A historical building health monitoring method and system

Abstract

The application belongs to the technical field of historical building health monitoring, and discloses a historical building health state monitoring method and system, which comprises the following steps: establishing a finite element model of a historical building to obtain a numerical analysis mode; arranging acceleration sensors and performing environmental vibration testing to obtain an experimental test mode; optimizing the elastic modulus of the finite element model with the minimum difference between the experimental test mode and the numerical analysis mode as the target, to obtain a preliminarily optimized finite element model; obtaining the optimal number and position of the acceleration sensors with the minimum maximum value of the off-diagonal elements of the mode shape MAC matrix as the target; adjusting the elastic modulus of the finite element model again according to the monitoring data of the acceleration sensors to obtain a deeply optimized finite element model; and analyzing the deeply optimized finite element model to obtain the arrangement position and number of crack sensors and tilt sensors. The application realizes reasonable arrangement of sensors, and further realizes effective and timely monitoring of the health state of historical buildings.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field related to health monitoring of historical buildings, and more particularly to a historical building health state monitoring method and system. BACKGROUND

[0002] In the historical building renovation project, due to the long construction time of historical buildings, long-term erosion by external environment and other influences, historical buildings have problems such as deterioration, damage, tilting and poor structural performance. Moreover, it is difficult to obtain the safety state of the building structure itself in real time during the renovation process. Once the performance of the historical building deteriorates due to construction disturbance and other factors, it cannot be found in time, and only passive repair and rescue work can be done with a lot of manpower and material resources. In severe cases, it can cause the collapse of the historical building structure, which not only endangers the safety of the site personnel, but also causes the cultural value of the historical building to disappear, resulting in immeasurable huge losses.

[0003] Currently, the historical building renovation project is often carried out by using the method of one-time structure detection and identification before renovation, which lacks control over the structure safety of the whole renovation process and has great limitations. Although the existing technology discloses the use of sensors to monitor buildings in real time, such as Chinese patents CN109099975 and CN109099962, the number of sensors arranged for large buildings, where to arrange them and what type of sensors to arrange are all arranged by technicians based on experience, which lacks scientific rationality and cannot effectively monitor the historical buildings. SUMMARY

[0004] In view of the above defects or improvement needs of the prior art, the present application provides a historical building health state monitoring method and system, which realizes the reasonable arrangement of tilt sensors, crack sensors and acceleration sensors, and further realizes the effective and timely monitoring of the health state of historical buildings.

[0005] To achieve the above object, according to one aspect of the present application, a historical building health state monitoring method is provided, which comprises: S1: establishing a finite element model of the historical building, and then performing dynamic analysis based on the finite element model to obtain a numerical analysis modal of the historical building; S2: arranging acceleration sensors at beam-column intersections of the historical building and performing environmental vibration testing on the historical building to obtain accelerations, velocities and displacements of different intersections under vibration interference, and then obtaining an experimental test modal based on the accelerations, velocities and displacements; S3: performing optimization on the elastic modulus of the finite element model based on a PSO algorithm with the minimum difference between the experimental test modal and the numerical analysis modal as the target, to obtain a preliminarily optimized finite element model; S4: obtaining the optimal number and position of the acceleration sensor arrangement using a genetic algorithm with the minimum maximum value of the off-diagonal elements of the mode shape MAC matrix as the target; S5: collecting monitoring data of the acceleration sensor in step S4, and adjusting the elastic modulus of the finite element model again according to the monitoring data to obtain a deeply optimized finite element model; S6: performing static analysis and dynamic analysis using the deeply optimized finite element model to obtain the optimized arrangement position and number of crack sensors and tilt sensors, and using the crack sensors, tilt sensors and acceleration sensors to perform real-time monitoring on the historical building.

[0006] Preferably, step S6 specifically comprises: S61: performing static analysis on the deeply optimized finite element model to obtain stress nephogram values and position changes to determine stress abnormal points, and arranging crack sensors at the stress abnormal points in combination with on-site crack distribution position survey; S62: performing dynamic analysis on the deeply optimized finite element model, and adjusting the position of the tilt sensor according to the mode shape of the torsional modal.

[0007] Preferably, the specific steps for determining the position of the tilt sensor according to the mode shape of the torsional modal in step S62 are: obtaining the first preset order natural frequency and mode shape from the result of dynamic analysis of the deeply optimized finite element model, obtaining the torsional and bending modals in the mode shape result, and arranging the tilt sensor at a position where the torsional and bending are greater than a preset value.

[0008] Preferably, step S5 further comprises outlier rejection and data completion processing on the monitoring data.

[0009] Preferably, the three-sigma rule is used for outlier detection and rejection, and the linear interpolation method is used for data completion on the monitoring data after rejection of outliers.

[0010] Preferably, the specific step of obtaining the experimental test modal in step S2 based on the acceleration, speed and displacement is: S21: inputting the acceleration, speed and displacement into Artemis Model Pro software for enhanced frequency domain decomposition (EFDD) and random subspace (SSI) modal analysis respectively to obtain two modal analysis results; S22: performing MAC value calculation on the two modal analysis results to select the modal with MAC value greater than 80% to determine the final modal.

[0011] Preferably, step S1 further comprises determining the measurement direction of the tilt sensor according to the maximum orientation of the modal torsional vibration offset of the first preset order of the numerical analysis modal, obtaining the stress and strain concentration position according to the static force analysis of the finite element model, and arranging the crack sensor in combination with the site crack distribution position survey at the stress and strain concentration position; further preferably, the distance between the two tilt sensors is not greater than 15 m.

[0012] Preferably, the method further comprises: S7: setting a multi-level early warning mechanism, each level of the early warning mechanism corresponding to a different tilt rate and crack width range, and then comparing the crack width monitored by the crack sensor and the tilt rate monitored by the tilt sensor with the early warning mechanism to perform early warning of the corresponding level.

[0013] According to another aspect of the present application, a historical building health state monitoring system is provided, the system comprising: a model establishing module for establishing a finite element model of the historical building and then performing dynamic analysis based on the finite element model to obtain numerical analysis modal of the historical building; an experimental modal obtaining module for arranging acceleration sensors at beam-column intersections of the historical building and performing environmental vibration test on the historical building to obtain acceleration, speed and displacement of different intersections under vibration interference, and then obtaining experimental test modal based on the acceleration, speed and displacement; a model preliminary optimization module for performing optimization on the elastic modulus of the finite element model based on a PSO algorithm with the difference between the experimental test modal and the numerical analysis modal being minimized as the target to obtain a preliminarily optimized finite element model; an acceleration sensor arrangement optimization module for obtaining the optimal number and position of acceleration sensor arrangement with the maximum value of off-diagonal elements of the mode shape MAC matrix being minimized as the target by using a genetic algorithm; a model deep optimization module for collecting the monitoring data of acceleration in the acceleration sensor arrangement optimization module, and adjusting the elastic modulus of the finite element model again according to the monitoring data to obtain a deeply optimized finite element model; and a crack sensor and tilt sensor arrangement optimization module for performing static analysis and dynamic analysis by using the deeply optimized finite element model to obtain the optimal arrangement position and number of crack sensors and tilt sensors respectively.

[0014] Preferably, the system further comprises a pre-warning module for setting a multi-level pre-warning mechanism, each level of the pre-warning mechanism corresponding to a different inclination rate and crack width range, and then performing pre-warning of the corresponding level according to the comparison between the crack width monitored by the crack sensor and the inclination rate monitored by the inclination sensor and the pre-warning mechanism.

[0015] Overall, compared with the prior art, the historical building health state monitoring method and system provided by the present application has the following beneficial effects:

[0016] 1. The present application finds weak positions in the structure through modal analysis based on the finite element model, and then the positions and number of the acceleration sensors, inclination sensors and crack sensors can be reasonably arranged, avoiding both redundancy and deficiency of the sensors, and thus the health state of the historical building can be effectively and timely monitored.

[0017] 2. The monitoring data is subjected to outlier rejection and data completion processing operations, ensuring the accuracy of the monitoring data, and thus further improving the accuracy of the elastic modulus optimization of the finite element model, which directly affects the determination of the positions and number of the subsequent crack sensors and inclination sensors, and thus ensures the rationality of the arrangement of the crack sensors and inclination sensors.

[0018] 3. The present application also sets a pre-warning mechanism, ensuring the accuracy of the data analysis work such as structure health state rating, and through the establishment of a monitoring data grading pre-warning mechanism, personnel can be timely warned to take corresponding emergency measures according to the structure health level, which has certain guiding value for historical building renovation engineering implementation. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is a step diagram of the historical building health state monitoring method of the embodiment of the present application;

[0020] Figure 2 is a schematic diagram of the finite element model of the historical building of the embodiment of the present application;

[0021] Figures 3A-3C is a schematic diagram of the first three modalities of the historical building of the embodiment of the present application; DETAILED DESCRIPTION

[0022] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0023] Please refer toFigure 1 The application provides a historical building health state monitoring method, and the method comprises S1-S6.

[0024] S1: a finite element model of the historical building is established, and then a numerical analysis mode of the historical building is obtained based on dynamic analysis of the finite element model.

[0025] According to historical building surveying drawings, the finite element model of the historical building is constructed, as shown in the figure. Figure 2 For example, the Abaqus software is used to pre-establish the finite element model of the historical building, conventional values are selected for material parameters, dynamic analysis is performed on the established finite element model, and corresponding frequencies and vibration modes under the first several orders of modes that can reflect overall building torsion and deflection are obtained.

[0026] The measuring direction of the tilt sensor is determined according to the maximum orientation of the first pre-set order mode torsional deflection of the numerical analysis mode, the stress and strain concentration positions are obtained according to static analysis of the finite element model, and the crack sensor is arranged in combination with the on-site crack distribution position survey at the stress and strain concentration positions; further preferably, the distance between the two tilt sensors is not greater than 15 m.

[0027] This embodiment takes the historical building Wuhan Minzhong Park as an example for explanation, after the finite element model of the building is established, dynamic analysis is performed, and the mode of the corresponding finite element model of the building is obtained, as shown in the figure. Figures 3A-3C The first three orders of modes are taken as the research objects in this embodiment, and as can be seen from the figure. Figures 3A-3C It can be seen that the maximum orientation is the southwest and northwest, and then the direction is taken as the measuring direction of the tilt sensor, in order to fully monitor the local torsion and deflection of the building, the distance between the two tilt sensors should be ensured to be not more than 15 m. The stress and strain concentration positions are set according to the stress and strain cloud atlas depth condition in combination with the on-site crack distribution position survey of the static analysis of the finite element model of the building. The model of the tilt sensor in this embodiment can be the work information MAS-WM400 wireless tilt monitor, in order to ensure that the early warning value accuracy is less than 0.1‰, so the accuracy is 0.005 when the measuring range is within 5 degrees, and the zero point temperature drift is ±0.001° / ℃; the model of the crack sensor can be the work information MAS-YTLF integrated crack meter, in order to ensure the early warning value accuracy, the crack sensor accuracy is set to 1 / 5 of the minimum value of the early warning threshold, so the range is 100 mm, the measuring accuracy is 0.02 mm, and the collection frequency is 3 times / day.

[0028] S2: the acceleration sensor is arranged at the beam-column intersection of the historical building, and the environmental vibration test of the historical building is performed to obtain the acceleration, speed and displacement of different intersections under vibration interference, and then the experimental test mode is obtained based on the acceleration, speed and displacement.

[0029] As many as possible low-frequency acceleration sensors with appropriate performance are arranged at the intersection of the upper beams and columns of the historical building. The sampling rate of the low-frequency acceleration sensor can be set to 128, and the sampling time is 18 minutes per group. The external environment vibration is set, the environmental vibration test of the historical building is carried out, and then the acceleration value, the speed value and the displacement value of the historical building at different position nodes under the interference of the external environment vibration are obtained.

[0030] The acceleration value, the speed value and the displacement value are introduced into the running modal analysis software Artemis Model Pro software, and the running modal analysis is carried out through two methods of enhanced frequency domain decomposition (EFDD) and random subspace (SSI). The enhanced frequency domain decomposition method is based on the singular value decomposition of the power spectrum density matrix of the structure response signal to obtain the structure modal parameters, and the random subspace method is based on the time domain data to obtain the modal parameters by using matrix QR decomposition and singular value decomposition method to identify the discrete system state space matrix, and then two modal analysis results are obtained. Then, the two modal analysis results need to be compared to determine the final modal analysis result. The MAC value of the two modal analysis results is calculated by using the Artemis Model Pro software, and the mode with the MAC value greater than eighty percent is determined as the final mode. The MAC value calculation formula is:

[0031]

[0032] Wherein And are two modes to be compared, and T is transposition.

[0033] S3: Based on the PSO algorithm, the difference between the experimental test mode and the numerical analysis mode is minimized to optimize the elastic modulus of the finite element model, and the preliminary optimized finite element model is obtained.

[0034] The PSO algorithm is adopted, the difference between the experimental test mode and the numerical analysis mode is taken as the objective function, the initial population number in the PSO algorithm is set to 30, the iteration number is 300 times, the learning factor is 0.2, the maximum speed is 6, the maximum weight and the minimum weight are respectively 0.8 and 0.3, the Young's modulus parameter of the material is automatically adjusted by the algorithm, the finite element model is updated, and the appropriate material elastic modulus parameter is generated, so that the dynamics of the finite element model is consistent with the actual situation.

[0035] For example, the experimental test mode and the numerical analysis mode in the embodiment are shown in Table 1 as follows:

[0036] order Experimental test frequency (Hz) Numerical analysis frequency (Hz) Difference 1 0.656 0.598 8.8％ 2 1.818 1.815 0.2％ 3 2.839 2.453 13.6％

[0037] Table 1

[0038] The objective function is:

[0039]

[0040] where f i represents the numerical analysis frequency; fex i represents the experimental test frequency; φ i represents the numerical analysis modal shape vector; φex i represents the experimental modal shape vector; w f represents the weighting factor of the frequency summation term; w φ represents the weighting factor of the modal shape summation term; the last two weight factors add up to 1.

[0041] The genetic algorithm is used to analyze the structural modal parameters of the preliminarily optimized finite element model, so that the maximum value of the off-diagonal elements of the modal shape MAC matrix is minimized. The iteration number is set to 150, the crossover rate is 0.8, and the mutation rate is 0.1. The optimal number and position of the acceleration sensors are obtained by solving.

[0042] The off-diagonal elements of the MAC matrix are:

[0043]

[0044] where φ i and φ j are the i-th and j-th column modal shape vectors in the modal shape matrix, i≠j, and T is the transpose.

[0045] The objective function f(x): f(x) = min{max i≠j (MAC i，j )}.

[0046] S5: Collect the monitoring data of the acceleration sensors in step S4, and adjust the elastic modulus of the finite element model again according to the monitoring data to obtain a deeply optimized finite element model.

[0047] According to the above acceleration sensor arrangement scheme, the historical building is monitored for a long time, the finite element model of the historical building is updated in real time, the material elastic modulus parameter is automatically adjusted, the results simulated by the finite element model are consistent with the actual status of the historical building, and a deeply optimized finite element model is obtained.

[0048] It is inevitable to have abnormal data in the long-term monitoring process, and therefore the step further includes outlier rejection and data completion processing on the monitoring data. Specifically, a three-sigma method can be used to detect outliers in the monitoring data, wherein the three-sigma method is to find data deviating from the mean by more than three standard deviations, and after finding the data, the data completion processing is performed on the monitoring data by using a linear interpolation method, wherein the linear interpolation method is to complete the missing values by linear interpolation of adjacent non-missing values.

[0049] S6: using the depth-optimized finite element model to perform static analysis and dynamic analysis to obtain optimized arrangement positions and quantities of crack sensors and tilt sensors, and using the crack sensors, tilt sensors and acceleration sensors to perform real-time monitoring on the historical building.

[0050] The depth-optimized finite element model is used to perform static analysis on the historical building multiple times, stress abnormal points are found according to changes in numerical values and positions of the finite element stress nephogram, and the crack sensor arrangement positions are adjusted in combination with the on-site crack distribution survey.

[0051] The depth-optimized finite element model is used to perform dynamic analysis on the historical building multiple times, and if the structural natural frequency is reduced, it can be judged that the structural material of the historical building has deteriorated, and the positions of the tilt sensors are adjusted according to changes in the mode shape of the torsional vibration mode of the depth-optimized finite element model dynamic analysis. Specifically, the first preset order natural frequency and mode shape are obtained according to the results of the depth-optimized finite element model dynamic analysis, the torsional and bending modes in the mode shape results are obtained, and the tilt sensors are arranged at positions where the torsion and bending are greater than a preset value. For example, the first three order natural frequencies and mode shapes in the mode are considered, the torsional and bending modes in the mode shape results are obtained, and the tilt sensors are arranged at positions where the torsion and bending are greater than a preset value according to Figures 3A-3C According to the analysis results, the tilt sensors are arranged at positions where the torsion and bending are the largest.

[0052] Through the above manner, the positions and quantities of the tilt sensors, crack sensors and acceleration sensors are reasonably arranged, and the historical building can be effectively monitored in real time.

[0053] The above method further includes a step S7 of setting multiple levels of early warning mechanisms, each level of early warning mechanism corresponding to different tilt rate and crack width ranges, and then comparing the crack width monitored by the crack sensors and the tilt rate monitored by the tilt sensors with the early warning mechanisms to perform early warning at a corresponding level.

[0054] For example, the health state of the historical building structure can be divided into four levels, i.e., first level, second level, third level and fourth level, and the specific conditions are shown in Table 2. When the health state of the historical building structure is at the first level, no pre-warning is performed and no corresponding measures need to be taken. When the health state of the historical building structure is at the second level, yellow pre-warning is performed and appropriate measures can be taken for prevention. When the health state of the historical building structure is at the third level, orange pre-warning is performed and measures should be taken for repair and reinforcement. When the health state of the historical building structure is at the fourth level, red pre-warning is performed and immediate rescue measures must be taken to ensure the safety of the on-site personnel and prevent safety accidents.

[0055]

[0056] Table 2

[0057] Another aspect of the present application provides a historical building health state monitoring system, which comprises a model establishing module, an experimental modal obtaining module, a model preliminary optimization module, an acceleration sensor layout optimization module, a model deep optimization module, a crack sensor and tilt sensor layout optimization module, wherein:

[0058] The model establishing module is used to establish a finite element model of the historical building, and then perform dynamics analysis based on the finite element model to obtain a numerical analysis modal of the historical building.

[0059] The experimental modal obtaining module is used to arrange acceleration sensors at beam-column intersections of the historical building and perform environmental vibration testing on the historical building to obtain accelerations, velocities and displacements of different intersections under vibration interference, and then obtain an experimental test modal based on the accelerations, velocities and displacements.

[0060] The model preliminary optimization module is used to optimize the elastic modulus of the finite element model based on a PSO algorithm with the minimum difference between the experimental test modal and the numerical analysis modal as the target, to obtain a preliminarily optimized finite element model.

[0061] The acceleration sensor layout optimization module is used to obtain the optimal number and position of the acceleration sensor layout by using a genetic algorithm with the minimum maximum value of the off-diagonal elements of the mode shape MAC matrix as the target.

[0062] The model deep optimization module is used to collect monitoring data of the accelerations in the acceleration sensor layout optimization module, and adjust the elastic modulus of the finite element model again according to the monitoring data to obtain a deeply optimized finite element model.

[0063] The crack sensor and tilt sensor layout optimization module is used to obtain the optimal layout position and number of the crack sensor and tilt sensor by using the deeply optimized finite element model for static analysis and dynamic analysis, respectively.

[0064] The system further comprises a pre-warning module, which is configured to set a multi-level pre-warning mechanism, each level of the pre-warning mechanism corresponding to a different inclination rate and crack width range, and then comparing the crack width monitored by the crack sensor and the inclination rate monitored by the inclination sensor with the pre-warning mechanism to perform pre-warning of the corresponding level.

[0065] Those skilled in the art will easily understand that the above description is only the preferred embodiment of the present application, and is not intended to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of monitoring the health of a historic building, characterized by, The method comprises: S1: establishing a finite element model of the historical building, and then performing dynamic analysis based on the finite element model to obtain a numerical analysis modal of the historical building; S2: arranging acceleration sensors at beam-column intersections of the historical building and performing environmental vibration testing on the historical building to obtain accelerations, velocities and displacements of different intersections under vibration interference, and then obtaining an experimental test modal based on the accelerations, velocities and displacements; S3: performing optimization on the elastic modulus of the finite element model based on a PSO algorithm, with the minimum difference between the experimental test modal and the numerical analysis modal as the target, to obtain a preliminarily optimized finite element model; S4: obtaining the optimal number and position of the acceleration sensor arrangement by using a genetic algorithm with the minimum maximum value of non-diagonal elements of a modal assurance criterion (MAC) matrix as the target; S5: collecting monitoring data of the acceleration sensor in step S4, and adjusting the elastic modulus of the finite element model again according to the monitoring data to obtain a deeply optimized finite element model; S6: performing static analysis and dynamic analysis by using the deeply optimized finite element model to obtain the optimal arrangement position and number of crack sensors and tilt sensors, and performing real-time monitoring on the historical building by using the crack sensors, tilt sensors and acceleration sensors.

2. The method of claim 1, wherein, Step S6 specifically comprises: S61: performing static analysis on the deeply optimized finite element model to obtain stress nephogram values and position changes to determine stress abnormal points, and arranging crack sensors at the stress abnormal points in combination with on-site crack distribution position surveying; S62: performing dynamic analysis on the deeply optimized finite element model, and adjusting the position of the tilt sensor according to the vibration mode of the torsional modal.

3. The method of claim 2, wherein, The specific steps for determining the position of the tilt sensor according to the vibration mode of the torsional modal in step S62 are as follows: obtaining the first preset order natural frequency and vibration mode from the result of dynamic analysis of the deeply optimized finite element model, obtaining the torsional and bending modes in the vibration mode result, and arranging the tilt sensor at a position where the torsional and bending modes are greater than a preset value.

4. The method of claim 1, wherein, Step S5 further comprises outlier rejection and data completion processing on the monitoring data.

5. The method of claim 4, wherein, The three-sigma rule is used for outlier detection and rejection, and the linear interpolation method is used for data completion on the monitoring data after rejection of outliers.

6. The method of claim 1, wherein, The specific steps for obtaining the experimental test modal based on the accelerations, velocities and displacements in step S2 are as follows: S21: inputting the accelerations, velocities and displacements into Artemis Model Pro software to perform enhanced frequency domain decomposition and random subspace modal analysis, respectively, to obtain two modal analysis results; S22: performing MAC value calculation on the two modal analysis results, and selecting the modal with a MAC value greater than 80% to determine the final modal.

7. The method of claim 1, wherein, Step S1 further comprises determining the measurement direction of the tilt sensor according to the maximum orientation of the torsional vibration offset of the first preset order modal of the numerical analysis modal, obtaining the stress and strain concentration position according to the static analysis of the finite element model, and arranging the crack sensor at the stress and strain concentration position in combination with on-site crack distribution position surveying; further preferably, the distance between the two tilt sensors is not greater than 15 m.

8. The method of claim 1, wherein, The method further comprises: S7: setting a multi-level early warning mechanism, each level of the early warning mechanism corresponding to different inclination rates and crack width ranges, and then comparing the crack width monitored by the crack sensor and the inclination rate monitored by the inclination sensor with the early warning mechanism to perform early warning of the corresponding level.

9. A historic building health monitoring system, characterized by, The system comprises: A model establishing module: used to establish a finite element model of the historical building, and then perform dynamics analysis based on the finite element model to obtain a numerical analysis modal of the historical building; An experimental modal obtaining module: used to arrange acceleration sensors at beam-column intersections of the historical building and perform environmental vibration testing on the historical building to obtain accelerations, speeds and displacements of different intersections under vibration interference, and then obtain an experimental test modal based on the accelerations, speeds and displacements; A model preliminary optimization module: used to optimize the elastic modulus of the finite element model based on a PSO algorithm, with the difference between the experimental test modal and the numerical analysis modal being minimized as the target, to obtain a preliminarily optimized finite element model; An acceleration sensor arrangement optimization module: used to obtain the optimal number and position of acceleration sensor arrangement by adopting a genetic algorithm, with the maximum value of off-diagonal elements of a mode shape MAC matrix being minimized as the target; A model deep optimization module: used to collect monitoring data of accelerations in the acceleration sensor arrangement optimization module, and adjust the elastic modulus of the finite element model again according to the monitoring data to obtain a deeply optimized finite element model; A crack sensor and inclination sensor arrangement optimization module: used to obtain the optimal arrangement position and number of crack sensors and inclination sensors by performing static analysis and dynamics analysis of the deeply optimized finite element model.

10. The system of claim 9, wherein, The system further comprises: An early warning module: used to set a multi-level early warning mechanism, each level of the early warning mechanism corresponding to different inclination rates and crack width ranges, and then compare the crack width monitored by the crack sensor and the inclination rate monitored by the inclination sensor with the early warning mechanism to perform early warning of the corresponding level.

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