A method for detecting stress state of horizontal sliding roof structure

Abstract

A method for detecting the stress state of a horizontally sliding roof structure comprises the following steps: Step 1: constructing a roof stress detection device; Step 2: installing the roof stress detection device; Step 3: designing a filter circuit to preprocess the collected electromagnetic signals; Step 4: establishing a network model for mapping electromagnetic signals to stress, wherein the network model comprises a pooling layer, a masked multi-head self-attention network, a normalization layer, a residual connection layer, a feedforward network, a normalization layer, a residual connection layer, and a feedforward network; Step 5: training the network model for mapping electromagnetic signals to stress; and Step 6: using the trained network model to assess the stress of the roof structure. By combining the advantages of multiple components, the network model of the present invention accurately detects and assesses the stress state of complex structures, monitors changes in the stress state of the structure in real time, and provides timely data support for structural safety monitoring.

Description

Technical Field

[0001] The present invention relates to the field of stress detection, and in particular to a method for detecting the stress state of a horizontally sliding roof structure. Background Art

[0002] Horizontally sliding roof structures, as an advanced architectural form, are widely used in large public buildings such as stadiums and exhibition halls. Their unique feature is that the roof can slide or expand horizontally to meet different usage requirements. Before the 1980s, horizontally sliding roofs primarily relied on membrane structures with pleated openings and closings. However, this opening method had inherent drawbacks, often causing malfunctions or tearing of the membrane material during operation due to wind and rain. Advances in construction technology in the 1980s made it possible to construct large-scale retractable structures. During this period, horizontally sliding roofs, which utilize rigid steel structures such as arched steel structures, arch shells, partial spherical shells, or flat grids as load-bearing structures, experienced rapid development. These roofing materials primarily consist of membranes, metal sheets, and other lightweight materials. The roof system is composed of several unit panels, which are opened and closed by overlapping and retracting them through movement and rotation. Representative buildings include the Toronto Skydome in Canada, the Marine Dome in Japan, and the Fukuoka Stadium in Japan. However, this structure will undergo complex mechanical changes during movement, such as internal force redistribution and stress concentration, so it is particularly important to monitor and evaluate the stress state of the structure in real time.

[0003] In response to the unique characteristics of horizontally sliding roof structures, this paper proposes a method for detecting the stress state of horizontally sliding roof structures. This method combines multiple steps, including electromagnetic signal acquisition, signal preprocessing, network model establishment and training, and stress assessment, to accurately detect the stress state of the roof structure. This method can be widely applied to stress state detection of various complex structures, providing strong technical support for safety monitoring and assessment in the field of structural engineering. Furthermore, with the continuous development of sensor technology and machine learning algorithms, the detection accuracy and real-time performance of this method will be further improved, providing a more solid guarantee for the safety and reliability of structural engineering. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a method for detecting the stress state of a horizontally sliding roof structure. In practical applications, the collected electromagnetic signals are input into a trained network model, and the model automatically calculates the stress state of the roof structure. The evaluation results can be used to guide the safety monitoring, maintenance, and management of the structure.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for detecting the stress state of a horizontal sliding roof structure comprises the following steps:

[0007] Step 1: Build a roof stress detection device, which consists of a U-shaped magnetic yoke, an excitation coil, a detection coil, and an A / D sampling circuit.

[0008] Step 2: Install the roof stress detection equipment in the selected detection area and debug it to ensure that the equipment can work properly and accurately collect data;

[0009] Step 3: Design a filtering circuit to pre-process the collected electromagnetic signal, including denoising and filtering to improve signal quality;

[0010] Step 4: Establish a network model for electromagnetic signal and stress mapping, which consists of a pooling layer, a masked multi-head self-attention network, a normalization layer, a residual connection layer, and a feedforward network.

[0011] Step 5: training a network model for electromagnetic signal and stress mapping, using a data set consisting of signals and stresses to train the network model for electromagnetic signal and stress mapping;

[0012] Step 6: Use the trained network model to evaluate the stress of the roof structure.

[0013] Furthermore, the process of installing the roof stress detection equipment in step 2 is expressed as follows:

[0014] The roof truss is a flat truss, with steel columns and diagonal braces serving as web members and main beams serving as upper and lower chords. All truss members are box-shaped and made of Q355B material. The stress detection system applies an alternating voltage signal to the excitation coil to magnetize the stress area of ​​the truss to be measured. The U-shaped magnetic yoke and detection coil are then placed in the stress area to detect the magnetic signal in that area.

[0015] Furthermore, the process of establishing the network model of electromagnetic signal and stress mapping in step 4 is expressed as follows:

[0016] The pooling layer can reduce the dimension of the data while retaining important information. The pooling layer is selected as average pooling, and its expression is as follows:

[0017]

[0018] Where, is the output of average pooling, is the electromagnetic signal after processing in step 3, i and j represent the index of the signal row and column, is the set of region indices, p and q are the position offsets within the region;

[0019] The masked multi-head self-attention network is used to improve the model's ability to understand the complex relationships within the data. Its expression is as follows:

[0020]

[0021] Where A represents the attention weight, sofmax() is the softmax function, is the output of the pooling layer, K is the key, V is the value, is the rank of the key;

[0022] The normalization layer can speed up the network training process and prevent the gradient from disappearing or exploding. LN normalization is used;

[0023] The residual connection layer allows the gradient to propagate directly to the deeper layers of the network, which helps to train deep networks. Its expression is as follows:

[0024]

[0025] Where y is the output of the residual connection layer, is the input of the residual connection layer, and is the weight matrix, is the activation function ReLU, is the weight matrix for dimension matching;

[0026] The feedforward network can perform nonlinear transformation on features and enhance the expressiveness of the model;

[0027]

[0028] Where, is the output of the feedforward network layer, y is the input of the feedforward network layer, is the activation function Sigmoid, are the feedforward network weights, is the feedforward network layer bias.

[0029] Furthermore, the stress assessment process in step 6 can be expressed as follows:

[0030] The trained model is integrated into a real-time monitoring system to continuously monitor the stress of the roof structure. The monitoring data is regularly analyzed and reports are generated to assess the long-term health of the roof and predict potential failures. When abnormal stress levels are detected, the system automatically triggers an alert, notifying relevant personnel to take necessary maintenance or emergency measures. Compared with existing technologies, the present invention has the following advantages:

[0031] 1. The present invention does not require direct contact with the structural surface, thus reducing interference and damage to the structure;

[0032] 2. The network model of the present invention achieves accurate detection and assessment of the stress state of complex structures by combining the advantages and strengths of multiple components;

[0033] 3. This network model combination not only improves model performance and stability, but also reduces training difficulty and computational cost;

[0034] 4. The present invention can monitor the stress state changes of the structure in real time and provide timely data support for structural safety monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Truss elevation layout diagram of the present invention;

[0036] Figure 2 This is a network model diagram of the present invention. DETAILED DESCRIPTION

[0037] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0038] The present invention proposes a method for detecting the stress state of a horizontal sliding roof structure, wherein the truss elevation layout diagram is as follows: Figure 1 As shown in the network model diagram Figure 2 As shown, the steps of the present invention are described in detail below.

[0039] Step 1: Build a roof stress detection device, which consists of a U-shaped magnetic yoke, an excitation coil, a detection coil, and an A / D sampling circuit.

[0040] Step 2: Install the roof stress detection equipment in the selected detection area and debug it to ensure that the equipment can work properly and accurately collect data;

[0041] The roof truss is a plane truss, with steel columns and diagonal braces as web members, and main beams as upper and lower chords. All truss members are box-shaped, and the cross-section material is Q355B. The truss elevation layout is shown in the figure below. Figure 1 As shown, the stress detection system applies an alternating voltage signal to the excitation coil to magnetize the stress area of ​​the truss to be measured, and then places the U-shaped magnetic yoke and the detection coil into the stress area to be measured to detect the magnetic signal in the area.

[0042] Step 3: Design a filtering circuit to pre-process the collected electromagnetic signal, including denoising and filtering to improve signal quality;

[0043] Step 4: Establish a network model for electromagnetic signal and stress mapping. The network model consists of a pooling layer, a masked multi-head self-attention network, a normalization layer, a residual connection layer, a feedforward network, a normalization layer, a residual connection layer, and a feedforward network. The network model is shown in the figure below. Figure 2 As shown;

[0044] The pooling layer can reduce the dimension of the data while retaining important information. The pooling layer is selected as average pooling, and its expression is as follows:

[0045]

[0046] Where, is the output of average pooling, is the electromagnetic signal after processing in step 3, i and j represent the index of the signal row and column, is the set of region indices, p and q are the position offsets within the region;

[0047] The masked multi-head self-attention network is used to improve the model's ability to understand the complex relationships within the data. Its expression is as follows:

[0048]

[0049] Where A represents the attention weight, sofmax() is the softmax function, is the output of the pooling layer, K is the key, V is the value, is the rank of the key;

[0050] The normalization layer can speed up the network training process and prevent the gradient from disappearing or exploding. LN normalization is used;

[0051] The residual connection layer allows the gradient to propagate directly to the deeper layers of the network, which helps to train deep networks. Its expression is as follows:

[0052]

[0053] Where y is the output of the residual connection layer, is the input of the residual connection layer, and is the weight matrix, is the activation function ReLU, is the dimensionally matched weight matrix.

[0054] The feedforward network can perform nonlinear transformation on features and enhance the expressiveness of the model;

[0055]

[0056] Where, is the output of the feedforward network layer, y is the input of the feedforward network layer, is the activation function Sigmoid, are the feedforward network weights, is the feedforward network layer bias.

[0057] Step 5: training a network model for electromagnetic signal and stress mapping, using a data set consisting of signals and stresses to train the network model for electromagnetic signal and stress mapping;

[0058] Step 6: Use the trained network model to evaluate the stress of the roof structure.

[0059] The trained model is integrated into a real-time monitoring system to continuously monitor the stress of the roof structure. The monitoring data is regularly analyzed and reports are generated to assess the long-term health of the roof and predict potential failures. When abnormal stress levels are detected, the system should automatically trigger an early warning and notify relevant personnel to take necessary maintenance or emergency measures.

[0060] The above description is merely a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent variation based on the technical essence of the present invention shall still fall within the scope of protection claimed by the present invention.

Claims

1. A method for detecting the stress state of a horizontal sliding roof structure, characterized in that: The following steps are involved: Step 1: Build a roof stress detection device, which consists of a U-shaped magnetic yoke, an excitation coil, a detection coil, and an A / D sampling circuit. Step 2: Install the roof stress detection equipment in the selected detection area and debug it to ensure that the equipment can work properly and accurately collect data; The process of installing the roof stress detection equipment in step 2 is expressed as follows: The roof truss is a flat truss, with steel columns and diagonal braces serving as web members, and main beams serving as upper and lower chords. All truss members are box-shaped and made of Q355B material. The stress detection system applies an alternating voltage signal to the excitation coil to magnetize the stress area of ​​the truss to be measured. A U-shaped magnetic yoke and detection coil are then placed in the stress area to detect the magnetic signal in that area. Step 3: Design a filtering circuit to pre-process the collected electromagnetic signal, including denoising and filtering steps to improve signal quality; Step 4: Establish a network model for electromagnetic signal and stress mapping, which consists of a pooling layer, a masked multi-head self-attention network, a normalization layer, a residual connection layer, and a feedforward network. The process of establishing the network model of electromagnetic signal and stress mapping in step 4 is as follows: The pooling layer can reduce the dimension of the data while retaining important information. The pooling layer is selected as average pooling, and its expression is as follows: ； Where, is the output of average pooling, is the electromagnetic signal after processing in step 3, i and j represent the index of the signal row and column, is the set of region indices, p and q are the position offsets within the region; The masked multi-head self-attention network is used to improve the model's ability to understand the complex relationships within the data. Its expression is as follows: ； Where A represents the attention weight, sofmax() is the softmax function, is the output of the pooling layer, K is the key, V is the value, is the rank of the key; The normalization layer can speed up the network training process and prevent the gradient from disappearing or exploding. LN normalization is used; The residual connection layer allows the gradient to propagate directly to the deeper layers of the network, which helps to train deep networks. Its expression is as follows: Where y is the output of the residual connection layer, is the input of the residual connection layer, and is the weight matrix, is the activation function ReLU, is the weight matrix for dimension matching; The feedforward network can perform nonlinear transformation on features and enhance the expressiveness of the model; Where, is the output of the feedforward network layer, y is the input of the feedforward network layer, is the activation function Sigmoid, are the feedforward network weights, is the feedforward network layer bias; Step 5: training a network model for electromagnetic signal and stress mapping, using a data set consisting of signals and stresses to train the network model for electromagnetic signal and stress mapping; Step 6: Use the trained network model to evaluate the stress of the roof structure.

2. The method for detecting the stress state of a horizontal sliding roof structure according to claim 1, characterized in that: The stress assessment process in step 6 is shown as follows: The trained model is integrated into a real-time monitoring system to continuously monitor the stress of the roof structure. The monitoring data is regularly analyzed and reports are generated to assess the long-term health of the roof and predict potential failures. When abnormal stress levels are detected, the system should automatically trigger an early warning and notify relevant personnel to take maintenance or emergency measures.

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