Bridge Safety Assessment Method during the Transportation of Large Vehicles Based on Mobile Monitoring

Abstract

The present application discloses a method, device and medium for bridge safety assessment during the transportation of large-sized vehicles based on mobile monitoring, which relates to the field of bridge engineering. The method provided by the present application assesses the safety status of bridges under large-sized vehicle transportation by detecting all bridges along the vehicle passing route, using on-vehicle detection devices and portable and detachable detection equipment. Compared with the current method of using artificial sensor layout for bridge safety detection and assessment, since artificial sensor layout for safety detection and assessment requires a set of independent detection equipment and equipment layout scheme for each bridge to be detected, and it is relatively complex to install the equipment on the bridge, while the safety detection and assessment method in this solution only requires one detection vehicle and a set of detachable and portable detection equipment to perform safety detection and assessment on all bridges. Therefore, the equipment is less, the cost is lower, and there is no need for installation, and the operation is relatively simple.

Description

Technical Field

[0001] The present application relates to the field of bridge engineering, and particularly to a method, device and readable storage medium for bridge safety assessment during the transportation of large-piece vehicles based on mobile monitoring. Background Art

[0002] In recent years, with the rapid development of road traffic infrastructure construction and driven by the country's industrialization development, the highway large-piece transportation business of non-detachable equipment has become increasingly heavy. The number of large-piece transportation vehicles passing through the border has been increasing, and their weights have been rising, seriously threatening the safety performance of highway bridges. In order to avoid large safety hazards in the bearing capacity of bridges caused by large-piece transportation vehicles passing through bridges, it is usually necessary to evaluate the safety performance of bridges when large-piece transportation vehicles pass through bridges.

[0003] For the existing bridge safety performance assessment during the transportation of large-piece vehicles, a large number of fixed sensors usually need to be installed on the bridge, and the safety information of the bridge is obtained by transmitting data through the sensors. Due to the difficulties in installing sensors and the need to use multiple sets of equipment when there are multiple bridges to be detected, the monitoring method using fixed sensors is limited due to high costs and other disadvantages.

[0004] In view of the above technology, finding a relatively simple and low-cost method for bridge safety assessment during the transportation of large-piece vehicles based on mobile monitoring is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of the present application is to provide a method for bridge safety assessment during the transportation of large-piece vehicles based on mobile monitoring, so as to solve the problems of difficult installation and high cost in current detection using fixed sensors.

[0006] To solve the above technical problems, the present application provides a method for bridge safety assessment during the transportation of large-piece vehicles based on mobile monitoring, including:

[0007] Controlling a detection vehicle to run on the bridge to be detected, where the detection vehicle is a vehicle equipped with a bridge detection device; controlling a detachable GPS-RTK mobile station to run on the bridge to be detected; controlling an in-vehicle camera and a portable and movable unmanned aerial vehicle to run on the bridge to be detected;

[0008] Obtaining the data measured by the bridge detection device when the detection vehicle runs on the bridge to be detected;

[0009] Generating the safety state of the bridge to be detected according to the data.

[0010] Preferably, the controlling the detection vehicle to run on the bridge to be detected includes:

[0011] Control the inspection vehicle to run alone on the bridge to be inspected;

[0012] Control the inspection vehicle and the large-piece vehicle to run simultaneously on the bridge to be inspected, where the large-piece vehicle is an over-limit transport vehicle for transporting non-dismantlable objects by road;

[0013] The data obtained when the inspection vehicle runs on the bridge to be inspected includes:

[0014] Obtain the initial data of the bridge detection device when the inspection vehicle runs alone on the bridge to be inspected;

[0015] Obtain the comparison data of the bridge detection device when the inspection vehicle and the large-piece vehicle run simultaneously on the bridge to be inspected;

[0016] The generation of the safety state of the bridge to be inspected based on the data includes:

[0017] Compare the comparison data with the initial data and generate the safety state of the bridge to be inspected.

[0018] Preferably, the bridge detection device includes an acceleration sensor, and the data obtained when the bridge detection device runs on the bridge to be inspected includes:

[0019] Obtain the acceleration response signal of the inspection vehicle measured by the acceleration sensor;

[0020] The generation of the safety state of the bridge to be inspected based on the data includes:

[0021] Use the stochastic subspace method, empirical mode decomposition method, and ensemble empirical mode decomposition to separate the dynamic response signals of the vehicle-bridge coupling system, and obtain the response signal of the vibration of the bridge to be inspected itself according to the acceleration response signal;

[0022] According to the response signal, obtain the dynamic characteristics of the bridge to be inspected, and generate the safety state of the bridge to be inspected according to the dynamic characteristics.

[0023] Preferably, the bridge detection device further includes a portable mobile video detection device, and the data obtained when the bridge detection device runs on the bridge to be inspected includes:

[0024] Obtain the apparent video of the bridge to be inspected measured by the on-vehicle camera and the portable mobile drone, and obtain the apparent diseases of the bridge to be inspected through the deep learning and image processing technologies;

[0025] The generation of the safety state of the bridge to be inspected based on the data includes:

[0026] Generate the safety status of the bridge to be detected according to the expressed diseases.

[0027] Preferably, the comparing the comparison data with the initial data and generating the safety status of the bridge to be detected includes:

[0028] Establish the square index of the bridge modal vibration mode before the passing of the large-piece transport vehicle as When the large-piece transport vehicle passes, establish the real-time square index of the bridge modal vibration mode as Calculate according to the following formula:

[0029]

[0030]

[0031]

[0032] where α is a parameter introduced to make the data difference obvious, Δ i is the damage index of different points of the bridge to be measured, and obtain the safety status of the bridge to be detected according to the Δ i ;

[0033] Among them, the establishment of the damage identification index is based on the square of the modal vibration mode:

[0034] is the vibration mode magnitude of the bridge to be detected, x i (i = 1, 2, 3…n) is the coordinate coefficient of the abscissa established with the bridge to be detected.

[0035] Preferably, the comparing the comparison data with the initial data and generating the safety status of the bridge to be detected includes:

[0036] Assume the initial damage index vector Solve the vehicle analytical acceleration response through the vehicle-bridge coupling dynamic equation and the sensitivity equation and the sensitivity matrix According to the number of measuring points (N) and the number of elements (m) of the damage index vector, select the sensitivity matrix corresponding size of for correction, and subtract the collected acceleration response of the detection vehicle from the analytical acceleration response of the detection vehicle according to the following formula:

[0037]

[0038] Correct the element stiffness matrix according to the following formula:

[0039]

[0040] Solve the above equation and correct the damage index according to the following formula:

[0041] P r+1 = P r + ΔP r

[0042] Substitute into the vehicle-bridge coupling motion equation to calculate the acceleration response at the (r + 1)-th iteration and the sensitivity matrix When the damage index correction vector ΔP r is less than the set value, obtain the stiffness reduction vector of each unit of the bridge to be detected, and calculate the safety state of the bridge to be detected according to the stiffness reduction vector;

[0043] Among them, the damage vector index is constructed by the stiffness reduction of the following local units:

[0044]

[0045] The vehicle-bridge coupling motion equation is as follows:

[0046]

[0047] Where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, S is the sensitivity matrix, and P is the index vector.

[0048] Preferably, the method further includes:

[0049] Set up a GPS-RTK mobile station at a preset position on the bridge to be detected;

[0050] Obtain the data collected by the GPS-RTK mobile station when the large-piece vehicle runs alone on the bridge to be detected;

[0051] Calculate the impact effect of the large-piece vehicle on the bridge to be detected according to the following formula;

[0052]

[0053] Where IM represents the impact coefficient, R dyn is the dynamic coefficient, and R sta is the static coefficient.

[0054] To solve the above problems, the present application also provides a safety detection device for large-piece transportation bridges, including:

[0055] A control module for controlling the detection vehicle to run on the bridge to be detected, and the detection vehicle is a vehicle equipped with a bridge detection device;

[0056] An acquisition module, configured to acquire data measured by the bridge detection device when the detection vehicle is running on the bridge to be detected;

[0057] A generation module, configured to generate a safety status of the bridge to be detected according to the data.

[0058] To solve the above problems, the present application further provides a bridge safety detection device, including a memory for storing a computer program;

[0059] A processor, configured to implement the steps of the above-mentioned bridge safety assessment method during the transportation of large-piece vehicles based on mobile monitoring when executing the computer program.

[0060] To solve the above problems, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned bridge safety assessment method during the transportation of large-piece vehicles based on mobile monitoring are implemented.

[0061] The bridge safety assessment method during the transportation of large-piece vehicles based on mobile monitoring provided by the present application enables a detection vehicle equipped with a detection device to pass through the bridge to be detected, so as to obtain data of the bridge to be detected by using the detection device, and then generate a safety status of the bridge to be detected according to the data. Compared with the current method of using artificial sensor erection for bridge safety detection, since using sensors for safety detection requires a corresponding set of equipment and installation plan for each bridge to be detected, and it is relatively complex to install the equipment on the bridge, while the safety detection method in this solution only requires one detection vehicle to detect multiple bridges to be detected. Therefore, the equipment is less, the cost is lower, and there is no need for installation. Only by assembling and controlling the detection vehicle to pass through the bridge to be detected can the detection of the bridge to be detected be completed, and the operation is relatively simple.

[0062] The bridge safety detection device and the computer-readable storage medium provided by the present application correspond to the above-mentioned bridge safety assessment method during the transportation of large-piece vehicles based on mobile monitoring, and have the same beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] To more clearly illustrate the embodiments of the present application, the following will briefly introduce the drawings required for the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0064] Figure 1 Schematic diagram of a bridge safety assessment method during the transportation of large-piece vehicles based on mobile monitoring provided by an embodiment of the present application;

[0065] Figure 2 Schematic diagram of a bridge safety detection device provided by an embodiment of the present application;

[0066] Figure 3 Structural diagram of a bridge safety detection device provided by another embodiment of the present application. Detailed implementation manners

[0067] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present application.

[0068] The core of the present application is to provide a method for bridge safety assessment during the transportation of large-piece vehicles based on mobile monitoring. It should be noted that in order to avoid large potential safety hazards in the bearing capacity of the bridge caused by large-piece transportation vehicles passing through the bridge, it is usually necessary to evaluate the safety performance of the bridge when large-piece transportation vehicles pass through the bridge. The evaluation methods in the prior art usually use finite element software for simulation analysis and load test analysis. However, due to the continuous degradation of material properties during the service process of the bridge, the true properties of the materials cannot be obtained, and the safety performance of the bridge when large-piece transportation vehicles pass through cannot be accurately evaluated by the finite element simulation method, resulting in large potential safety hazards in the bridge when large-piece transportation vehicles pass through, and the true stress performance of the bridge cannot be accurately evaluated, posing a great threat to life and property safety. In addition, evaluating the safety performance of the bridge through traditional load tests requires huge manpower and material resources, and it is impossible to quickly evaluate the safety performance of all bridges along the large-piece transportation route. In response to the above problems, the following solutions are given in the present application.

[0069] In order to enable those skilled in the art to better understand the solutions of the present application, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0070] Figure 1 Schematic diagram of a method for bridge safety assessment during the transportation of large-piece vehicles based on mobile monitoring provided by an embodiment of the present application, as Figure 1 shown, the method includes:

[0071] S10: Control the detection vehicle to run on the bridge to be detected;

[0072] It should be noted that the inspection vehicle is a vehicle equipped with a bridge inspection device and is also equipped with portable and detachable bridge inspection equipment. In this embodiment, the types and other properties of the inspection vehicle and the inspection device itself are not limited. That is, the inspection device involved in this embodiment may include, but is not limited to, video inspection devices, acceleration sensors, dynamic response sensors, etc. And since the running state of the vehicle on the bridge to be inspected is not limited in this embodiment, it can be understood that in order to obtain complete and accurate measurement results, the inspection vehicle can be controlled to pass through the bridge to be inspected from beginning to end for a complete inspection of the bridge to be inspected, and the type of the bridge is not limited. It can be understood that the detachable displacement monitoring equipment and the portable and mobile apparent monitoring equipment have strong adaptability to the displacement monitoring and apparent inspection of key points of different types of bridges. And the state of other vehicles on the bridge to be inspected when the inspection vehicle is running is not limited. It can be inspected under the normal daily use state, that is, when there are other pedestrians and ordinary vehicles passing by, or the inspection vehicle can be used alone for inspection, or inspected when large overweight vehicles pass by, etc. One or several combinations of the above inspection schemes can be adopted to obtain data.

[0073] S11: Obtain the data measured by the bridge inspection device when the inspection vehicle is running on the bridge to be inspected;

[0074] It should be noted that in this embodiment, the specific steps for the bridge inspection device to obtain bridge data are not limited, and different inspection devices have different acquisition methods. In this application, the specific time for the bridge inspection device to perform inspection when the vehicle is running is not limited and depends on different bridge inspection devices. It can be a full-process inspection when the vehicle is running, or an inspection at a certain position of the bridge to be inspected.

[0075] S12: Generate the safety state of the bridge to be inspected according to the data.

[0076] In practical applications, according to the obtained data, calculations and statistics are carried out to obtain the safety state of the bridge to be inspected. It can be understood that since the inspection device is not limited, the types of data obtained are different. Therefore, the methods and algorithms for generating the safety state of the bridge to be inspected are also different. In this embodiment, the specific method for generating the safety state is not limited.

[0077] The bridge safety assessment method during the transportation of large-piece vehicles based on mobile monitoring provided in this embodiment enables a detection vehicle equipped with a detection device to pass through the bridge to be detected, thereby obtaining data of the bridge to be detected by using the detection device, and then generating the safety status of the bridge to be detected based on this data. Compared with the current method of using sensors for bridge safety detection, since using fixed sensors for safety detection requires a corresponding set of equipment and installation plan for each bridge to be detected, and it is relatively complex to install the equipment on the bridge. However, the safety detection method in this solution only requires one detection vehicle to detect multiple bridges to be detected. Therefore, the equipment is less, the cost is lower, and there is no need for installation. Only by assembling and controlling the detection vehicle to pass through the bridge to be detected can the detection of the bridge to be detected be completed, and the operation is relatively simple.

[0078] Considering the integrity of the bridge safety detection data, a preferred solution is proposed here. Controlling the detection vehicle to run on the bridge to be detected includes:

[0079] Controlling the detection vehicle to run alone on the bridge to be detected;

[0080] Controlling the detection vehicle and the large-piece vehicle to run on the bridge to be detected simultaneously. The large-piece vehicle is a vehicle for over-limit transportation that carries non-dismantlable objects by road. The data obtained when the detection vehicle runs on the bridge to be detected includes:

[0081] Obtaining the initial data of the bridge detection device when the detection vehicle runs alone on the bridge to be detected;

[0082] Obtaining the comparison data of the bridge detection device when the detection vehicle and the large-piece vehicle run on the bridge to be detected simultaneously;

[0083] Generating the safety status of the bridge to be detected based on the data includes:

[0084] Comparing the comparison data with the initial data and generating the safety status of the bridge to be detected.

[0085] It can be understood that in this embodiment, the data detected when the detection vehicle passes through the bridge to be detected alone is different from the data detected when passing through the bridge to be detected simultaneously with the large-piece vehicle. The former obtains the basic historical damage data of the bridge to be detected, that is, the initial data, and the latter obtains the real-time damage data of the bridge when the current large-piece vehicle passes through. It should be noted that in this embodiment, the nature of the large-piece vehicle itself, such as quantity, quality, etc., is not limited.

[0086] It should be noted that in this embodiment, by performing two detections, the historical damage data of the bridge to be detected when the detection vehicle passes the bridge to be detected alone is obtained respectively, and the damage data feedback in real time of the bridge to be detected when the detection vehicle and the large-piece vehicle pass the bridge to be detected simultaneously, so that the data of the bridge to be detected obtained is more complete, the safety state of the generated bridge is more accurate, and the accuracy of the solution is increased.

[0087] The bridge detection device is not limited in the above embodiment. Here, a preferred solution is proposed. The bridge detection device includes an acceleration sensor. The data obtained by the bridge detection device when the detection vehicle runs on the bridge to be detected includes:

[0088] Obtaining the acceleration response signal of the detection vehicle measured by the acceleration sensor;

[0089] Generating the safety state of the bridge to be detected according to the data includes:

[0090] Using the stochastic subspace method, the empirical mode decomposition method, and the ensemble empirical mode decomposition to separate the dynamic response signal of the vehicle-bridge coupling system, and obtaining the response signal of the vibration of the bridge to be detected itself according to the acceleration response signal;

[0091] According to the response signal, obtaining the dynamic characteristics of the bridge to be detected, and generating the safety state of the bridge to be detected according to the dynamic characteristics.

[0092] It can be understood that in this embodiment, only the bridge detection device is limited to include an acceleration sensor, that is, the bridge detection device may also include other devices, which are not limited here.

[0093] It should be noted that the vehicle-bridge coupling system refers to the interaction system between the detection vehicle and the bridge to be detected. The stochastic subspace method is a kind of integrated learning. The stochastic subspace reduces the correlation between each classifier by using random partial features instead of all features to train each classifier. Here, the classifier can represent each unit distance in the bridge. The empirical mode decomposition method is an adaptive data processing or mining method, which is very suitable for the processing of non-linear and non-stationary time series. Essentially, it is a stationary processing of the data sequence or signal. The ensemble empirical mode decomposition is a new time-frequency analysis method and an adaptive time-frequency localization analysis method. Based on the above three methods, the response signals of the detection vehicle running at different positions and different times on the bridge to be detected in the coupling system are obtained, so as to obtain the vibration signal of the bridge to be detected, and the safety state of the bridge to be detected is calculated according to the vibration signal.

[0094] In this embodiment, it is defined that the detection device includes an acceleration sensor, so as to obtain the acceleration response signal of the detected vehicle through the acceleration sensor. Through the vehicle-bridge coupling system, the acceleration response signal of the detected vehicle can be converted into the vibration response signal of the bridge. The fast Fourier transform is performed on the vibration response signal of the bridge to obtain the initial natural vibration frequency of the bridge. Then, the Hilbert transform is performed on the vibration response signal of the bridge, and the vibration mode and damping ratio of the bridge are identified according to the modal confidence criterion, so as to analyze and obtain the safety state of the bridge, and thus obtain the safety state of the bridge with respect to the vibration mode and damping ratio.

[0095] In the above embodiment, the bridge detection device is not fully defined. Here, a preferred solution is proposed. The bridge detection device further includes a video detection device. The data obtained when the bridge detection device detects the vehicle running on the bridge to be detected includes:

[0096] Obtain the apparent diseases of the bridge to be detected measured by the portable mobile video detection device;

[0097] Generating the safety state of the bridge to be detected according to the data includes:

[0098] Generate the safety state of the bridge to be detected according to the apparent diseases.

[0099] It should be noted that in this embodiment, the video detection device is not limited, and may include but is not limited to a high-speed camera mounted on the detection vehicle's turret, a drone, a Global Navigation Satellite System (GNSS), etc. Due to the different devices, the installation positions in the detection vehicle are different, which will not be elaborated here.

[0100] It can be understood that the apparent diseases refer to the dangerous states of the bridge that can be observed according to the images of the bridge to be detected, such as cracks, peeling, rust, etc. Based on the GNSS, the three-dimensional coordinates of the video image are obtained, and its three-dimensional coordinates are printed on the corresponding image in real time to obtain the initial disease image of the bridge to be detected and perform real-time positioning on the disease image. For how to generate the safety state of the bridge to be detected according to the apparent diseases, generally, a trained deep learning network and digital image processing technology are used to identify the apparent diseases of the bridge to be detected, combine the three-dimensional coordinates on the crack image to locate the main cracks, and use image processing technology to quantitatively analyze the length, width and area of the cracks, determine the bridges with serious apparent diseases, facilitate the real-time monitoring of the development trend of the bridge cracks when large-piece transport vehicles pass through, and realize the fatigue life assessment of the bridge, so as to obtain the safety state of the bridge to be detected.

[0101] It can be understood that in this embodiment, by obtaining the image of the bridge to be detected to generate the safety state of the bridge to be detected, the basic safety state of the bridge to be detected can be judged more intuitively and simply.

[0102] In the above embodiment, there is no limitation on how to generate the safety state of the bridge to be detected. Here, a preferred solution is proposed. Comparing the comparison data with the initial data and generating the safety state of the bridge to be detected includes:

[0103] Establish the square index of the bridge modal vibration mode before the passing of the large-piece transport vehicle as When the large-piece transport vehicle passes, establish the real-time square index of the bridge modal vibration mode as

[0104]

[0105]

[0106]

[0107] where α is a parameter introduced to make the data difference obvious, and Δ i is the damage index of different points of the bridge to be measured. According to Δ i Obtain the safety state of the bridge to be detected;

[0108] Among them, the establishment of the damage identification index is based on the square of the modal vibration mode:

[0109] is the vibration mode size of the bridge to be detected, and x i (i = 1, 2, 3…n) is the coordinate coefficient of the abscissa established for the bridge to be detected.

[0110] Through the calculation method of this embodiment, the vibration mode of each unit distance of the bridge to be detected can be calculated more systematically, so as to accurately obtain the safety state based on the vibration mode in different regions of the bridge to be detected in the unit distance.

[0111] In the above embodiment, a safety state of different regions of the bridge to be detected judged based on the vibration mode is provided. Here, a preferred solution is proposed. Comparing the comparison data with the initial data and generating the safety state of the bridge to be detected includes:

[0112] Assume the initial damage index vector Solve the vehicle analytical acceleration response through the vehicle-bridge coupling dynamic equation and the sensitivity equation and the sensitivity matrix According to the number of measurement points (N) and the number of elements (m) of the damage index vector, select the sensitivity matrix corresponding size of Make corrections by taking the difference between the collected acceleration response of the test vehicle and the analyzed acceleration response of the test vehicle according to the following formula:

[0113]

[0114] Correct the element stiffness matrix according to the following formula:

[0115]

[0116] Solve the above equation and correct the damage index according to the following formula:

[0117] P r+1 = P r + ΔP r

[0118] Substitute into the vehicle-bridge coupling motion equation to calculate the acceleration response at the (r + 1)-th iteration

[0119] and the sensitivity matrix When the damage index correction vector ΔP r is less than the set value, obtain the stiffness reduction vector of each element of the bridge to be tested, and calculate the safety state of the bridge to be tested according to the stiffness reduction vector;

[0120] Among them, the damage vector index is constructed by the stiffness reduction of the following local elements:

[0121]

[0122] The vehicle-bridge coupling motion equation is as follows:

[0123]

[0124] where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, S is the sensitivity matrix, and P is the index vector.

[0125] Through the calculation method of this embodiment, the stiffness reduction vector of the motion of each element of the bridge to be tested can be calculated more systematically, so as to accurately obtain the safety state based on stiffness in different regions of the bridge to be tested per unit distance.

[0126] Considering that the impact generated by the heavy vehicle on the bridge to be tested is an important data for judging the safety state of the bridge to be tested, a preferred solution is proposed here. This method further includes:

[0127] Install a detachable GPS-RTK mobile station at a preset position on the bridge to be tested;

[0128] Obtain the data collected by the GPS-RTK rover when a large vehicle runs alone on the bridge to be detected;

[0129] Calculate the impact effect of the large vehicle on the bridge to be detected according to the following formula;

[0130]

[0131] where IM represents the impact coefficient, R dyn is the dynamic coefficient, R sta is the static coefficient.

[0132] Carrier phase differential technology (Real-time kinematic, RTK) is a differential method for real-time processing of carrier phase observations at two measurement stations. It sends the carrier phase collected by the reference station to the user receiver for differential solution of coordinates. This is a new and commonly used satellite positioning measurement method. In the past, static, fast static, and dynamic measurements all required post-processing to obtain centimeter-level accuracy, while RTK is a measurement method that can obtain centimeter-level positioning accuracy in the field in real time. It uses the carrier phase dynamic real-time differential method, which is a major milestone in GPS applications and greatly improves the operation efficiency.

[0133] By using the RTK-GPS system, the impact coefficient of the large vehicle passing through the bridge to be detected can be accurately obtained. Thus, based on the impact coefficient each time the large vehicle passes through the bridge to be detected, combined with the traffic flow of the bridge to be detected, that is, the current safety status, the life of the bridge to be detected can be assisted in judgment, increasing the practicability of this method.

[0134] In the above embodiment, the method for bridge safety assessment during the transportation of large vehicles based on mobile monitoring is described in detail. The present application also provides an embodiment corresponding to the bridge safety detection device. It should be noted that the present application describes the embodiment of the device part from two perspectives, one is from the perspective of functional modules, and the other is from the perspective of hardware.

[0135] Figure 2 The following is a schematic diagram of a bridge safety detection device provided by an embodiment of the present application. The device includes:

[0136] A control module 10, configured to control the detection vehicle to run on the bridge to be detected, where the detection vehicle is a vehicle equipped with a bridge detection device;

[0137] An acquisition module 11, configured to acquire the data measured by the bridge detection device when the detection vehicle runs on the bridge to be detected;

[0138] A generation module 12, configured to generate the safety status of the bridge to be detected according to the data.

[0139] Since the embodiments in the apparatus part correspond to those in the method part, for the embodiments in the apparatus part, please refer to the descriptions of the embodiments in the method part, which will not be elaborated here. And because the apparatus part corresponds to the method part, the beneficial effects are the same as those in the method part.

[0140] Figure 3 The structural diagram of the bridge safety detection device provided by another embodiment of this application is as follows Figure 3 As shown, the bridge safety detection device includes: a memory 20 for storing computer programs;

[0141] A processor 21 for implementing the steps of the bridge safety assessment method during the transportation of large-piece vehicles based on mobile monitoring as mentioned in the above embodiments when executing the computer programs.

[0142] The bridge safety detection device provided in this embodiment may include, but is not limited to, a smart phone, a tablet computer, a laptop computer, or a desktop computer, etc.

[0143] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 may be implemented in at least one of the hardware forms of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, and the AI processor is used to process the computational operations related to machine learning.

[0144] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201. After the computer program is loaded and executed by the processor 21, it can implement the relevant steps of the bridge safety assessment method based on mobile monitoring disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, etc., and the storage method may be transient storage or permanent storage. Among them, the operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, the data involved in the bridge safety assessment method based on mobile monitoring during the transportation of large-piece vehicles as described above.

[0145] In some embodiments, the bridge safety detection device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0146] Those skilled in the art can understand that Figure 3 the structure shown in

[0147] does not constitute a limitation on the bridge safety detection device, and may include more or fewer components than those shown in the figure.

[0148] Since the embodiments of the device part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the device part, which will not be elaborated here. And since the device part corresponds to the method part, the beneficial effects are the same as those of the method part.

[0149] Finally, the present application also provides an embodiment corresponding to a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the steps recorded in the above method embodiments.

[0150] It can be understood that if the methods in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0151] Since the embodiments of the readable storage medium correspond to the embodiments of the method part, for the embodiments of the device part, please refer to the description of the embodiments of the method part, which will not be elaborated here. And because the device part corresponds to the method part, the beneficial effects are the same as those of the method part.

[0152] The above has introduced in detail a method, device, and readable storage medium for bridge safety assessment during the transportation of large-piece vehicles based on mobile monitoring provided by the present application. The embodiments in the specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method part. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

[0153] It should also be noted that in this specification, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

Claims

1. A method for bridge safety assessment during the transportation of large-sized vehicles based on mobile monitoring, characterized in that, Including: Controlling a detection vehicle to run on a bridge to be detected, where the detection vehicle is a vehicle equipped with a bridge detection device and is also equipped with portable and detachable detection equipment; Obtaining data measured by the bridge detection device when the detection vehicle and the portable detection equipment are running on the bridge to be detected; Generating a safety state of the bridge to be detected according to the data; The controlling the detection vehicle to run on the bridge to be detected includes: Controlling the detection vehicle to run alone on the bridge to be detected; Controlling the detection vehicle and a large-piece vehicle to run on the bridge to be detected at the same time, where the large-piece vehicle is an over-limit transportation vehicle that transports non-detachable objects by road; Obtaining the data measured when the detection vehicle is running on the bridge to be detected includes: Obtaining initial data of the bridge detection device when the detection vehicle is running alone on the bridge to be detected; Obtaining comparison data of the bridge detection device when the detection vehicle and the large-piece vehicle are running on the bridge to be detected at the same time; The generating the safety state of the bridge to be detected according to the data includes: Comparing the comparison data with the initial data and generating the safety state of the bridge to be detected; The comparing the comparison data with the initial data and generating the safety state of the bridge to be detected includes: The squared index of the bridge modal vibration mode before the passing of the large-piece transport vehicle is , and when the large-piece transport vehicle passes, the real-time squared index of the bridge modal vibration mode is , and the calculation is carried out according to the following formula: ； ； ； where α is a parameter introduced to make the data difference obvious, is the damage index of different points of the bridge to be detected, and according to the the safety status of the bridge to be detected is obtained; Among them, the damage identification index is established based on the square of the modal vibration mode: , φ n is the modal amplitude of the bridge to be detected, and x i (i = 1, 2, 3... n) is the coordinate coefficient of the abscissa established with the bridge to be detected.

2. The bridge safety assessment method during the transportation of large-sized vehicles based on mobile monitoring according to claim 1, wherein The bridge detection device includes an acceleration sensor, and the obtaining the data measured by the bridge detection device when the detection vehicle is running on the bridge to be detected includes: Obtaining the acceleration response signal of the detection vehicle measured by the acceleration sensor; the generating the safety state of the bridge to be detected according to the data includes: Separating the dynamic response signal of the vehicle-bridge coupling system by using the stochastic subspace method, the empirical mode decomposition method, and the ensemble empirical mode decomposition, and obtaining the response signal of the vibration of the bridge to be detected itself according to the acceleration response signal; According to the response signal, obtaining the dynamic characteristics of the bridge to be detected, and generating the safety state of the bridge to be detected according to the dynamic characteristics.

3. The bridge safety assessment method during the transportation of large vehicles based on mobile monitoring according to claim 2, characterized in that, The bridge detection device further includes a portable and mobile video detection device, and the obtaining the data measured by the bridge detection device when the detection vehicle is running on the bridge to be detected includes: Obtaining the apparent diseases of the bridge to be detected measured by the video detection device; The generating the safety state of the bridge to be detected according to the data includes: Generating the safety state of the bridge to be detected according to the apparent diseases.

4. The bridge safety assessment method during the transportation of large-sized vehicles based on mobile monitoring according to claim 1, wherein The comparing the comparison data with the initial data and generating the safety state of the bridge to be detected includes: Assume the initial damage index vector , solve the analytical acceleration response of the vehicle through the vehicle-bridge coupling dynamic equation and the sensitivity equation and the sensitivity matrix . According to the number of measurement points N and the number of elements m of the damage index vector, select the sensitivity matrix corresponding to the size of (N×m) for correction, and subtract the collected acceleration response of the test vehicle from the analytical acceleration response of the test vehicle according to the following formula: ； Correcting the element stiffness matrix according to the following formula: ； Solving the above equation and correcting the damage index according to the following formula: ； Substitute into the vehicle-bridge coupling motion equation to calculate the acceleration response at the (r + 1)-th iteration and the sensitivity matrix , when the damage index correction vector is less than the set value, the stiffness reduction vector of each unit of the bridge to be detected is obtained, and the safety state of the bridge to be detected is calculated according to the stiffness reduction vector; Among them, the damage vector index is constructed by reducing the element stiffness below the local area: ； The vehicle-bridge coupling motion equation is as follows: ； Where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, S is the sensitivity matrix, and P is the index vector.

5. The bridge safety assessment method during the transportation of large-sized vehicles based on mobile monitoring according to any one of claims 1 to 4, characterized in that, Also including: Setting up a detachable GPS-RTK mobile station at a preset position of the bridge to be detected; Obtain the data collected by the GPS-RTK mobile station when the large-piece vehicle runs alone on the bridge to be detected; Calculate the impact effect of the large-piece vehicle on the bridge to be detected according to the following formula; ； Among them, IM represents the impact coefficient, and R dyn is the dynamic coefficient, and R sta is the static coefficient.

6. A bridge safety detection device, characterized in that, Including: A control module for controlling a detection vehicle to run on a bridge to be detected, where the detection vehicle is a vehicle equipped with a bridge detection device; An acquisition module for acquiring the data measured by the bridge detection device when the detection vehicle runs on the bridge to be detected; A generation module for generating the safety status of the bridge to be detected based on the data; The controlling the detection vehicle to run on the bridge to be detected includes: Controlling the detection vehicle to run alone on the bridge to be detected; Controlling the detection vehicle and the large-piece vehicle to run on the bridge to be detected at the same time, where the large-piece vehicle is an over-limit transport vehicle carrying non-dismantlable objects by road; The obtaining the data measured when the detection vehicle runs on the bridge to be detected includes: Obtaining the initial data of the bridge detection device when the detection vehicle runs alone on the bridge to be detected; Obtaining the comparison data of the bridge detection device when the detection vehicle and the large-piece vehicle run on the bridge to be detected at the same time; The generation module is used for: Comparing the comparison data with the initial data and generating the safety status of the bridge to be detected; The comparing the comparison data with the initial data and generating the safety status of the bridge to be detected includes: The square index of the bridge modal vibration mode before the passing of the large-piece transport vehicle is , and when the large-piece transport vehicle passes, the real-time square index of the bridge modal vibration mode is , and the calculation is carried out according to the following formula: ； ； ； where α is a parameter introduced to make the data difference obvious, is the damage index of different points of the bridge to be detected, and according to the the safety status of the bridge to be detected is obtained; Among them, the damage identification index is established based on the modal vibration mode square: is the vibration mode magnitude of the bridge to be detected, and x n is the vibration mode magnitude of the bridge to be detected, and x i (i = 1, 2, 3... n) is the coordinate coefficient of the abscissa established with the bridge to be detected.

7. A bridge safety detection device, characterized in that, Including a memory for storing a computer program; A processor for implementing the steps of the bridge safety assessment method during the transportation of large-piece vehicles based on mobile monitoring according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the bridge safety assessment method during the transportation of large-piece vehicles based on mobile monitoring according to any one of claims 1 to 5 are implemented.

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