Tunnel lining bearing test method and device

Abstract

The invention provides a tunnel lining bearing test method and device, and the method comprises the steps: automatically collecting a sound wave detection signal which passes through a tunnel lining through an intelligent sound wave sensor, and determining the sound wave propagation characteristics and a plurality of sound energy densities when the sound wave detection signal passes through the tunnel lining; determining the residual stress quantification of the sound wave detection signal according to the sound wave propagation characteristics and all the sound energy densities, and performing stress reconstruction on the stress state of the tunnel lining through the residual stress quantification to obtain the stress distribution characteristics of the tunnel lining; the method comprises the following steps: acquiring electromagnetic wave signals penetrating through a tunnel lining through an intelligent photoelectric sensor to obtain a scattering characteristic diagram, extracting a plurality of evaluation intervals and scattering characteristics when the electromagnetic wave signals penetrate through a tunnel lining corrosion layer from the scattering characteristic diagram, and determining the unbalance degree of the tunnel lining according to the scattering characteristics and all the evaluation intervals; and detecting the bearing state of the tunnel lining based on the stress distribution characteristics and the unbalance degree. According to the scheme, the accuracy of measuring the bearing capacity of the tunnel lining can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of tunnel lining testing, and more specifically, to a tunnel lining load-bearing testing method and device. Background Art

[0002] Load-bearing test is a technology used to evaluate and test the load-bearing capacity and performance of structures or equipment. Its main purpose is to ensure that the load-bearing capacity of the target object remains within a safe range. Through accurate data collection and detection and analysis methods, the load-bearing capacity of the target object can be accurately tested to prevent safety hazards caused by insufficient load.

[0003] The bearing test of tunnel lining is a technology to evaluate the bearing capacity and stability of tunnel lining under various working conditions. Its main purpose is to ensure that the tunnel can safely withstand the pressure of the ground and surrounding soil, traffic load and other possible external influences during construction and use. In the existing tunnel lining bearing test technology, dynamic loads (such as vibration) are applied to the tunnel lining or known static loads (such as placing objects of known weight) are applied to certain parts of the tunnel, and then the deformation and strain of the tunnel lining are measured to evaluate the bearing capacity of the tunnel lining. However, the above method is not only difficult to simulate the dynamic loads that the actual tunnel is subjected to during operation, but also the application of loads to the tunnel will cause additional damage to the tunnel lining. At the same time, it can only detect the influence of fatigue load on the tunnel lining, ignoring the corrosion effect of the surrounding environment. Therefore, how to combine corrosion and fatigue loads to measure the performance of the tunnel lining, and weaken the additional fatigue and damage to the tunnel lining during the measurement process, so as to improve the accuracy of measuring the bearing capacity of the tunnel lining has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a tunnel lining load-bearing test method and device, which can measure the performance of the tunnel lining in combination with corrosion and fatigue loads, and reduce the extra fatigue and damage to the tunnel lining during the measurement process, thereby improving the accuracy of measuring the tunnel lining's load-bearing capacity.

[0005] In a first aspect, the present application provides a tunnel lining load-bearing test method, comprising the following steps: Sending sonic detection signals to the tunnel lining; Automatically collecting the acoustic wave detection signal passing through the tunnel lining by means of an intelligent acoustic wave sensor, and determining the acoustic wave propagation characteristics and a plurality of acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining; Determine the residual stress quantity that causes the acoustic wave to attenuate when the acoustic wave detection signal passes through the tunnel lining according to the acoustic wave propagation characteristics and all acoustic energy densities, reconstruct the stress state of the tunnel lining through the residual stress quantity, and then obtain the stress distribution characteristics of the tunnel lining under fatigue load; The electromagnetic wave signal passing through the tunnel lining is collected by an intelligent photoelectric sensor, and then a scattering characteristic diagram of the electromagnetic wave signal is obtained. Multiple evaluation intervals where the tunnel lining is affected by corrosion and the scattering characteristics of the electromagnetic wave signal when passing through the corrosion layer of the tunnel lining are extracted from the scattering characteristic diagram, and the imbalance degree of the tunnel lining after being affected by corrosion is determined according to the scattering characteristics and all the evaluation intervals; The bearing state of the tunnel lining is detected based on the stress distribution characteristics and the imbalance degree.

[0006] In some embodiments, determining the acoustic wave propagation characteristics and multiple acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining specifically includes: collecting acoustic wave data when the acoustic wave detection signal passes through the tunnel lining; Decoupling the acoustic wave data to obtain an acoustic wave response when the acoustic wave detection signal passes through the tunnel lining; The acoustic wave propagation characteristics of the acoustic wave detection signal when passing through the tunnel lining and a plurality of acoustic energy densities when the acoustic wave detection signal passes through the tunnel lining are determined according to the acoustic wave response.

[0007] In some embodiments, determining the residual stress that causes the acoustic wave to attenuate during the acoustic wave detection signal passing through the tunnel lining according to the acoustic wave propagation characteristics and all acoustic energy densities specifically includes: Determining the elastic domain and the plastic domain of the tunnel lining according to the acoustic wave propagation characteristics; Determine the elastic stress acoustic wave field of the tunnel lining due to the residual elastic stress causing the acoustic wave attenuation according to the elastic action domain and all acoustic energy densities; Determining the plastic stress acoustic wave field of the tunnel lining due to acoustic wave attenuation caused by residual plastic stress according to the plastic action domain and all acoustic energy densities; determining an acoustic-elastic coupling of the tunnel lining; The residual stress that causes the acoustic wave to attenuate during the acoustic wave detection signal passing through the tunnel lining is quantitatively determined based on the acoustic-elastic coupling, the plastic stress acoustic wave field, and the elastic stress acoustic wave field.

[0008] In some embodiments, the stress state of the tunnel lining is reconstructed by quantitatively analyzing the residual stress, and the stress distribution characteristics of the tunnel lining under fatigue load are obtained, which specifically include: Determine the residual stress at each test point and take all residual stresses as stress states; Selecting a detection point as a selected detection point, and determining the residual stress corresponding to the selected detection point quantitatively according to the residual stress; If the residual stress corresponding to the selected detection point is less than the residual stress quantification, the residual stress corresponding to the selected detection point is used as the residual stress reconstructed at the selected detection point; If the residual stress corresponding to the selected detection point is greater than or equal to the residual stress quantification, the residual stress quantification is added to the residual stress corresponding to the selected detection point, and the added value is used as the residual stress reconstructed at the selected detection point; Continue to judge and reconstruct the residual stresses corresponding to the remaining detection points; The stress distribution characteristics of the tunnel lining under fatigue loading are determined based on all reconstructed residual stresses.

[0009] In some embodiments, extracting multiple evaluation intervals where the tunnel lining is affected by corrosion and scattering characteristics of the electromagnetic wave signal when passing through the tunnel lining corrosion layer from the scattering characteristic map specifically includes: Determine the detection window for corrosion inspection of tunnel linings; Extracting a plurality of evaluation intervals where the tunnel lining is affected by corrosion from the scattering characteristic map based on the detection window; The scattering characteristics of the electromagnetic wave signal when passing through the tunnel lining corrosion layer are determined based on all evaluation intervals.

[0010] In some embodiments, determining the imbalance degree of the tunnel lining after being affected by corrosion according to the scattering characteristics and all evaluation intervals specifically includes: Determine the local imbalance degree of each evaluation interval; The imbalance degree of the tunnel lining after being affected by corrosion is determined based on all local imbalance degrees and the scattering characteristics.

[0011] In some embodiments, detecting the bearing state of the tunnel lining based on the stress distribution characteristics and the imbalance degree specifically includes: Determining stiffness components of each residual stress region according to the stress distribution characteristics; Determining the strain stiffness of the tunnel lining based on all stiffness components and the unbalance; determining a stiffness threshold of the tunnel lining; comparing the strain stiffness to the stiffness threshold; If the strain stiffness is less than the stiffness threshold, marking the tunnel lining load-bearing state as high-risk; If the strain stiffness is greater than or equal to the stiffness threshold, the tunnel lining bearing state is marked as low risk.

[0012] In a second aspect, the present application provides a tunnel lining load-bearing test device, comprising: A sending module, used for sending an acoustic wave detection signal to the tunnel lining; a processing module, for automatically collecting the acoustic wave detection signal passing through the tunnel lining through an intelligent acoustic wave sensor, and determining the acoustic wave propagation characteristics and a plurality of acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining; The processing module is further used to determine the residual stress quantity that causes the acoustic wave to attenuate during the acoustic wave detection signal passing through the tunnel lining according to the acoustic wave propagation characteristics and all acoustic energy densities, and to reconstruct the stress state of the tunnel lining through the residual stress quantity, thereby obtaining the stress distribution characteristics of the tunnel lining under fatigue load; The processing module is further used to collect electromagnetic wave signals passing through the tunnel lining through an intelligent photoelectric sensor, thereby obtaining a scattering characteristic diagram of the electromagnetic wave signals, extracting from the scattering characteristic diagram a plurality of evaluation intervals where the tunnel lining is affected by corrosion and scattering characteristics of the electromagnetic wave signals when passing through the corrosion layer of the tunnel lining, and determining the degree of imbalance of the tunnel lining after being affected by corrosion according to the scattering characteristics and all evaluation intervals; A detection module is used to detect the bearing state of the tunnel lining based on the stress distribution characteristics and the imbalance degree.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned tunnel lining load-bearing test method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned tunnel lining load-bearing test method is implemented.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the tunnel lining load-bearing test method and device provided in the present application, an acoustic wave detection signal is first sent to the tunnel lining; the acoustic wave detection signal passing through the tunnel lining is automatically collected by an intelligent acoustic wave sensor, and the acoustic wave propagation characteristics and multiple acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining are determined; the residual stress that causes the acoustic wave to attenuate during the acoustic wave detection signal passing through the tunnel lining is quantitatively determined based on the acoustic wave propagation characteristics and all acoustic energy densities, and the stress state of the tunnel lining is stress reconstructed based on the residual stress quantification, thereby obtaining the stress distribution characteristics of the tunnel lining under fatigue load; the electromagnetic wave signal passing through the tunnel lining is collected by an intelligent photoelectric sensor, thereby obtaining a scattering characteristic diagram of the electromagnetic wave signal, and multiple evaluation intervals affected by corrosion of the tunnel lining and the scattering characteristics of the electromagnetic wave signal when passing through the tunnel lining corrosion layer are extracted from the scattering characteristic diagram, and the imbalance degree of the tunnel lining after being affected by corrosion is determined based on the scattering characteristics and all evaluation intervals; the bearing state of the tunnel lining is detected based on the stress distribution characteristics and the imbalance degree.

[0016] It can be seen that in the process of tunnel lining load-bearing test, the present application first sends an acoustic wave detection signal to the tunnel lining; automatically collects the acoustic wave detection signal passing through the tunnel lining through an intelligent acoustic wave sensor, and determines the acoustic wave propagation characteristics and multiple acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining; secondly, determines the residual stress quantification that causes the acoustic wave to attenuate in the process of the acoustic wave detection signal passing through the tunnel lining based on the acoustic wave propagation characteristics and all acoustic energy densities, and the residual stress quantification is used to analyze the size and distribution of the residual stress inside the tunnel lining under load and fatigue conditions, so as to facilitate the evaluation of the safety performance of the tunnel lining during use, thereby quantifying the residual stress. The stress state of the tunnel lining is reconstructed to obtain the stress distribution characteristics of the tunnel lining under fatigue load; the electromagnetic wave signal passing through the tunnel lining is collected by an intelligent photoelectric sensor to obtain a scattering characteristic diagram of the electromagnetic wave signal, and multiple evaluation intervals affected by corrosion of the tunnel lining and the scattering characteristics of the electromagnetic wave signal when passing through the corrosion layer of the tunnel lining are extracted from the scattering characteristic diagram, and the scattering characteristics are used to reflect the corrosion characteristics of the tunnel lining through the scattering of the electromagnetic wave signal, and then the imbalance degree of the tunnel lining after being affected by corrosion is determined according to the scattering characteristics and all the evaluation intervals, and finally the bearing state of the tunnel lining is detected based on the stress distribution characteristics and the imbalance degree. The above scheme can measure the performance of the tunnel lining in combination with corrosion and fatigue load, and weaken the extra fatigue and damage to the tunnel lining during the measurement process, thereby improving the accuracy of measuring the bearing capacity of the tunnel lining. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1is an exemplary flow chart of a tunnel lining load-bearing test method according to some embodiments of the present application; Figure 2 is an exemplary flow chart for determining residual stress quantification according to some embodiments of the present application; Figure 3 is a model example diagram of a smart sensor according to some embodiments of the present application; Figure 4 is a schematic structural diagram of a tunnel lining load-bearing test device according to some embodiments of the present application; Figure 5 It is a structural schematic diagram of a computer device for implementing a tunnel lining load-bearing test method according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is an exemplary flow chart of a tunnel lining load-bearing test method according to some embodiments of the present application. The tunnel lining load-bearing test method 100 mainly includes the following steps: In step 101, an acoustic wave detection signal is sent to a tunnel lining.

[0020] In a specific implementation, a high-frequency sound wave detection signal is sent to the tunnel through a high-frequency sound wave excitation device (such as a laser speaker) arranged in the tunnel, and a low-frequency sound wave detection signal is sent to the tunnel lining through a low-frequency sound wave excitation device (such as a linear low-frequency electromagnetic exciter); the high-frequency sound wave detection signal and the low-frequency sound wave detection signal are sent alternately at a specified frequency, wherein the specified frequency is 100 Hz; in other embodiments, other methods may also be used for determination, which is not limited here.

[0021] It should be noted that in the present application, after sending an acoustic wave detection signal to the tunnel lining, the high-frequency acoustic wave signal and the low-frequency acoustic wave signal of the acoustic wave detection signal passing through the tunnel lining are collected at multiple detection points arranged in the tunnel, so as to facilitate the analysis of the bearing capacity of the tunnel lining.

[0022] In step 102, the acoustic wave detection signal passing through the tunnel lining is automatically collected by an intelligent acoustic wave sensor, and the acoustic wave propagation characteristics and multiple acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining are determined.

[0023] In specific implementation, multiple detection points are set up in the tunnel, and the high-frequency sound wave detection signals and low-frequency sound wave detection signals of each monitoring point are automatically collected by intelligent sound wave sensors arranged at each detection point; in other embodiments, other methods can also be used to obtain, which will not be repeated here.

[0024] In some embodiments, determining the acoustic wave propagation characteristics and multiple acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining can be achieved by the following steps: collecting acoustic wave data when the acoustic wave detection signal passes through the tunnel lining; Decoupling the acoustic wave data to obtain an acoustic wave response when the acoustic wave detection signal passes through the tunnel lining; The acoustic wave propagation characteristics of the acoustic wave detection signal when passing through the tunnel lining and a plurality of acoustic energy densities when the acoustic wave detection signal passes through the tunnel lining are determined according to the acoustic wave response.

[0025] In specific implementation, the collection of acoustic wave data when the acoustic wave detection signal passes through the tunnel lining can be achieved in the following manner, namely: arranging multiple detection points in the tunnel lining, and using intelligent acoustic wave sensors installed at each detection point to automatically collect the acoustic wave transmission speed, acoustic wave amplitude, acoustic wave duration, and sound pressure of the high-frequency acoustic wave detection signal at each detection point, and using all collected data as high-frequency acoustic wave data; collecting the acoustic wave transmission speed, acoustic wave amplitude, acoustic wave duration, and sound pressure of the low-frequency acoustic wave detection signal at each detection point, and using all collected data as low-frequency acoustic wave data; using a set of high-frequency acoustic wave data and low-frequency acoustic wave data as the acoustic wave data of the acoustic wave detection signal passing through the tunnel lining; in other embodiments, other methods can also be used to obtain, which are not limited here.

[0026] In a specific implementation, the acoustic wave data is decoupled, and the acoustic wave response when the acoustic wave detection signal passes through the tunnel lining can be obtained by the following method, namely: initializing an acoustic wave response model, using the high-frequency acoustic wave data in the acoustic wave response as the initialization parameter of the acoustic wave response model, and using the low-frequency acoustic wave data in the acoustic wave response as the constraint parameter of the acoustic wave response model, and then obtaining the acoustic wave response when the acoustic wave detection signal propagates in the tunnel lining through the acoustic wave response model, wherein the acoustic wave response model uses an acoustic wave response model established by a machine learning algorithm (such as a decision tree, a neural network, etc.). The acoustic wave response model is, for example: acoustic wave response = high-frequency acoustic wave data * A + low-frequency acoustic wave data * B, wherein A and B are weight coefficients, and A and B can be determined according to a large number of acoustic wave responses. The acoustic wave response refers to the state of the high-frequency acoustic wave detection signal and the low-frequency acoustic wave detection signal after being absorbed, reflected or scattered when propagating in the tunnel lining, including the acoustic wave transmission speed, acoustic wave amplitude, acoustic wave duration, acoustic wave frequency and sound pressure of all acoustic wave detection signals collected at each detection point after being absorbed and reflected; in other embodiments, other methods can also be used for determination, which is not limited here.

[0027] In specific implementation, the acoustic wave propagation characteristics of the acoustic wave detection signal when passing through the tunnel lining can be determined according to the acoustic wave response in the following manner, namely: obtaining the initial amplitudes of the high-frequency acoustic wave signal and the low-frequency acoustic wave signal from the operating table of the acoustic wave excitation device; selecting a detection point as the selected detection point, subtracting the acoustic wave amplitude of the selected detection point in the acoustic wave response from the average of the initial amplitude of the high-frequency acoustic wave and the initial amplitude of the low-frequency acoustic wave, performing a logarithmic operation with natural numbers as the base on the value obtained by the subtraction, taking the opposite of the value obtained by the logarithmic operation as the acoustic wave attenuation coefficient of the selected detection point, and continuing to determine the acoustic wave attenuation coefficients of the remaining detection points; taking all the acoustic wave attenuation coefficients and all the acoustic wave transmission speeds as the acoustic wave propagation characteristics of the acoustic wave detection signal when passing through the tunnel lining; in other embodiments, other methods can also be used for determination, which are not limited here.

[0028] In specific implementation, determining multiple acoustic energy densities when the acoustic wave detection signal passes through the tunnel lining based on the acoustic wave response can be achieved in the following manner, namely: selecting a detection point as the selected detection point, taking the acoustic wave duration corresponding to the selected detection point in the acoustic wave response as the upper limit of integration, taking 0 as the lower limit of integration, and integrating the square of the sound pressure at the selected detection point with time as the microelement in combination with the above upper and lower limits, dividing the value obtained by the integration operation by the acoustic wave duration of the selected detection point, and taking the value obtained by the division as the acoustic energy density of the selected detection point, and continuing to determine the acoustic energy densities of the remaining detection points to obtain multiple acoustic energy densities when the acoustic wave detection signal passes through the tunnel lining; in other embodiments, other methods can also be used for determination, which are not limited here.

[0029] It should be noted that the acoustic wave propagation characteristics in this application refer to the speed of the acoustic wave detection signal when propagating in the tunnel lining and the behavior of the acoustic wave being absorbed by the tunnel lining, including the above-mentioned acoustic wave propagation speed and acoustic wave attenuation coefficient, which are used to describe the propagation of the acoustic wave and its interaction with the tunnel lining, so as to facilitate the detection of the degree of damage inside the tunnel lining; the acoustic energy density is the acoustic wave energy contained in the tunnel lining per unit volume; the acoustic energy density is used to analyze the transfer of acoustic wave energy; In step 103, the residual stress that causes the acoustic wave to attenuate when the acoustic wave detection signal passes through the tunnel lining is quantitatively determined based on the acoustic wave propagation characteristics and all acoustic energy densities. The stress state of the tunnel lining is reconstructed through the residual stress quantification to obtain the stress distribution characteristics of the tunnel lining under fatigue load.

[0030] In some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of the process of determining the residual stress quantification in some embodiments of the present application. In this embodiment, the residual stress quantification of the acoustic wave attenuation during the acoustic wave detection signal passing through the tunnel lining can be achieved by the following steps according to the acoustic wave propagation characteristics and all acoustic energy densities: First, in step 1031, the elastic domain and the plastic domain of the tunnel lining are determined according to the acoustic wave propagation characteristics; Secondly, in step 1032, the elastic stress acoustic wave field of the tunnel lining resulting from acoustic wave attenuation due to residual elastic stress is determined according to the elastic action domain and all acoustic energy densities; Furthermore, in step 1033, the plastic stress acoustic wave field of the tunnel lining resulting from acoustic wave attenuation due to residual plastic stress is determined according to the plastic action domain and all acoustic energy densities; Next, in step 1034, the acoustic-elastic coupling of the tunnel lining is determined; Finally, in step 1035, the residual stress that causes the acoustic wave to attenuate when the acoustic wave detection signal passes through the tunnel lining is quantitatively determined based on the acoustic-elastic coupling, the plastic stress acoustic wave field, and the elastic stress acoustic wave field.

[0031] In specific implementation, the elastic domain and plastic domain of the tunnel lining can be determined according to the acoustic wave propagation characteristics in the following manner, namely: the mean of all acoustic wave attenuation coefficients is taken as the strain critical point of the tunnel lining, wherein the strain critical point is the critical point at which the tunnel lining changes from the elastic stage to the plastic stage under stress, the set consisting of all detection points corresponding to the acoustic wave attenuation coefficients less than the strain critical point is taken as the elastic domain of the tunnel lining, the set consisting of all detection points corresponding to the acoustic wave attenuation coefficients greater than or equal to the strain critical point is taken as the plastic domain of the tunnel lining, wherein the elastic domain refers to the area where the tunnel lining maintains elastic deformation when subjected to stress, and the plastic domain refers to the area where the tunnel lining maintains plastic deformation when subjected to stress. In other embodiments, other methods may also be used for determination, which are not limited here.

[0032] In specific implementation, the elastic stress acoustic wave field of the tunnel lining that causes acoustic wave attenuation due to residual elastic stress can be determined based on the elastic domain and all acoustic energy densities in the following manner, namely: the initial yield stress and density of the tunnel lining material are obtained from the material database, and the initial acoustic wave attenuation coefficient and the low-frequency acoustic wave frequency are obtained from the operating console of the acoustic wave excitation device, the average of all the corresponding acoustic wave attenuation coefficients within the elastic domain is subtracted from the initial acoustic wave attenuation coefficient, the subtracted value is divided by the low-frequency acoustic wave frequency, and the result of the division is used as the elastic hardening parameter of the tunnel lining, wherein the elastic hardening parameter is a parameter value that describes the hardening degree of the tunnel lining in the elastic stage, the elastic hardening parameter is subtracted from the initial yield stress of the tunnel lining material, and the subtracted value is used as the elastic hardening parameter of the tunnel lining. stress, wherein the elastic hardening stress refers to the stress that causes the tunnel lining to elastically harden; the elastic hardening stress is divided by the material density of the tunnel lining to obtain a first value, the cube of the mean value of all corresponding acoustic energy densities in the elastic action domain is multiplied by 2, the multiplied value is added to the mean value of the acoustic energy density, the added value is logarithmically calculated with natural numbers as the base, the value obtained by the logarithm calculation is added to the first value, and the added value is multiplied by 3, the multiplied value is multiplied by the material density of the tunnel lining, and finally the multiplied value is used as the elastic stress acoustic wave field of the tunnel lining due to the residual elastic stress causing the acoustic wave attenuation; wherein the elastic stress acoustic wave field refers to the field strength when the acoustic wave propagates inside the tunnel lining under the elastic stress state; in other embodiments, other methods can also be used for determination, which are not limited here.

[0033] In specific implementation, the plastic stress acoustic wave field of the tunnel lining caused by the residual plastic stress causing the acoustic wave attenuation can be determined according to the plastic action domain and all acoustic energy densities in the following manner, namely: obtaining the high-frequency acoustic wave frequency from the operating console of the acoustic wave excitation device, subtracting the average of all the acoustic wave attenuation coefficients corresponding to the plastic action domain from the initial acoustic wave attenuation coefficient, dividing the subtracted value by the high-frequency acoustic wave frequency, and using the result of the division as the plastic hardening parameter of the tunnel lining, wherein the plastic hardening parameter is a parameter value that describes the hardening degree of the tunnel lining in the plastic stage, subtracting the plastic hardening parameter from the initial yield stress of the tunnel lining material, and using the subtracted value as the plastic hardening stress of the tunnel lining, wherein the plastic hardening stress refers to the degree of hardening of the tunnel lining. The stress at which plastic hardening of the masonry occurs; the plastic hardening stress is divided by the material density of the tunnel lining to obtain a first value, the cube of the mean value of all corresponding acoustic energy densities within the plastic action domain is multiplied by 2, the multiplied value is added to the mean value of the acoustic energy density, the added value is logarithmically calculated with natural numbers as the base, the value obtained by the logarithm calculation is added to the first value, and the added value is multiplied by 3, the multiplied value is multiplied by the material density of the tunnel lining, and finally the multiplied value is used as the plastic stress acoustic wave field of the tunnel lining due to the residual plastic stress causing the acoustic wave attenuation; wherein the plastic stress acoustic wave field refers to the field intensity when the acoustic wave propagates inside the tunnel lining under the plastic stress state; in other embodiments, other methods can also be used for determination, which are not limited here.

[0034] In specific implementation, the acoustic-elastic coupling of the tunnel lining can be determined in the following manner, namely: the initial acoustic wave propagation velocity is obtained from the operating table of the acoustic wave excitation device, the mean of all the corresponding acoustic wave attenuation coefficients in the elastic domain is subtracted by one, the subtracted value is multiplied by the initial acoustic wave propagation velocity, the multiplied value is divided by the elastic hardening stress, and the divided value is multiplied by the mean of all the corresponding acoustic energy densities in the elastic domain, and finally the absolute value of the multiplied value is used as the elastic-acoustic field coupling degree of the tunnel lining, wherein the elastic-acoustic field coupling degree describes the degree of interaction between the propagation of acoustic waves in the tunnel lining and the elastic stress; the mean of all the acoustic wave attenuation coefficients in the plastic domain is subtracted by one , multiply the subtracted value by the initial sound wave propagation velocity, divide the multiplied value by the plastic hardening stress, and then multiply the divided value by the mean of all corresponding sound energy densities in the plastic action domain, and finally use the absolute value of the multiplied value as the plastic-acoustic field coupling degree of the tunnel lining, wherein the plastic-acoustic field coupling degree describes the degree of interaction between the propagation of sound waves in the tunnel lining and the plastic stress; use the set of the plastic-acoustic field coupling degree and the elastic-acoustic field coupling degree as the acoustic-elastic coupling of the tunnel lining, wherein the acoustic-elastic coupling refers to the coupling caused by the mutual influence between the sound wave and the stress inside the tunnel lining during the propagation process; in other embodiments, other methods can also be used for determination, which is not limited here.

[0035] In specific implementation, the quantitative determination of the residual stress that causes the acoustic wave to attenuate during the acoustic wave detection signal passing through the tunnel lining according to the acoustic-elastic coupling, the plastic stress acoustic wave field and the elastic stress acoustic wave field can be achieved in the following manner, namely: multiplying the plastic acoustic field coupling degree in the acoustic-elastic coupling with the plastic stress acoustic wave field, performing a logarithmic operation with a base of 2 on the multiplied value to obtain a first value, multiplying the elastic acoustic field coupling degree in the acoustic field coupling with the elastic stress acoustic wave field, performing a logarithmic operation with a base of natural numbers on the multiplied value, adding the value obtained by the logarithmic operation to the first value, and then dividing the added value by the strain critical point, and finally using the divided value as the quantitative residual stress that causes the acoustic wave to attenuate during the acoustic wave detection signal passing through the tunnel lining; in other embodiments, other methods can also be used for determination, which are not limited here.

[0036] It should be noted that the residual stress quantification in this application refers to the quantification of the residual stress inside the tunnel lining under fatigue load, which is used to analyze the size and distribution of the residual stress inside the tunnel lining under load and fatigue conditions, so as to facilitate the evaluation of the safety performance of the tunnel lining during use.

[0037] In some embodiments, the stress state of the tunnel lining is reconstructed by quantitatively analyzing the residual stress, and the stress distribution characteristics of the tunnel lining under fatigue load are obtained by the following steps: Determine the residual stress at each test point and take all residual stresses as stress states; Selecting a detection point as a selected detection point, and determining the residual stress corresponding to the selected detection point quantitatively according to the residual stress; If the residual stress corresponding to the selected detection point is less than the residual stress quantification, the residual stress corresponding to the selected detection point is used as the residual stress reconstructed at the selected detection point; If the residual stress corresponding to the selected detection point is greater than or equal to the residual stress quantification, the residual stress quantification is added to the residual stress corresponding to the selected detection point, and the added value is used as the residual stress reconstructed at the selected detection point; Continue to judge and reconstruct the residual stresses corresponding to the remaining detection points; The stress distribution characteristics of the tunnel lining under fatigue loading are determined based on all reconstructed residual stresses.

[0038] In specific implementation, the residual stress of each detection point is determined, and all residual stresses are regarded as stress states. The following method can be used to achieve this, namely: select a detection point as the selected detection point, subtract the sound wave attenuation coefficient corresponding to the selected detection point from the initial sound wave attenuation coefficient to obtain a first value, multiply the inverse of the average of the high-frequency sound wave frequency and the low-frequency sound wave frequency by the first value, and then subtract the multiplied value from the initial yield stress, multiply the subtracted value by the sound energy density component corresponding to the selected detection point, and finally use the multiplied value as the residual stress of the selected detection point, and continue to determine the residual stress of the remaining detection points; all residual stresses are regarded as stress states; in other embodiments, other methods can also be used for determination, which are not limited here.

[0039] In specific implementation, the stress distribution characteristics of the tunnel lining under fatigue load conditions can be determined based on all reconstructed residual stresses in the following manner, namely: the horizontal coordinate of the detection point is used as the horizontal axis, the vertical coordinate of the detection point is used as the vertical axis, and the residual stress of the detection point is used as the vertical axis. A reconstructed total stress distribution diagram is obtained by combining the above-mentioned horizontal axis, vertical axis, and vertical axis through a data visualization platform (such as Tableau). The areas surrounded by all coordinates in the total stress distribution diagram where the residual stress is greater than or equal to the residual stress quantification and the coordinate positions are similar are taken as residual stress areas to obtain multiple residual stress areas. The set consisting of each residual stress area and the residual stress corresponding to each residual stress area is taken as the stress distribution characteristics of the tunnel lining under fatigue load conditions. In other embodiments, other methods can also be used for determination, which are not limited here.

[0040] It should be noted that the stress distribution characteristics in this application refer to the residual stress distribution inside the tunnel lining; after being subjected to external stress, the tunnel lining is still continuously affected when no external force is applied to the tunnel lining. The stress distribution characteristics are used to describe the situation in which various areas inside the tunnel lining are affected by this influence, so as to facilitate comprehensive monitoring and analysis of the structural stability of the tunnel lining.

[0041] In step 104, electromagnetic wave signals passing through the tunnel lining are collected by intelligent photoelectric sensors to obtain a scattering characteristic diagram of the electromagnetic wave signals. Multiple evaluation intervals where the tunnel lining is affected by corrosion and scattering characteristics of the electromagnetic wave signals passing through the tunnel lining corrosion layer are extracted from the scattering characteristic diagram. The degree of imbalance of the tunnel lining after being affected by corrosion is determined based on the scattering characteristics and all evaluation intervals.

[0042] In a specific implementation, a radar antenna is used to send high-frequency electromagnetic waves to the tunnel lining, and electromagnetic wave signals passing through all detection points of the tunnel lining are collected by photoelectric sensors. All collected electromagnetic wave signals are uploaded to a three-dimensional modeling software (such as Autodesk Recap), wherein the collected signals include the scattering intensity, relative propagation time and scattering direction of the electromagnetic wave; areas with low scattering intensity are marked in red, and the scattering intensity of each detection point is represented by the brightness value of the pixel to obtain a three-dimensional scattering characteristic diagram of the tunnel lining, wherein the scattering characteristic diagram is a diagram representing the scattering condition of the electromagnetic wave signal, and is used to analyze the scattering signal generated when the electromagnetic wave signal encounters structural inhomogeneity (such as cracks, voids, corrosion layers, etc.) inside or on the surface of the tunnel lining to generate an image; in other embodiments, other methods can also be used to obtain, which are not limited here.

[0043] In some embodiments, extracting multiple evaluation intervals where the tunnel lining is affected by corrosion and the scattering characteristics of the electromagnetic wave signal when passing through the tunnel lining corrosion layer from the scattering characteristic map can be achieved by the following steps: Determine the detection window for corrosion inspection of tunnel linings; Extracting a plurality of evaluation intervals where the tunnel lining is affected by corrosion from the scattering characteristic map based on the detection window; The scattering characteristics of the electromagnetic wave signal when passing through the tunnel lining corrosion layer are determined based on all evaluation intervals.

[0044] In specific implementation, the detection window for corrosion detection of tunnel lining can be determined in the following manner, namely: the total area of ​​the tunnel lining is calculated by the regional analysis tool of the three-dimensional modeling software, the total area of ​​the tunnel lining is logarithmically calculated with a base of 10, and the value obtained by the logarithmic operation is used as the area to construct a square, and the square is used as the detection window for corrosion detection of the tunnel lining, wherein the detection window refers to the range used to accurately analyze the corrosion of the tunnel lining. Based on the detection window, multiple evaluation intervals affected by corrosion of the tunnel lining are extracted from the scattering characteristic map, which can be implemented in the following manner, namely: the coordinates of all corrosion defects in the scattering characteristic map are marked in red; a detection window is divided in the upper left corner of the corrosion distribution map, and the area surrounded by the red coordinates in the detection window of the scattering characteristic map is calculated by the regional analysis tool of the three-dimensional modeling software, and the area is divided by the detection window area. If the value obtained by the division is greater than or equal to 0.5, the area surrounded by the detection window on the corrosion distribution map is used as the evaluation interval, and then according to the movement from left to right and from top to bottom, the step length of each movement is the window length, so that the detection window moves on the corrosion distribution map, and continues to move. The detection window after the movement is judged by the area included in it, until the entire corrosion distribution map is traversed, and multiple evaluation intervals of the tunnel lining affected by corrosion are obtained; the scattering characteristics of the electromagnetic wave signal when passing through the tunnel lining corrosion layer can be determined by the following method, namely: select an evaluation interval as the selected evaluation interval, divide the brightness value of the red area in the selected evaluation interval by the total brightness value of the selected evaluation interval, and use the value obtained by the division as the scattering intensity attenuation degree of the selected evaluation interval; continue to determine the scattering intensity attenuation degrees of the remaining evaluation intervals, and use the set of all the obtained scattering intensity attenuations as the scattering characteristics of the tunnel lining; in other embodiments, other methods can also be used to obtain, which are not limited here.

[0045] It should be noted that the evaluation interval in the present application refers to the area divided on the tunnel lining for evaluating the corrosion condition of the tunnel lining, which is used to analyze the impact of corrosion on the tunnel lining structure; the scattering feature is the scattering condition of the electromagnetic wave signal when passing through the corroded area on the surface of the tunnel lining, which is used to reflect the corrosion condition of each area of ​​the tunnel lining.

[0046] In some embodiments, determining the imbalance degree of the tunnel lining after being affected by corrosion according to the scattering characteristics and all evaluation intervals can be achieved by the following steps: Determine the local imbalance degree of each evaluation interval; The imbalance degree of the tunnel lining after being affected by corrosion is determined based on all local imbalance degrees and the scattering characteristics.

[0047] In specific implementation, the local imbalance degree of each evaluation interval can be determined in the following manner, namely: obtaining an initial image of the tunnel lining from the database of the three-dimensional modeling software; selecting an evaluation interval as the selected evaluation interval, calculating the area of ​​the corrosion region of the selected evaluation interval through the regional analysis tool of the three-dimensional modeling software, and calculating the original area of ​​the corrosion region corresponding to the initial image of the tunnel lining, dividing the area of ​​the corrosion region by the original area, and taking the value obtained by the division as the local imbalance degree of the selected evaluation interval, and continuing to determine the local imbalance degree of the remaining selected evaluation intervals, wherein the local imbalance degree is a parameter value that describes the degree of imbalance of the structure of each part of the tunnel lining after being corroded; in other embodiments, other methods can also be used for determination, which are not limited here.

[0048] In specific implementation, the imbalance degree of the tunnel lining after being affected by corrosion can be determined according to all local imbalance degrees and the scattering characteristics. The following method is adopted, namely: an evaluation interval is selected as the selected evaluation interval, and the local imbalance degree corresponding to the selected evaluation interval is subjected to a logarithmic operation with a base of 2, and the value obtained by the logarithmic operation is multiplied by the scattering intensity attenuation degree corresponding to the selected evaluation interval in the scattering characteristics, and the multiplied value is used as the imbalance component of the selected evaluation interval, and the imbalance components of the remaining evaluation intervals are continuously determined, and all the obtained imbalance degree components are added to obtain a first value, and all the scattering intensity attenuation degrees in the scattering characteristics are added, and the inverse of the added value is multiplied by the first value, and the multiplied value is used as the imbalance degree of the tunnel lining after being affected by corrosion; in other embodiments, other methods can also be used for determination, which are not limited here.

[0049] It should be noted that the imbalance degree in this application refers to the instability of the structure of each part of the tunnel lining under the action of corrosion, which is used to predict the failure period of the tunnel lining under the current environment, so as to facilitate early warning of structural degradation of the tunnel lining.

[0050] In step 105, the bearing state of the tunnel lining is detected based on the stress distribution characteristics and the imbalance degree.

[0051] In some embodiments, the detection of the bearing state of the tunnel lining based on the stress distribution characteristics and the imbalance degree can be achieved by using the following steps: Determining stiffness components of each residual stress region according to the stress distribution characteristics; Determining the strain stiffness of the tunnel lining based on all stiffness components and the unbalance; determining a stiffness threshold of the tunnel lining; comparing the strain stiffness to the stiffness threshold; If the strain stiffness is less than the stiffness threshold, marking the tunnel lining load-bearing state as high-risk; If the strain stiffness is greater than or equal to the stiffness threshold, the tunnel lining bearing state is marked as low risk.

[0052] In specific implementation, the following method can be used to determine the stiffness component of each residual stress area according to the stress distribution characteristics, namely: select a residual stress area as the selected residual stress area, multiply the mean value of the acoustic wave attenuation coefficient of all detection points in the selected residual stress area by the mean value of the residual stress of all detection points in the area, add 1 to the multiplied value, and then use the reciprocal of the added value as the stiffness component of the selected residual stress area, and continue to determine the stiffness component of the remaining residual stress areas. The following method can be used to determine the strain stiffness of the tunnel lining according to all stiffness components and the imbalance degree, namely: add all stiffness components, add 1 to the reciprocal of the added value, and then multiply the reciprocal of the added value by the imbalance degree, and use the multiplied value as the strain stiffness of the tunnel lining; wherein, the stiffness component refers to the strain capacity of the tunnel lining in different areas, and the strain stiffness refers to the strain capacity of the tunnel lining as a whole under the combined action of corrosion and fatigue loads; in other embodiments, other methods can also be used to determine, which are not limited here.

[0053] It should be noted that the stiffness threshold in the present application can be set according to the specific environmental conditions to the stiffness change threshold. For example, if the tunnel lining is under high dynamic load or frequent vibration, the stiffness threshold can be set in a high range. If the tunnel lining is under low load, the stiffness threshold can be set in a low range. In other embodiments, for example, when the tunnel lining is in saline-alkali land, the stiffness threshold can be set in a high range, thereby improving the safety of the use of the tunnel lining.

[0054] In addition, in another aspect of the present application, in some embodiments, the present application provides a tunnel lining load-bearing test device, referring to Figure 4 , which is a schematic diagram of the structure of a tunnel lining load-bearing test device according to some embodiments of the present application, the tunnel lining load-bearing test device 400 includes: a sending module 401, a processing module 402 and a detection module 403, which are respectively described as follows: The sending module 401 in this application is mainly used to send an acoustic wave detection signal to the tunnel lining; Processing module 402, in the present application, the processing module 402 is used to automatically collect the acoustic wave detection signal passing through the tunnel lining through the intelligent acoustic wave sensor, and determine the acoustic wave propagation characteristics and multiple acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining; It should be noted that the processing module 402 in the present application is also used to determine the residual stress quantity that causes the sound wave to attenuate during the sound wave detection signal passing through the tunnel lining according to the sound wave propagation characteristics and all sound energy densities, and to reconstruct the stress state of the tunnel lining through the residual stress quantity, thereby obtaining the stress distribution characteristics of the tunnel lining under fatigue load; In addition, it should be noted that the processing module 402 in the present application is also used to collect electromagnetic wave signals passing through the tunnel lining through intelligent photoelectric sensors, thereby obtaining a scattering characteristic diagram of the electromagnetic wave signals, extracting multiple evaluation intervals where the tunnel lining is affected by corrosion and scattering characteristics of the electromagnetic wave signals when passing through the tunnel lining corrosion layer from the scattering characteristic diagram, and determining the degree of imbalance of the tunnel lining after being affected by corrosion based on the scattering characteristics and all evaluation intervals; Detection module 403: In the present application, the detection module 403 is mainly used to detect the bearing state of the tunnel lining based on the stress distribution characteristics and the imbalance degree.

[0055] In addition, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned tunnel lining load-bearing test method.

[0056] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a tunnel lining load-bearing test method according to some embodiments of the present application. The tunnel lining load-bearing test method in the above embodiment can be Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0057] The processor 501 may be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0058] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0059] The memory 503 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0060] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0061] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0062] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0063] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0064] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned tunnel lining load-bearing test method is implemented.

[0065] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0066] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A tunnel lining load-bearing test method, characterized in that: The steps include: Sending sonic detection signals to the tunnel lining; Automatically collecting the acoustic wave detection signal passing through the tunnel lining by means of an intelligent acoustic wave sensor, and determining the acoustic wave propagation characteristics and a plurality of acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining; Determine the residual stress quantity that causes the acoustic wave to attenuate when the acoustic wave detection signal passes through the tunnel lining according to the acoustic wave propagation characteristics and all acoustic energy densities, reconstruct the stress state of the tunnel lining through the residual stress quantity, and then obtain the stress distribution characteristics of the tunnel lining under fatigue load; The electromagnetic wave signal passing through the tunnel lining is collected by an intelligent photoelectric sensor, and then a scattering characteristic diagram of the electromagnetic wave signal is obtained. Multiple evaluation intervals where the tunnel lining is affected by corrosion and the scattering characteristics of the electromagnetic wave signal when passing through the corrosion layer of the tunnel lining are extracted from the scattering characteristic diagram, and the imbalance degree of the tunnel lining after being affected by corrosion is determined according to the scattering characteristics and all the evaluation intervals; The bearing state of the tunnel lining is detected based on the stress distribution characteristics and the imbalance degree.

2. The method according to claim 1, characterized in that Determining the acoustic wave propagation characteristics and multiple acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining specifically includes: collecting acoustic wave data when the acoustic wave detection signal passes through the tunnel lining; Decoupling the acoustic wave data to obtain an acoustic wave response when the acoustic wave detection signal passes through the tunnel lining; The acoustic wave propagation characteristics of the acoustic wave detection signal when passing through the tunnel lining and a plurality of acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining are determined according to the acoustic wave response.

3. The method according to claim 1, characterized in that Determining the residual stress quantification that causes the acoustic wave to attenuate during the acoustic wave detection signal passing through the tunnel lining according to the acoustic wave propagation characteristics and all acoustic energy densities specifically includes: Determining the elastic domain and the plastic domain of the tunnel lining according to the acoustic wave propagation characteristics; Determine the elastic stress acoustic wave field of the tunnel lining due to the residual elastic stress causing the acoustic wave attenuation according to the elastic action domain and all acoustic energy densities; Determining the plastic stress acoustic wave field of the tunnel lining due to acoustic wave attenuation caused by residual plastic stress according to the plastic action domain and all acoustic energy densities; determining an acoustic-elastic coupling of the tunnel lining; The residual stress that causes the acoustic wave to attenuate during the acoustic wave detection signal passing through the tunnel lining is quantitatively determined based on the acoustic-elastic coupling, the plastic stress acoustic wave field, and the elastic stress acoustic wave field.

4. The method according to claim 1, characterized in that The stress state of the tunnel lining is quantitatively reconstructed by the residual stress, and the stress distribution characteristics of the tunnel lining under fatigue load are obtained, which specifically include: Determine the residual stress at each test point and take all residual stresses as stress states; Selecting a detection point as a selected detection point, and determining the residual stress corresponding to the selected detection point quantitatively according to the residual stress; If the residual stress corresponding to the selected detection point is less than the residual stress quantification, the residual stress corresponding to the selected detection point is used as the residual stress reconstructed at the selected detection point; If the residual stress corresponding to the selected detection point is greater than or equal to the residual stress quantification, the residual stress quantification is added to the residual stress corresponding to the selected detection point, and the added value is used as the residual stress reconstructed at the selected detection point; Continue to judge and reconstruct the residual stresses corresponding to the remaining detection points; The stress distribution characteristics of the tunnel lining under fatigue loading are determined based on all reconstructed residual stresses.

5. The method according to claim 1, characterized in that Extracting multiple evaluation intervals affected by the corrosion of the tunnel lining and the scattering characteristics of the electromagnetic wave signal when passing through the tunnel lining corrosion layer from the scattering characteristic diagram specifically includes: Determine the detection window for corrosion inspection of tunnel linings; Extracting a plurality of evaluation intervals where the tunnel lining is affected by corrosion from the scattering characteristic map based on the detection window; The scattering characteristics of the electromagnetic wave signal when passing through the tunnel lining corrosion layer are determined based on all evaluation intervals.

6. The method according to claim 1, characterized in that Determining the imbalance degree of the tunnel lining after being affected by corrosion based on the scattering characteristics and all evaluation intervals specifically includes: Determine the local imbalance degree of each evaluation interval; The imbalance degree of the tunnel lining after being affected by corrosion is determined based on all local imbalance degrees and the scattering characteristics.

7. The method according to claim 1, characterized in that Detecting the bearing state of the tunnel lining based on the stress distribution characteristics and the imbalance degree specifically includes: Determining stiffness components of each residual stress region according to the stress distribution characteristics; Determining the strain stiffness of the tunnel lining based on all stiffness components and the unbalance; determining a stiffness threshold of the tunnel lining; comparing the strain stiffness to the stiffness threshold; If the strain stiffness is less than the stiffness threshold, marking the tunnel lining load-bearing state as high-risk; If the strain stiffness is greater than or equal to the stiffness threshold, the tunnel lining bearing state is marked as low risk.

8. A tunnel lining load-bearing test device, characterized in that: include: A sending module, used for sending an acoustic wave detection signal to the tunnel lining; a processing module, for automatically collecting the acoustic wave detection signal passing through the tunnel lining through an intelligent acoustic wave sensor, and determining the acoustic wave propagation characteristics and a plurality of acoustic energy densities of the acoustic wave detection signal when passing through the tunnel lining; The processing module is further used to determine the residual stress quantity that causes the acoustic wave to attenuate during the acoustic wave detection signal passing through the tunnel lining according to the acoustic wave propagation characteristics and all acoustic energy densities, and to reconstruct the stress state of the tunnel lining through the residual stress quantity, thereby obtaining the stress distribution characteristics of the tunnel lining under fatigue load; The processing module is further used to collect electromagnetic wave signals passing through the tunnel lining through an intelligent photoelectric sensor, thereby obtaining a scattering characteristic diagram of the electromagnetic wave signals, extracting from the scattering characteristic diagram a plurality of evaluation intervals where the tunnel lining is affected by corrosion and scattering characteristics of the electromagnetic wave signals when passing through the corrosion layer of the tunnel lining, and determining the degree of imbalance of the tunnel lining after being affected by corrosion according to the scattering characteristics and all evaluation intervals; A detection module is used to detect the bearing state of the tunnel lining based on the stress distribution characteristics and the imbalance degree.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the tunnel lining load-bearing test method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the tunnel lining load-bearing test method according to any one of claims 1 to 7 is implemented.

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