A method and system for monitoring the structural health of prefabricated houses

Abstract

The present application discloses a method and system for monitoring the structural health of prefabricated houses, which relates to the field of house monitoring technology. The method includes: establishing a three-dimensional model of the prefabricated house, simulating the three-dimensional model using historical environmental data, and obtaining deformation and stress data of the three-dimensional model; dividing the three-dimensional model into multiple monitoring areas; establishing an objective function; solving the objective function to obtain sensor installation positions; installing sensors in the prefabricated house to collect real-time status data; obtaining real-time environmental data of the prefabricated house; inverting and correcting the real-time status data based on the real-time environmental data, and using the corrected data to perform structural health assessment on the prefabricated house. The present application optimizes the sensor installation positions and also inverts and corrects the data collected by the sensors. Using only a few sensors, it is possible to obtain data that is as accurate as possible at multiple locations, laying the foundation for improving the accuracy of structural health assessment results.

Description

Technical Field

[0001] The present application relates to the field of housing monitoring technology, and in particular to a method and system for monitoring the health of prefabricated housing structures. Background Art

[0002] Prefabricated houses are a new type of building form. The various components of the house are pre-fabricated in the factory and then assembled on the construction site. Buildings constructed in this way not only have basically the same performance as traditional cast-in-place buildings, but also have a very short construction time on the construction site, which can greatly save construction time and costs.

[0003] Since prefabricated houses are made up of a large number of parts, these joints are easily subject to large deformations due to factors such as materials and connection methods, which can adversely affect the health of the house structure. Therefore, real-time monitoring of the structural health of prefabricated houses is very necessary.

[0004] Chinese invention patent publication number CN116542111A discloses a method and system for monitoring the structural health of connection nodes in prefabricated buildings. This system uses CAE (Computer-Aided Engineering) simulation to identify locations of significant structural deformation or stress. Sensors are then installed at these locations to collect deformation and stress data, enabling real-time assessment of the building's structural health. However, as the building undergoes use, changes in the external environment, such as geology or strong winds, can alter its deformation and stress. Consequently, locations with the highest deformation and stress may deviate from the sensor installation location, resulting in inaccurate sensor data and, in turn, impacting the accuracy of the structural health assessment. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for monitoring the health of prefabricated house structures, which are used to solve the problem in the prior art that sensors set at locations with maximum deformation and stress determined by CAE simulation cannot be applied to various external environments.

[0006] In one aspect, an embodiment of the present application provides a method for monitoring the structural health of a prefabricated house, comprising:

[0007] Obtain historical environmental data for housing assembly locations;

[0008] Establish a 3D model of the prefabricated house, simulate the 3D model using historical environmental data, and obtain the deformation and stress data of the 3D model;

[0009] According to the distribution of deformation and stress data in the 3D model, the 3D model is divided into multiple monitoring areas;

[0010] Analyze the local maximum values of deformation and stress data in each monitoring area, and establish an objective function with the goal of minimizing the sum of the distances between the locations of all local maxima in a monitoring area and the sensor installation location;

[0011] Genetic algorithm is used to solve the objective function and obtain the sensor installation location in each monitoring area;

[0012] Install corresponding sensors at each sensor installation location of the prefabricated house to collect real-time status data of the prefabricated house;

[0013] Obtain real-time environmental data of prefabricated houses;

[0014] Inverse and correct the real-time state data according to the real-time environmental data to obtain the measured state data;

[0015] Conduct structural health assessments of prefabricated houses using measured condition data.

[0016] In one possible implementation, after a three-dimensional model is established using CAE technology, historical environmental data at different times are sequentially imported, and the three-dimensional model is simulated under the influence of the historical environmental data to obtain deformation and stress data.

[0017] In one possible implementation, simulations are performed sequentially using historical environmental data at different times to obtain multiple corresponding deformation and stress data. The multiple deformation and stress data are superimposed on the same three-dimensional model to obtain the distribution of the deformation and stress data in the three-dimensional model.

[0018] In one possible implementation, when the three-dimensional model is divided into multiple monitoring areas, the number of deformation monitoring areas and the number of stress monitoring areas are determined according to a pre-set number of sensors, and the K-means clustering algorithm is used to perform cluster analysis on the local maximum deformation and local maximum stress in the deformation and stress data, respectively, to obtain multiple deformation monitoring areas and multiple stress monitoring areas.

[0019] In one possible implementation, after the deformation and stress data are superimposed in the same three-dimensional model, each deformation monitoring area has multiple local deformation maxima, and each stress monitoring area also has multiple local stress maxima. Each local deformation maximum and each local stress maximum are respectively located at a position in the three-dimensional model. After determining the position of each local deformation maximum and each local stress maximum, an objective function is established.

[0020] In one possible implementation, when inverting and correcting real-time status data, the three-dimensional model is simulated using real-time environmental data to obtain simulation status data for each position in the three-dimensional model, extract the simulation status data corresponding to the installation position of the sensor in the three-dimensional model, determine the difference between the simulation status data and the real-time status data, correct the simulation status data for each position in the three-dimensional model based on the difference, and extract the local maximum value in the corrected simulation status data as the measurement status data.

[0021] In a possible implementation, the historical environmental data includes historical geological data and historical wind data, and the real-time environmental data includes real-time geological data and real-time wind data.

[0022] In a possible implementation, after the measurement status data is obtained, the measurement status data is displayed in the three-dimensional model, and the result of the structural health assessment is also displayed simultaneously with the three-dimensional model integrating the measurement status data.

[0023] On the other hand, the present application also provides a prefabricated house structure health monitoring system, including:

[0024] A data acquisition module for acquiring historical environmental data of the housing assembly location;

[0025] The 3D simulation module is used to build a 3D model of the prefabricated house and simulate the 3D model using historical environmental data to obtain the deformation and stress data of the 3D model;

[0026] The region division module is used to divide the 3D model into multiple monitoring regions according to the distribution of deformation and stress data in the 3D model;

[0027] The target establishment module is used to analyze the local maximum values of deformation and stress data in each monitoring area, and establish an objective function with the goal of minimizing the sum of the distances between the locations of all local maxima in a monitoring area and the sensor installation location;

[0028] A location determination module is used to solve the objective function using a genetic algorithm to obtain the sensor installation location in each monitoring area;

[0029] Sensors are installed at each sensor installation location of the prefabricated house to collect real-time status data and real-time environmental data of the prefabricated house;

[0030] A data correction module is used to invert and correct the real-time state data according to the real-time environmental data to obtain the measurement state data;

[0031] The structural assessment module is used to perform structural health assessment of prefabricated houses using measurement status data.

[0032] The present invention provides a method and system for monitoring the health of prefabricated housing structures, which has the following advantages:

[0033] The sensor installation locations are optimized to ensure better responses in a variety of external environments. At the same time, the data collected by the sensors is inverted and corrected. With only a few sensors, the most accurate status data possible can be obtained at multiple locations in the prefabricated house, laying the foundation for improving the accuracy of structural health assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0035] Figure 1 This is a flow chart of a method for monitoring the structural health of a prefabricated house provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0037] Figure 1 This is a flow chart of a method for monitoring the health of a prefabricated house structure provided in an embodiment of the present application. This embodiment of the present application provides a method for monitoring the health of a prefabricated house structure, comprising:

[0038] S100, obtaining historical environmental data of a housing assembly location.

[0039] For example, historical environmental data includes historical geological data and historical wind data, which can be collected by multiple sensors installed at the housing assembly location. For geological data, a fiber Bragg grating inclinometer can be used to collect ground subsidence data and use it as historical geological data. For wind data, an anemometer can be installed at an appropriate height above the ground to collect wind data at multiple different times. It should be understood that since the impact of wind on a house is not limited to the magnitude of the wind but also related to the wind direction, wind direction data can be collected at the same time as the wind force and used as part of the historical environmental data.

[0040] S110, establishing a three-dimensional model of the prefabricated house, simulating the three-dimensional model using historical environmental data, and obtaining deformation and stress data of the three-dimensional model.

[0041] For example, CAE software can be used to build a three-dimensional model, and then historical environmental data at different times can be imported into the CAE software in sequence. The three-dimensional model can be simulated in the CAE software under the influence of the historical environmental data to obtain deformation and stress data.

[0042] Commonly used CAE software includes ANSYS, ABAQUS, etc. These software can be used to perform mechanical analysis on prefabricated houses and generate deformation and stress data of prefabricated houses at various locations.

[0043] S120 , dividing the three-dimensional model into a plurality of monitoring areas according to the distribution of deformation and stress data in the three-dimensional model.

[0044] For example, since the historical environmental data is a plurality of data groups at different times, each data group will generate a set of deformation and stress data, and after simulation, these deformation and stress data will be displayed on the three-dimensional model, thereby forming multiple three-dimensional models that integrate deformation and stress data. In order to estimate the impact of future environmental data on prefabricated houses through historical environmental data over a period of time, and thus obtain a more accurate sensor installation location, it is necessary to merge all three-dimensional models that integrate deformation and stress data together. Since the three-dimensional models are the same, the data in each three-dimensional model and its position in the three-dimensional model can be extracted, and then superimposed and displayed in the same three-dimensional model. At this time, the three-dimensional model can show the impact of all historical environmental data, and by analyzing these superimposed data, its distribution in the three-dimensional model can be obtained.

[0045] In the embodiment of the present application, the deformation and stress data include two parts, namely deformation data and stress data. Since these two types of data are analyzed separately using CAE software, after superposition, two three-dimensional models that integrate deformation data and stress data respectively will be obtained.

[0046] Furthermore, when the three-dimensional model is divided into multiple monitoring areas, the number of deformation monitoring areas and the number of stress monitoring areas are determined according to the pre-set number of sensors, and the K-means clustering algorithm is used to perform cluster analysis on the local maximum deformation and local maximum stress in the deformation and stress data, respectively, to obtain multiple deformation monitoring areas and multiple stress monitoring areas.

[0047] Specifically, the number of sensors installed in a prefabricated house is generally predetermined, but the installation locations need to be determined based on actual conditions. The sensors in the embodiments of the present application include deformation sensors and stress sensors. Each deformation sensor is used to monitor conditions in a deformation monitoring area, while each stress sensor is used to monitor conditions in a stress monitoring area. Therefore, the number of deformation monitoring areas needs to be the same as the number of deformation sensors, and the number of stress monitoring areas needs to be the same as the number of stress sensors.

[0048] After determining the number of monitoring areas, the number of clusters can be directly adopted when clustering using the K-means clustering algorithm. Based on this setting, the deformation data and stress data in the three-dimensional model can be clustered and analyzed separately to obtain multiple deformation monitoring areas and stress monitoring areas.

[0049] Before cluster analysis, it is necessary to determine the local maximum values of deformation and stress at each position after superposition in the three-dimensional model. It should be noted that, in the embodiment of the present application, when superimposing deformation data and stress data in the three-dimensional model, the deformation data and stress data at different times are independent of each other, that is, the values are not superimposed, and only the data of multiple time points are displayed simultaneously at the same position of the three-dimensional model. Therefore, the determination of the local maximum is carried out in the deformation data and stress data at the same time point. In the embodiment of the present application, the local maximum is the maximum value after comparison with the data of adjacent points in all directions around it. Based on this principle, a large number of local maximum values of deformation and local maximum values of stress can be calculated from the deformation data and stress data at a time point. These local maxima are distributed in the three-dimensional model, so each local maximum has a corresponding position. By performing cluster analysis on the values and positions of these local maxima using the K-means clustering algorithm, several local maxima can be clustered into one monitoring area, thereby obtaining multiple monitoring areas.

[0050] S130 , analyzing the local maximum values of deformation and stress data in each monitoring area, and establishing an objective function with the goal of minimizing the sum of distances between the locations of all local maximum values in a monitoring area and the sensor installation location.

[0051] Exemplarily, after superimposing the deformation and stress data in the same three-dimensional model, each deformation monitoring area has multiple local deformation maxima, and each stress monitoring area also has multiple local stress maxima. Each local deformation maximum and each local stress maximum are respectively located at a position in the three-dimensional model. After determining the position of each local deformation maximum and each local stress maximum, the objective function is established.

[0052] Before establishing the objective function, the local maximum values in each monitoring area can be normalized. According to the largest local maximum value and the smallest local maximum value at all time points, each local maximum value can be normalized to the range of [0, 1]. These normalized local maxima can be added as weights in the objective function to reflect the impact of local maxima on distance.

[0053] The objective function can be expressed as:

[0054]

[0055] in, F represents the objective function, i is the label of the location of the local maximum in the three-dimensional model, N is the number of locations with local maxima, After normalization, i The weight of the location of the local maximum, For the i The distance between the location of the local maximum and the sensor installation location.

[0056] S140, using a genetic algorithm to solve the objective function to obtain the sensor installation position in each monitoring area.

[0057] S150: Install corresponding sensors at each sensor installation position of the prefabricated house to collect real-time status data of the prefabricated house.

[0058] Exemplarily, the real-time status data includes real-time deformation data and real-time stress data. The real-time deformation data can be collected using a deformation sensor, and the real-time stress data can be collected using a stress sensor.

[0059] S160, obtaining real-time environmental data of the prefabricated house.

[0060] For example, the types of real-time environmental data are the same as those of historical environmental data, including real-time geological data and real-time wind data. It should be understood that real-time wind direction associated with the real-time wind data should also be included. These data can be collected by sensors that collect historical environmental data.

[0061] S170, performing inversion correction on the real-time state data according to the real-time environmental data to obtain measurement state data.

[0062] For example, when inverting and correcting real-time status data, the three-dimensional model is simulated using real-time environmental data to obtain simulation status data for each position in the three-dimensional model, extract the simulation status data corresponding to the installation position of the sensor in the three-dimensional model, determine the difference between the simulation status data and the real-time status data, correct the simulation status data for each position in the three-dimensional model based on the difference, and extract the local maximum value in the corrected simulation status data as the measurement status data.

[0063] Specifically, while historical environmental data and real-time environmental data are somewhat correlated, there are still some differences between the two. Therefore, using real-time environmental data, simulation can be used to generate simulated state data that reflects the current state. It should be understood that simulated state data is the data obtained after simulating all locations in the three-dimensional model, and this data necessarily corresponds to the sensor installation location. Therefore, after extracting the data corresponding to the sensor installation location from the simulated state data, the difference between the extracted data and the real-time state data is calculated. This difference reflects the deviation caused by the simulation process from the actual situation. The smaller this difference, the more accurate the simulation.

[0064] After subtracting the difference from the simulation state data of all positions in the three-dimensional model, the corrected simulation state data can be obtained.

[0065] Since the sensor installation position has been optimized in this application, one sensor can be used to collect deformation and stress data under various possible conditions in a monitoring area. Since the deformation and stress data are continuous within a certain distance range, after optimizing the sensor installation position, the deformation and stress data collected at one position can more accurately reflect the deformation and stress conditions at other positions. Combined with the inversion correction process, the accuracy of the estimation of the local maximum value can be improved.

[0066] S180, using measurement status data to perform structural health assessment of prefabricated houses.

[0067] For example, after the measurement status data is obtained, the measurement status data is displayed in the three-dimensional model, and the result of the structural health assessment is also displayed simultaneously with the three-dimensional model in which the measurement status data is integrated.

[0068] The present application also provides a prefabricated house structure health monitoring system, which includes:

[0069] A data acquisition module for acquiring historical environmental data of the housing assembly location;

[0070] The 3D simulation module is used to build a 3D model of the prefabricated house and simulate the 3D model using historical environmental data to obtain the deformation and stress data of the 3D model;

[0071] The region division module is used to divide the 3D model into multiple monitoring regions according to the distribution of deformation and stress data in the 3D model;

[0072] The target establishment module is used to analyze the local maximum values of deformation and stress data in each monitoring area, and establish an objective function with the goal of minimizing the sum of the distances between the locations of all local maxima in a monitoring area and the sensor installation location;

[0073] A location determination module is used to solve the objective function using a genetic algorithm to obtain the sensor installation location in each monitoring area;

[0074] Sensors are installed at each sensor installation location of the prefabricated house to collect real-time status data and real-time environmental data of the prefabricated house;

[0075] A data correction module is used to invert and correct the real-time state data according to the real-time environmental data to obtain the measurement state data;

[0076] The structural assessment module is used to perform structural health assessment of prefabricated houses using measurement status data.

[0077] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional 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 that fall within the scope of the present application.

[0078] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for monitoring the health of prefabricated house structures, characterized in that: include: Obtain historical environmental data for housing assembly locations; Establishing a three-dimensional model of the prefabricated house, and simulating the three-dimensional model using the historical environmental data to obtain deformation and stress data of the three-dimensional model; Dividing the three-dimensional model into a plurality of monitoring areas according to the distribution of the deformation and stress data in the three-dimensional model; Analyzing the local maximum values of the deformation and stress data in each monitoring area, and establishing an objective function with the goal of minimizing the sum of distances between the locations of all the local maximum values in a monitoring area and the sensor installation location; Solving the objective function using a genetic algorithm to obtain the sensor installation position in each monitoring area; Installing a corresponding sensor at each sensor installation position of the prefabricated house to collect real-time status data of the prefabricated house; Obtain real-time environmental data of prefabricated houses; Invert and correct the real-time state data according to the real-time environmental data to obtain measurement state data; Performing a structural health assessment on the prefabricated house using the measured status data; wherein, when inversion correction is performed on the real-time state data, the three-dimensional model is simulated using the real-time environmental data to obtain simulation state data of each position in the three-dimensional model, the simulation state data corresponding to the installation position of the sensor in the three-dimensional model is extracted, the difference between the simulation state data and the real-time state data is determined, the simulation state data of each position in the three-dimensional model is corrected according to the difference, and the local maximum value in the corrected simulation state data is extracted as the measurement state data; After the three-dimensional model is established using CAE technology, the historical environmental data at different times are sequentially imported, and simulations are performed on the three-dimensional model under the influence of the historical environmental data to obtain the deformation and stress data; After performing simulations in sequence using the historical environmental data at different times, a plurality of corresponding deformation and stress data are obtained respectively, and the plurality of deformation and stress data are superimposed on the same three-dimensional model to obtain the distribution of the deformation and stress data in the three-dimensional model; When dividing the three-dimensional model into the plurality of monitoring areas, determining the number of deformation monitoring areas and the number of stress monitoring areas according to a preset number of sensors, and performing cluster analysis on the local maximum values of deformation and the local maximum values of stress in the deformation and stress data using a K-means clustering algorithm, respectively, to obtain the plurality of deformation monitoring areas and the plurality of stress monitoring areas; After superimposing the deformation and stress data in the same three-dimensional model, each deformation monitoring area has a plurality of the deformation local maxima, and each stress monitoring area also has a plurality of the stress local maxima, each of the deformation local maximum and each of the stress local maximum are respectively located at a position of the three-dimensional model, and after determining the position of each of the deformation local maximum and the stress local maximum, establishing the objective function; The objective function is expressed as: in, F represents the objective function, i is the label of the position of the local maximum in the three-dimensional model, N is the number of locations with local maxima, After normalization, i The weights of the positions of the local maxima are normalized to the range of [0,1] according to the largest local maximum and the smallest local maximum at all time points. These normalized local maxima are added as weights in the objective function. For the i The distance between the location of the local maximum and the sensor installation location.

2. A method for monitoring the health of prefabricated house structures according to claim 1, characterized in that: The historical environmental data includes historical geological data and historical wind data, and the real-time environmental data includes real-time geological data and real-time wind data.

3. A method for monitoring the health of prefabricated house structures according to claim 1, characterized in that: After the measurement status data is obtained, the measurement status data is displayed in the three-dimensional model, and the result of the structural health assessment is also displayed simultaneously with the three-dimensional model incorporating the measurement status data.

4. A system using the method for monitoring the health of prefabricated house structures according to any one of claims 1 to 3, characterized in that: include: A data acquisition module for acquiring historical environmental data of the housing assembly location; A three-dimensional simulation module is used to establish a three-dimensional model of the prefabricated house, and simulate the three-dimensional model using the historical environmental data to obtain deformation and stress data of the three-dimensional model; A region division module, configured to divide the three-dimensional model into a plurality of monitoring regions according to the distribution of the deformation and stress data in the three-dimensional model; a target establishment module, configured to analyze the local maximum values of the deformation and stress data in each monitoring area, and establish an objective function with the goal of minimizing the sum of the distances between the locations of all the local maxima in a monitoring area and the sensor installation location; a position determination module, configured to solve the objective function using a genetic algorithm to obtain the sensor installation position in each monitoring area; A sensor is installed at each sensor installation location of the prefabricated house to collect real-time status data and real-time environmental data of the prefabricated house; A data correction module is used to perform inversion correction on the real-time state data according to the real-time environmental data to obtain measurement state data; The structural assessment module is used to perform a structural health assessment on the prefabricated house using the measurement status data.

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