point, constructing a neighborhood graph, reflecting the local similarity of data points and providing a structural basis for subsequent calculations; For any two data points in a high-dimensional dataset and , calculate the Mahalanobis distance between them ,in is the covariance matrix of the data set. The Mahalanobis distance takes into account the correlation and scale of the data and can more accurately measure the distance between data points.

[0063] Use the kd tree data structure to efficiently determine the The kd tree is a binary search tree that reduces the time complexity of the nearest neighbor search by recursively dividing the data space into two half spaces.

[0064] According to each data point nearest neighbor points and build a neighborhood graph. It is a data point of One of the nearest neighbors, then add a line from arrive The edge weights are the Mahalanobis distances between them.

[0065] S34. Use BellmanFord algorithm to calculate the shortest path between any two points in the neighborhood graph as the geodesic distance, which reflects the real structural relationship of the bridge samples and is used for low-dimensional embedding and feature extraction, providing key information for evaluation and prediction; Application of BellmanFord algorithm: For any two nodes in the neighborhood graph and , use the BellmanFord algorithm to calculate the shortest path between them. Initialize the distance estimates of all nodes is infinite, where is the starting node, Then proceed Iterations, for each edge in the neighborhood graph ,if , then update ,in It is the edge Finally, check whether there is a negative weight loop. If so, the algorithm fails. Otherwise, That is from arrive The shortest path length.

[0066] Repeat the above process to calculate the shortest path between any two points in the neighborhood graph and construct the geodesic distance matrix ,in Represents data points and The geodesic distance between .

[0067] S35. Using the kernel Isomap method, the Laplace kernel function is introduced to perform eigendecomposition after centering the kernel matrix, and the data is projected into a low-dimensional space. The spectral clustering algorithm is used to cluster the bridge samples in the low-dimensional space, and the technical condition scores are assigned to the bridge samples based on the clustering results and related factors. Using the Kernel Isomap method: Introducing the Laplace kernel function Calculate the kernel matrix ,in is the bandwidth parameter of the kernel function. Centralized processing, obtained . For the centralized kernel matrix Perform eigendecomposition and obtain eigenvalues and the eigenvector . Before selecting The eigenvector corresponding to the largest eigenvalue projects the data into a low-dimensional space. The coordinates after projection are .

[0068] In low-dimensional space, construct a similarity matrix ,in . Calculate the degree matrix ,in . Construct the Laplacian matrix . For the Laplacian matrix Perform feature decomposition and select The eigenvectors corresponding to the smallest eigenvalues ​​form the matrix . For the matrix Each row of is normalized to obtain the matrix .use Mean Algorithm for Matrix The rows are clustered and the bridge samples are divided into kind.

[0069] A technical condition score is assigned to each bridge sample based on factors such as clustering results, bridge structural characteristics, and disease history. For example, samples that are clustered into the category of better health are assigned a higher score, while samples that are clustered into the category of more serious disease are assigned a lower score.

[0070] S36, using long short-term memory network, taking low-dimensional spatial feature sequence as input, and predicting the disease development trend based on historical data by training the network; Construct an LSTM network, which includes an input layer, an LSTM layer, and an output layer. The input layer receives a low-dimensional spatial feature sequence, and the LSTM layer contains multiple LSTM units to process sequence data and capture long-term dependencies. The output layer outputs the predicted value of disease development.

[0071] The LSTM network is trained by taking the low-dimensional spatial feature sequence as input and the corresponding disease development data as output. The weight parameters of the network are updated using the back propagation algorithm to minimize the loss function between the predicted value and the true value, such as the mean square error loss function. ,in is the true value, is the predicted value.

[0072] Use the trained LSTM network and input the current low-dimensional spatial feature sequence to obtain the prediction results of disease development.

[0073] S37. Use fuzzy comprehensive evaluation method to divide bridge health level according to technical condition score and disease development prediction results. Combined with bridge importance factors, use data envelopment analysis method to determine maintenance priority.

[0074] Determine the evaluation factor set , such as technical condition score, disease development trend, etc.; determine the comment set , such as health, sub-health, disease, serious disease, etc. Establish a fuzzy relationship matrix ,in Indicates The evaluation factors Determine the weight vector of each evaluation factor , through fuzzy transformation Get the comprehensive evaluation result vector , the health grade of the bridge is determined according to the maximum membership principle.

[0075] Considering the importance of bridges, such as traffic flow and economic impact, a decision-making unit (DMU) is constructed. Each DMU contains input indicators (such as maintenance cost, maintenance time, etc.) and output indicators (such as improvement of bridge health level, slowing of disease development, etc.). The DEA model is used to calculate the efficiency value of each DMU. The higher the efficiency value, the higher the output of the DMU under certain input. The bridges are sorted according to the efficiency value to determine the maintenance priority.

[0076] Capturing complex relationships: The technical condition and disease development of bridges are affected by a variety of factors, and there are complex nonlinear relationships between these factors. High-dimensional data space can integrate these factors together, so that these complex relationships can be captured in this space. When conducting evaluation and prediction, the model built based on such a high-dimensional space can better simulate the actual situation and improve the accuracy of evaluation and prediction.

[0077] The relationship between neighborhood graph construction and bridge technical condition assessment and disease development prediction: Reflecting local similarity: Neighborhood graph construction is based on the distance relationship between data points, and the graph structure is constructed by determining the nearest neighbor points of each data point. In bridge condition assessment, the neighborhood graph can reflect the local similarity between data points. If two bridge samples are neighbors in the neighborhood graph, it means that they are similar in multiple features, then their technical conditions and disease development trends may also be similar. This helps to refer to the situation of similar samples during evaluation and prediction, and improve the reliability of evaluation and prediction.

[0078] Providing a structural basis for subsequent calculations: The structure of the neighborhood graph provides a basis for subsequent geodesic distance calculations, which plays a key role in evaluation and prediction. At the same time, the topological structure of the neighborhood graph can also be used as a feature information to be applied to the evaluation and prediction model to further improve the performance of the model.

[0079] The relationship between geodesic distance calculation and bridge technical condition assessment and disease development prediction: Reflecting the real structural relationship: Geodesic distance takes into account the global structure of data points in high-dimensional space and is obtained by calculating the shortest path length between any two points in the neighborhood graph. In bridge condition assessment, geodesic distance can more accurately reflect the real structural relationship between bridge samples. Compared with simple Euclidean distance, it can better reflect the similarities and differences of bridges under complex environments and multiple factors. When evaluating the technical condition of bridges, bridge samples can be classified according to geodesic distance. Samples with similar distances may be in similar health states. In disease development prediction, geodesic distance can better analyze the correlation between disease development between different bridge samples, thereby more accurately predicting the spread and development trend of diseases.

[0080] For low-dimensional embedding and feature extraction: Geodesic distance is a key input for low-dimensional embedding. By retaining the geodesic distance between high-dimensional data points in low-dimensional space, the intrinsic characteristics of the data can be extracted. These low-dimensional features can more effectively represent the technical status and disease development information of the bridge, providing more representative and discriminative input for subsequent evaluation and prediction models, thereby improving the accuracy of evaluation and prediction.

[0081] Example 5 Further described in conjunction with Example 2, in step S4, multiple curing methods are generated based on the particle swarm optimization simulated annealing hybrid algorithm as follows: S41. Obtain the bridge technical condition assessment and disease development prediction data from step S3, clarify the maintenance objectives, determine the decision variables, including the type of maintenance measures, maintenance time nodes, maintenance resource allocation, and set the upper limit of the maintenance cost. , Maintenance time limit Constraints; Setting the particle swarm size , randomly initialize the position vector of each particle and the velocity vector ,in is the number of decision variables; Define the fitness function as ,in , , is the weight coefficient and , Indicates the maintenance effect. represents the maintenance cost, Indicates maintenance time; In each iteration, the fitness value of each particle is calculated , update the particle's own historical optimal position and the global optimal position of the entire particle swarm , through the formula and Update particle velocity and position, where is the inertia weight, and is the learning factor, and is a random number uniformly distributed in the interval [0, 1]. Indicates the current iteration number; like , accept the new position; otherwise, with probability Accept new position where For the current temperature, press Cooling, is the temperature reduction coefficient; After each update of the particle position, check whether the constraint conditions are met. If not, correct the particle position to return to the feasible solution space; When the preset maximum number of iterations is reached Or the fitness value changes less than the set threshold in several consecutive iterations When , the iteration is stopped, and the optimal particle position is recorded to generate multiple maintenance plans; The evaluation and optimization steps using the combined method of analytic hierarchy process and grey clustering analysis include: S42. Construct a hierarchical model, including selecting the optimal maintenance plan, maintenance effect, cost-effectiveness, implementation difficulty, environmental impact and the multiple maintenance plans generated above; construct a judgment matrix And assign values; calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector , normalize to get the weight vector, and calculate the consistency index and random consistency ratio ,when When the judgment matrix is ​​considered to have satisfactory consistency, otherwise it will be readjusted; in, represents the order of the judgment matrix, RI represents the random consistency index; parameter : represents the order of the judgment matrix, that is, the number of rows or columns of elements in the judgment matrix. When constructing the judgment matrix of the hierarchical analysis method, its size depends on the number of elements in the criterion layer or the scheme layer. For example, when evaluating the bridge maintenance scheme from the four criteria of maintenance effect, cost-effectiveness, implementation difficulty, and environmental impact, the constructed judgment matrix is At this time . It plays a key role in the calculation of the consistency index CI. It affects the value of CI, which in turn affects the judgment of the consistency of the judgment matrix. CI is used to measure the degree of deviation of the judgment matrix from consistency. The smaller the CI value, the better the consistency of the judgment matrix.

[0082] Parameter RI: stands for random consistency index, which is a reference index introduced to test the consistency of judgment matrices. It is an average consistency index calculated by a large number of randomly generated judgment matrices. Judgment matrices of different orders correspond to different $RI$ values, which are obtained through a large number of experiments and statistical analysis by predecessors. For example, when When , $RI$ is about 0.58; When , RI is about 0.90. When calculating the random consistency ratio CR, $RI$ is used as the denominator, and the calculated consistency index CI is divided by it to obtain the CR value. When , the judgment matrix is ​​considered to have satisfactory consistency; if , it means that the consistency of the judgment matrix is ​​poor, and it is necessary to readjust the element values ​​in the judgment matrix to improve its consistency and ensure that the weight results obtained by the hierarchical analysis method are reliable and reasonable.

[0083] The weight of each criterion at the criterion level and the weight of each solution at the solution level under the corresponding criterion are weighted and summed to obtain the comprehensive weight of each solution; Grey cluster analysis is used to cluster the maintenance schemes for evaluation: the cluster evaluation of the maintenance schemes includes the improvement rate of maintenance effect, the rate of cost reduction, the rate of shortening the construction period and the whitening weight function; For each maintenance plan And each clustering index , according to the indicator value Calculate its belonging to each gray class The clustering coefficient ,in is the number of clustering indicators, For indicators weight; divide each maintenance plan into corresponding gray categories according to the clustering coefficient, and rank the maintenance plans based on the comprehensive weight obtained by the hierarchical analysis method; Determine the best solution by combining expert experience with actual conditions: Further evaluate the top-ranked solutions based on expert experience and actual project conditions, and ultimately select the best bridge maintenance solution.

[0084] The calculation steps of the particle swarm optimization simulated annealing hybrid algorithm are: 1. Initialize the particle swarm and set the particle swarm size , randomly initialize the position vector of each particle and the velocity vector .

[0085] 2. Define the fitness function , determine the weight coefficient according to the actual needs of bridge maintenance , , .

[0086] 3. In each iteration, calculate the fitness value of each particle and update the particle's own historical optimal position and the global optimal position of the entire particle swarm .

[0087] 4. Update the particle speed and position according to the formula, and use the simulated annealing algorithm to accept poor solutions with a certain probability to avoid falling into the local optimum.

[0088] 5. After each particle position update, check whether the constraints (such as the upper limit of the maintenance cost) are met. , Maintenance time limit ), if it is not satisfied, the particle position is corrected to return to the feasible solution space.

[0089] 6. When the preset maximum number of iterations is reached Or the fitness value changes less than the set threshold in several consecutive iterations When , the iteration is stopped, and the optimal particle position is recorded to generate multiple maintenance plans.

[0090] Constructing hierarchical model for scheme evaluation and decision making 1. Build a hierarchical model and construct a judgment matrix And assign a value.

[0091] 2. Calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector , normalized to obtain the weight vector, and simultaneously calculated the consistency index CI and the random consistency ratio CR to ensure that the judgment matrix has satisfactory consistency.

[0092] 3. The weight of each criterion at the criterion level and the weight of each solution at the solution level under the corresponding criterion are weighted and summed to obtain the comprehensive weight of each solution.

[0093] 4. Use grey cluster analysis to cluster and evaluate the maintenance schemes, calculate the clustering coefficient of each maintenance scheme belonging to each grey class, and classify each maintenance scheme into the corresponding grey class according to the clustering coefficient.

[0094] 5. The maintenance plans are ranked according to the comprehensive weights obtained by the hierarchical analysis method. The top-ranked plans are further evaluated based on expert experience and actual engineering conditions, and the optimal bridge maintenance plan is finally selected.

[0095] The implementation cases are as follows: After the evaluation in the previous step S3, it was found that the bridge had a certain degree of damage, including structural cracks, concrete spalling, etc., and it was predicted that the damage would develop further. In order to ensure the safe use of the bridge, a scientific and reasonable maintenance plan needs to be formulated.

[0096] Generate maintenance plan based on particle swarm optimization and simulated annealing hybrid algorithm: 1. Data acquisition and target determination From step S3, the technical condition assessment data of the bridge (such as crack width, depth, structural strain, etc.) and disease development prediction data (such as crack expansion speed in the next year, etc.) are obtained. The maintenance goal is to maximize the maintenance effect of the bridge and extend the service life of the bridge under the premise of meeting cost and time constraints. The decision variables are determined as follows: Types of maintenance measures, including crack repair, structural reinforcement, etc.; Maintenance time nodes, including the month in which maintenance should be started; The amount of maintenance resources allocated, including material usage, man-hours, etc.; Also set a maintenance cost cap Ten thousand yuan, upper limit of maintenance time Months.

[0097] 2. Particle swarm initialization Setting the particle swarm size , the number of decision variables (Corresponding to the three decision variables mentioned above). Randomly initialize the position vector of each particle and the velocity vector For example, particles The position vector of may be expressed as , and the velocity vector represents the speed of change of these decision variables.

[0098] 3. Define the fitness function Define the fitness function , determine the weight coefficient according to the actual situation , , .in, The calculation is done through a professional bridge assessment model, which takes into account factors such as the degree of improvement of bridge defects and the improvement of structural performance; is the maintenance cost, which is calculated based on the type of maintenance measures and resource allocation; The maintenance time is determined by the complexity of the maintenance measures and the resource input.

[0099] 4. Iterative Calculation In each iteration: calculate the fitness value of each particle .

[0100] Update the particle's own historical optimal position and the global optimal position of the entire particle swarm .

[0101] By formula and Update particle velocity and position. Set inertia weight , learning factor , and is a random number uniformly distributed in the interval $[0, 1]$, Indicates the current iteration number.

[0102] like , accept the new position; otherwise, with probability Accept the new position. Initial temperature , cooling coefficient ,according to Cool down.

[0103] After each particle position update, check whether the constraints are met (the maintenance cost does not exceed , the maintenance time shall not exceed ), if it is not satisfied, the particle position is corrected to return to the feasible solution space. For example, if the maintenance cost corresponding to a particle exceeds , then reduce the amount of resource allocation.

[0104] 5. Stop Iteration When the preset maximum number of iterations $T_{maxIter} = 200$ is reached or the fitness value changes less than the set threshold $\epsilon = 0.01$ in 10 consecutive iterations, the iteration is stopped. Record the optimal particle position and generate multiple maintenance plans, such as: Option 1: Adopt crack repair and local reinforcement measures, start maintenance in the third month, invest 60 tons of materials, the estimated cost is 1.8 million yuan, and the maintenance time is 5 months.

[0105] Option 2: Carry out comprehensive structural reinforcement, start maintenance in the first month, invest 80 tons of materials, estimate the cost to be RMB 2 million, and the maintenance time is 6 months.

[0106] The method of combining analytic hierarchy process and grey cluster analysis is used to evaluate and optimize the scheme. 1. Build a hierarchical model A hierarchical model is constructed. The target layer is to select the optimal maintenance plan. The criterion layer includes maintenance effect, cost-effectiveness, implementation difficulty, and environmental impact. The plan layer includes the multiple maintenance plans generated above (Scheme 1 and Scheme 2).

[0107] 2. Construct the judgment matrix and calculate the weights Constructing a judgment matrix And assign values, for example, for the judgment matrix of the criterion layer: ; Calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector , normalized to get the weight vector. Calculate the consistency index , random consistency ratio ,for , ,but , it is believed that the judgment matrix has satisfactory consistency.

[0108] For the scheme layer, a judgment matrix is ​​constructed and the weight is calculated under each criterion, such as under the maintenance effect criterion: ; The weights of Scheme 1 and Scheme 2 under the maintenance effect criterion are calculated as follows: .

[0109] The weight of each criterion at the criterion level and the weight of each solution at the solution level under the corresponding criterion are weighted and summed to obtain the comprehensive weight of each solution.

[0110] 3. Grey cluster analysis Select the maintenance effect improvement rate, cost reduction rate, and construction period reduction rate as clustering indicators, and determine the weight of each indicator , , . Determine the whitening weight function based on the actual data.

[0111] For each maintenance plan And each clustering index , calculate its belonging to each gray class The clustering coefficient Assuming that it is divided into three gray categories (excellent, good, and medium), the clustering coefficients of Scheme 1 and Scheme 2 are calculated and divided into the corresponding gray categories.

[0112] The clustering coefficient of scheme 1 is , , , then option 1 belongs to the optimal class; the clustering coefficient of option 2 is , , , then Scheme 2 belongs to the good category.

[0113] The maintenance plans are ranked according to the comprehensive weights obtained by the hierarchical analysis method, and plan 1 is ranked first because of its higher comprehensive score.

[0114] 4. Determine the best solution Combining the experience of experts and the actual project situation, the experts believe that Plan 1 has a more reasonable cost and time control while ensuring the maintenance effect, and is relatively easy to implement and has less impact on the environment. Plan 1 was finally selected as the optimal bridge maintenance plan.

[0115] Through the above examples, it is demonstrated how to use the particle swarm optimization simulated annealing hybrid algorithm to generate a variety of bridge maintenance plans, and use the method of combining hierarchical analysis and grey clustering analysis to evaluate and optimize the plans, so as to determine the optimal maintenance plan.

[0116] Example 6 In combination with Example 2, the solution in S6 includes: S61, the data collected by the fiber grating sensor, the vibration sensor and the high-definition camera are transmitted to the data processing center in real time by using wireless transmission technology and adopting data encryption and retransmission mechanism, and stored in the distributed time series database; the sensor error correction formula is used Perform error correction on sensor data, where is the original measurement value, is the true value, and are the coefficients obtained by least squares fitting; Data preprocessing uses linear interpolation formula Fill in missing data values, where is the time point where the missing value is located, and For the adjacent time points before and after it, and is the corresponding measured value; The method based on local outlier factor is used to detect and remove outliers; the maximum and minimum normalization is used to normalize the sensor data of different types; S62: The pre-processed data is transmitted to the bridge condition assessment module in S3, and the current state of the bridge is assessed based on the Isomap algorithm model and the relevant assessment index system; the change value of the bridge health status score is calculated. and crack growth rate ; in is the current rating, is the pre-maintenance score, where is the current bridge damage expansion rate, is the bridge damage expansion rate before maintenance; Constructing comprehensive evaluation indicators for maintenance effectiveness ,in , , is the corresponding weight and , is the actual maintenance cost, is the budgeted maintenance cost; S63. Comprehensive evaluation index based on maintenance effectiveness With the high threshold set and low threshold Make comparative judgments; like , maintain the current maintenance strategy; like , re-analyze the causes and development trends of bridge diseases, use the particle swarm optimization simulated annealing hybrid algorithm to generate new maintenance plans, and use the method of combining hierarchical analysis and grey clustering analysis to evaluate and optimize the new plans to select the optimal strategy; like , and make appropriate fine-tuning to the frequency of implementation of maintenance measures and the proportion of maintenance resource allocation.

[0117] Specific implementation example: Bridge maintenance effectiveness evaluation and strategy adjustment A cross-river bridge adopts an advanced monitoring and maintenance management system, and is equipped with fiber grating sensors, vibration sensors, high-definition cameras and other equipment to monitor the bridge status in real time. After a period of maintenance, it is necessary to evaluate the maintenance results and adjust the maintenance strategy based on the evaluation results.

[0118] Step S61: Data collection, transmission, processing and preprocessing 1. Data collection and transmission Fiber Bragg grating sensors, vibration sensors and high-definition cameras collect data on the structural strain, vehicle vibration and appearance of the bridge. Wireless transmission technology is used to transmit the collected data to the data processing center in real time. To ensure the security and reliability of data transmission, data encryption and retransmission mechanisms are used. For example, when packet loss occurs during data transmission, the system will automatically retransmit the lost data. The collected data is finally stored in a distributed time series database to facilitate subsequent analysis and processing.

[0119] 2. Sensor error correction For the raw data collected by the sensor, use the sensor error correction formula To correct the error. First, the coefficients are obtained by least squares fitting. and Assuming that the FBG sensor measures strain data, a certain number of raw measurements are collected and the corresponding true value samples (obtained through high-precision calibration equipment), calculated using the least squares method , If the original measurement value of the fiber Bragg grating sensor at a certain moment , then the corrected true value .

[0120] 3. Data Preprocessing When data is missing, the linear interpolation formula is used. Fill it up. The vibration sensor is The data of the moment is missing, and the adjacent time points before and after The corresponding measured value , The corresponding measured value ,but The interpolation result at time is .

[0121] The outliers are detected and removed using a method based on local outlier factors. The local outlier factor of each data point is calculated, and when the local outlier factor exceeds the set threshold, the data point is determined to be an outlier and removed from the data set.

[0122] The maximum and minimum normalization is used to normalize the data of different types of sensors and map the data to the $[0, 1]$ interval. For the strain data of the fiber Bragg grating sensor, its maximum value is , the minimum value is , the value of a data point is , then the normalized value is .

[0123] Step S62: Calculation of comprehensive evaluation indexes for bridge condition assessment and maintenance effectiveness 1. Bridge condition assessment The preprocessed data is sent to the bridge condition assessment module in S3 to assess the current state of the bridge based on the Isomap algorithm model and related assessment index system. Assume that the current health status score of the bridge obtained by the assessment model is , the score before maintenance was , then the bridge health score change value .

[0124] At the same time, the current bridge crack expansion speed is measured mm / month, bridge crack expansion rate before maintenance mm / month, the crack expansion rate change rate .

[0125] 2. Calculation of comprehensive evaluation indicators of maintenance effectiveness Setting weights , , , actual maintenance cost Ten thousand yuan, budgeted maintenance cost Comprehensive evaluation index of maintenance effect .

[0126] Step S63: Maintenance strategy adjustment 1. Threshold setting Set high threshold , low threshold .

[0127] 2. Strategic judgment and adjustment because ,satisfy Therefore, the maintenance measures implementation frequency and maintenance resource allocation ratio are appropriately adjusted. For example, the routine inspection from once a month is adjusted to once every half month, and the material resource allocation ratio for crack repair is increased from Increase to , the proportion of resources allocated for structural reinforcement has changed from Reduce to .

[0128] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions described in the claims, including equivalent replacement solutions of the method features in the technical solutions described in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A highway bridge intelligent maintenance decision-making system, characterized by: The decision-making system includes a data acquisition module, a data processing and storage module, a bridge condition assessment module, a maintenance decision-making module, a maintenance implementation and management module, and a maintenance monitoring and feedback module; The bridge condition assessment module includes the bridge technical condition module and the disease development trend prediction module; The technical condition module and the block prediction disease development trend module in the bridge condition assessment module both use the Isomap algorithm to assess the technical condition of the bridge; Extract bridge data and process missing values ​​and outliers. Select features using ReliefF to build a high-dimensional data set. Use Mahalanobis distance and kd tree to build a neighborhood graph. Use BellmanFord algorithm to measure geodetic distance. Use kernel Isomap to introduce Laplace kernel function and project it into low-dimensional space to evaluate technical status by clustering score. Use long short-term memory network to predict disease trend. The maintenance implementation and management module includes a BIM bridge 3D model module; The monitoring and feedback module is set on the bridge. The monitoring and feedback module includes a fiber grating sensor, a vibration sensor and a high-definition camera. The data monitored by the fiber grating sensor, the vibration sensor and the high-definition camera are transmitted to the data processing and storage module. The data in the data processing and storage module is input into the bridge condition assessment module to assess the technical condition of the bridge and predict the development trend of the disease.

2. According to claim 1, a highway bridge intelligent maintenance decision-making system is characterized by: The acquisition modules include fiber grating sensors, vibration sensors, high-definition cameras, drone detection equipment, and manual inspection methods; The collection module collects status data, environmental data, traffic data and historical maintenance record data from the highway intranet and the Internet.

3. According to claim 1, a highway bridge intelligent maintenance decision-making system is characterized by: The data processing and storage module uses the DBSCAN algorithm to clean the collected data, uses the one-hot encoding method to encode the classified data, and builds a distributed database based on blockchain technology to store the processed data.

4. A decision-making method for a highway bridge intelligent maintenance decision-making system, applied to the system according to any one of claims 1 to 3, characterized in that: The method includes: S1. Collect various data of bridges using fiber grating sensors, vibration sensors, high-definition cameras, drone detection equipment and manual inspections. At the same time, collect bridge condition data, environmental data, traffic data and historical maintenance record data through the highway intranet and the Internet; S2, the data processing and storage module uses the DBSCAN algorithm to clean the data collected in step S1, encodes the classified data using the one-hot encoding method, and stores the processed data in a distributed database based on blockchain technology; S3, the bridge condition assessment module extracts the data stored in the database, uses the Isomap algorithm to assess the technical condition of the bridge, predicts the development of the disease, and determines the health level and maintenance priority of the bridge; S4. Evaluate the technical conditions obtained in step S3, predict the disease development data, generate multiple maintenance plans using a hybrid algorithm of particle swarm optimization and simulated annealing, evaluate and optimize the plans using a combination of analytic hierarchy process and grey clustering analysis, and select the optimal maintenance plan; S5. Based on the selected maintenance plan, use BIM technology to organize construction in accordance with construction specifications and safety standards, and manage construction progress and quality; BIM technology manages construction progress and quality by building a three-dimensional model of the bridge and associating it with construction progress information; S6, the data collected by the fiber Bragg grating sensor, vibration sensor and high-definition camera in the monitoring and feedback module monitors the bridge status in real time; The monitoring data is then transmitted to the bridge condition assessment module in S3, which regularly re-evaluates the maintenance effectiveness and provides feedback to adjust the maintenance strategy.

5. According to claim 4, a decision-making method for a highway bridge intelligent maintenance decision-making system is characterized by: step In S1, FBG sensors are installed in the mid-span of the bridge, around the support, and at the connection between the pier and the beam; Apply special glue to the installation position in the middle of the bridge span and around the support, and stick the fiber grating sensor on the bridge surface; The connection between the pier and the beam is embedded, a groove is chiseled out at the connection between the pier and the beam, the fiber grating sensor is placed in the groove, and the groove is sealed with concrete slurry; Fiber Bragg grating sensors monitor the strain and temperature of bridge structures; High-definition cameras and drone inspection equipment obtain images of the bridge's exterior; Manual inspections were conducted to record the detailed location and morphology of the disease.

6. The decision-making method of the highway bridge intelligent maintenance decision-making system according to claim 4 is characterized by: The vibration sensor analyzes the vibration information of vehicles passing through the bridge. The specific steps include: A1. First, use the time-frequency analysis algorithm to reveal the frequency characteristics of the signal at different times, including using short-time Fourier transform or wavelet transform to expand the signal in two dimensions of time and frequency to reveal the frequency characteristics of the signal at different times. Use the multi-resolution analysis characteristics of wavelet transform to automatically adjust the window size according to the signal frequency to capture the transient changes in the bridge vibration signal; A2. Then use the wavelet packet decomposition algorithm to achieve fine signal decomposition to obtain sub-signals in different frequency bands. By analyzing the energy distribution and amplitude change characteristics of the sub-signals, and comparing the characteristic differences of the sub-signals under normal and abnormal conditions, it is possible to determine whether the bridge structure is damaged or abnormal. A3. Use the empirical mode decomposition algorithm to adaptively process nonlinear and non-stationary signals. Use the empirical mode decomposition algorithm to decompose the bridge vibration signal into multiple intrinsic mode functions, extract the main characteristic components related to the vibration of the bridge structure, remove noise and interference components, calculate the energy and frequency parameters of the intrinsic mode function components and observe their changing trends over time, and then evaluate the stability and health of the bridge structure; A4. Finally, the support vector machine algorithm is used to identify abnormal vibration patterns. The support vector machine algorithm is used to identify abnormal vibration patterns. The amplitude, frequency, and energy characteristic parameters are extracted from the vibration signal as the input of the support vector machine. A large amount of bridge vibration data under normal and abnormal conditions is used to train the support vector machine so that it can learn the characteristic differences between normal and abnormal vibration patterns. During actual monitoring, the vibration signal features collected in real time are input into the trained support vector machine model to determine whether the current vibration state is normal and the type and severity of the abnormality.

7. The decision-making method of the highway bridge intelligent maintenance decision-making system according to claim 4 is characterized by: The steps of the bridge condition assessment method using the Isomap algorithm are as follows: S31. Extract bridge data from a distributed database based on blockchain, including bridge structure strain, displacement, crack size, traffic data, environmental parameters, and past maintenance records. Use multiple filling methods to process missing data and use the local outlier factor algorithm to detect outliers. S32. Use the ReliefF algorithm for feature selection and use the selected features as dimensions to construct a high-dimensional data set, providing a comprehensive information basis for bridge technical condition assessment and disease development prediction and capturing complex relationships; S33, using Mahalanobis distance to measure the distance between data points, using kd tree to determine the k nearest neighbor points of each data point, constructing a neighborhood graph, reflecting the local similarity of data points and providing a structural basis for subsequent calculations; S34. Use BellmanFord algorithm to calculate the shortest path between any two points in the neighborhood graph as the geodesic distance, which reflects the real structural relationship of the bridge samples and is used for low-dimensional embedding and feature extraction, providing key information for evaluation and prediction; S35. Using the kernel Isomap method, the Laplace kernel function is introduced to perform eigendecomposition after centering the kernel matrix, and the data is projected into a low-dimensional space. The spectral clustering algorithm is used to cluster the bridge samples in the low-dimensional space, and the technical condition scores are assigned to the bridge samples based on the clustering results and related factors. S36, using long short-term memory network, taking low-dimensional spatial feature sequence as input, and predicting the disease development trend based on historical data by training the network; S37. Use fuzzy comprehensive evaluation method to divide bridge health level according to technical condition score and disease development prediction results. Combined with bridge importance factors, use data envelopment analysis method to determine maintenance priority.

8. The decision-making method of the highway bridge intelligent maintenance decision-making system according to claim 4 is characterized by: In step S4, multiple maintenance methods are generated based on the particle swarm optimization simulated annealing hybrid algorithm: S41. Obtain the bridge technical condition assessment and disease development prediction data from step S3, clarify the maintenance objectives, determine the decision variables, including the type of maintenance measures, maintenance time nodes, maintenance resource allocation, and set the upper limit of the maintenance cost. , Maintenance time limit Constraints; Define the fitness function as ,in , , is the weight coefficient and , Indicates the maintenance effect. represents the maintenance cost, Indicates maintenance time; In each iteration, the fitness value of each particle is calculated , update the particle's own historical optimal position and the global optimal position of the entire particle swarm , initialize the position vector of each particle and the velocity vector , through the formula and Update particle velocity and position, where is the inertia weight, and is the learning factor, and is a random number uniformly distributed in the interval [0, 1]. Indicates the current iteration number; like , accept the new position; otherwise, with probability Accept new position where For the current temperature, press Cooling, is the temperature reduction coefficient; After each update of the particle position, check whether the constraint conditions are met. If not, correct the particle position to return to the feasible solution space; When the preset maximum number of iterations is reached Or the fitness value changes less than the set threshold in several consecutive iterations When , the iteration is stopped, and the optimal particle position is recorded to generate multiple maintenance plans; The evaluation and optimization steps using the combined method of analytic hierarchy process and grey clustering analysis include: S42. Construct a hierarchical model, including selecting the optimal maintenance plan, maintenance effect, cost-effectiveness, implementation difficulty, environmental impact and the multiple maintenance plans generated above; construct a judgment matrix And assign values; calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector , normalize to get the weight vector, and calculate the consistency index and random consistency ratio ,when When the judgment matrix is ​​considered to have satisfactory consistency, otherwise it will be readjusted; in, represents the order of the judgment matrix, RI represents the random consistency index; The weight of each criterion at the criterion level and the weight of each solution at the solution level under the corresponding criterion are weighted and summed to obtain the comprehensive weight of each solution; Grey cluster analysis is used to cluster the maintenance schemes for evaluation: the cluster evaluation of the maintenance schemes includes the improvement rate of maintenance effect, the rate of cost reduction, the rate of shortening the construction period and the whitening weight function; For each maintenance plan And each clustering index , according to the indicator value Calculate its belonging to each gray class The clustering coefficient ,in is the number of clustering indicators, For indicators weight; divide each maintenance plan into corresponding gray categories according to the clustering coefficient, and rank the maintenance plans based on the comprehensive weight obtained by the hierarchical analysis method; Determine the best solution by combining expert experience with actual conditions: Further evaluate the top-ranked solutions based on expert experience and actual project conditions, and ultimately select the best bridge maintenance solution.

9. The decision-making method of the highway bridge intelligent maintenance decision-making system according to claim 4 is characterized by: The solutions in S6 include: The data collected by S61, fiber grating sensor, vibration sensor and high-definition camera are transmitted to the data processing center in real time by wireless transmission technology and data encryption and retransmission mechanism, and stored in the distributed time series database; the sensor error correction formula is used to correct the data. Perform error correction on sensor data, where is the original measurement value, is the true value, and are the coefficients obtained by least squares fitting; Data preprocessing uses linear interpolation formula Fill in missing data values, where is the time point where the missing value is located, and For the adjacent time points before and after it, and is the corresponding measured value; The method based on local outlier factor is used to detect and remove outliers; the maximum and minimum normalization is used to normalize the sensor data of different types; S62: The pre-processed data is transmitted to the bridge condition assessment module in S3, and the current state of the bridge is assessed based on the Isomap algorithm model and the relevant assessment index system; the change value of the bridge health status score is calculated. and crack growth rate ; in is the current rating, is the pre-maintenance score, where is the current bridge damage expansion rate, is the bridge damage expansion rate before maintenance; Constructing comprehensive evaluation indicators for maintenance effectiveness ,in , , is the corresponding weight and , is the actual maintenance cost, is the budgeted maintenance cost; S63. Comprehensive evaluation index based on maintenance effectiveness With the high threshold set and low threshold Make comparative judgments; like , maintain the current maintenance strategy; like , re-analyze the causes and development trends of bridge diseases, use the particle swarm optimization simulated annealing hybrid algorithm to generate new maintenance plans, and use the method of combining hierarchical analysis and grey clustering analysis to evaluate and optimize the new plans to select the optimal strategy; like , and make appropriate fine-tuning to the frequency of implementation of maintenance measures and the proportion of maintenance resource allocation.

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