A method and system for monitoring the stability of a fabricated building structure

Abstract

The application provides a prefabricated building structure stability monitoring method and system, and relates to the technical field of building safety monitoring.The method comprises the following steps: acquiring node monitoring data through a sensor combination arranged at a node at multiple time points in a monitoring period; acquiring real-time environmental data at multiple time points in the monitoring period; acquiring building design information; determining a node health coefficient according to the node monitoring data; determining a dynamic node weight according to the node health coefficient, the building design information and the node monitoring data; determining an overall building stability coefficient according to the real-time environmental data, the dynamic node weight and the node health coefficient; and monitoring the structural stability of a building to be measured according to the node health coefficient and the overall building stability coefficient.According to the application, the health conditions of nodes and the overall building can be monitored respectively, and the accuracy of building structure stability monitoring is improved.

Description

Technical Field

[0001] This invention relates to the field of building safety monitoring technology, and in particular to a method and system for monitoring the structural stability of prefabricated buildings. Background Technology

[0002] In related technologies, the stability of building structures can be monitored by professionals in conjunction with sensor data. However, this relies heavily on human factors, resulting in a very large workload. Therefore, excessive reliance on human factors may make it difficult to accurately identify safety hazards, leading to inaccurate monitoring results for the stability of prefabricated building structures.

[0003] The information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides a method and system for monitoring the structural stability of prefabricated buildings, which can solve the technical problem that related technologies cannot guarantee the accuracy of the monitoring results of the structural stability of prefabricated buildings.

[0005] According to a first aspect of the present invention, a method for monitoring the structural stability of a prefabricated building is provided, comprising: acquiring node monitoring data at multiple times during a monitoring period by means of a combination of sensors installed at nodes; acquiring real-time environmental data at multiple times during the monitoring period; acquiring building design information; determining node health coefficients based on the node monitoring data; determining dynamic node weights based on the node health coefficients, the building design information, and the node monitoring data; determining an overall building stability coefficient based on the real-time environmental data, the dynamic node weights, and the node health coefficients; and performing structural stability monitoring on the building under test based on the node health coefficients and the overall building stability coefficients.

[0006] According to the present invention, determining the node health coefficient based on the node monitoring data includes: determining the cumulative value of node acoustic emission energy, node strain value, strain anomaly fluctuation energy value, and spectrum deviation coefficient based on the node monitoring data; obtaining historical reference acoustic emission energy value, historical reference node strain value, historical reference strain anomaly fluctuation energy value, and historical reference spectrum deviation coefficient; and determining the node health coefficient based on the strain anomaly fluctuation energy value, the historical reference strain anomaly fluctuation energy value, the node strain value, the cumulative value of node acoustic emission energy, the historical reference acoustic emission energy value, the historical reference node strain value, the spectrum deviation coefficient, and the historical reference spectrum deviation coefficient.

[0007] According to the present invention, determining the node health coefficient based on the strain anomaly fluctuation energy value, the historical reference strain anomaly fluctuation energy value, the node strain value, the cumulative value of node acoustic emission energy, the historical reference acoustic emission energy value, the historical reference node strain value, the spectral deviation coefficient, and the historical reference spectral deviation coefficient includes: according to the formula: Determine the node health coefficient of the i-th node at the j-th time of the monitoring period. ,in, , and The first preset parameter is `if`, where `if` is a conditional function. Let be the node strain value of the i-th node at the j-th time point of the monitoring period. The preset node strain value threshold is the i-th node. Let be the cumulative acoustic emission energy of the i-th node at the j-th time point of the monitoring period. Let be the historical reference acoustic emission energy value of the i-th node. Let be the strain anomaly fluctuation energy value of the i-th node at the j-th time of the monitoring period. Let be the historical reference strain anomaly fluctuation energy value of the i-th node. Let be the spectral deviation coefficient of the i-th node at the j-th time of the monitoring period. is the historical reference spectrum deviation coefficient of the i-th node.

[0008] According to the present invention, determining dynamic node weights based on the node health coefficient, the building design information, and the node monitoring data includes: determining the adjacent nodes of each node based on the building design information; determining the adjacent node health coefficients of the adjacent nodes of each node based on the node monitoring data; obtaining the bending stiffness of node components and the betweenness centrality of nodes; constructing a building finite element model based on the building design information; determining the bearing capacity reduction ratio based on the building finite element model; determining the adjacent node influence coefficients of the adjacent nodes of each node based on the building finite element model; determining the foundation node weights based on the bending stiffness of node components, the betweenness centrality of nodes, and the bearing capacity reduction ratio; and determining the dynamic node weights based on the adjacent node health coefficients, the adjacent node influence coefficients, the node health coefficients, and the foundation node weights.

[0009] According to the present invention, determining dynamic node weights based on the neighboring node health coefficients, the neighboring node influence coefficients, the node health coefficients, and the basic node weights includes: determining dynamic node weights according to the formula: Determine the dynamic node weight of the i-th node at the j-th time of the monitoring period. Where, if is a conditional function, , , and This is the second preset parameter. Let the weight of the i-th node be the base node weight. Let be the node health coefficient of the i-th node at the j-th time point in the monitoring period. Let be the neighboring node influence coefficient of the k-th neighboring node of the i-th node. Let be the health coefficient of the k-th neighboring node of the i-th node at the j-th time of the monitoring period. The preset neighbor node health coefficient threshold is the threshold value for the k-th neighbor node of the i-th node. K is the preset node health coefficient threshold, K is the number of adjacent nodes, k≤K, and both k and K are positive integers.

[0010] According to the present invention, determining the overall building stability coefficient based on the real-time environmental data, the dynamic node weights, and the node health coefficients includes: determining the node health coefficient change rate based on the node health coefficients; determining a first standard deviation based on the node health coefficient change rates of multiple nodes; determining a real-time environmental load coefficient based on the real-time environmental data; and determining the overall building stability coefficient based on the dynamic node weights, the node health coefficients, the first standard deviation, and the real-time environmental load coefficients.

[0011] According to the present invention, determining the real-time environmental load coefficient based on the real-time environmental data includes: determining real-time wind pressure, real-time snow depth, and real-time number of people based on the real-time environmental data; acquiring historical environmental data; determining a reference wind pressure, a reference snow depth, and a reference number of people based on the historical environmental data; and determining the real-time environmental load coefficient based on the real-time wind pressure, the real-time snow depth, the real-time number of people, the reference wind pressure, the reference snow depth, and the reference number of people.

[0012] According to the present invention, the overall building stability coefficient is determined based on the dynamic node weight, the node health coefficient, the first standard deviation, and the real-time environmental load coefficient, including: according to the formula: Determine the overall building stability coefficient at time j of the monitoring period. Where, if is a conditional function. and The third preset weight, Let be the node health coefficient of the i-th node at the j-th time point in the monitoring period. Let be the dynamic node weight of the i-th node at the j-th time point in the monitoring period. Let the first standard deviation be the value at time j of the monitoring period. Let be the real-time environmental load coefficient of the i-th node at the j-th moment of the monitoring period, where n is the number of nodes, i ≤ n, and i and n are both positive integers.

[0013] According to a second aspect of the present invention, a prefabricated building structural stability monitoring system is provided, comprising: a node data module for acquiring node monitoring data at multiple times during a monitoring cycle via a combination of sensors installed at the nodes; an environmental data module for acquiring real-time environmental data at multiple times during the monitoring cycle; a design information module for acquiring building design information; a node health module for determining a node health coefficient based on the node monitoring data; a dynamic weighting module for determining a dynamic node weight based on the node health coefficient, the building design information, and the node monitoring data; a stability coefficient module for determining an overall building stability coefficient based on the real-time environmental data, the dynamic node weight, and the node health coefficient; and a real-time monitoring module for monitoring the structural stability of the building under test based on the node health coefficient and the overall building stability coefficient.

[0014] Technical Effects: According to the present invention, the health status of each node of the building under test can be accurately monitored, the node health coefficient can be determined, and dynamic node weights can be set for each node based on the node health coefficient and building design information. Furthermore, based on real-time environmental data, dynamic node weights, and node health coefficients, the overall stability of the building under test can be monitored, and the overall building stability coefficient can be determined, thus improving the accuracy of building structural stability monitoring. When determining the node health coefficient, it can be determined based on the abnormal strain fluctuation energy value, historical reference abnormal strain fluctuation energy value, node strain value, cumulative node acoustic emission energy value, historical reference acoustic emission energy value, historical reference node strain value, spectral deviation coefficient, and historical reference spectral deviation coefficient. During the calculation process, the node strain value can be used to determine whether there is a stress exceeding the limit at the node. Furthermore, when there is no stress exceeding the limit at the node, the rate and activity of the damage event and the degree of damage already caused can be accurately analyzed based on the cumulative node acoustic emission energy value, abnormal strain fluctuation energy value, and spectral deviation coefficient. Based on the rate and activity of the damage event and the degree of damage already caused, the node health coefficient can be determined, thus improving the comprehensiveness and accuracy of the node health coefficient. When determining dynamic node weights, the weights can be determined based on the health coefficients of adjacent nodes, the influence coefficients of adjacent nodes, the node health coefficients, and the weights of basic nodes. During the calculation process, the impact of node health coefficients on node health and the risk of sudden changes can be fully analyzed, as can the damage impact of adjacent nodes, based on their health coefficients and influence coefficients. Furthermore, dynamic node weights are determined based on the weights of basic nodes, the impact of node health, the risk of sudden changes, and the damage impact of adjacent nodes, thus improving the comprehensiveness of dynamic node weights. When determining the overall building stability coefficient, the weights can be determined based on dynamic node weights, node health coefficients, the first standard deviation, and the real-time environmental load coefficient. During the calculation process, the overall building stability coefficient is determined based on three aspects: the overall health status of the building, the consistency of health status degradation across all nodes, and the impact of real-time environmental loads, thus improving the comprehensiveness and accuracy of the overall building stability coefficient.

[0015] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Other features and aspects of the invention will become clearer from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic flowchart of a prefabricated building structure stability monitoring method according to an embodiment of the present invention is shown as an example.

[0018] Figure 2 An exemplary flowchart illustrating the determination of node health coefficients according to an embodiment of the present invention is shown;

[0019] Figure 3 An exemplary flowchart illustrating the determination of dynamic node weights according to an embodiment of the present invention is shown;

[0020] Figure 4 An exemplary flowchart illustrating the determination of the overall building stability coefficient according to an embodiment of the present invention is shown;

[0021] Figure 5 A block diagram of a prefabricated building structure stability monitoring system according to an embodiment of the present invention is shown as an example. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0024] Figure 1An exemplary flowchart of a prefabricated building structure stability monitoring method according to an embodiment of the present invention is shown. The method includes: step S1, acquiring node monitoring data at multiple times during a monitoring cycle using a combination of sensors installed at the nodes; step S2, acquiring real-time environmental data at multiple times during the monitoring cycle; step S3, acquiring building design information; step S4, determining node health coefficients based on the node monitoring data; step S5, determining dynamic node weights based on the node health coefficients, the building design information, and the node monitoring data; step S6, determining an overall building stability coefficient based on the real-time environmental data, the dynamic node weights, and the node health coefficients; and step S7, performing structural stability monitoring on the building under test based on the node health coefficients and the overall building stability coefficients.

[0025] According to the present invention, the prefabricated building structure stability monitoring method can accurately monitor the health status of each node of the building under test, determine the node health coefficient, and set the dynamic node weight of each node based on the node health coefficient and building design information. Furthermore, based on real-time environmental data, dynamic node weight and node health coefficient, the overall stability status of the building under test is monitored and the overall building stability coefficient is determined, thereby improving the accuracy of building structure stability monitoring.

[0026] According to one embodiment of the present invention, in step S1, node monitoring data is acquired at multiple times during the monitoring cycle by a combination of sensors installed at the node.

[0027] For example, node monitoring data can be obtained by setting up a sensor combination at nodes (such as beam-column connection nodes, wall panel connection nodes, primary and secondary beam connection nodes, supports and connection parts of large precast components). The sensor combination includes strain sensors, acoustic emission sensors and micro-vibration sensors. The node monitoring data includes acoustic emission energy values, strain values ​​and vibration spectra, etc. The monitoring cycle is ten minutes apart between adjacent moments.

[0028] According to one embodiment of the present invention, in step S2, real-time environmental data is acquired at multiple moments during the monitoring period.

[0029] For example, real-time environmental data can be obtained by installing wind pressure sensors and weighing snow depth sensors on the exterior of the building under test.

[0030] According to one embodiment of the present invention, in step S3, architectural design information is obtained.

[0031] For example, obtaining architectural design information, such as design drawings and design parameters.

[0032] According to one embodiment of the present invention, in step S4, the node health coefficient is determined based on the node monitoring data.

[0033] Figure 2 A flowchart for determining the health coefficient of a node according to an embodiment of the present invention is shown as an example.

[0034] According to an embodiment of the present invention, step S4 includes: step S41, determining the cumulative value of node acoustic emission energy, node strain value, strain anomaly fluctuation energy value, and spectrum deviation coefficient based on the node monitoring data; step S42, acquiring historical reference acoustic emission energy value, historical reference node strain value, historical reference strain anomaly fluctuation energy value, and historical reference spectrum deviation coefficient; step S43, determining the node health coefficient based on the strain anomaly fluctuation energy value, the historical reference strain anomaly fluctuation energy value, the node strain value, the cumulative value of node acoustic emission energy, the historical reference acoustic emission energy value, the historical reference node strain value, the spectrum deviation coefficient, and the historical reference spectrum deviation coefficient.

[0035] For example, by using high-sensitivity acoustic emission sensors pre-installed on or inside the node, the cumulative value of node acoustic emission energy can be obtained. For instance, the original waveform signal is acquired in real time, filtered, and amplitude discriminated to identify acoustic emission events exceeding a threshold. The energy of each event is calculated and summed to determine the cumulative value of node acoustic emission energy. Through strain sensors, the node strain value is obtained. The node strain value at the second moment of the monitoring cycle is the average strain value between the second moment and the first moment of the monitoring cycle, thus obtaining the strain anomaly fluctuation energy value. For instance, wavelet packet transform is performed on the original strain signal to decompose it into different frequency bands, and the energy of the anomaly fluctuation signal at multiple moments in the monitoring cycle is calculated, i.e., the strain anomaly fluctuation energy value. Through micro-vibration sensors pre-installed on or inside the node, the spectral deviation coefficient is obtained. For instance, spectral analysis (e.g., fast Fourier transform) or modal analysis is performed on acceleration time history data to extract the first few dominant frequencies and mode shapes, forming an eigenvector. The eigenvector and the reference are then calculated. The Mahalanobis distance between vectors (dynamic fingerprint features identified by environmental vibration testing when the node is in an initial healthy and undamaged state) is the spectral deviation coefficient. Historical reference acoustic emission energy values, historical reference node strain values, historical reference strain anomaly fluctuation energy values, and historical reference spectral deviation coefficients are obtained. For example, after the building under test is completed and put into normal use, a period of continuous and stable monitoring (e.g., 24-72 hours) is conducted. The average value of the cumulative node acoustic emission energy, node strain value, strain anomaly fluctuation energy value, and spectral deviation coefficient detected during this period is determined as the historical reference acoustic emission energy value, historical reference node strain value, and historical reference spectral deviation coefficient. Based on the strain anomaly fluctuation energy value, historical reference strain anomaly fluctuation energy value, node strain value, cumulative node acoustic emission energy value, historical reference acoustic emission energy value, historical reference node strain value, spectral deviation coefficient, and historical reference spectral deviation coefficient, the health status of each node is monitored, and the node health coefficient is determined.

[0036] According to an embodiment of the present invention, step S43 includes: determining the node health coefficient of the i-th node at the j-th moment of the monitoring period according to formula (1). ,

[0037] (1)

[0038] in, , and The first preset parameter is `if`, where `if` is a conditional function. Let be the node strain value of the i-th node at the j-th time point of the monitoring period. The preset node strain value threshold is the i-th node. Let be the cumulative acoustic emission energy of the i-th node at the j-th time point of the monitoring period. Let be the historical reference acoustic emission energy value of the i-th node. Let be the strain anomaly fluctuation energy value of the i-th node at the j-th time of the monitoring period. Let be the historical reference strain anomaly fluctuation energy value of the i-th node. Let be the spectral deviation coefficient of the i-th node at the j-th time of the monitoring period. is the historical reference spectrum deviation coefficient of the i-th node.

[0039] According to an embodiment of the present invention, in formula (1), the condition function The value includes the following two cases, when the following conditions are met: When the condition is met, if the strain value of node i at time j of the monitoring period is greater than or equal to the preset strain value threshold of node i, it indicates that the stress at that node exceeds the limit, which may mean that the connecting plate or bolt group at that location has reached the yield strength and has begun to undergo irreversible deformation. The health condition of the building structure at that node is poor, and the value of the condition function is 0. The preset strain value threshold can be determined according to documents such as the "Steel Structure Design Standard" and the "Concrete Structure Design Code". When the condition is met, it indicates that there is no stress exceeding the limit at the i-th node, and the value of the condition function is... , Let be the cumulative acoustic emission energy of the i-th node at the j-th moment of the monitoring period, representing the total transient elastic wave energy released within the material due to microcrack generation, propagation, fiber breakage, or interfacial friction. This is the ratio of the cumulative acoustic emission energy of the i-th node at the j-th time of the monitoring period to the historical reference acoustic emission energy value of the i-th node. The larger this ratio, the higher the probability that microscopic defects are actively being generated and developing inside the i-th node. This is the ratio of the strain anomaly fluctuation energy value of the i-th node at the j-th moment of the monitoring period to the historical reference strain anomaly fluctuation energy value of the i-th node. The larger this ratio, the greater the possibility of mechanical failures such as bolt loosening, loss of preload, contact surface slippage, or gasket damage at the i-th node joint. This indicates that the node health coefficient of the i-th node decreases exponentially as the strain anomaly fluctuation energy value and the cumulative value of node acoustic emission energy increase. This indicates the rate and activity of the damage event. This is the ratio of the spectral deviation coefficient of the i-th node at the j-th moment of the monitoring period to the historical reference spectral deviation coefficient. The larger this ratio, the greater the overall and trend-like decrease in the effective stiffness of the i-th node, and the greater the cumulative damage to the i-th node. This indicates that the node health coefficient of the i-th node is negatively correlated with the spectrum deviation coefficient. Indicates the extent of damage that has already occurred. This indicates that the node health coefficient is determined based on the rate and activity of damage events and the extent of damage already caused, where the first preset parameter... , and It can be determined based on an optimized calibration method using laboratory destructive testing. , and They can be set to 1.2, 1.5, and 1.3 respectively.

[0040] In this way, the node health coefficient can be determined based on the strain anomaly fluctuation energy value, historical reference strain anomaly fluctuation energy value, nodal strain value, cumulative nodal acoustic emission energy value, historical reference acoustic emission energy value, historical reference nodal strain value, spectral deviation coefficient, and historical reference spectral deviation coefficient. During the calculation process, the node strain value can be used to determine whether there is a stress over-limit condition at the node. Furthermore, when there is no stress over-limit condition at the node, the rate and activity of the damage event and the degree of damage already caused can be accurately analyzed based on the cumulative nodal acoustic emission energy value, strain anomaly fluctuation energy value, and spectral deviation coefficient. Based on the rate and activity of the damage event and the degree of damage already caused, the node health coefficient is determined, thus improving the comprehensiveness and accuracy of the node health coefficient.

[0041] According to an embodiment of the present invention, in step S5, dynamic node weights are determined based on the node health coefficient, the building design information, and the node monitoring data.

[0042] Figure 3 A flowchart for determining dynamic node weights according to an embodiment of the present invention is shown as an example.

[0043] According to an embodiment of the present invention, step S5 includes: step S51, determining the adjacent nodes of each node based on the architectural design information; step S52, determining the adjacent node health coefficient of each node's adjacent nodes based on the node monitoring data; step S53, obtaining the bending stiffness of the node components and the betweenness centrality of the nodes; step S54, constructing a building finite element model based on the architectural design information; step S55, determining the bearing capacity reduction ratio based on the building finite element model; step S56, determining the adjacent node influence coefficient of each node's adjacent nodes based on the building finite element model; step S57, determining the foundation node weight based on the bending stiffness of the node components, the betweenness centrality of the nodes, and the bearing capacity reduction ratio; and step S58, determining the dynamic node weight based on the adjacent node health coefficient, the adjacent node influence coefficient, the node health coefficient, and the foundation node weight.

[0044] For example, based on the design drawings of the building under test, the adjacent nodes of each node are determined; based on the node monitoring data, the health coefficient of the adjacent nodes of each node is determined. The method for determining the health coefficient of the adjacent nodes is similar to that for determining the node health coefficient, and will not be repeated here; the bending stiffness and betweenness centrality of the node components are obtained. For example, the bending stiffness of the components where the nodes are located is obtained from the design drawings and design parameters of the building under test. That is, the bending stiffness of the node components, the beams, columns, supports and other components of the building under test are abstracted as "edges", and the connection points between components are abstracted as "points", an undirected weighted graph is established, and the standard shortest path algorithm is used. Calculate the shortest path between all pairs of points in the graph. For each node, count the number of times it appears on the shortest path between all node pairs; this is the node betweenness centrality. Based on architectural design information, construct a finite element model of the building. For example, based on the design drawings and parameters of the building to be tested, establish a refined structural model that reflects material nonlinearity and geometric nonlinearity; based on the finite element model, determine the bearing capacity reduction ratio. For example, perform pushover analysis on the finite element model to obtain the first ultimate bearing capacity of the i-th node. In the finite element model, remove the i-th node or set its stiffness close to zero, and simulate the i-th node. If node i fails completely, a push-over analysis is performed on the damaged structure again to obtain the second ultimate bearing capacity of node i. The reduced ultimate bearing capacity is determined by subtracting the second ultimate bearing capacity from the first ultimate bearing capacity. The bearing capacity reduction ratio is determined based on the ratio of the reduced ultimate bearing capacity to the first ultimate bearing capacity. Based on the building finite element model, the adjacent node influence coefficients of each node are determined. For example, in the building finite element model, a unit force is applied to neighboring nodes along the possible main force transmission direction (e.g., vertical, horizontal), or a unit displacement is forcibly applied (simulating damage settlement or slippage of node j). The analysis and calculation are performed on the adjacent nodes. Under unit force, the key response of the target node (e.g., displacement relative to the ground) is normalized based on the response value (vector synthesis of responses in different directions is required) to determine the influence coefficient of adjacent nodes. The weight of the foundation node is determined based on the bending stiffness of the node component, the betweenness centrality of the node, and the bearing capacity reduction ratio. For example, the weight of the foundation node of the i-th node is determined by weighting and summing the ratio of the bending stiffness of the node component of the i-th node to the average bending stiffness of the node components of all nodes, the betweenness centrality of the node, and the bearing capacity reduction ratio with three preset weights. The preset weights can be set to 0.4, 0.2, and 0.4. The larger the ratio of the bending stiffness of the node at node i to the average bending stiffness of all nodes, the more critical the role of the member connected to node i in bearing and transmitting bending moments. A higher node betweenness centrality indicates that more internal force flows need to be redistributed through this node. If this node fails, internal forces will be forced to take long alternative paths, greatly disrupting the stress state of the structure and potentially triggering a chain reaction. A larger decrease in bearing capacity indicates that this node is a "critical component" maintaining the robustness of the structural system; its failure will lead to a sharp loss of redundancy, and the risk of the structure evolving from a "statically indeterminate" state to a "mechanism" or even a "collapse" state is extremely high. Dynamic node weights are determined based on the health coefficients of adjacent nodes, the influence coefficients of adjacent nodes, the node health coefficients, and the weights of the foundation nodes.

[0045] According to an embodiment of the present invention, step S58 includes: determining the dynamic node weight of the i-th node at the j-th moment of the monitoring period according to formula (2). ,

[0046] (2)

[0047] Where if is a conditional function. , , and This is the second preset parameter. Let the weight of the i-th node be the base node weight. Let be the node health coefficient of the i-th node at the j-th time point in the monitoring period. Let be the neighboring node influence coefficient of the k-th neighboring node of the i-th node. Let be the health coefficient of the k-th neighboring node of the i-th node at the j-th time of the monitoring period. The preset neighbor node health coefficient threshold is the threshold value for the k-th neighbor node of the i-th node. K is the preset node health coefficient threshold, K is the number of adjacent nodes, k≤K, and both k and K are positive integers.

[0048] According to one embodiment of the present invention, Let be the node health coefficient of the i-th node at the j-th time point in the monitoring period. and It can be set to 2 and 0.35 respectively, when When it approaches 1, The value is also close to 1, indicating that when the node is in a relatively healthy state, it has almost no impact on the dynamic node weight. At lower levels, A significant increase indicates that as the severity of the injury worsens, its risk weight should also increase sharply. This represents the self-health impact coefficient of the i-th node. When the node health coefficient of the i-th node is lower at the j-th moment of the monitoring period, the weight of the less healthy node is increased. The larger the self-health impact coefficient, the more attention needs to be paid to the node.

[0049] According to one embodiment of the present invention, The preset threshold for the neighboring node influence coefficient of the kth neighboring node of the i-th node can be determined based on the average value of the neighboring node influence coefficients of the kth neighboring node of the i-th node during a stable monitoring period after the completion of the building under test. This represents the total degradation of the influence coefficient of the k-th neighboring node of the i-th node at the j-th time of the monitoring period. The product of the neighboring node influence coefficient of the k-th neighboring node of the i-th node and the total degradation of the neighboring node influence coefficients represents the influence of the damage level of the k-th neighboring node of the i-th node on the i-th node. This represents the impact of the damage levels of all adjacent nodes of the i-th node on the i-th node. This means that the influence quantity is mapped to the interval from -1 to 1 using the hyperbolic tangent function. It is usually set between 0.1 and 0.5 for structures with low redundancy and fast risk transmission (such as certain trusses). A larger value should be taken, such as 0.4; for structures with high redundancy and strong load redistribution capacity (such as frame-shear wall structures). A smaller value can be chosen, such as 0.2. This represents the damage impact coefficient of adjacent nodes. The larger the coefficient, the greater the impact of damage to adjacent nodes on this node, and the more attention should be paid to this node.

[0050] According to one embodiment of the present invention, The preset node health coefficient threshold represents the critical health level. It can be set to 0.65. In formula (2), the conditional function Including the following two situations, when the following conditions are met When the condition is met, if the node health coefficient of the i-th node at the j-th time of the monitoring period is greater than or equal to the preset node health coefficient threshold, it indicates that the i-th node is in a healthy state, and the value of the condition function is 1. Otherwise, if the condition is not met... When the condition is met, it means that the i-th node itself is in a damaged state, and the value of the condition function is... In practice, the process of a node transitioning from a damaged state to a failed state is often accelerated and nonlinear. Therefore, This represents the speed at which the i-th node transitions from a damaged state to a failed state. It can be set to 0.271. This represents the risk mutation coefficient when the i-th node is close to complete failure. The larger the value, the faster the i-th node transitions to a failure state, and the more attention should be paid to this node.

[0051] According to one embodiment of the present invention, This means that the dynamic node weight is determined based on the basic node weight, its own health impact coefficient, the damage impact coefficient of adjacent nodes, and the risk mutation coefficient.

[0052] In this way, dynamic node weights can be determined based on the health coefficients of adjacent nodes, the influence coefficients of adjacent nodes, the node health coefficients, and the weights of basic nodes. During the calculation process, the influence of node health on its own health and the risk mutation status can be fully analyzed based on the node health coefficients, and the influence of adjacent node damage can be fully analyzed based on the health coefficients and influence coefficients of adjacent nodes. Furthermore, dynamic node weights are determined based on the weights of basic nodes, the influence of node health on its own health, the risk mutation status, and the influence of adjacent node damage, thus improving the comprehensiveness of dynamic node weights.

[0053] According to an embodiment of the present invention, in step S6, the overall building stability coefficient is determined based on the real-time environmental data, the dynamic node weights, and the node health coefficients.

[0054] Figure 4 A flowchart for determining the overall building stability coefficient according to an embodiment of the present invention is shown as an example.

[0055] According to an embodiment of the present invention, step S6 includes: step S61, determining the node health coefficient change rate based on the node health coefficient; step S62, determining the first standard deviation based on the node health coefficient change rates of multiple nodes; step S63, determining the real-time environmental load coefficient based on the real-time environmental data; and step S64, determining the overall building stability coefficient based on the dynamic node weight, the node health coefficient, the first standard deviation, and the real-time environmental load coefficient.

[0056] For example, based on the node health coefficient, the rate of change of the node health coefficient can be determined, such as by the difference between the node health coefficients at adjacent times; the standard deviation of the rate of change of the node health coefficients of multiple nodes can be calculated to determine the first standard deviation; based on real-time environmental data, the load conditions brought by the external environment and the flow of people can be monitored to determine the real-time environmental load coefficient; based on the dynamic node weights, node health coefficients, the first standard deviation, and the real-time environmental load coefficient, the overall stability of the building under test can be monitored to determine the overall building stability coefficient.

[0057] According to an embodiment of the present invention, step S63 includes: step S631, determining real-time wind pressure, real-time snow depth, and real-time number of people based on the real-time environmental data; step S632, acquiring historical environmental data; step S633, determining reference wind pressure, reference snow depth, and reference number of people based on the historical environmental data; and step S634, determining a real-time environmental load coefficient based on the real-time wind pressure, the real-time snow depth, the real-time number of people, the reference wind pressure, the reference snow depth, and the reference number of people.

[0058] For example, real-time wind pressure is obtained through wind pressure sensors on the surface of the building under test; real-time snow depth is obtained through weighing snow depth sensors located at the eaves and gutters of the building under test; and real-time number of people is obtained through video crowd counting systems installed at main entrances and exits. Historical environmental data is also acquired, namely, the historical wind pressure, historical snow depth, and historical number of people for the building under test over a past period. Based on the historical environmental data, baseline wind pressure, baseline snow depth, and baseline number of people are determined. For example, baseline wind pressure, baseline snow depth, and baseline number of people are determined based on the average values ​​of historical wind pressure, historical snow depth, and historical number of people, respectively. Finally, based on real-time wind pressure, real-time snow depth, real-time number of people, baseline wind pressure, baseline snow depth, and baseline number of people, the baseline wind pressure, baseline snow depth, and baseline number of people are determined. The number of people determines the real-time environmental load coefficient. For example, the real-time wind pressure at each measuring point is integrated to obtain the real-time wind load acting on the building under test. The real-time snow load is calculated based on the real-time snow depth and the roof snow distribution coefficient (determined according to the roof type of the building under test). The real-time pedestrian density is determined based on the real-time number of people. The real-time number of people load is determined by multiplying the real-time pedestrian density by the average weight per person. The benchmark wind load, benchmark snow load, and benchmark number of people are determined based on the benchmark wind load, benchmark snow load, and benchmark number of people load. The real-time environmental load coefficient is determined by multiplying the ratio of the real-time wind load to the benchmark wind load, the ratio of the real-time snow load to the benchmark snow load, and the ratio of the real-time number of people load to the benchmark number of people load.

[0059] According to an embodiment of the present invention, step S64 includes: determining the overall building stability coefficient at the j-th moment of the monitoring period according to formula (3). ,

[0060] (3)

[0061] Where if is a conditional function. and The third preset weight, Let be the node health coefficient of the i-th node at the j-th time point in the monitoring period. Let be the dynamic node weight of the i-th node at the j-th time point in the monitoring period. Let the first standard deviation be the value at time j of the monitoring period. Let be the real-time environmental load coefficient of the i-th node at the j-th moment of the monitoring period, where n is the number of nodes, i ≤ n, and i and n are both positive integers.

[0062] According to one embodiment of the present invention, Let be the product of the node health coefficient and the dynamic node weight of the i-th node at the j-th time of the monitoring period, representing the weighted health status of the i-th node. Indicates the overall health condition of the building. Let be the first standard deviation at the j-th moment of the monitoring period. The larger the first standard deviation, the greater the difference in the rate of change of the node health coefficient of all nodes of the building under test, the more inconsistent the degradation of the health status of all nodes, and the worse the overall stability of the building under test.

[0063] According to one embodiment of the present invention, The value of the condition function includes the following two cases, when the condition is satisfied. When the condition is met, the real-time environmental load coefficient of the i-th node at the j-th moment of the monitoring period is less than or equal to 1, indicating that the real-time environmental load has not had any additional impact on the building under test, and the value of the condition function is 0. If this condition is not met... When the condition is met, it indicates that the real-time environmental load has an additional impact on the building under test, which may cause a temporary, recoverable decline in the building's health status. The value of the condition function is... .

[0064] According to one embodiment of the present invention, This indicates that the overall building stability coefficient is determined based on the building's overall health condition, the consistency of health degradation across all nodes, and the impact of real-time environmental loads. and They can be set to 0.4 and 1.2 respectively.

[0065] In this way, the overall building stability coefficient can be determined based on dynamic node weights, node health coefficients, first standard deviation, and real-time environmental load coefficients. During the calculation process, the overall building stability coefficient can be determined based on three aspects: the overall health status of the building, the consistency of the health status degradation of all nodes, and the impact of real-time environmental loads, thereby improving the comprehensiveness and accuracy of the overall building stability coefficient.

[0066] According to an embodiment of the present invention, in step S7, the structural stability of the building under test is monitored based on the node health coefficient and the overall building stability coefficient.

[0067] For example, when the node health coefficient is less than 0.65, it indicates that the node's health condition is poor; when the overall building stability coefficient is less than 0.7, it indicates that the overall stability of the building under test is poor.

[0068] The prefabricated building structure stability monitoring method according to embodiments of the present invention can accurately monitor the health status of each node of the building under test, determine the node health coefficient, and set dynamic node weights for each node based on the node health coefficient and building design information. Furthermore, based on real-time environmental data, dynamic node weights, and node health coefficients, the overall stability of the building under test is monitored, and the overall building stability coefficient is determined, thus improving the accuracy of building structure stability monitoring. When determining the node health coefficient, it can be determined based on the abnormal strain fluctuation energy value, historical reference abnormal strain fluctuation energy value, node strain value, cumulative node acoustic emission energy value, historical reference acoustic emission energy value, historical reference node strain value, spectral deviation coefficient, and historical reference spectral deviation coefficient. During the calculation process, the node strain value can be used to determine whether there is a stress exceeding the limit at the node. Furthermore, when there is no stress exceeding the limit at the node, the rate and activity of the damage event and the degree of damage already caused can be accurately analyzed based on the cumulative node acoustic emission energy value, abnormal strain fluctuation energy value, and spectral deviation coefficient. Based on the rate and activity of the damage event and the degree of damage already caused, the node health coefficient is determined, improving the comprehensiveness and accuracy of the node health coefficient. When determining dynamic node weights, the weights can be determined based on the health coefficients of adjacent nodes, the influence coefficients of adjacent nodes, the node health coefficients, and the weights of basic nodes. During the calculation process, the impact of node health coefficients on node health and the risk of sudden changes can be fully analyzed, as can the damage impact of adjacent nodes, based on their health coefficients and influence coefficients. Furthermore, dynamic node weights are determined based on the weights of basic nodes, the impact of node health, the risk of sudden changes, and the damage impact of adjacent nodes, thus improving the comprehensiveness of dynamic node weights. When determining the overall building stability coefficient, the weights can be determined based on dynamic node weights, node health coefficients, the first standard deviation, and the real-time environmental load coefficient. During the calculation process, the overall building stability coefficient is determined based on three aspects: the overall health status of the building, the consistency of health status degradation across all nodes, and the impact of real-time environmental loads, thus improving the comprehensiveness and accuracy of the overall building stability coefficient.

[0069] Figure 5An exemplary block diagram of a prefabricated building structural stability monitoring system according to an embodiment of the present invention is shown. The system includes: a node data module for acquiring node monitoring data at multiple times during a monitoring cycle using a combination of sensors installed at the nodes; an environmental data module for acquiring real-time environmental data at multiple times during the monitoring cycle; a design information module for acquiring building design information; a node health module for determining a node health coefficient based on the node monitoring data; a dynamic weighting module for determining a dynamic node weight based on the node health coefficient, the building design information, and the node monitoring data; a stability coefficient module for determining an overall building stability coefficient based on the real-time environmental data, the dynamic node weight, and the node health coefficient; and a real-time monitoring module for monitoring the structural stability of the building under test based on the node health coefficient and the overall building stability coefficient.

[0070] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0071] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

Claims

1. A method for monitoring the structural stability of prefabricated buildings, characterized in that, include: At multiple points in the monitoring cycle, node monitoring data is acquired through a combination of sensors installed at the nodes. Real-time environmental data is acquired at multiple points during the monitoring cycle; Obtain architectural design information; The node health coefficient is determined based on the node monitoring data. The dynamic node weight is determined based on the node health coefficient, the building design information, and the node monitoring data. The overall building stability coefficient is determined based on the real-time environmental data, the dynamic node weights, and the node health coefficients. Based on the node health coefficient and the overall building stability coefficient, the structural stability of the building under test is monitored. Based on the node monitoring data, the node health coefficient is determined, including: determining the cumulative value of node acoustic emission energy, node strain value, abnormal strain fluctuation energy value, and spectral deviation coefficient based on the node monitoring data; obtaining historical reference acoustic emission energy value, historical reference node strain value, historical reference abnormal strain fluctuation energy value, and historical reference spectral deviation coefficient; and determining the node health coefficient based on the abnormal strain fluctuation energy value, the historical reference abnormal strain fluctuation energy value, the node strain value, the cumulative value of node acoustic emission energy, the historical reference acoustic emission energy value, the historical reference node strain value, the spectral deviation coefficient, and the historical reference spectral deviation coefficient. The node health coefficient is determined based on the strain anomaly fluctuation energy value, the historical reference strain anomaly fluctuation energy value, the nodal strain value, the cumulative nodal acoustic emission energy value, the historical reference acoustic emission energy value, the historical reference nodal strain value, the spectral deviation coefficient, and the historical reference spectral deviation coefficient, including: according to the formula: Determine the node health coefficient of the i-th node at the j-th time of the monitoring period. ,in, , and The first preset parameter is `if`, where `if` is a conditional function. Let be the node strain value of the i-th node at the j-th time point of the monitoring period. The preset node strain value threshold is the i-th node. Let be the cumulative acoustic emission energy of the i-th node at the j-th time point of the monitoring period. Let be the historical reference acoustic emission energy value of the i-th node. Let be the strain anomaly fluctuation energy value of the i-th node at the j-th time of the monitoring period. Let be the historical reference strain anomaly fluctuation energy value of the i-th node. Let be the spectral deviation coefficient of the i-th node at the j-th time of the monitoring period. The historical reference spectrum deviation coefficient of the i-th node; The dynamic node weights are determined based on the node health coefficients, the building design information, and the node monitoring data, including: determining the adjacent nodes of each node based on the building design information; determining the adjacent node health coefficients of the adjacent nodes of each node based on the node monitoring data; obtaining the bending stiffness and betweenness centrality of the node components; constructing a building finite element model based on the building design information; determining the bearing capacity reduction ratio based on the building finite element model; determining the adjacent node influence coefficients of the adjacent nodes of each node based on the building finite element model; determining the foundation node weights based on the bending stiffness of the node components, the betweenness centrality of the nodes, and the bearing capacity reduction ratio; and determining the dynamic node weights based on the adjacent node health coefficients, the adjacent node influence coefficients, the node health coefficients, and the foundation node weights. The dynamic node weight is determined based on the adjacent node health coefficient, the adjacent node influence coefficient, the node health coefficient, and the basic node weight, including: according to the formula: Determine the dynamic node weight of the i-th node at the j-th time of the monitoring period. Where, if is a conditional function, , , and This is the second preset parameter. Let the weight of the i-th node be the base node weight. Let be the node health coefficient of the i-th node at the j-th time point in the monitoring period. Let be the neighboring node influence coefficient of the k-th neighboring node of the i-th node. Let be the health coefficient of the k-th neighboring node of the i-th node at the j-th time of the monitoring period. The preset neighbor node health coefficient threshold is the threshold value for the k-th neighbor node of the i-th node. The preset node health coefficient threshold is K, where K is the number of adjacent nodes, k≤K, and both k and K are positive integers. The overall building stability coefficient is determined based on the real-time environmental data, the dynamic node weights, and the node health coefficients, including: determining the node health coefficient change rate based on the node health coefficients; determining the first standard deviation based on the node health coefficient change rates of multiple nodes; determining the real-time environmental load coefficient based on the real-time environmental data; and determining the overall building stability coefficient based on the dynamic node weights, the node health coefficients, the first standard deviation, and the real-time environmental load coefficients. The overall building stability coefficient is determined based on the dynamic node weights, the node health coefficients, the first standard deviation, and the real-time environmental load coefficients, including: according to the formula: Determine the overall building stability coefficient at time j of the monitoring period. Where, if is a conditional function. and The third preset weight, Let be the node health coefficient of the i-th node at the j-th time point in the monitoring period. Let be the dynamic node weight of the i-th node at the j-th time point in the monitoring period. Let the first standard deviation be the value at time j of the monitoring period. Let be the real-time environmental load coefficient of the i-th node at the j-th moment of the monitoring period, where n is the number of nodes, i ≤ n, and i and n are both positive integers.

2. The method for monitoring the structural stability of prefabricated buildings according to claim 1, characterized in that, Determining the real-time environmental load coefficient based on the real-time environmental data includes: determining real-time wind pressure, real-time snow depth, and real-time number of people based on the real-time environmental data; acquiring historical environmental data; determining a reference wind pressure, reference snow depth, and reference number of people based on the historical environmental data; and determining the real-time environmental load coefficient based on the real-time wind pressure, the real-time snow depth, the real-time number of people, the reference wind pressure, the reference snow depth, and the reference number of people.

3. A prefabricated building structure stability monitoring system, characterized in that, A method for performing any one of claims 1-2, comprising: a node data module for acquiring node monitoring data at multiple times during a monitoring period using a combination of sensors installed at the nodes; an environmental data module for acquiring real-time environmental data at multiple times during the monitoring period; a design information module for acquiring architectural design information; a node health module for determining a node health coefficient based on the node monitoring data; a dynamic weighting module for determining a dynamic node weight based on the node health coefficient, the architectural design information, and the node monitoring data; a stability coefficient module for determining an overall building stability coefficient based on the real-time environmental data, the dynamic node weight, and the node health coefficient; and a real-time monitoring module for monitoring the structural stability of the building under test based on the node health coefficient and the overall building stability coefficient.

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