Building collapse risk assessment method based on monitoring data and numerical simulation

Abstract

The invention relates to a building collapse risk assessment method based on monitoring data and numerical simulation, and the method comprises the steps: building a large number of building finite element model samples for a target building through obtaining the macroscopic size of the target building; building modal analysis is carried out based on the building finite element model sample, and a bottom floor axial compression ratio prediction model based on the basic period is established based on a modal analysis result and by referring to a basic period theoretical estimation formula. And performing collapse simulation based on the building finite element model sample, and establishing a collapse probability calculation model based on the bottom floor axial compression ratio. And predicting a bottom floor axial load ratio based on the actual measurement basic period and the bottom floor axial load ratio prediction model, and predicting a real-time collapse probability based on the bottom floor axial load ratio and the collapse probability prediction model.

Description

Technical Field

[0001] The present application relates to the technical field of building structure health monitoring, and particularly to a method for evaluating the risk of building collapse based on monitoring data and numerical simulation. Background Art

[0002] In recent years, there have been several accidents of sudden building collapse during the use period in China, which have claimed hundreds of lives and had a serious impact on social security. The main reasons for building collapse include: non-standard design and construction, resulting in a chaotic structural system of the main structure and insufficient structural bearing capacity; non-standard additional construction and renovation, increasing the gravity load borne by the main structure and weakening the bearing capacity of the main structure; non-standard use, causing the gravity load borne by the main structure to far exceed its bearing capacity.

[0003] Although the investigation of potential hazards of relevant houses is carried out after each collapse accident, similar incidents still occur from time to time. On the one hand, this is because the number of houses to be investigated is huge, while the number of professional housing safety appraisal personnel is relatively insufficient; on the other hand, it is because the unauthorized additional construction, renovation, and non-standard use are relatively hidden and difficult to detect. In view of the above problems, regular monitoring of building structures is one of the feasible solutions.

[0004] Structural health monitoring technology has been applied in practical engineering since the 1990s of the 20th century, but it is mainly used for large and important engineering structures, and there is no research on the monitoring methods and technologies for ordinary houses with a large quantity and wide area. Compared with large and important engineering structures, due to the large number of ordinary houses, in order to control the cost of purchasing and installing the monitoring system hardware, and considering the cost and technical difficulty of data transmission, storage and processing, it is necessary to reduce the number of sensors installed in each building. In addition, there is no mature method for building collapse risk in the existing health monitoring technology and the corresponding damage identification method. Therefore, there is an urgent need for a method for evaluating the risk of collapse of ordinary buildings based on the monitoring data of a small number of sensors, that is, a method for evaluating the risk of building collapse based on monitoring data and numerical simulation. Summary of the Invention

[0005] Based on this, in view of the reasons for building collapse, it is necessary to propose a method for evaluating the risk of building collapse based on monitoring data and numerical simulation.

[0006] The present application provides a method for evaluating the risk of building collapse based on monitoring data and numerical simulation, including: establishing a parameter sampling pool;

[0007] Constructing a finite element analysis model;

[0008] Performing a first sampling combination on the parameter sampling pool to obtain a parameter sampling result of the parameter sampling pool;

[0009] Based on the parameter sampling results of the parameter sampling pool, all parameters in the parameter sampling results are incorporated into the finite element analysis model to generate a dataset for predicting the average axial compression ratio of the bottom walls regarding the fundamental period formula;

[0010] Call all the parameters in the parameter sampling results and the finite element analysis model to establish a dataset for the collapse distribution probability of the vertical member damage based on the average axial compression ratio of the bottom walls;

[0011] Return the result of a sampling combination of the parameter sampling pool to obtain the parameter sampling results of the parameter sampling pool until all the parameter combinations in the sampling pool have completed the sampling combination;

[0012] Fit all the datasets for predicting the average axial compression ratio of the bottom walls regarding the fundamental period formula into a prediction model for the average axial compression ratio of the bottom walls regarding the fundamental period formula;

[0013] Fit all the datasets for the collapse distribution probability of the vertical member damage based on the average axial compression ratio of the bottom walls into a collapse distribution probability model;

[0014] Receive acceleration monitoring data;

[0015] Perform fundamental period identification on the acceleration monitoring data to obtain the data value of the fundamental period regarding the acceleration monitoring data;

[0016] Incorporate the data value of the fundamental period into the prediction model for the average axial compression ratio of the bottom walls regarding the fundamental period formula to obtain the average axial compression ratio of the bottom walls;

[0017] Incorporate the average axial compression ratio of the bottom walls into the collapse distribution probability model to obtain the building collapse probability.

[0018] This application relates to a method for assessing the building collapse risk based on monitoring data and numerical simulation. By obtaining the macroscopic dimensions of the target building, a large number of building finite element model samples for the target building are established. Building modal analysis is performed based on the building finite element model samples. Based on the modal analysis results and referring to the fundamental period theory estimation formula, a prediction model for the axial compression ratio of the bottom floors based on the fundamental period is established. Collapse simulation is performed based on the building finite element model samples, and a collapse probability calculation model based on the axial compression ratio of the bottom floors is established. The axial compression ratio of the bottom floors is predicted based on the measured fundamental period and the prediction model for the axial compression ratio of the bottom floors, and the real-time collapse probability is predicted based on the axial compression ratio of the bottom floors and the collapse probability prediction model. Description of the Drawings

[0019] Figure 1 It is a schematic flowchart of the method for assessing the building collapse risk based on monitoring data and numerical simulation provided by an embodiment of this application.

[0020] Figure 2This is a modeling step diagram of a building collapse risk assessment method based on monitoring data and numerical simulation provided by an embodiment of the present application. Detailed implementation manners

[0021] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0022] The present application provides a building collapse risk assessment method based on monitoring data and numerical simulation.

[0023] As Figure 1 shown, in an embodiment of the present application, a building collapse risk assessment method based on monitoring data and numerical simulation includes:

[0024] S100, establishing a parameter sampling pool.

[0025] S200, constructing a finite element analysis model.

[0026] Specifically, collect the number of floors, floor height, and wall distribution of the masonry structure through on-site investigation, and establish a basic finite element model of the target building according to the above data.

[0027] S300, performing a first sampling combination on the parameter sampling pool to obtain a parameter sampling result of the parameter sampling pool.

[0028] Specifically, refer to the theoretical calculation formula of the building basic period, initially establish the relationship between the basic period and the average axial compression ratio of the bottom layer wall, and further establish a one-to-one correspondence between the basic period and the average axial compression ratio of the bottom layer wall based on the finite element model.

[0029] It can be understood that each sample is used to establish a specific finite element model and perform modal analysis to generate a data point in the dataset. Establishing a one-to-one correspondence between the basic period and the average axial compression ratio of the bottom layer wall based on the finite element model actually means only calculating the data of one sample. Perform modal analysis on the data of each sample once.

[0030] S400, based on the parameter sampling result of the parameter sampling pool, incorporate all the parameters in the parameter sampling result into the finite element analysis model to generate a dataset for predicting the average axial compression ratio of the bottom layer wall of the basic period formula.

[0031] Specifically, initially determine the relationship between the basic period and the average axial compression ratio of the bottom layer wall by referring to the theoretical calculation formula of the basic period.

[0032] For a single-degree-of-freedom structure, the theoretical calculation formula of its basic period is shown in Equation (1).

[0033]

[0034] In the formula, T is the basic period of the building; m is the lumped mass; k is the horizontal stiffness. The lumped mass is proportional to the total gravity load of the structure, then:

[0035] M = G / g (2)

[0036] In the formula, G is the total gravity load of the building; g is the acceleration due to gravity.

[0037] k is the unidirectional horizontal stiffness of the structure. The horizontal stiffness of the masonry structure is provided by the masonry walls. According to the code provisions, the calculation of the horizontal stiffness of the wall should take into account the influence of the height-width ratio of the wall. When the height-width ratio is less than 1, only the shear deformation needs to be calculated; when the height-width ratio is greater than 1 and less than 4, both the shear deformation and the bending deformation should be calculated; when the height-width ratio is greater than 4, the equivalent lateral stiffness can be taken as 0.

[0038] And the shear deformation stiffness k1 of the wall is shown in the following formula:

[0039] k1 = A1G v / h = vA1E / h (3)

[0040] In the formula, A1 is the cross-sectional area of the wall in a single direction; Gv is the shear modulus of the masonry material; h is the height of the wall; is the Poisson's ratio of the masonry material; E is the elastic modulus of the masonry material.

[0041]

[0042] In the formula, c1 is the ratio between k and k1.

[0043] It should be noted that there is the following relationship between the elastic modulus E and the compressive strength f of the masonry:

[0044] E = c2f (5)

[0045] In the formula, c2 is a constant, approximately 1600. Therefore, the basic period can be further derived as follows:

[0046]

[0047] In the formula, A is the cross-sectional area of all load-bearing walls; c3 is the ratio of A1 to A; u is the average axial compression ratio of the wall, as shown in the following formula:

[0048] u = G / Af (7)

[0049] Then formula (6) establishes the relationship between the basic period T and the axial compression ratio of the walls of the masonry structure. In the formula, h, g, v, c2, c3 are relatively easy to determine, and c1 is relatively difficult to determine.

[0050] The above is the derivation of the theoretical formula for the axial compression ratio at the bottom layer based on the fundamental period for a single-degree-of-freedom structural system. It can be seen that there is a relatively clear relationship between the building's axial compression ratio and the fundamental period.

[0051] Multi-story masonry is a multi-degree-of-freedom structure, and its corresponding fundamental period can be expressed by the following formula:

[0052]

[0053] In the formula, Meq is the equivalent single-degree-of-freedom mass, and Keq is the equivalent single-degree-of-freedom stiffness. According to "Structural Dynamics", for a structure with uniformly distributed mass and stiffness on each floor, the equivalent single-degree-of-freedom mass and stiffness can be calculated according to the following formula:

[0054]

[0055] In the formula, M is the mass matrix of the multi-degree-of-freedom structure, which is a diagonal matrix; φ n is the vibration mode of the nth order; m is the sum of the masses of each floor.

[0056]

[0057] In the formula, K is the stiffness matrix of the multi-degree-of-freedom structure, which is a non-diagonal matrix; k is the stiffness of the bottom-layer wall.

[0058] Then, after replacing m and k in formula (1) with Meq and Keq, formula (6) becomes the following formula:

[0059]

[0060] That is, the axial compression ratio of the bottom-layer wall of the multi-story masonry structure is:

[0061] u = (T / 2π) 2 ×(c5c1gvc3c2) / (c4h)(12)

[0062] S500, call all the parameters in the parameter sampling results and the finite element analysis model, and establish a dataset of the collapse distribution probability of the vertical member damage based on the average axial compression ratio of the bottom-layer wall.

[0063] Specifically, take the wall unit weight, floor load, height of each floor, elastic modulus of the masonry material, and wall thickness as random variables respectively, sample according to the possible value ranges, and form a large number of structural finite element model samples by combining the samples of each random variable; then perform modal analysis on the structural finite element models to extract parameters such as the fundamental period and the basic axial compression ratio.

[0064] S600, return the parameter sampling result of the parameter sampling pool for one sampling combination until all the parameter combinations in the sampling pool have completed sampling combinations.

[0065] Specifically, based on the cyclic operations from S300 to S600, each sample is used to establish a specific finite element model and perform modal analysis to generate a data point in the dataset.

[0066] Briefly, the method for assessing the risk of building collapse based on monitoring data and numerical simulation includes the following steps.

[0067] Obtain the macroscopic dimensions of the target building and establish a large number of building finite element model samples for the target building.

[0068] Perform building modal analysis based on the building finite element model samples, and establish a prediction model for the axial compression ratio of the bottom floor based on the fundamental period, referring to the fundamental period theory estimation formula based on the modal analysis results.

[0069] Perform collapse simulation based on the building finite element model samples and establish a calculation model for the collapse probability based on the axial compression ratio of the bottom floor.

[0070] Predict the axial compression ratio of the bottom floor based on the measured fundamental period and the prediction model for the axial compression ratio of the bottom floor, and predict the real-time collapse probability based on the axial compression ratio of the bottom floor and the collapse probability prediction model. In S700, fit all the datasets of the predicted average axial compression ratio of the bottom-layer wall based on the fundamental period formula into a prediction model for the average axial compression ratio of the bottom-layer wall based on the fundamental period formula.

[0071] Specifically, with the fundamental period as the independent variable and the average axial compression ratio of the bottom layer as the dependent variable, fit to obtain the calculation formula for the average axial compression ratio of the bottom layer.

[0072] In S800, fit all the datasets of the collapse distribution probability of the vertical member damage based on the average axial compression ratio of the bottom-layer wall into a collapse distribution probability model.

[0073] Specifically, establish a large number of finite element model samples, perform simulation of the collapse process, extract the average axial compression ratio of the bottom-layer wall of each sample structure, and record whether the structure sample collapses. Then, fit the collapse probability distribution according to the average axial compression ratio of the bottom-layer wall of a large number of structure samples and the discrimination results of whether they collapse.

[0074] S900, receive acceleration monitoring data.

[0075] S910, identify the fundamental period of the acceleration monitoring data to obtain the data value of the fundamental period regarding the acceleration monitoring data.

[0076] S920, incorporate the data value of the fundamental period into the prediction model for the average axial compression ratio of the bottom-layer wall based on the fundamental period formula to obtain the average axial compression ratio of the bottom-layer wall.

[0077] Specifically, the measured fundamental period of the structure is extracted from the structural acceleration monitoring data. Then, the average axial compression ratio of the bottom-layer wall of the building structure is calculated using the formula obtained by fitting the measured fundamental period.

[0078] In S930, the average axial compression ratio of the bottom-layer wall is incorporated into the collapse distribution probability model to obtain the building collapse probability.

[0079] Specifically, the collapse probability of the structure is calculated using the collapse probability distribution obtained by fitting the average axial compression ratio of the bottom-layer wall calculated above.

[0080] In an embodiment of the present application, the incorporating the average axial compression ratio of the bottom-layer wall into the collapse distribution probability model to obtain the building collapse probability includes:

[0081] A prediction method for the average axial compression ratio of the bottom floor based on the measured fundamental period of the building, and a calculation method for the building collapse probability based on the average axial compression ratio of the bottom floor. By establishing a one-to-one correspondence relationship among the building collapse probability, the average axial compression ratio of the building bottom floor, and the measured fundamental period of the building, the stress state of the building structure is reflected to realize the real-time assessment of the building collapse risk.

[0082] Establish a sampling pool.

[0083] Input the floor load, wall thickness, wall shear deformation parameter, Poisson's ratio of the masonry material, and elastic modulus of the masonry material into the sampling pool.

[0084] Perform sampling based on the sampling pool.

[0085] Obtain at least one sampling result.

[0086] Based on each sampling result, establish a building finite element model of the target building.

[0087] This embodiment relates to a building finite element model. The macroscopic dimensions of the finite element model are the same as those of the target building. The floor load, wall thickness, etc. are used as variables, sampled according to possible situations, and the samplings of each variable are combined to obtain a large number of finite element model samples. It is also possible to collect the number of floors, floor height, and wall distribution of the masonry structure through on-site investigations. Based on the above data, establish a basic finite element model of the target building.

[0088] The main reasons for building collapse mainly include non-standard design and construction, resulting in a chaotic structural system of the main structure and insufficient structural bearing capacity.

[0089] Non-standard additional construction and renovation increase the gravity load borne by the main structure and weaken the bearing capacity of the main structure.

[0090] Non-standard use causes the gravity load borne by the main structure to far exceed its bearing capacity.

[0091] By selecting the factors that affect the building stability, such as floor load, wall thickness, wall shear deformation parameter, Poisson's ratio of masonry material, and elastic modulus of masonry material, it is possible to simulate as many ordinary houses with large quantities and wide areas as possible. Furthermore, high-efficiency stability assessments can be carried out for ordinary houses with different structures and different structural problems.

[0092] In an embodiment of the present application, the step of incorporating the average axial compression ratio of the bottom-layer wall into the collapse distribution probability model to obtain the building collapse probability further includes:

[0093] The one-to-one correspondence between the average axial compression ratio of the bottom floor of the building and the building's fundamental period is based on the deduced results of the theoretical calculation formula for the fundamental period. By establishing a large number of building finite element model samples and performing modal analysis on the models, the correspondence is obtained by fitting based on the building's fundamental period and average axial compression ratio obtained from the analysis.

[0094] Determine the sampling results of the building finite element model.

[0095] In the simulation environment, perform modal analysis on the building finite element model.

[0096] Obtain no less than 2 modes of the building finite element model.

[0097] Map the first mode of each finite element model sample to the sampling results of the building finite element model.

[0098] Specifically, the one-to-one correspondence between the average axial compression ratio of the bottom floor of the building and the building's fundamental period is based on the deduced results of the theoretical calculation formula for the fundamental period. By establishing a large number of building finite element model samples and performing modal analysis on the models, the correspondence is obtained by fitting based on the building's fundamental period and average axial compression ratio obtained from the analysis.

[0099] In an embodiment of the present application, based on the parameter sampling results of the parameter sampling pool, all the parameters in the parameter sampling results are incorporated into the finite element analysis model to generate a dataset for predicting the average axial compression ratio of the bottom-layer wall of the fundamental period formula, including:

[0100] The macroscopic dimensions of the finite element model are the same as those of the target building. Floor load, wall thickness, etc. are used as variables, sampled according to possible situations, and the samplings of each variable are combined to obtain a large number of finite element model samples.

[0101] Refer to the theoretical calculation formula for the building's fundamental period, initially establish the relationship between the fundamental period and the average axial compression ratio of the bottom-layer wall, and further establish the one-to-one correspondence between the fundamental period and the average axial compression ratio of the bottom-layer wall based on the finite element model. The steps are as follows:

[0102] Preliminarily determine the relationship between the fundamental period and the average axial compression ratio of the bottom layer walls according to the theoretical calculation formula of the fundamental period theory. For a single-degree-of-freedom structure, the theoretical calculation formula of its fundamental period is shown in Equation (1).

[0103]

[0104] In Equation (1), T is the fundamental period of the building, m = M are both lumped masses, and k is the horizontal stiffness.

[0105] The lumped mass is proportional to the total gravity load of the structure, as shown in Equation (2).

[0106] M = G / g Equation (2)

[0107] In Equation (2), G is the total gravity load of the building, g is the acceleration due to gravity, and M is the lumped mass.

[0108] k is the horizontal stiffness of the structure in a single direction. The horizontal stiffness of the masonry structure is provided by the masonry walls. According to the code provisions, the calculation of the wall horizontal stiffness should take into account the influence of the wall height-width ratio. When the height-width ratio is less than 1, only the shear deformation can be calculated; when the height-width ratio is greater than 1 and less than 4, both the shear deformation and the bending deformation should be calculated; when the height-width ratio is greater than 4, the equivalent lateral stiffness can be taken as 0. And the shear deformation stiffness k1 of the wall is shown in Equation (3):

[0109] k1 = A1G v / h = vA1E / h Equation (3)

[0110] In Equation (3), A1 is the cross-sectional area of the wall in a single direction, G v is the shear modulus of the masonry material, h is the wall height, v is the Poisson's ratio of the masonry material, and E is the elastic modulus of the masonry material.

[0111] By combining Equation (1), Equation (2), and Equation (3), Equation (4) is obtained:

[0112]

[0113] In Equation (4), c1 is the ratio between k and k1.

[0114] There is the following relationship between the elastic modulus E and the compressive strength f of the masonry, as shown in Equation (5):

[0115] E = c2f Equation (5)

[0116] In Equation (5), c2 is a constant, approximately 1600. Therefore, the fundamental period can be further deduced as follows:

[0117]

[0118] Wherein, A is the cross-sectional area of all load-bearing walls, c3 is the ratio of A1 to A, u is the average axial compression ratio of the wall, and the average axial compression ratio of the wall is shown in the following formula (7):

[0119] u = G / Af Formula (7)

[0120] Then, Formula (6) establishes the relationship between the fundamental period T and the axial compression ratio of the masonry structure wall. Among them, h, g, v, c2, and c3 are relatively easy to determine, while c1 is relatively difficult to determine.

[0121] The above is the derivation of the theoretical formula for the axial compression ratio of the bottom layer based on the fundamental period for a single-degree-of-freedom structural system. It can be seen that there is a relatively clear relationship between the building axial compression ratio and the fundamental period.

[0122] Multi-layer masonry is a multi-degree-of-freedom structure, and the corresponding fundamental period can be expressed by the following formula (8):

[0123]

[0124] In Formula (8), Meq is the equivalent single-degree-of-freedom mass, and Keq is the equivalent single-degree-of-freedom stiffness. For a structure with uniform mass and stiffness distribution on each floor, the equivalent single-degree-of-freedom mass and stiffness can be calculated according to the following formula (9):

[0125]

[0126] Wherein, M is the mass matrix of the multi-degree-of-freedom structure, is a diagonal matrix, φ n is the vibration mode of the nth order, and m is the total mass of each floor.

[0127]

[0128] In Formula (10), K is the stiffness matrix of the multi-degree-of-freedom structure, and k is the stiffness of the bottom layer wall.

[0129] Then, after replacing m and k in Formula (1) with Meq and Keq, Formula (6) becomes the following formula (11):

[0130]

[0131] The axial compression ratio of the bottom layer wall of the multi-layer masonry structure is Formula (12):

[0132] u = (T / 2π) 2 ×(c5c1gvc3c2) / (c4h) Formula (12)

[0133] In an embodiment of the present application, fitting all the datasets for predicting the average axial compression ratio of the bottom layer wall of the fundamental period formula into a prediction model for the average axial compression ratio of the bottom layer wall of the fundamental period formula includes:

[0134] The input neurons include, but are not limited to, information such as the measured fundamental period and the height of each floor. The output neuron is the average axial compression ratio of the bottom floor, and it includes more than 2 hidden layers, with each layer containing more than 4 neurons.

[0135] Based on the artificial neural network, a neural network for predicting the axial compression ratio of the bottom floor is established.

[0136] Incorporate at least 2 hidden layers into the neural network for predicting the axial compression ratio of the bottom floor.

[0137] Add at least 4 neurons to each hidden layer.

[0138] Define the average axial compression ratio of the bottom floor as the output neuron.

[0139] Specifically, the wall density, floor load, height of each floor, elastic modulus of the masonry material, and wall thickness are respectively taken as random variables, adopted according to the possible value ranges, and a large number of structural finite element model samples are formed by combining the samples of each random variable. Then, modal analysis is performed on the structural finite element model to extract parameters such as the fundamental period and the fundamental axial compression ratio.

[0140] The fitting method includes, but is not limited to, the method of training an artificial neural network (ANN), and information such as the fundamental period and the height of each floor is used as the input neurons, and the average axial compression ratio of the bottom floor is used as the output neuron, including more than 2 hidden layers, with each layer containing more than 4 neurons.

[0141] Specifically, the fitting method includes, but is not limited to, the method of training an artificial neural network (ANN), and information such as the fundamental period and the height of each floor is used as the input neurons, and the average axial compression ratio of the bottom floor is used as the output neuron, including more than 2 hidden layers, with each layer containing more than 4 neurons.

[0142] The fitting method can also be polynomial fitting, and the dependent variable of the polynomial only includes the measured fundamental period, and the order of the polynomial is not less than 2. In an embodiment of the present application, fitting all the datasets for predicting the average axial compression ratio of the bottom layer wall of the fundamental period formula into a prediction model for the average axial compression ratio of the bottom layer wall of the fundamental period formula includes:

[0143] The dependent variable of the polynomial only includes the measured fundamental period, and the order of the polynomial is not less than 2.

[0144] Call the building finite element model of the target building corresponding to the sampling result.

[0145] Determine the axial compression ratio of the bottom floor of the building finite element model of the target building.

[0146] Use the polynomial fitting method to fit the relationship between the average axial compression ratio of the bottom wall and the fundamental period. Define that the polynomial order of the polynomial fitting method is not less than the second order.

[0147] Taking the fundamental period as the independent variable and the average axial compression ratio of the bottom layer as the dependent variable, the calculation formula for the average axial compression ratio of the bottom layer is obtained by fitting.

[0148] In an embodiment of the present application, the building finite element analysis model is constructed, including:

[0149] The method generates a large number of building finite element model samples, and uses a numerical simulation method combining finite element and rigid body dynamics to simulate the building collapse process.

[0150] Analyze the vertical load-bearing member model of the building finite element model of the target building.

[0151] Incorporate the analyzed vertical load-bearing member model into the collapse probability calculation model of the axial compression ratio of the bottom floor.

[0152] Specifically, the building collapse probability calculation method based on the average axial compression ratio of the bottom floor establishes a building collapse probability distribution model based on the average axial compression ratio of the bottom layer extracted from the collapse simulation and the damage ratio of the vertical load-bearing members.

[0153] Based on the axial compression ratio of the bottom floor of the building finite element model of the target building and the damage ratio of the vertical load-bearing member model of the building finite element model of the target building, a collapse probability calculation model is established.

[0154] Specifically, conduct a collapse process simulation, extract the average axial compression ratio of the bottom wall of each sample structure, and record whether the structure sample collapses. Then, fit the collapse probability distribution according to the average axial compression ratio of the bottom wall of a large number of structure samples and the discrimination results of whether they collapse.

[0155] Extract the measured fundamental period of the structure through the structural acceleration monitoring data. Then, calculate the average axial compression ratio of the bottom wall of the building structure based on the measured fundamental period using the fitted formula. Finally, calculate the collapse probability of the structure using the collapse probability distribution obtained by fitting the average axial compression ratio of the bottom wall calculated above.

[0156] Installing an acceleration sensor in a building can meet the requirements of monitoring data, and the monitoring system has a low cost. It can realize the real-time assessment of building collapse risk and real-time early warning of building collapse danger. It can realize long-term automatic monitoring, assessment and collapse danger early warning.

[0157] In an embodiment of the present application, the step of incorporating the average axial compression ratio of the bottom wall into the collapse distribution probability model to obtain the building collapse probability includes:

[0158] A building collapse probability distribution model based on the average axial compression ratio of the ground floor is established according to the average axial compression ratio of the ground floor extracted from the collapse simulation and the damage ratio of the vertical load-bearing members.

[0159] This embodiment relates to a method for obtaining the building collapse probability by incorporating the average axial compression ratio of the ground floor wall into the collapse distribution probability model. The measured fundamental period is transmitted through an acceleration sensor to realize the data input of the axial compression ratio prediction model for the bottom floor. It can be determined that based on the macroscopic dimensions of the target building, the established building finite element model of at least one target building is credible. This greatly reduces the number of sensors deployed, thereby reducing the application cost of the structural health monitoring technology. Based on the building finite element model of the target building and the axial compression ratio prediction model of the bottom floor, a collapse probability calculation model based on the axial compression ratio of the bottom floor is established. The collapse probability calculation model utilizes a highly credible building finite element model and can obtain a relatively reliable conclusion for building stability assessment. Based on the building stability assessment conclusion, the design of the building reinforcement plan can be carried out.

[0160] The technical features of the above embodiments can be combined arbitrarily, and there is no limitation on the execution order of the method steps. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0161] The above embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it cannot be understood as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for assessing the risk of building collapse based on monitoring data and numerical simulation, characterized in that, Including: Establish a parameter sampling pool; Construct a finite element analysis model; Perform a sampling combination on the parameter sampling pool to obtain the parameter sampling result of the parameter sampling pool; Based on the parameter sampling result of the parameter sampling pool, incorporate all the parameters in the parameter sampling result into the finite element analysis model to generate a dataset for predicting the average axial compression ratio of the bottom wall regarding the fundamental period formula; Call all the parameters in the parameter sampling result and the finite element analysis model to establish a dataset for the collapse distribution probability of the vertical member damage based on the average axial compression ratio of the bottom wall; Return the step of performing a sampling combination on the parameter sampling pool to obtain the parameter sampling result of the parameter sampling pool until all the parameter combinations in the sampling pool have completed the sampling combination; Fit all the datasets for predicting the average axial compression ratio of the bottom wall regarding the fundamental period formula into a prediction model for the average axial compression ratio of the bottom wall regarding the fundamental period formula; Fit all the datasets for the collapse distribution probability of the vertical member damage based on the average axial compression ratio of the bottom wall into a collapse distribution probability model; Receive acceleration monitoring data; Perform fundamental period identification on the acceleration monitoring data to obtain the data value of the fundamental period regarding the acceleration monitoring data; Incorporate the data value of the fundamental period into the prediction model for the average axial compression ratio of the bottom wall regarding the fundamental period formula to obtain the average axial compression ratio of the bottom wall; Incorporate the average axial compression ratio of the bottom wall into the collapse distribution probability model to obtain the building collapse probability.

2. The method for evaluating the building collapse risk based on monitoring data and numerical simulation according to claim 1, wherein, The step of incorporating the average axial compression ratio of the bottom wall into the collapse distribution probability model to obtain the building collapse probability includes: Based on the prediction method for the average axial compression ratio of the bottom floor of the building's measured fundamental period and the calculation method for the building collapse probability based on the average axial compression ratio of the bottom floor, by establishing a one-to-one correspondence among the building collapse probability, the average axial compression ratio of the building's bottom floor, and the building's measured fundamental period, reflect the stress state of the building structure to achieve real-time assessment of the building collapse risk.

3. The method for evaluating the risk of building collapse based on monitoring data and numerical simulation according to claim 2, wherein The step of incorporating the average axial compression ratio of the bottom wall into the collapse distribution probability model to obtain the building collapse probability further includes: The one-to-one correspondence between the average axial compression ratio of the building's bottom floor and the building's fundamental period is the result deduced from referring to the theoretical calculation formula of the fundamental period. By establishing a large number of building finite element model samples and performing modal analysis on the models, it is obtained by fitting based on the building's fundamental period and average axial compression ratio obtained from the analysis.

4. The method for assessing the risk of building collapse based on monitoring data and numerical simulation according to claim 2, wherein The step of, based on the parameter sampling result of the parameter sampling pool, incorporating all the parameters in the parameter sampling result into the finite element analysis model to generate a dataset for predicting the average axial compression ratio of the bottom wall regarding the fundamental period formula includes: The macroscopic dimensions of the finite element model are the same as those of the target building. The floor load, wall thickness, etc. are used as variables, sampled according to possible situations, and the samplings of each variable are combined to obtain a large number of finite element model samples.

5. The method for evaluating the risk of building collapse based on monitoring data and numerical simulation according to claim 2, wherein, The step of fitting all the datasets for predicting the average axial compression ratio of the bottom wall regarding the fundamental period formula into a prediction model for the average axial compression ratio of the bottom wall regarding the fundamental period formula includes: The input neurons include but are not limited to information such as the measured fundamental period, floor heights of each floor, etc. The output neuron is the average axial compression ratio of the bottom floor, including more than 2 hidden layers, and each layer contains more than 4 neurons.

6. The method for evaluating the risk of building collapse based on monitoring data and numerical simulation according to claim 2, characterized in that, Fitting all datasets for predicting the average axial compression ratio of the bottom walls of the basic period formula into a prediction model for the average axial compression ratio of the bottom walls of the basic period formula includes: The dependent variable of the polynomial only includes the measured basic period, and the order of the polynomial is not less than 2.

7. The method for evaluating the risk of building collapse based on monitoring data and numerical simulation according to claim 1, wherein Constructing the finite element analysis model includes: Generating a large number of building finite element model samples by the method described, and simulating the building collapse process using a numerical simulation method combining finite element and rigid body dynamics.

8. The method for assessing the risk of building collapse based on monitoring data and numerical simulation according to claim 1, wherein Incorporating the average axial compression ratio of the bottom walls into the collapse distribution probability model to obtain the building collapse probability includes: Establishing a building collapse probability distribution model based on the average axial compression ratio of the bottom layer according to the average axial compression ratio of the bottom layer extracted from the collapse simulation and the damage ratio of the vertical load-bearing members.

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