Ultrasonic-based monitoring method for performance of fabricated steel beam-column connections

Abstract

The application provides an ultrasonic-based assembled steel structure beam-column connection performance monitoring method, and relates to the technical field of performance monitoring. The method comprises the following steps: restoring the steel structure design information of a target building through BIM technology modeling, extracting connection node parameters, and classifying the nodes using a node importance evaluation model. Based on the stress conduction fitting results, M beam-column connection monitoring trees are constructed. A pre-constructed mechanical monitoring and early warning device and a performance fault identifier are used to monitor the performance of the top connection node in real time and locate the fault node. This process improves monitoring efficiency and structural safety. The application can solve the technical problems of the prior art, such as the lack of effective information integration and modeling methods, the inability to differentiate monitoring of different nodes, the differences between design information and actual construction, the reduction of efficiency, and the further impact on structural safety. The application improves the accuracy, real-time performance and efficiency of beam-column connection node performance monitoring in assembled steel structure buildings.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of performance monitoring, and particularly relates to an ultrasonic-based performance monitoring method for beam-column connection of fabricated steel structure. BACKGROUND

[0002] Fabricated steel structure buildings prefabricate steel beams, steel columns and other components in factories, and then are transported to construction sites for rapid assembly. This building method can effectively improve construction efficiency, shorten construction period, reduce environmental pollution caused by on-site construction, and also can guarantee the quality and safety of buildings.

[0003] At present, the existing structure monitoring method may lack effective information integration means, resulting in differences between design information and actual construction. It may monitor all nodes equally, which is not only inefficient, but also may ignore the potential risks of some key nodes. Through a node importance evaluation model, the most critical nodes such as top nodes can be identified for key monitoring.

[0004] To sum up, the prior art lacks effective information integration and modeling means, cannot differentiate monitoring of different nodes, results in differences between design information and actual construction, reduces efficiency, and further affects the safety of the structure. SUMMARY

[0005] The purpose of the present application is to provide an ultrasonic-based performance monitoring method for beam-column connection of fabricated steel structure, to solve the technical problems that the prior art lacks effective information integration and modeling means, cannot differentiate monitoring of different nodes, results in differences between design information and actual construction, reduces efficiency, and further affects the safety of the structure. The accuracy, real-time performance and efficiency of performance monitoring of beam-column connection nodes in fabricated steel structure buildings are improved.

[0006] In view of the above problems, the present application provides an ultrasonic-based performance monitoring method for beam-column connection of fabricated steel structure.

[0007] The application provides an ultrasonic-based assembly type steel structure beam-column connection performance monitoring method, which comprises the following steps: obtaining steel structure design information of a target building interactively, and restoring the steel structure design information based on BIM technology to obtain a building steel structure model; extracting connection node parameters based on the building steel structure model to obtain a plurality of node parameter sets of a plurality of beam-column connection nodes; introducing a node importance evaluation model to analyze the plurality of node parameter sets, and dividing the plurality of beam-column connection nodes into a top node set, an intermediate node set and a basic node set based on the analysis result, wherein the top node set comprises M top connection nodes; fitting stress conduction based on the building steel structure model, and assembling the top node set, the intermediate node set and the basic node set based on the fitting result to obtain M beam-column connection monitoring trees, wherein the M beam-column connection monitoring trees are tree-shaped monitoring structures constructed by taking the M top connection nodes as top events; pre-constructing a mechanical monitoring early warning device and a performance fault identifier; performing real-time performance monitoring on the M top connection nodes based on the mechanical monitoring early warning device, and generating an early warning signal when performance fault early warning occurs in the M top connection nodes, and activating the performance fault identifier to locate a fault node based on the M beam-column connection monitoring trees as a fault positioning guide to obtain a target performance fault node.

[0008] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0009] The steel structure design information of the target building is obtained interactively, and the steel structure design information is modeled and reconstructed based on BIM technology to obtain a building steel structure model. Connection node parameters are extracted based on the building steel structure model to obtain multiple node parameter sets for multiple beam-column connection nodes. A node importance evaluation model is introduced to analyze the multiple node parameter sets, and based on the analysis results, the multiple beam-column connection nodes are divided into a top node set, an intermediate node set, and a foundation node set, wherein the top node set includes M top connection nodes. Force transmission fitting is performed based on the building steel structure model, and the top node set, intermediate node set, and foundation node set are assembled based on the fitting results to obtain M beam-column connection monitoring trees, wherein the M beam-column connection... The monitoring tree is a tree-like monitoring structure built with the M top connection nodes as top events. A mechanical monitoring and early warning device and a performance fault identifier are pre-built. Based on the mechanical monitoring and early warning device, real-time performance monitoring is performed on the M top connection nodes. When a performance fault warning occurs at one of the M top connection nodes, the mechanical monitoring and early warning device generates an early warning signal and activates the performance fault identifier to locate the fault node using the M beam-column connection monitoring tree as a fault location guide. This effectively solves the technical problem in existing technologies where the lack of effective information integration and modeling methods prevents differentiated monitoring of different nodes, leading to discrepancies between design information and actual construction, reducing efficiency, and further affecting structural safety. This improves the accuracy, real-time performance, and efficiency of beam-column connection node performance monitoring in prefabricated steel structure buildings.

[0010] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application 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 merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the ultrasound-based method for monitoring the connection performance of prefabricated steel structure beams and columns according to this application.

[0013] Figure 2This is a schematic diagram illustrating the process of calculating the load ratio of multiple nodes in the ultrasound-based prefabricated steel structure beam-column connection performance monitoring method of this application. Detailed Implementation

[0014] This application provides an ultrasound-based method for monitoring the performance of beam-column connections in prefabricated steel structures. This addresses the technical problem in existing technologies where the lack of effective information integration and modeling methods prevents differentiated monitoring of different nodes, leading to discrepancies between design information and actual construction, reduced efficiency, and ultimately, compromised structural safety. The method improves the accuracy, real-time performance, and efficiency of beam-column connection performance monitoring in prefabricated steel structures.

[0015] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0016] Example 1

[0017] Please see the appendix Figure 1 This application provides a method for monitoring the connection performance of prefabricated steel structure beams and columns based on ultrasound, wherein the method specifically includes the following steps:

[0018] S1: Interact to obtain the steel structure design information of the target building, and model and restore the steel structure design information based on BIM technology to obtain the building steel structure model.

[0019] Specifically, this involves collecting steel structure design information for the building project, including design drawings, structural analysis reports, material specifications, and construction details. The collected design information is then imported into BIM software such as Autodesk Revit, Bentley Systems, and ArchiCAD. In the BIM software, a 3D model is created using the imported design information. This includes accurately drawing the location, dimensions, and relationships of steel beams, columns, nodes, and other structural elements. Relevant parameters, such as material properties, cross-sectional dimensions, and connection types, are assigned to each structural element. The BIM model can be continuously updated to reflect changes and adjustments during actual construction. Ideally, steel structure design drawings and specifications are obtained from the design team, and the CAD drawings are imported into Autodesk Revit as the base map. Steel beams and columns are created using Revit's structural framing tools, with the corresponding geometric dimensions and locations entered. Beam-column connection nodes are simulated using the structural connection tools, and appropriate connection types are selected. Material and cross-sectional properties are assigned to each element in the model. Revit's parametric capabilities are used to assign parameters to each structural element, such as steel grade and cross-sectional dimensions, and constraints are set, such as the rotational degrees of freedom of connection nodes. In Revit, compare the model with the original design drawings to check for accuracy, and use Revit's clash detection function to check for interference problems in the model.

[0020] S2: Based on the building steel structure model, extract the connection node parameters to obtain multiple node parameter sets for multiple beam-column connection nodes.

[0021] Specifically, this involves identifying all beam-column connection nodes in the BIM model. This is accomplished by analyzing the geometric relationships and connectivity of the structural frame within the model. For each identified node, relevant parameter information is extracted. These parameters include geometric parameters such as node location, connection angle, and cross-sectional dimensions; material properties such as yield strength, modulus of elasticity, and density of steel; connection type such as welding, bolting, hinged, or fixed connection; and stress state such as design load, bending moment, shear force, and axial force. The extracted parameters are then organized into a parameter set for each node.

[0022] S3: Introduce a node importance evaluation model to analyze the multiple node parameter sets, and based on the analysis results, divide the multiple beam-column connection nodes into a top node set, an intermediate node set, and a foundation node set, wherein the top node set includes M top connection nodes.

[0023] Specifically, the node importance assessment model evaluates the importance of each node based on its parameter set. This includes the node's stress conditions, such as maximum bending moment, shear force, and axial force; its geometric location, such as whether it is located at a frame corner, edge, or support point; its function in the structure, such as whether it is a node on the critical path of load transfer; and its redundancy, such as whether the structure can remain stable in the event of node failure. The established model analyzes the parameter set of each node and calculates its importance score. Based on the analysis results, nodes are divided into different sets: The top node set, which has the highest importance in the structure and bears the largest load or is most critical to structural stability. The top node set includes M top connection nodes. The intermediate node set, which has less importance than the top nodes but still performs important load transfer functions; and the basic node set, which has relatively low importance in the structure and includes nodes that have a smaller impact on the overall structural performance.

[0024] S4: Based on the building steel structure model, perform force transmission fitting, and assemble the top node set, intermediate node set and basic node set based on the fitting results to obtain M beam-column connection monitoring trees, wherein the M beam-column connection monitoring trees are tree-like monitoring structures constructed by taking the M top connection nodes as top events.

[0025] Specifically, structural analysis software is used to perform force transmission analysis on the building's steel structure model. This simulates the structure's response under load, determining the magnitude and direction of the force at each node. Based on the results of the force transmission analysis, a mathematical model is fitted to describe the force transmission process within the structure. This model is linear. The fitted model is used to identify the force transmission paths within the structure. These paths start from the load application point, pass through beam-column connection nodes, and finally reach the foundation. Based on the identified transmission paths, M beam-column connection monitoring trees are constructed. Each tree structure has a top connection node as its top event, containing all relevant nodes from which the force is transmitted downwards. These nodes are organized according to the magnitude of the force and the importance of the transmission path, forming a hierarchical structure. The top node set, intermediate node set, and foundation node set are assembled into the monitoring trees according to the force transmission paths. Each node in the top node set becomes a top event in a monitoring tree, and nodes in the intermediate and foundation node sets are assigned to the corresponding monitoring trees based on their position on the transmission path. The constructed monitoring tree is validated to ensure that each node is correctly placed in the appropriate position in the tree structure and that the magnitude and direction of the forces are correctly represented in the tree.

[0026] S5: Pre-built mechanical monitoring and early warning device and performance fault identifier.

[0027] Specifically, monitoring parameters include strain, displacement, acceleration, temperature, and crack width. Appropriate sensors are selected based on these parameters. For example, strain gauges are used to measure strain, accelerometers to measure vibration, and temperature sensors to monitor temperature changes. Sensors are installed at critical locations on the structure, particularly areas under high stress and previously identified top, intermediate, and foundation node sets. Warning thresholds are set for each monitoring parameter based on structural design specifications and safety standards. These thresholds should be able to distinguish between minor changes under normal operating conditions and significant changes that may indicate structural performance degradation. A mechanical monitoring and warning system is trained using neural networks and historical data. The warning system is tested and validated to ensure it can accurately identify warning signals in practical operation. Historical failure cases of similar structures are collected and compiled to establish a failure database. These failure cases should include descriptions, causes, effects, and solutions. Cases in the failure database are analyzed to identify common failure modes and their corresponding symptoms. Based on these failure modes, a performance failure identification model is trained, including pattern recognition and fault tree analysis. The failure identifier is also tested and validated to ensure it can accurately identify failures in practical applications.

[0028] S6: Based on the mechanical monitoring and early warning device, real-time performance monitoring is performed on the M top connection nodes. When a performance fault warning occurs at the M top connection nodes, the mechanical monitoring and early warning device generates an early warning signal and activates the performance fault identifier to locate the fault node using the M beam-column connection monitoring tree as the fault location guide, thereby obtaining the target performance fault node.

[0029] Specifically, the installed sensors and data acquisition system monitor the M top connection nodes in real time. The sensors collect data including strain, displacement, vibration, and temperature, reflecting the real-time state of the structure. This real-time data is input into a mechanical monitoring and early warning system, which generates an early warning signal. When an early warning signal is generated, a performance fault identifier is activated. The fault identifier uses the collected data and a historical fault database to analyze the current structural state. Utilizing the M beam-column connection monitoring tree as a guide for fault location, the fault identifier analyzes the data at each node to determine the specific location of the fault. The monitoring tree provides the path of force transmission, helping to identify the root cause of the fault and the affected area. By analyzing the monitoring data and the monitoring tree, the fault identifier locates the specific performance fault node.

[0030] Furthermore, this application also includes:

[0031] The node importance evaluation model includes an evaluation parameter calling layer, a node importance calculation layer, and a node level identification layer. It pre-sets an importance evaluation index set and adapts a text scheduling engine based on this set, synchronizing the text scheduling engine to the evaluation parameter calling layer. It pre-constructs a node importance evaluation function and synchronizes this function to the node importance calculation layer. Based on the building steel structure model, it identifies the properties of connection nodes to obtain H hinged nodes. It pre-sets top node judgment constraints and stores these constraints and the H hinged nodes in the node level identification layer, thus completing the construction of the node importance evaluation model.

[0032] Specifically, the importance evaluation index set includes the node's stress condition, geometric location, connection type, redundancy, etc. A text scheduling engine is used to manage and invoke evaluation parameters. Based on the preset importance evaluation index set, the text scheduling engine is adapted to enable it to retrieve and provide corresponding parameters as needed. The adapted text scheduling engine is synchronized to the evaluation parameter invocation layer, allowing for quick access and invocation of evaluation parameters when needed. A function for calculating node importance is constructed based on the evaluation indices. This function is a mathematical formula used to convert the evaluation indices into quantitative scores of node importance. The constructed evaluation function is synchronized to the node importance calculation layer, enabling it to receive parameters from the evaluation parameter invocation layer and output the node's importance score. Based on the building steel structure model, the properties of all connected nodes are identified, such as hinged nodes, fixed nodes, etc., with H hinged nodes specifically identified. Top node judgment constraints are based on the node's importance score; for example, the top 10% of nodes with the highest scores are defined as top nodes. The top node judgment constraints and the information of the identified H hinged nodes are stored in the node level identification layer.

[0033] Furthermore, this application also includes:

[0034] The node importance evaluation function is as follows: Where I is the node importance index, The proportion of nodal loads. The number of node load paths. Mean ratio of stiffness of adjacent connections For load-bearing capacity, For shear force, It represents the bending moment.

[0035] Specifically, the node importance index measures the significance of a node in a structure. It is composed of multiple evaluation indicators, including the node's stress state, location, and connection type. The node load ratio reflects the proportional relationship between the load borne by the node and the loads of the surrounding structure. A higher ratio generally indicates a higher node importance. The number of load paths through a node includes the number of load paths passing through it. If a node has multiple load paths passing through it, its importance is generally higher. The ratio of the stiffness of adjacent connections to the mean compares the stiffness of a node with that of its adjacent connections. If the stiffness of a node is significantly higher or lower than that of its surrounding nodes, this can have a significant impact on the overall performance of the structure. Bearing capacity directly considers the node's load-bearing capacity, i.e., the maximum load the node can withstand. Shear force focuses on the magnitude of the shear force borne by the node. Nodes with large shear forces are more prone to shear failure. Bending moment focuses on the magnitude of the bending moment borne by the node. Nodes with large bending moments are more prone to bending failure.

[0036] Furthermore, this application also includes:

[0037] The importance evaluation index set includes load ratio index, load path number index, connection stiffness ratio index, bearing capacity index, shear force index, and bending moment index.

[0038] Specifically, the load proportion index reflects the ratio between the load borne by a node and the total load of its structural portion. A higher load proportion borne by a node indicates a greater impact on structural performance, and therefore, its importance. The number of load paths index includes the number of load transfer paths passing through the node. If a node has multiple load paths, its impact on structural stability and function will be greater. The connection stiffness ratio index compares the stiffness of a node with its adjacent connections. Differences in stiffness between a node and its surrounding nodes can affect the load distribution and response of the structure. The bearing capacity index directly measures the bearing capacity of a node, i.e., the maximum load the node can withstand. The shear force index focuses on the magnitude of the shear force borne by the node. The bending moment index focuses on the magnitude of the bending moment borne by the node. Bending moment is an important parameter for measuring the degree of compression and bending of a node; nodes with large bending moments may be more prone to bending failure.

[0039] Furthermore, such as Figure 2 As shown, this application also includes:

[0040] Based on the building steel structure model, adjacent connections of the multiple beam-column connection nodes are determined to obtain multiple sets of adjacent connection nodes. Based on the node composition of the multiple sets of adjacent connection nodes, the stiffness data of the multiple node parameter sets is retrieved to obtain multiple sets of adjacent node stiffness parameters. Adjacent connection stiffness ratios are calculated using the multiple beam-column connection nodes as the numerator and the multiple sets of adjacent node stiffness parameters as the denominator, resulting in multiple sets of adjacent stiffness ratios. The average of these multiple sets of adjacent stiffness ratios is calculated to obtain the average of multiple adjacent connection stiffness ratios, which is then mapped and updated to the multiple node parameter sets. Load parameters are retrieved based on the multiple node parameter sets to obtain multiple node load parameters. These multiple node load parameters are summed to obtain the total structural load. Based on the total structural load and the multiple node load parameters, the multiple node load proportions of the multiple beam-column connection nodes are calculated, and these multiple node load proportions are mapped and updated to the multiple node parameter sets.

[0041] Specifically, based on the building steel structure model, adjacent connections are determined for each beam-column connection node to identify the set of adjacent connected nodes for each node. These adjacent nodes are those directly connected to the target node. The adjacent connected node set for each node is traversed, and the stiffness parameters of these nodes are retrieved from the node parameter set. These parameters include the node's elastic modulus and moment of inertia. For each beam-column connection node, the adjacent connection stiffness ratio is calculated using its own stiffness as the numerator and the average stiffness of its adjacent nodes as the denominator. This ratio reflects the stiffness relationship between the node and its adjacent nodes. The average adjacent stiffness ratios for each node are averaged to obtain multiple average adjacent connection stiffness ratios. These averages reflect the stiffness characteristics of the node in the entire structure. The calculated average adjacent connection stiffness ratios are mapped and updated to the corresponding node parameter sets. Thus, the parameter set of each node contains the latest stiffness ratio information. The load parameters for each node are retrieved from the node parameter set; these parameters include the stress state of the node under design loads. The load parameters of all nodes are summed to obtain the total structural load. This represents the sum of the loads borne by the structure at all nodes. Based on the total structural load and the load parameters of each node, the load proportion for each node is calculated. This proportion reflects the importance of each node's load within the total structural load. The calculated load proportions for multiple nodes are then mapped and updated to the corresponding node parameter sets. In this way, each node's parameter set contains the latest load proportion information.

[0042] Furthermore, this application also includes:

[0043] The first node parameter set is synchronized to the node importance evaluation model. The evaluation parameter is invoked through the evaluation parameter invocation layer in the node importance evaluation model, and the evaluation parameter invocation result is calculated through the node importance calculation layer to obtain the first node importance index. The first node parameter set is any one of the plurality of node parameter sets. Similarly, the plurality of node parameter sets are synchronized to the node importance evaluation model. The evaluation parameter is invoked through the evaluation parameter invocation layer in the node importance evaluation model, and the evaluation parameter invocation result is calculated through the node importance calculation layer to obtain multiple node importance indices. In the node level identification layer, data is removed from the multiple node importance indices based on the H hinge nodes to obtain multiple updated importance indices, and the H hinge nodes are used as the basic node set. The multiple updated importance indices are traversed using a top node judgment constraint to obtain the top node set and the intermediate node set. The updated importance index of any beam-column connection node in the top node set is greater than the top node judgment constraint, and the updated importance index of any beam-column connection node in the intermediate node set is less than the top node judgment constraint.

[0044] Specifically, one node parameter set is selected from multiple node parameter sets and synchronized to the node importance evaluation model. The node importance evaluation model calls the evaluation parameters for that node through the evaluation parameter call layer. The evaluation parameter call result is calculated by the node importance calculation layer to obtain the first node importance index for that node. The remaining node parameter sets are then synchronized to the node importance evaluation model. Each node parameter set is processed through the evaluation parameter call layer and the node importance calculation layer to calculate the node importance index for each node. In the node level identification layer, data is removed from the node importance indices of all nodes based on H hinged nodes. The removal operation includes deleting data from nodes whose importance cannot be accurately assessed due to the characteristics of hinged nodes. After data removal, multiple updated importance indices are obtained. Using the H hinged nodes as the basic node set, all updated importance indices are traversed using the top node judgment constraint. The updated importance index of any beam-column connection node in the top node set is greater than the top node judgment constraint. The updated importance index of any beam-column connection node in the intermediate node set is less than the top node judgment constraint, thus determining the top node set and the intermediate node set.

[0045] Furthermore, step S4 of this application also includes:

[0046] The first top connection node is called from the M top connection nodes, and the remaining M-1 top connection nodes are marked as force transmission taboos in the building steel structure model. In the building steel structure model, the first top connection node is used as the starting point of force transmission to perform force transmission fitting and obtain the first associated transmission node set. The first associated transmission node set is used to traverse the basic node set to perform node identification, and the first associated transmission node set is divided based on the identification results to obtain the first basic node group and the first intermediate node group. The first top connection node is used as the top event, and the first basic node group and the first intermediate node group are connected step by step based on the force transmission relationship to perform tree monitoring structure construction and generate the first beam-column connection monitoring tree. Similarly, the M top connection nodes are used as top events to assemble the top node set, the intermediate node set and the basic node set to obtain the M beam-column connection monitoring trees.

[0047] Specifically, the first top connection node is selected from M top connection nodes. The remaining M-1 top connection nodes are marked as force transmission taboos in the building steel structure model, meaning these nodes will not be considered in subsequent force transmission processes. Using the selected first top connection node as the starting point for force transmission, force transmission fitting is performed in the building steel structure model. Force transmission fitting uses structural analysis software to simulate the structure's response under load, determining how forces are transmitted from top nodes to other nodes. Through force transmission fitting, the first associated transmission node set is obtained; these nodes transmit forces from top nodes to foundation nodes. The first associated transmission node set is used to traverse the foundation node set, identifying which nodes belong to the first foundation node group and which belong to the first intermediate node group based on the traversal results. Using the first top connection node as the top event, the first foundation node group and the first intermediate node group are connected level by level based on the force transmission relationship. A tree-like monitoring structure is constructed, generating the first beam-column connection monitoring tree. Iteratively, using the remaining M-1 top connection nodes as top events, the top node set, intermediate node set, and foundation node set are assembled. Each iteration builds a new beam-column connection monitoring tree until all M top connection nodes are used to build the monitoring tree.

[0048] Furthermore, this application also includes:

[0049] The system interacts with the multiple node parameter sets to obtain M sets of stress constraints, shear constraints, and axial force constraints for the M top-connected nodes, and constructs M performance comparison and early warning branches based on the M sets of stress constraints, shear constraints, and axial force constraints; it then connects the M performance comparison and early warning branches in parallel to obtain the mechanical monitoring and early warning device; it interactively obtains multiple sample fault image sets of various apparent performance faults, and constructs multiple apparent fault identification branches based on the multiple sample fault image sets; it interactively obtains multiple sample fault ultrasonic signal sets of various internal performance faults, and constructs multiple internal fault identification branches based on the multiple sample fault ultrasonic signal sets; it then connects the multiple apparent fault identification branches in parallel to obtain an apparent fault identification module, connects the multiple internal fault identification branches in parallel to obtain an internal fault identification module, and finally connects the apparent fault identification module and the internal fault identification module in parallel to complete the construction of the performance fault identifier.

[0050] Specifically, the system interactively acquires M sets of stress constraints, shear constraints, and axial force constraints for M top-connection nodes. These constraints are set based on structural design codes and safety standards and are used to define the normal operating range of the nodes. Based on the M sets of stress constraints, shear constraints, and axial force constraints, M performance comparison and early warning branches are constructed. Each branch corresponds to a top-connection node and is used to compare the relationship between the actual stress, shear force, and axial force of the node and the constraint values ​​in real time. The M performance comparison and early warning branches are connected in parallel to form a mechanical monitoring and early warning device. The parallel early warning device can simultaneously monitor the performance of all top-connection nodes and issue an early warning signal when any node exceeds the constraint range. The system also interactively acquires multiple sample fault image sets for various apparent performance faults. These image sets contain known fault images and are used to train the fault identification model. Based on the multiple sample fault image sets, multiple apparent fault identification branches are constructed. Each branch corresponds to a type of apparent fault and is used to identify faults such as cracks and deformations on the structural surface. Finally, the system interactively acquires multiple sample fault ultrasonic signal sets for various internal performance faults. These signal sets contain known fault ultrasonic signals and are used to train the fault identification model. Based on the multiple sample fault ultrasonic signal sets, multiple internal fault identification branches are constructed. Each branch corresponds to a type of internal fault, used to identify internal structural faults such as cracks and corrosion. Multiple apparent fault identification branches are connected in parallel to form an apparent fault identification module, and multiple internal fault identification branches are connected in parallel to form an internal fault identification module. Connecting the apparent fault identification module and the internal fault identification module in parallel completes the construction of the performance fault identifier.

[0051] Furthermore, this application also includes:

[0052] Based on the mechanical monitoring and early warning device, a first performance comparison and early warning branch is initiated. This first performance comparison and early warning branch, among the M performance comparison and early warning branches, is used to perform targeted mechanical performance early warning judgments on the first top connection node. The first performance comparison and early warning branch includes a first stress constraint, a first shear force constraint, and a first axial force constraint. Real-time performance monitoring is performed on the first top connection node based on the first performance comparison and early warning branch. When the real-time stress of the first top connection node does not meet the first stress constraint and / or the real-time shear force does not meet the first shear force constraint and / or the real-time axial force does not meet the first axial force constraint, the mechanical monitoring and early warning device generates a first early warning signal. The first early warning signal is used to activate the performance fault identifier to perform apparent fault detection and identification and internal fault detection and identification on the first top connection node. If the performance fault identifier's fault detection and identification result for the first top connection node is empty, then the first beam-column connection monitoring tree is used as the fault location guide, and the fault node is located by traversing the first beam-column connection monitoring tree layer by layer to obtain the target performance fault node.

[0053] Specifically, based on the mechanical monitoring and early warning system, a first performance comparison and early warning branch is initiated. This branch is used for targeted monitoring of the mechanical properties of the first top connection node. Real-time performance monitoring is performed on the first top connection node, collecting real-time stress, shear force, and axial force data. When the real-time stress, shear force, or axial force of the first top connection node does not meet the constraints in the first performance comparison and early warning branch, the mechanical monitoring and early warning system generates a first early warning signal. This first early warning signal activates the performance fault identifier. The performance fault identifier performs apparent fault detection and identification as well as internal fault detection and identification on the first top connection node. If the fault detection and identification result for the first top connection node is empty, i.e., no fault is found, the next step is performed. Using the first beam-column connection monitoring tree as a fault location guide, the tree is traversed layer by layer. By traversing the monitoring tree, the specific fault node, i.e., the target performance fault node, can be located.

[0054] In summary, the ultrasound-based method for monitoring the connection performance of prefabricated steel structure beams and columns provided in this application has the following technical advantages:

[0055] The steel structure design information of the target building is obtained interactively, and the steel structure design information is modeled and reconstructed based on BIM technology to obtain a building steel structure model. Connection node parameters are extracted based on the building steel structure model to obtain multiple node parameter sets for multiple beam-column connection nodes. A node importance evaluation model is introduced to analyze the multiple node parameter sets, and based on the analysis results, the multiple beam-column connection nodes are divided into a top node set, an intermediate node set, and a foundation node set, wherein the top node set includes M top connection nodes. Force transmission fitting is performed based on the building steel structure model, and the top node set, intermediate node set, and foundation node set are assembled based on the fitting results to obtain M beam-column connection monitoring trees, wherein the M beam-column connection... The monitoring tree is a tree-like monitoring structure built with the M top connection nodes as top events. A mechanical monitoring and early warning device and a performance fault identifier are pre-built. Based on the mechanical monitoring and early warning device, real-time performance monitoring is performed on the M top connection nodes. When a performance fault warning occurs at one of the M top connection nodes, the mechanical monitoring and early warning device generates an early warning signal and activates the performance fault identifier to locate the fault node using the M beam-column connection monitoring tree as a fault location guide. This effectively solves the technical problem in existing technologies where the lack of effective information integration and modeling methods prevents differentiated monitoring of different nodes, leading to discrepancies between design information and actual construction, reducing efficiency, and further affecting structural safety. This improves the accuracy, real-time performance, and efficiency of beam-column connection node performance monitoring in prefabricated steel structure buildings.

[0056] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0057] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for monitoring the connection performance of prefabricated steel structure beams and columns based on ultrasound, characterized in that, The method includes: The steel structure design information of the target building is obtained interactively, and the steel structure design information is modeled and restored based on BIM technology to obtain the building steel structure model; Based on the aforementioned building steel structure model, connection node parameters are extracted to obtain multiple node parameter sets for multiple beam-column connection nodes; A node importance evaluation model is introduced to analyze the multiple node parameter sets, and based on the analysis results, the multiple beam-column connection nodes are divided into a top node set, an intermediate node set, and a foundation node set, wherein the top node set includes M top connection nodes; Based on the building steel structure model, the force transmission is fitted, and based on the fitting results, the top node set, intermediate node set and foundation node set are assembled to obtain M beam-column connection monitoring trees, wherein the M beam-column connection monitoring trees are tree-like monitoring structures constructed by taking the M top connection nodes as top events; Pre-built mechanical monitoring and early warning system and performance fault identifier; Based on the mechanical monitoring and early warning device, real-time performance monitoring is performed on the M top connection nodes. When a performance failure warning occurs on the M top connection nodes, the mechanical monitoring and early warning device generates an early warning signal and activates the performance failure identifier to locate the fault node using the M beam-column connection monitoring tree as the fault location guide, thereby obtaining the target performance failure node. The ultrasound-based method for monitoring the connection performance of prefabricated steel structure beams and columns also includes: The node importance evaluation model includes an evaluation parameter calling layer, a node importance calculation layer, and a node level identification layer; A set of importance evaluation indicators is preset, and a text scheduling engine is adapted based on the set of importance evaluation indicators, and the text scheduling engine is synchronized to the evaluation parameter calling layer; A node importance evaluation function is pre-constructed, and the node importance evaluation function is synchronized to the node importance calculation layer; Based on the steel structure model of the building, the properties of the connection nodes are identified to obtain H hinged nodes; Preset top node judgment constraints, and store the top node judgment constraints and the H hinge nodes in the node level identification layer to complete the construction of the node importance evaluation model; The importance evaluation index set includes load ratio index, load path number index, connection stiffness ratio index, bearing capacity index, shear force index, and bending moment index. A node importance evaluation model is introduced to analyze the multiple node parameter sets, and based on the analysis results, the multiple beam-column connection nodes are divided into a top node set, an intermediate node set, and a foundation node set, wherein the top node set includes M top connection nodes. The method includes: The first node parameter set is synchronized to the node importance evaluation model. The evaluation parameter is called through the evaluation parameter calling layer in the node importance evaluation model, and the evaluation parameter calling result is calculated through the node importance calculation layer to obtain the first node importance index. The first node parameter set is any one of the multiple node parameter sets. Similarly, the multiple node parameter sets are synchronized to the node importance evaluation model. The evaluation parameters are called through the evaluation parameter calling layer in the node importance evaluation model, and the evaluation parameter calling results are calculated through the node importance calculation layer to obtain multiple node importance indices. In the node level identification layer, data is removed from the multiple node importance indices based on the H hinge nodes to obtain multiple updated importance indices, and the H hinge nodes are used as the basic node set; The multiple update importance indices are traversed using the top node judgment constraint to obtain the top node set and the intermediate node set. The update importance index of any beam-column connection node in the top node set is greater than the top node judgment constraint, and the update importance index of any beam-column connection node in the intermediate node set is less than the top node judgment constraint. Based on the aforementioned building steel structure model, force transmission is fitted, and based on the fitting results, the top node set, intermediate node set, and foundation node set are assembled to obtain M beam-column connection monitoring trees. The method includes: The first top connection node is called from the M top connection nodes, and the remaining M-1 top connection nodes are marked as force transmission taboos in the building steel structure model; In the steel structure model of the building, the first top connection node is used as the starting point of force transmission for force transmission fitting to obtain the first set of associated transmission nodes; The first associated transmission node set is used to traverse the basic node set to identify nodes, and the first associated transmission node set is divided based on the identification results to obtain a first basic node group and a first intermediate node group. Using the first top connection node as the top event, the first basic node group and the first intermediate node group are connected step by step according to the force transmission relationship to construct a tree-like monitoring structure and generate the first beam-column connection monitoring tree. Similarly, by taking the M top connection nodes as top events, the top node set, intermediate node set, and basic node set are assembled to obtain the M beam-column connection monitoring trees; The method for pre-constructing a mechanical monitoring and early warning device and a performance fault identifier includes: The multiple node parameter sets are interacted to obtain M sets of stress constraints, shear constraints and axial force constraints for the M top connection nodes, and M performance comparison and early warning branches are constructed based on the M sets of stress constraints, shear constraints and axial force constraints. The M performance comparison and early warning branches are connected in parallel to obtain the mechanical monitoring and early warning device; Multiple sample fault image sets of various apparent performance faults are obtained interactively, and multiple apparent fault identification branches are constructed based on the multiple sample fault image sets. Multiple sample fault ultrasound signal sets of various internal performance faults are obtained interactively, and multiple internal fault identification branches are constructed based on the multiple sample fault ultrasound signal sets. By connecting the multiple apparent fault identification branches in parallel, an apparent fault identification module is obtained; by connecting the multiple internal fault identification branches in parallel, an internal fault identification module is obtained; and by connecting the apparent fault identification module and the internal fault identification module in parallel, the performance fault identifier is constructed.

2. The method for monitoring the connection performance of prefabricated steel structure beams and columns based on ultrasound as described in claim 1, characterized in that, The node importance evaluation function is as follows: ； Where I is the node importance index, The proportion of nodal loads. The number of node load paths. Mean ratio of stiffness of adjacent connections For load-bearing capacity, For shear force, It represents the bending moment.

3. The method for monitoring the connection performance of prefabricated steel structure beams and columns based on ultrasound as described in claim 2, characterized in that, Based on the aforementioned building steel structure model, connection node parameters are extracted to obtain multiple node parameter sets for multiple beam-column connection nodes. Subsequently, the method includes: Based on the building steel structure model, the adjacent connection of the multiple beam-column connection nodes is determined to obtain a set of multiple adjacent connection nodes of the multiple beam-column connection nodes. Based on the nodes of the multiple adjacent connected node sets, the stiffness data is retrieved by traversing the multiple node parameter sets to obtain multiple sets of adjacent node stiffness parameters. Using the multiple beam-column connection nodes as the numerator and the multiple sets of adjacent node stiffness parameters as the denominator, the adjacent connection stiffness ratio is calculated to obtain multiple sets of adjacent stiffness ratios. The average of the multiple sets of adjacent stiffness ratios is calculated to obtain multiple average values ​​of adjacent connection stiffness ratios. The average values ​​of multiple adjacent connection stiffness ratios are then mapped and updated to the multiple node parameter sets. Load parameters are called based on the multiple node parameter sets to obtain multiple node load parameters. The multiple node load parameters are summed to obtain the total structural load. Based on the total structural load and the multiple node load parameters, the multiple node load ratios of the multiple beam-column connection nodes are calculated, and the multiple node load ratios are mapped and updated to the multiple node parameter sets.

4. The method for monitoring the connection performance of prefabricated steel structure beams and columns based on ultrasound as described in claim 1, characterized in that, The mechanical monitoring and early warning device performs real-time performance monitoring on the M top connection nodes. When a performance failure warning occurs at one of the M top connection nodes, the mechanical monitoring and early warning device generates an early warning signal and activates the performance failure identifier to locate the fault node using the M beam-column connection monitoring tree as a fault location guide, thereby obtaining the target performance failure node. The method further includes: Based on the mechanical monitoring and early warning device, the first performance comparison and early warning branch is activated. The first performance comparison and early warning branch is used to make a targeted mechanical performance early warning judgment of the first top connection node among the M performance comparison and early warning branches. The first performance comparison and early warning branch includes a first stress constraint, a first shear force constraint, and a first axial force constraint. Based on the real-time performance monitoring of the first top connection node in the first performance comparison and early warning branch, when the real-time stress of the first top connection node does not meet the first stress constraint and / or the real-time shear force does not meet the first shear force constraint and / or the real-time axial force does not meet the first axial force constraint, the mechanical monitoring and early warning device generates a first early warning signal. The first warning signal is used to activate the performance fault identifier to perform apparent fault detection and identification and internal fault detection and identification on the first top connection node. If the performance fault identifier finds no fault detection result for the first top connection node, then the first beam-column connection monitoring tree is used as the fault location guide, and the fault node is located by traversing the first beam-column connection monitoring tree layer by layer to obtain the target performance fault node.

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