CN115577587A - Historical building health state monitoring method and system - Google Patents
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Abstract
The invention belongs to the technical field related to historical building health monitoring, and discloses a historical building health state monitoring method and system, wherein the method comprises the following steps: establishing a finite element model of a historical building to obtain a numerical analysis mode; arranging an acceleration sensor and carrying out an environmental vibration test to obtain an experimental test mode; optimizing the elasticity modulus of the finite element model by taking the minimum difference value between the experimental testing mode and the numerical analysis mode as a target to obtain a preliminarily optimized finite element model; obtaining the optimal number and position of the acceleration sensor layout by taking the minimum maximum value of the non-diagonal elements of the vibration mode MAC matrix as a target; adjusting the elasticity modulus of the finite element model again according to the monitoring data of the acceleration sensor to obtain a depth-optimized finite element model; and analyzing by adopting a finite element model with optimized depth to obtain the arrangement positions and the number of the crack sensors and the inclination sensors. The reasonable arrangement of the sensors is realized, and then the health state of the historical building is effectively monitored in time.
Description
Technical Field
The invention belongs to the technical field related to historical building health monitoring, and particularly relates to a historical building health state monitoring method and system.
Background
In the historical building updating project, the historical building is influenced by external environment erosion and the like for a long time due to the fact that the historical building is long in construction age, the historical building has the problems of deterioration, damage, inclination, poor structural performance and the like, the safety state of the building structure is difficult to acquire in real time in the updating process, once the deterioration of the performance of the historical building cannot be found in time due to factors such as construction disturbance and the like, a large amount of manpower and material resources can be spent for passive repair and rescue work, the collapse accident of the historical building structure is caused in serious cases, the safety of field personnel is damaged, the cultural value of the historical building is lost, and immeasurable huge loss is caused.
At present, the construction of the historical building updating project in China usually adopts a method of carrying out one-time structure detection and identification before updating, lacks the control on the structure safety condition of the whole updating project process, and has great limitation. Although the prior art discloses that the building is monitored in real time by adopting sensors, such as Chinese patents CN109099975 and CN109099962, etc., the arrangement of a plurality of sensors in a large-size building, the arrangement of the sensors where and the arrangement of the sensors of any type are carried out by technicians according to experience, and the prior art is lack of scientific rationality and cannot effectively monitor historical buildings.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a method and a system for monitoring the health state of a historical building, which realize reasonable arrangement of an inclination sensor, a crack sensor and an acceleration sensor, and further realize effective and timely monitoring on the health state of the historical building.
To achieve the above object, according to one aspect of the present invention, there is provided a historical building health status monitoring method, the method comprising: s1: establishing a finite element model of the historical building, and further performing dynamic analysis on the basis of the finite element model to obtain a numerical analysis mode of the historical building; s2: arranging an acceleration sensor at the beam-column intersection of the historical building, carrying out environmental vibration test on the historical building to obtain acceleration, speed and displacement of different intersections under vibration interference, and further obtaining an experimental test mode based on the acceleration, the speed and the displacement; s3: optimizing the elasticity modulus of the finite element model by taking the minimum difference value between the experimental testing mode and the numerical analysis mode as a target on the basis of a PSO algorithm to obtain a preliminarily optimized finite element model; s4: acquiring the optimal number and position of the acceleration sensor layout by adopting a genetic algorithm and taking the minimum maximum value of the non-diagonal elements of the vibration mode MAC matrix as a target; s5: collecting monitoring data of the acceleration sensor in the step S4, and adjusting the elasticity modulus of the finite element model again according to the monitoring data to obtain a depth-optimized finite element model; s6: and performing static analysis and dynamic analysis by using the depth-optimized finite element model to respectively obtain the optimized arrangement positions and the number of the crack sensors and the inclination sensors, and performing real-time monitoring on the historical building by using the crack sensors, the inclination sensors and the acceleration sensors.
Preferably, step S6 specifically includes: s61: performing static analysis on the depth-optimized finite element model to obtain the change of the numerical value and the position of a stress cloud map to determine a stress abnormal point, and setting a crack sensor at the stress abnormal point by combining the investigation condition of the distribution position of the on-site cracks; s62: and carrying out dynamic analysis on the finite element model with the optimized depth, and adjusting the position of the inclination sensor according to the vibration mode of the torsional mode.
Preferably, the specific step of determining the position of the tilt sensor according to the mode shape of the torsional mode in step S62 is: and obtaining the self-vibration frequency and the vibration mode of a preset order according to the result of the finite element model dynamic analysis of the depth optimization, obtaining the torsion and bending modes in the vibration mode result, and arranging an inclined sensor at the position where the torsion and the bending are greater than the preset value.
Preferably, step S5 further includes performing outlier rejection and data completion processing on the monitoring data.
Preferably, the abnormal value is detected and removed by adopting a triple standard deviation method, and the data completion is carried out on the monitoring data after the abnormal value is removed by adopting a linear interpolation method.
Preferably, the step S2 of obtaining an experimental test mode based on the acceleration, the velocity, and the displacement includes the specific steps of: s21: inputting the acceleration, the speed and the displacement into Artemis Model Pro software to carry out Enhanced Frequency Domain Decomposition (EFDD) and random subspace (SSI) modal analysis respectively to obtain two modal analysis results; s22: and calculating the MAC value of the two modal analysis results, and selecting the mode with the MAC value larger than 80% to determine the final mode.
Preferably, the step S1 further includes determining a measurement direction of the tilt sensor according to a maximum orientation of a modal torsional vibration excursion of a pre-preset order of the numerical analysis mode, obtaining a stress-strain concentration position according to a static analysis of the finite element model, and laying a crack sensor at the stress-strain concentration position in combination with a field crack distribution position investigation condition; further preferably, the distance between the two inclination sensors is not more than 15m.
Preferably, the method further comprises: s7: and setting a multi-stage early warning mechanism, wherein each stage of early warning mechanism corresponds to different inclination rates and crack width ranges, and then performing early warning at a corresponding stage according to the crack width monitored by the crack sensor and the inclination rate monitored by the inclination sensor compared with the early warning mechanism.
According to another aspect of the present invention, there is provided a historical building health monitoring system, the system comprising: a model building module: the finite element model is used for establishing the historical building, and then dynamic analysis is carried out on the basis of the finite element model to obtain a numerical analysis mode of the historical building; an experimental mode acquisition module: the system comprises a historical building, a beam-column intersection and a vibration sensor, wherein the historical building is used for arranging the acceleration sensor at the beam-column intersection of the historical building, carrying out environmental vibration test on the historical building to obtain acceleration, speed and displacement of different intersections under vibration interference, and further obtaining an experimental test mode based on the acceleration, the speed and the displacement; a model preliminary optimization module: the elastic modulus of the finite element model is optimized based on a PSO algorithm by taking the minimum difference value between the experimental testing mode and the numerical analysis mode as a target to obtain a preliminarily optimized finite element model; an acceleration sensor layout optimization module: the method is used for obtaining the optimal number and the optimal positions of the acceleration sensors by adopting a genetic algorithm and taking the minimum maximum value of the non-diagonal elements of the vibration mode MAC matrix as a target; a model depth optimization module: the system comprises a finite element model, an acceleration sensor layout optimization module, a data acquisition module and a data processing module, wherein the finite element model is used for acquiring monitoring data of acceleration in the acceleration sensor layout optimization module and adjusting the elasticity modulus of the finite element model again according to the monitoring data to obtain a depth-optimized finite element model; the crack sensor and inclination sensor layout optimization module comprises: and performing static analysis and dynamic analysis by using the depth-optimized finite element model to respectively obtain the optimized arrangement positions and the number of the crack sensors and the inclination sensors.
Preferably, the system further comprises: the early warning module: the early warning system is used for setting a multi-stage early warning mechanism, each stage of early warning mechanism corresponds to different dip rates and crack width ranges, and then early warning of corresponding stages is carried out according to the crack width monitored by the crack sensor and the dip rate monitored by the dip sensor.
Generally, compared with the prior art, the method and the system for monitoring the health state of the historical building, which are provided by the invention, have the following beneficial effects:
1. this application carries out modal analysis based on finite element model and finds the weak position in the structure, and then can rationally lay acceleration sensor, tilt sensor and crack sensor's position and quantity, has both avoided the redundancy of sensor and has avoided the not enough of sensor again, and then can effectively in time monitor the health status of historical building.
2. Abnormal value elimination and data completion processing operation are carried out on the monitoring data, the accuracy of the monitoring data is guaranteed, the accuracy of elastic modulus optimization of the finite element model is further improved, the finite element model directly influences the determination of the positions and the number of the follow-up crack sensors and the inclination sensors, and the reasonability of arrangement of the crack sensors and the inclination sensors is guaranteed.
3. The method and the device have the advantages that the early warning mechanism is further arranged, accuracy of data analysis work such as structure health state rating is guaranteed, corresponding emergency measures can be timely warned to personnel according to the structure health level by establishing the monitoring data grading early warning mechanism, and certain guiding value is provided for implementation of historical building updating engineering.
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FIG. 1 is a diagram of steps in a method for monitoring historical building health in accordance with an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a finite element model of a historic building according to an embodiment of the present application;
3A-3C are schematic diagrams of the first three modes of a historic building according to an embodiment of the application;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Referring to fig. 1, the present invention provides a method for monitoring health status of a historical building, which includes steps S1 to S6.
S1: and establishing a finite element model of the historical building, and further performing dynamic analysis on the basis of the finite element model to obtain a numerical analysis mode of the historical building.
And (3) constructing a finite element model of the historical building according to the historical building survey drawing paper, for example, pre-establishing the historical building finite element model by using Abaqus software, selecting a conventional value for material parameters, and performing dynamic analysis on the established finite element model to obtain stable frequency and vibration mode which can reflect the corresponding modes of the previous orders of the overall building torsion, the deflection and the like, as shown in figure 2.
Determining the measurement direction of the inclination sensor according to the maximum orientation of the modal torsional vibration offset of the numerical analysis mode with the pre-set order, acquiring a stress-strain concentration position according to the static analysis of the finite element model, and arranging a crack sensor at the stress-strain concentration position in combination with the investigation condition of the site crack distribution position; further preferably, the distance between the two inclination sensors is not more than 15m.
In this embodiment, the historical building wuhan citizen paradise is taken as an example for explanation, a finite element model is established for the building, and then a dynamics analysis is performed, so that a mode of the finite element model corresponding to the building is obtained as shown in fig. 3A to 3C. In the embodiment, the first three-order mode is used as a research object, and it can be seen from fig. 3A to 3C that the maximum directions are the southwest and northwest, and further, the direction is used as the measurement direction of the tilt sensor, so that in order to fully monitor the local torsion and offset of the building, the distance between the two tilt sensors should be ensured not to exceed 15m. And (4) carrying out static analysis on the finite element model of the building, judging the stress-strain concentration position according to the depth condition of the stress-strain cloud map, and setting a crack sensor according to the investigation condition of the site crack distribution position. In the embodiment, the type of the inclination sensor can be an industrial information MAS-WM400 wireless inclination angle monitor, and in order to ensure that the precision of the early warning value is less than 0.1 per thousand, the precision is 0.005 when the measurement range is within 5 degrees, and the zero temperature drift is +/-0.001 degrees/DEG C; the type of the crack sensor can be an industrial signal MAS-YTLF integrated crack meter, in order to ensure the precision of an early warning value, the precision of the crack sensor is set to be 1/5 of the minimum value of an early warning threshold value, so the measuring range is 100mm, the measuring precision is 0.02mm, and the acquisition frequency is 3 times/day.
S2: arranging an acceleration sensor at the beam-column intersection of the historical building, carrying out environmental vibration test on the historical building to obtain acceleration, speed and displacement of different intersections under vibration interference, and further obtaining an experimental test mode based on the acceleration, the speed and the displacement.
And arranging a proper number of low-frequency acceleration sensors with proper performance at the upper beam-column junction of the historic building as much as possible. The low frequency acceleration sensor sampling rate may be set to 128 with a sampling duration of 18 minutes per group. And setting external environment vibration, and carrying out environmental vibration test on the historical building to further obtain an acceleration value, a speed value and a displacement value of the historical building under the external environment vibration interference at different position nodes.
The method comprises the steps of importing an acceleration value, a speed value and a displacement value into operation mode analysis software, performing operation mode analysis by using Enhanced Frequency Domain Decomposition (EFDD) and random subspace (SSI), wherein the enhanced frequency domain decomposition method is to perform singular value decomposition on a power spectrum density matrix based on a structural response signal to obtain structural mode parameters, the random subspace method is to identify a scattered system state space matrix by using matrix QR decomposition, singular value decomposition and other methods based on time domain data to obtain mode parameters, further obtain two mode analysis results, then comparing the two mode analysis results to determine a final mode analysis result, performing MAC value calculation on the two mode analysis results by using the Artemis Model Pro software, determining a mode with the MAC value being more than eighty percent as a final mode, and using an MAC value calculation formula as follows:
wherein
And
is the two-order mode to be compared, and T is transposed.
S3: and optimizing the elasticity modulus of the finite element model by taking the minimum difference value between the experimental testing mode and the numerical analysis mode as a target on the basis of a PSO algorithm to obtain a preliminarily optimized finite element model.
Adopting a PSO algorithm, setting the initial population number in the PSO algorithm to be 30, the iteration times to be 300, the learning factor to be 0.2, the maximum speed to be 6, and the maximum weight and the minimum weight to be 0.8 and 0.3 respectively, automatically adjusting the Young modulus parameter of the material through the algorithm, updating the finite element model, generating a proper material elastic modulus parameter, and enabling the dynamic performance of the finite element model to be consistent with the actual situation by taking the minimum difference value between the experimental testing mode and the numerical analysis mode as an objective function.
For example, the experimental test mode and the numerical analysis mode in this embodiment are shown in table 1 below:
| order of the scale | Frequency of experimental tests (Hz) | Numerical analysis frequency (Hz) | Difference in |
| 1 | 0.656 | 0.598 | 8.8％ |
| 2 | 1.818 | 1.815 | 0.2％ |
| 3 | 2.839 | 2.453 | 13.6％ |
TABLE 1
The objective function is then:
wherein f is i Represents the numerical analysis frequency; fex i RepresentsTesting frequency in an experiment; phi is a i Representing a numerical analysis mode shape vector; phi ex i Representing an experimental modal shape vector; w is a f A weighting factor representing a frequency summation term; w is a φ A weighting factor representing a mode shape summation term; the last two weight factors add up to 1. S4: and obtaining the optimal quantity and position of the acceleration sensor layout by adopting a genetic algorithm and taking the minimum maximum value of the non-diagonal elements of the vibration mode MAC matrix as a target.
And analyzing the structural modal parameters of the preliminarily optimized finite element model by adopting a genetic algorithm to minimize the maximum value of the non-diagonal elements of the vibration mode MAC matrix, setting the iteration times to be 150 times, the cross rate to be 0.8 and the variation rate to be 0.1 by taking the maximum value as an objective function, and solving to obtain the optimal number of the acceleration sensors and the optimal layout positions of the acceleration sensors.
The non-diagonal elements of the MAC matrix are:
wherein phi is i And phi j The i-th and j-th columns of vibration mode vectors in the vibration mode matrix are shown, i is not equal to j, and T is a transposition;
objective function f (x): f (x) = min { max = i≠j (MAC i，j )}。
S5: and collecting the monitoring data of the acceleration sensor in the step S4, and adjusting the elasticity modulus of the finite element model again according to the monitoring data to obtain the depth-optimized finite element model.
Monitoring the historical building for a long time according to the layout scheme of the acceleration sensor, updating the finite element model of the historical building in real time, and automatically adjusting the parameter of the elasticity modulus of the material, so that the simulation result of the finite element model is consistent with the actual current situation of the historical building, and further the deeply optimized finite element model is obtained.
The data abnormality in the long-term monitoring process is unavoidable, so the step also comprises the steps of removing abnormal values and completing the data of the monitored data. Specifically, the abnormal value detection may be performed on the monitoring data by a triple standard deviation method, wherein the triple standard deviation method is to find out data deviating from the mean value by more than a triple standard deviation, remove the data after finding out the data, and then perform data completion on the monitoring data by a linear interpolation method, wherein the linear interpolation method is to perform linear interpolation to complete the missing value by the adjacent non-missing value.
S6: and performing static analysis and dynamic analysis by using the depth-optimized finite element model to respectively obtain the optimized arrangement positions and the number of the crack sensors and the inclination sensors, and performing real-time monitoring on the historical building by using the crack sensors, the inclination sensors and the acceleration sensors.
And (3) performing static analysis on the historical building for many times by adopting a depth-optimized finite element model, finding out stress abnormal points according to the change of the numerical value and the position of a finite element stress cloud chart, and adjusting the arrangement position of the crack sensor according to the situation of field crack distribution investigation.
And performing dynamic analysis on the historical building for multiple times by adopting the depth-optimized finite element model, judging that the structural material of the historical building is degraded if the natural frequency of the structure is reduced, and adjusting the position of the inclination sensor according to the vibration mode change of the torsional vibration mode of the dynamic analysis of the depth-optimized finite element model. Specifically, the self-vibration frequency and the vibration mode of the preset order are obtained according to the result of the finite element model dynamic analysis of the depth optimization, the torsion and bending modes in the vibration mode result are obtained, and the inclination sensor is arranged at the position where the torsion and the bending are larger than the preset value. For example, considering the first three natural frequencies and the mode shapes in the modes, the torsional and bending modes in the mode shape result are obtained, and according to the analysis results of fig. 3A to 3C, the tilt sensor is arranged at the position where the torsional bending is the largest.
The reasonable arrangement of the positions and the number of the inclination sensors, the crack sensors and the acceleration sensors is realized through the above mode, and then the historical buildings can be effectively monitored in real time.
The above method further comprises step S7: and setting a multi-stage early warning mechanism, wherein each stage of early warning mechanism corresponds to different inclination rates and crack width ranges, and then performing early warning at a corresponding stage according to the crack width monitored by the crack sensor and the inclination rate monitored by the inclination sensor compared with the early warning mechanism.
For example, the health status of the historical building structure can be divided into four levels, i.e., a first level, a second level, a third level and a fourth level, as shown in table 2 below, when the health status of the historical building structure is the first level, no early warning is performed, and no corresponding measures need to be taken; when the health status grade of the historical building structure is two-grade, performing yellow early warning, and taking appropriate measures to prevent; when the health state grade of the historical building structure is three-grade, carrying out orange early warning, and adopting measures to repair and reinforce; when the health state grade of the historical building structure is four, red early warning is carried out, emergency measures must be taken immediately, the safety of field personnel is guaranteed, and safety accidents are prevented.
TABLE 2
This application on the other hand provides a historical architecture health status monitoring system, the system includes that model establishment module, experiment modality acquire module, the preliminary optimization module of model, acceleration sensor lay optimization module, model degree of depth optimization module, crack sensor and inclination sensor lay optimization module, wherein:
a model building module: the finite element model is used for establishing the historical building, and then dynamic analysis is carried out on the basis of the finite element model to obtain a numerical analysis mode of the historical building;
an experimental mode acquisition module: the system comprises a historical building, a beam-column intersection and a vibration sensor, wherein the historical building is used for arranging the acceleration sensor at the beam-column intersection of the historical building, carrying out environmental vibration test on the historical building to obtain acceleration, speed and displacement of different intersections under vibration interference, and further obtaining an experimental test mode based on the acceleration, the speed and the displacement;
a model preliminary optimization module: the elastic modulus of the finite element model is optimized based on a PSO algorithm by taking the minimum difference value between the experimental testing mode and the numerical analysis mode as a target to obtain a preliminarily optimized finite element model;
an acceleration sensor layout optimization module: the method is used for obtaining the optimal number and the optimal positions of the acceleration sensors by adopting a genetic algorithm and taking the minimum maximum value of the non-diagonal elements of the vibration mode MAC matrix as a target;
a model depth optimization module: the system comprises a finite element model, an acceleration sensor layout optimization module, a data acquisition module and a data processing module, wherein the finite element model is used for acquiring monitoring data of acceleration in the acceleration sensor layout optimization module and adjusting the elasticity modulus of the finite element model again according to the monitoring data to obtain a depth-optimized finite element model;
the crack sensor and inclination sensor layout optimization module comprises: and performing static analysis and dynamic analysis by using the depth-optimized finite element model to respectively obtain the optimized arrangement positions and the number of the crack sensors and the inclination sensors.
The system further comprises an early warning module, wherein the early warning module is used for setting a multi-stage early warning mechanism, each stage of early warning mechanism corresponds to different inclination rates and crack width ranges, and then early warning of corresponding stages is carried out according to the crack width monitored by the crack sensor and the inclination rate monitored by the inclination sensor.
It will be understood by those skilled in the art that the foregoing is only an exemplary embodiment of the present invention, and is not intended to limit the invention to the particular forms disclosed, since various modifications, substitutions and improvements within the spirit and scope of the invention are possible and within the scope of the appended claims.
Claims (10)
1. A method of monitoring historical building health, the method comprising:
s1: establishing a finite element model of the historical building, and further performing dynamic analysis based on the finite element model to obtain a numerical analysis mode of the historical building;
s2: arranging an acceleration sensor at the beam-column intersection of the historical building, carrying out environmental vibration test on the historical building to obtain acceleration, speed and displacement of different intersections under vibration interference, and further obtaining an experimental test mode based on the acceleration, the speed and the displacement;
s3: optimizing the elasticity modulus of the finite element model by taking the minimum difference value of the experimental testing mode and the numerical analysis mode as a target based on a PSO algorithm to obtain a preliminarily optimized finite element model;
s4: acquiring the optimal number and position of the acceleration sensor layout by adopting a genetic algorithm and taking the minimum maximum value of the non-diagonal elements of the vibration mode MAC matrix as a target;
s5: collecting monitoring data of the acceleration sensor in the step S4, and adjusting the elasticity modulus of the finite element model again according to the monitoring data to obtain a depth-optimized finite element model;
s6: and performing static analysis and dynamic analysis by using the depth-optimized finite element model to respectively obtain the optimized arrangement positions and the optimized arrangement number of the crack sensors and the inclined sensors, and performing real-time monitoring on the historical building by using the crack sensors, the inclined sensors and the acceleration sensors.
2. The method according to claim 1, wherein step S6 specifically comprises:
s61: performing static analysis on the depth-optimized finite element model to obtain the change of the numerical value and the position of a stress cloud map to determine a stress abnormal point, and setting a crack sensor at the stress abnormal point by combining the investigation condition of the distribution position of the on-site cracks;
s62: and carrying out dynamic analysis on the finite element model with the optimized depth, and adjusting the position of the inclination sensor according to the vibration mode of the torsional mode.
3. The method of claim 2, wherein the step of determining the position of the tilt sensor according to the mode shape of the torsional mode in step S62 comprises the steps of: and obtaining the self-vibration frequency and the vibration mode of a preset order according to the result of the finite element model dynamic analysis of the depth optimization, obtaining the torsion and bending modes in the vibration mode result, and arranging an inclined sensor at the position where the torsion and the bending are greater than the preset value.
4. The method according to claim 1, wherein step S5 further comprises performing outlier rejection and data completion processing on the monitoring data.
5. The method of claim 4, wherein the abnormal value is detected and removed by a triple standard deviation method, and the monitored data after the abnormal value is removed is subjected to data completion by a linear interpolation method.
6. The method according to claim 1, wherein the step S2 of obtaining an experimental test mode based on the acceleration, the velocity and the displacement comprises the specific steps of:
s21: inputting the acceleration, the speed and the displacement into an Artemis Model Pro software to carry out enhanced frequency domain decomposition and random subspace modal analysis respectively to obtain two modal analysis results;
s22: and calculating the MAC value of the two modal analysis results, and selecting the mode with the MAC value larger than 80% to determine the final mode.
7. The method according to claim 1, wherein step S1 further comprises determining a measurement direction of a tilt sensor according to a maximum orientation of a modal torsional vibration excursion of a previous preset order of the numerical analysis mode, obtaining a stress-strain concentration location according to a static analysis of the finite element model, and laying a crack sensor at the stress-strain concentration location in combination with an in-situ crack distribution location survey; further preferably, the distance between the two inclination sensors is not more than 15m.
8. The method of claim 1, further comprising:
s7: and setting a multi-stage early warning mechanism, wherein each stage of early warning mechanism corresponds to different inclination rates and crack width ranges, and then performing early warning at a corresponding stage according to the crack width monitored by the crack sensor and the inclination rate monitored by the inclination sensor compared with the early warning mechanism.
9. A historical building health monitoring system, the system comprising:
a model building module: the finite element model is used for establishing the historical building, and then the dynamic analysis is carried out on the basis of the finite element model to obtain the numerical analysis mode of the historical building;
an experimental mode acquisition module: the system comprises a historical building, a beam-column intersection and a vibration sensor, wherein the historical building is used for arranging the acceleration sensor at the beam-column intersection of the historical building, carrying out environmental vibration test on the historical building to obtain acceleration, speed and displacement of different intersections under vibration interference, and further obtaining an experimental test mode based on the acceleration, the speed and the displacement;
a model preliminary optimization module: the elastic modulus optimization module is used for optimizing the elastic modulus of the finite element model based on a PSO algorithm by taking the minimum difference value of the experimental testing mode and the numerical analysis mode as a target to obtain a preliminarily optimized finite element model;
an acceleration sensor layout optimization module: the method is used for obtaining the optimal number and the optimal positions of the acceleration sensors by adopting a genetic algorithm and taking the minimum maximum value of the non-diagonal elements of the vibration mode MAC matrix as a target;
a model depth optimization module: the system comprises a finite element model, an acceleration sensor layout optimization module, a data acquisition module and a data processing module, wherein the finite element model is used for acquiring monitoring data of acceleration in the acceleration sensor layout optimization module and adjusting the elasticity modulus of the finite element model again according to the monitoring data to obtain a depth-optimized finite element model;
the crack sensor and inclination sensor layout optimization module comprises: and performing static analysis and dynamic analysis by using the depth-optimized finite element model to respectively obtain the optimized arrangement positions and the number of the crack sensors and the inclination sensors.
10. The system of claim 9, further comprising:
the early warning module: the early warning system is used for setting a multi-stage early warning mechanism, each stage of early warning mechanism corresponds to different dip rates and crack width ranges, and then early warning of corresponding stages is carried out according to the crack width monitored by the crack sensor and the dip rate monitored by the dip sensor.
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| CN116362076A (en) * | 2023-03-07 | 2023-06-30 | 武汉理工大学 | Sensor optimal arrangement method and system considering damage degree of metal structure |
| CN116861544B (en) * | 2023-09-04 | 2024-01-09 | 深圳大学 | Building abnormal vibration source positioning method based on edge cloud cooperation and related equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116362076A (en) * | 2023-03-07 | 2023-06-30 | 武汉理工大学 | Sensor optimal arrangement method and system considering damage degree of metal structure |
| CN116861544B (en) * | 2023-09-04 | 2024-01-09 | 深圳大学 | Building abnormal vibration source positioning method based on edge cloud cooperation and related equipment |
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