Intelligent monitoring system for light steel structure
By using an intelligent monitoring system to analyze the temperature gradient field and stress distribution, the energy trap area of the light steel structure is identified, which solves the problem of local vibration energy not being able to diffuse in large light steel grid structures, realizes the safety early warning and reinforcement of the structure, and ensures its long-term operation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHENGZHOU QIANJING CONSTRUCTION ENGINEERING CO LTD
- Filing Date
- 2026-01-08
- Publication Date
- 2026-04-21
AI Technical Summary
Large light steel grid structures are susceptible to dynamic coupling effects of internal temperature field gradients caused by uneven solar radiation and instantaneous wind pressure during service. This results in local vibration energy failing to diffuse as designed, forming an 'energy trap region' that accelerates local fatigue damage to the structure. Existing monitoring systems struggle to effectively identify and warn of such damage.
An intelligent monitoring system is adopted, which analyzes the temperature gradient field through the gradient sensing module, identifies local stress concentration elements through the coupled stress module, analyzes the stiffness of nodes through the stiffness symmetry module, identifies energy trap regions through the energy trap identification module, and assesses the damage risk level through the damage grading module, providing targeted reinforcement and operation and maintenance suggestions.
It enables precise monitoring of local temperature and stress in light steel structures, identifies pre-energy trap areas, provides risk assessment and reinforcement recommendations, and ensures safe service and long-term operation of the structure.
Smart Images
Figure CN121898764A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of building engineering technology, and more specifically to an intelligent monitoring system for light steel structures. Background Technology
[0002] In the field of monitoring technology, specialized environmental monitoring instruments have developed rapidly. Various organic matter measuring instruments and water pollution monitoring instruments, among others, have achieved the capture and dynamic analysis of target parameters in their respective monitoring fields thanks to their high-resolution, high-frequency data acquisition capabilities. However, in the field of health monitoring of large-scale light steel grid structures, traditional monitoring systems still have significant shortcomings. Large-scale lightweight steel grid structures, with their advantages of light weight, large span, and convenient construction, have been widely used in large-span public buildings such as stadium roofs and airport terminals. However, during service, these structures are highly susceptible to the dynamic coupling effect of internal temperature field gradients caused by uneven solar radiation and instantaneous wind pressure, which can induce abnormally strong local vibrations in certain areas. More critically, the slight asymmetry in node stiffness is highly concealed and uncontrollable, preventing local vibration energy from dissipating as designed, thus forming "energy trap zones." Continuous local high-stress cycles significantly accelerate fatigue damage in these areas, causing local fatigue damage to accumulate unnoticed, severely restricting the safe service and long-term operation of large-scale lightweight steel grid structures.
[0003] Therefore, the present invention provides an intelligent monitoring system for light steel structures. Summary of the Invention
[0004] The purpose of this invention is to provide an intelligent monitoring system for light steel structures to solve the aforementioned background problems.
[0005] The objective of this invention can be achieved through the following technical solution: an intelligent monitoring system for light steel structures, comprising the following modules: Gradient sensing module: The temperature gradient field is obtained by performing gradient analysis on the temperature data of the grid monitoring units divided by the light steel structure of the stadium roof during the wind-temperature coupling period. Coupled stress module: Collects the effective instantaneous wind pressure value of the grid monitoring unit during the wind-temperature coupling period, and obtains the actual resultant stress value by combining it with the temperature gradient field joint analysis, and identifies local stress concentration units; Stiffness Symmetrical Module: Performs stiffness stress analysis on the bolt tightening data of each node of the local stress concentration element to obtain the actual comprehensive stiffness value of each node, and performs symmetrical stress analysis to determine the pre-energy trap region; Energy trap identification module: Based on the energy flow intensity and energy divergence obtained from energy flow analysis in the pre-energy trap region, the energy trap region in the pre-energy trap region is determined; Damage rating module: Performs damage and fatigue analysis on the energy trap area, obtains the local damage potential index and fatigue life margin, constructs a damage risk level judgment matrix, and determines the risk level of the energy trap area.
[0006] Furthermore, the process of performing the gradient analysis is as follows: The surface temperature of the component and the internal temperature of the light steel are obtained for each grid monitoring unit during the wind-temperature coupling period, and the difference is calculated to obtain the real-time temperature difference between the inside and outside. Obtain the real-time temperature difference between the inside and outside of all grid monitoring units, and calculate the temperature gradient difference by comparing the real-time temperature difference between the inside and outside of adjacent grid monitoring units. The temperature gradient field is obtained by arranging the temperature gradient differences of all grid monitoring units according to the actual spatial location of the grid.
[0007] Furthermore, the method for identifying localized stress concentration elements is as follows: Obtain the actual resultant stress value of the grid monitoring unit; For each grid monitoring unit, the actual resultant stress value is compared with the actual resultant stress values of all orthogonally adjacent grid monitoring units. If the actual resultant stress value of the grid monitoring unit is greater than the actual resultant stress value of each orthogonally adjacent grid monitoring unit, then the grid monitoring unit is determined to be a local stress concentration unit.
[0008] Furthermore, the actual resultant stress value of the grid monitoring unit is obtained as follows: Obtain the effective instantaneous wind pressure value of the grid monitoring unit; obtain the thermal stress coefficient and wind pressure stress coefficient; The thermal stress value is calculated by multiplying the temperature gradient difference of the grid monitoring unit with the thermal stress coefficient, and the wind pressure stress value is calculated by multiplying the effective instantaneous wind pressure value of the grid monitoring unit with the wind pressure stress coefficient. The actual combined stress value is obtained by summing the thermal stress value and the wind pressure stress value.
[0009] Furthermore, the stiffness stress analysis is performed as follows: Obtain the equivalent preload at each node of the local stress concentration element; Obtain the axial stiffness coefficient of the bolt; calculate the ratio between the equivalent preload of each node and the axial stiffness coefficient of the bolt to obtain the axial tightening stiffness value of the bolt at that node. Obtain the friction coefficient of the contact surface of the light steel mesh node, the elastic modulus of the light steel component, the effective bearing area of the node contact surface, and the nominal bonding thickness of the contact surface; The bearing stiffness value of the node is calculated by multiplying the friction coefficient of the contact surface of the light steel mesh node, the elastic modulus of the light steel component, and the effective bearing area of the contact surface of the node. The result of the multiplication calculation is then compared with the nominal bonding thickness of the contact surface. The axial fastening stiffness value and the bearing stiffness value are summed to obtain the actual comprehensive stiffness value of each node.
[0010] Furthermore, the equivalent preload of each node in the local stress concentration element is obtained as follows: Obtain the digital construction archive of the stadium roof project and retrieve the bolt tightening data of each node of all local stress concentration units; Obtain the nominal diameter of the bolt based on its specifications and model. The preload of a single bolt in each node is calculated by multiplying the actual tightening torque, the nominal diameter of the bolt, and the torque coefficient. The equivalent preload of the node is obtained by calculating the arithmetic mean of the preload of all single bolts within the node.
[0011] Furthermore, the process of conducting energy flow analysis is as follows: The vibration velocity during the wind-temperature coupling period is obtained and averaged to obtain the average vibration velocity. Measure the cross-sectional area of the rods within the pre-energy trap region and the surface area of the pre-energy trap region; The energy flow intensity is obtained by multiplying the actual resultant stress value, the average vibration velocity value and the cross-sectional area of the rod in the pre-energy trap region, and then comparing the result of the product calculation with the area of the region. The energy divergence is obtained by divergence-aggregation analysis based on the energy flow intensity in the pre-energy trap region.
[0012] Furthermore, the process of performing divergent-aggregate analysis is as follows: The energy flow intensity of adjacent grid monitoring units in the pre-energy trap region is obtained, and the difference between the adjacent energy flow intensity in the pre-energy trap region and the energy flow intensity of adjacent grid monitoring units is calculated to obtain the adjacent energy difference. The energy divergence is obtained by averaging the adjacent energy differences between all adjacent grid monitoring units and the pre-energy trap region.
[0013] Furthermore, the process of conducting damage fatigue analysis is as follows: Obtain the material fatigue limit value of the light steel components in the energy trap region; The stress concentration coefficient is obtained by calculating the ratio of the actual resultant stress value in the energy trap region to the material fatigue limit value. The stiffness-energy coupling coefficient is calculated by multiplying the discrete values of element stiffness in the energy trap region with the absolute value of energy divergence. The stress concentration coefficient and stiffness-energy coupling coefficient are dimensionless, and the local damage potential index is obtained by multiplying the stress concentration coefficient and stiffness-energy coupling coefficient. The fatigue life margin is obtained by fatigue life analysis based on the local damage potential index.
[0014] Furthermore, the process of performing fatigue life analysis is as follows: The fatigue life attenuation coefficient is obtained by calculating the ratio of the local damage potential index to the material fatigue limit value. Obtain the baseline fatigue life; calculate the fatigue life margin by comparing the baseline fatigue life with the fatigue life attenuation coefficient.
[0015] The beneficial effects of this invention are as follows: 1. The lightweight steel structure of the stadium roof is divided into different grid monitoring units. Temperature data of each grid monitoring unit during the wind-temperature coupling period is collected and gradient analysis is performed to obtain the temperature gradient field. By dividing the monitoring into grids and analyzing the temperature gradient field during the wind-temperature coupling period, the local temperature distribution differences of the stadium roof's lightweight steel structure can be captured. The effective instantaneous wind pressure value of each grid monitoring unit during the wind-temperature coupling period is collected. The effective instantaneous wind pressure value and the temperature gradient field are jointly analyzed to obtain the actual resultant stress value. Based on the actual resultant stress value, local stress concentration units are identified. By combining the effective instantaneous wind pressure and the temperature gradient field during the wind-temperature coupling period... By calculating the actual combined stress, local stress concentration units of the stadium roof's light steel structure can be located. Bolt tightening data for each node of the local stress concentration unit is obtained, and stiffness-stress analysis is performed based on this data to obtain the actual comprehensive stiffness value of each node. Symmetrical stress analysis is then conducted based on the actual comprehensive stiffness values of each node in the local stress concentration unit to identify pre-energy trap regions. By acquiring bolt tightening data for each node of the local stress concentration unit and conducting stiffness-stress and symmetric stress analysis, pre-energy trap regions can be identified, providing crucial judgment criteria for early risk prevention and targeted reinforcement and maintenance of the stadium roof's light steel structure.
[0016] 2. Energy flow analysis is performed on the pre-energy trap area to obtain energy flow intensity and energy divergence. Based on the energy flow intensity and energy divergence, energy trap areas within the pre-energy trap area are identified. By conducting energy flow analysis on the pre-energy trap area and identifying energy trap areas based on energy flow intensity and divergence, key areas in the stadium roof light steel structure with genuine energy accumulation risks can be screened, providing core targeting basis for proactive risk prevention of the structure. Damage fatigue analysis is performed on the energy trap area to obtain the local damage potential index and fatigue life margin. Based on the local damage potential index and fatigue life margin, a damage risk level judgment matrix is constructed to determine the risk level of the energy trap area. This provides core decision-making basis for differentiated priority reinforcement, operation and maintenance intervention, and full life cycle safety management of the stadium roof light steel structure. Attached Figure Description
[0017] The invention will now be further described with reference to the accompanying drawings.
[0018] Figure 1 This is a functional module diagram of an intelligent monitoring system for light steel structures according to the present invention; Figure 2 This is a logic diagram for identifying local stress concentration units in this invention; Figure 3 This is a flowchart of the steps of an intelligent monitoring method for light steel structures in this invention. Detailed Implementation
[0019] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments. Example 1:
[0020] like Figures 1-2 As shown, an intelligent monitoring system for light steel structures includes: Gradient sensing module: Divide the light steel structure of the stadium roof into different grid monitoring units, collect temperature data of the grid monitoring units during the wind-temperature coupling period and perform gradient analysis to obtain the temperature gradient field; The process of dividing the stadium roof's light steel structure into different grid monitoring units is as follows: The light steel grid structure of the stadium roof is obtained. Based on the spatial orientation of its main load-bearing truss and the grid arrangement of the secondary purlins, the grid division of the light steel grid structure is completed by the unit area equal division method to obtain the grid monitoring unit. It should be noted that the unit region equal division method is a method of uniformly dividing a region in two-dimensional or three-dimensional space (such as finite element mesh, CAD graphics, geospatial units, etc.) into several equal parts; Obtain all grid monitoring units and assign a unique code to each grid monitoring unit; The process of collecting temperature data from the grid monitoring unit during the wind-temperature coupling period and performing gradient analysis to obtain the temperature gradient field is as follows: It should be noted that the wind-temperature coupling period is from 12:00 to 15:00 on a certain day in summer. The wind-temperature coupling period is a high-frequency period when the light steel grid structure of the stadium roof experiences solar temperature differences, which easily forms a significant temperature gradient. In addition, gusts often accompany the afternoon in summer, and the pulsating wind pressure (such as wind speed of 5-10 m / s) and the thermal stress generated by the temperature gradient form a dynamic coupling. Collect the component surface temperature and the internal temperature of the light steel in each grid monitoring unit; It should be noted that the method for collecting the surface temperature of the component and the internal temperature of the light steel is as follows: miniature temperature sensing nodes are deployed on the surface of the component and inside the light steel in each grid monitoring unit, and the sampling frequency is set to 1 minute / time. The difference between the surface temperature of the component and the internal temperature of the light steel at each collection time point is calculated to obtain the real-time internal and external temperature difference value; Obtain the real-time temperature difference between the inside and outside of all grid monitoring units, and calculate the temperature gradient difference by comparing the real-time temperature difference between the inside and outside of adjacent grid monitoring units. It is understandable that there are 4 adjacent grid monitoring units, and 4 orthogonally adjacent units (top, bottom, left, and right) are selected. The temperature gradient field is obtained by arranging the temperature gradient differences of all grid monitoring units according to the actual spatial location of the grid. Coupled stress module: Collects the effective instantaneous wind pressure value of the grid monitoring unit during the wind-temperature coupling period, performs joint analysis of the effective instantaneous wind pressure value and temperature gradient field to obtain the actual resultant stress value, and identifies local stress concentration units based on the actual resultant stress value; The process of collecting the effective instantaneous wind pressure value of the grid monitoring unit during the wind-temperature coupling period is as follows: Synchronous wind pressure monitoring nodes are deployed in the grid monitoring unit, and the acquisition frequency is set to be consistent with that of the micro temperature sensing node to acquire the original instantaneous wind pressure value. The raw instantaneous wind pressure values collected by each grid monitoring unit are filtered for time-domain characteristics; It should be noted that the process of time-domain feature screening is as follows: wind pressure values without load effect caused by environmental airflow turbulence and local eddies are removed, and only wind pressure data that actually exert pressure and tension on the light steel grid components are retained, so as to determine the effective instantaneous wind pressure value of each grid monitoring unit area. The process of jointly analyzing the effective instantaneous wind pressure value and the temperature gradient field to obtain the actual resultant stress value, and then identifying local stress concentration elements based on the actual resultant stress value, is as follows: The thermal stress value is calculated by multiplying the temperature gradient difference of the grid monitoring unit with the thermal stress coefficient. The wind pressure stress value is calculated by multiplying the effective instantaneous wind pressure value of the grid monitoring unit with the wind pressure stress coefficient. The actual resultant stress value is calculated by summing the thermal stress value and the wind pressure stress value. For example, the thermal stress coefficient and the wind pressure stress coefficient are determined by those skilled in the art through stress analysis experiments; Obtain the actual resultant stress values of all grid monitoring units; For each grid monitoring unit, the actual resultant stress value is compared with the actual resultant stress values of all orthogonally adjacent grid monitoring units; If the actual resultant stress value of a grid monitoring unit is greater than the actual resultant stress value of each orthogonally adjacent grid monitoring unit, then the grid monitoring unit is determined to be a local stress concentration unit. Stiffness Symmetry Module: Obtain bolt tightening data of each node in the local stress concentration element, perform stiffness stress analysis based on the bolt tightening data to obtain the actual comprehensive stiffness value of each node, and perform symmetry stress analysis based on the actual comprehensive stiffness value of each node in the local stress concentration element to obtain the pre-energy trap region. The process of obtaining bolt tightening data for each node of the local stress concentration element, and then performing stiffness-stress analysis based on the bolt tightening data to obtain the actual comprehensive stiffness value of each node is as follows: Obtain the digital construction archive of the stadium roof project and retrieve the bolt tightening data of each node of all local stress concentration units; It should be noted that the bolt tightening data includes the bolt specification (such as M24, 10.9 grade), design preload (or torque) value, actual tightening torque value of each bolt (usually recorded by a smart wrench), and bolt axial stiffness coefficient. Obtain the nominal diameter of the bolt based on its specifications and model. The preload of a single bolt in each node is calculated by multiplying the actual tightening torque, the nominal diameter of the bolt, and the torque coefficient. It should be noted that the torque coefficient is obtained through standardized physical tests, sampling and statistical analysis of bolts in the same batch, and its essence is a statistical value reflecting the friction state under a specific process. It should be noted that each node of the light steel grid structure of the stadium roof is arranged in a multi-bolt combination. For multiple bolts of the same specification in the same node, the arithmetic mean of the preload of all single bolts in the node needs to be calculated to obtain the equivalent preload of the node, which is used as the final value of the preload of the node. Based on the equivalent preload of each node and combined with the bolt connection mechanical properties of the light steel mesh structure, the actual stiffness of different nodes in the local stress concentration element is calculated, specifically: The axial tightening stiffness value of the bolt at each node is obtained by calculating the ratio of the equivalent preload force at each node to the axial stiffness coefficient of the bolt. Obtain the friction coefficient of the contact surface of the light steel mesh node, the elastic modulus of the light steel component, the effective bearing area of the node contact surface, and the nominal bonding thickness of the contact surface; The bearing stiffness value of the node is calculated by multiplying the friction coefficient of the contact surface of the light steel mesh node, the elastic modulus of the light steel component, and the effective bearing area of the contact surface of the node. The result of the multiplication calculation is then compared with the nominal bonding thickness of the contact surface. The axial fastening stiffness value and the bearing stiffness value are summed to obtain the actual comprehensive stiffness value of each node; It should be noted that the physical meaning of the actual comprehensive stiffness value reflects the overall stiffness characteristics of the local stress concentration unit node under the combined action of bolt preload and external load. It is the result of the synergistic effect of axial tightening stiffness and bearing stiffness. Among them, the axial tightening stiffness value characterizes the elastic constraint capacity of the bolt group during the axial force transmission process, and is directly related to the bolt specifications, preload level, and axial deformation control of the connector. The bearing stiffness value reflects the bearing deformation characteristics and frictional force transmission efficiency of the node contact surface under normal pressure. It is a key mechanical parameter to ensure the stable bearing capacity of the node and suppress local stress concentration. The process of obtaining the pre-energy trap region by performing symmetrical stress analysis based on the actual comprehensive stiffness values of each node of the local stress concentration element is as follows: The actual combined stiffness values of different nodes of the local stress concentration element are obtained and calculated to obtain the variance, which is then labeled as the element stiffness discrete value. In some embodiments, the discrete values of element stiffness are compared with a discrete threshold. It should be noted that the discrete threshold is obtained by those skilled in the art through stiffness discrete analysis of local areas of the light steel structure; If the discrete value of the element stiffness is greater than the discrete threshold, it indicates that there is an actual stiffness asymmetry between different nodes of the local stress concentration element, and the local stress concentration element is identified as a pre-energy trap region. If the discrete value of the element stiffness is less than or equal to the discrete threshold, it is determined to be a non-pre-energy trap region and no processing is performed. The technical solution of this embodiment is as follows: The light steel structure of the stadium roof is divided into different grid monitoring units. Temperature data of the grid monitoring units during the wind-temperature coupling period is collected and gradient analysis is performed to obtain the temperature gradient field. By dividing the grid monitoring and analyzing the temperature gradient field during the wind-temperature coupling period, the local temperature distribution differences of the stadium roof light steel structure can be captured. The effective instantaneous wind pressure value of the grid monitoring units during the wind-temperature coupling period is collected. The effective instantaneous wind pressure value and the temperature gradient field are jointly analyzed to obtain the actual resultant stress value. Based on the actual resultant stress value, local stress concentration units are identified. By combining the effective instantaneous wind pressure during the wind-temperature coupling period with… The temperature gradient field calculation of the actual resultant stress can locate the local stress concentration units of the stadium roof light steel structure. Bolt tightening data of each node in the local stress concentration unit is obtained, and stiffness-stress analysis is performed based on this data to obtain the actual comprehensive stiffness value of each node. Symmetrical stress analysis is then performed based on the actual comprehensive stiffness value of each node in the local stress concentration unit to identify the pre-energy trap region. By obtaining the bolt tightening data of each node in the local stress concentration unit and conducting stiffness-stress and symmetric stress analysis, the pre-energy trap region can be identified, providing a crucial basis for early risk prevention and targeted reinforcement maintenance of the stadium roof light steel structure. Example 2:
[0021] Please see Figure 1 As shown, an intelligent monitoring system for light steel structures includes: Energy trap identification module: Analyzes energy flow in the pre-energy trap region to obtain energy flow intensity and energy divergence; precipitates the energy trap region within the pre-energy trap region based on the energy flow intensity and energy divergence. The process of obtaining energy flow intensity and energy divergence by performing energy flow analysis based on the pre-energy trap region is as follows: Miniature triaxial vibration acceleration sensors were deployed in the pre-energy trap area to collect raw multidimensional vibration parameters. The data collection period coincided with the wind-temperature coupling period, and the sampling frequency was set to 100Hz. It should be noted that the multidimensional vibration parameters include the time-domain signal of vibration acceleration, vibration velocity, and vibration displacement; Multidimensional vibration parameters are obtained by preprocessing the original multidimensional vibration parameters. It should be noted that the preprocessing process involves using a 5Hz low-pass filter to remove electromagnetic interference noise, using time-domain peak detection to remove sudden impact signals (such as bird strikes), and retaining the effective vibration response related to wind-temperature coupled loads. The vibration velocity at each time point within the data collection period is obtained and averaged to obtain the average vibration velocity. Measure the cross-sectional area of the rods within the pre-energy trap region and the surface area of the pre-energy trap region; The energy flow intensity is obtained by multiplying the actual resultant stress value, the average vibration velocity value and the cross-sectional area of the rod in the pre-energy trap region, and then comparing the result of the product calculation with the area of the region. The energy flow intensity of adjacent grid monitoring units in the pre-energy trap region is obtained, and the difference between the adjacent energy flow intensity in the pre-energy trap region and the energy flow intensity of adjacent grid monitoring units is calculated to obtain the adjacent energy difference. It should be noted that there are 4 grid monitoring units adjacent to the pre-energy trap area, and 4 of them are taken as orthogonal adjacent units (up, down, left, and right). The energy divergence is obtained by averaging the adjacent energy differences between all adjacent grid monitoring units and the pre-energy trap region. It should be noted that energy divergence is a core indicator describing the accumulation or diffusion of energy within a cell. When the energy divergence is negative, it indicates that the energy flow intensity of adjacent cells is greater than that of the pre-energy trap region, and energy tends to accumulate in this region (i.e., the inflow is greater than the outflow). When the energy divergence is positive, it indicates that the energy flow intensity of the pre-energy trap region is greater than that of adjacent cells, and energy tends to diffuse outward from this region (i.e., the outflow is greater than the inflow). This averaging process based on the difference in energy flow intensity between adjacent cells can effectively reflect the dynamic distribution characteristics of energy within the pre-energy trap region. The process of extracting the energy trap region from the pre-energy trap region based on the energy flow intensity and energy divergence is as follows: The energy flow intensity of all pre-energy trap regions is obtained and averaged to obtain the energy balance index. If the energy flow intensity of the pre-energy trap region is greater than the energy balance index and the energy divergence of the pre-energy trap region is negative, then the pre-energy trap region is marked as an energy trap region. Conversely, no action is taken; It should be noted that the energy trap region is a phenomenon where abnormally strong vibration energy generated in a local area cannot be rapidly diffused and dissipated through the designed path due to the interaction between the internal temperature field gradient caused by uneven solar radiation and instantaneous wind pressure, and is trapped in a specific area. Its formation mechanism is closely related to the slight stiffness asymmetry at the node connection. This defect, which is common in construction but difficult to detect and control completely, will disrupt the uniformity of energy transfer in the structure. However, continuous local high stress cycles will significantly accelerate the fatigue damage process in this part, and such damage locations are precisely blind spots that are difficult to detect in the overall analysis model. Ultimately, it may lead to local failure of the structure without overall monitoring and early warning. Damage rating module: Performs damage and fatigue analysis on the energy trap area to obtain the local damage potential index and fatigue life margin. Constructs a damage risk level judgment matrix based on the local damage potential index and fatigue life margin and judges the risk level of the energy trap area. The process of obtaining the local damage potential index and fatigue life margin by performing damage and fatigue analysis on the energy trap region is as follows: Obtain the material fatigue limit value of the light steel components in the energy trap region; It should be noted that the material fatigue limit value of the light steel components in the energy trap area is obtained by referring to the project material acceptance file or fatigue test data of the same batch of steel, which is the critical stress value that characterizes the material's resistance to fatigue damage. The stress concentration coefficient is obtained by calculating the ratio of the actual resultant stress value in the energy trap region to the material fatigue limit value. Understandably, a stress concentration factor greater than 1 indicates that the local stress exceeds the material's fatigue resistance threshold, and the larger the value, the more significant the damage caused by a single stress cycle; a stress concentration factor less than or equal to 1 indicates that the stress has not reached the fatigue critical value and the damage potential is low. The stiffness-energy coupling coefficient is obtained by multiplying the discrete value of the element stiffness in the energy trap region with the absolute value of the energy divergence. It is understandable that the larger the stiffness-energy coupling coefficient, the stronger the amplification effect of stiffness asymmetry on energy accumulation, and the easier it is for damage to accumulate locally and not spread to the surrounding area. The stress concentration coefficient and stiffness-energy coupling coefficient are normalized to remove dimensions, and the local damage potential index is obtained by multiplying the stress concentration coefficient and stiffness-energy coupling coefficient. The fatigue life attenuation coefficient is obtained by calculating the ratio of the local damage potential index to the material fatigue limit value. It should be noted that the fatigue life attenuation coefficient reflects the degree of attenuation of the fatigue life of a light steel structure due to the damage potential. The larger the fatigue life attenuation coefficient, the faster the life of the light steel structure attenuates. The fatigue life margin is calculated by comparing the baseline fatigue life with the fatigue life attenuation coefficient. Preferably, the baseline fatigue life is 50 years; The process of constructing a damage risk level determination matrix based on the local damage potential index and fatigue life margin, and then determining the risk level of the energy trap region, is as follows: A damage risk level determination matrix is constructed based on the local damage potential index and fatigue life margin. The specific method for constructing the damage risk level determination matrix is as follows: In some embodiments, the local damage potential index is compared with the damage potential index threshold, and the fatigue life margin is compared with the fatigue life threshold. It should be noted that the damage potential index threshold and fatigue life threshold were set by those skilled in the art by collecting fatigue damage case data of similar light steel mesh structures and in conjunction with the "Standard for Acceptance of Construction Quality of Steel Structures". High-risk level: If the local damage potential index is greater than the damage potential index threshold and the fatigue life margin is less than the fatigue life threshold, it indicates that the damage in the energy trap area is accumulating rapidly and the remaining life is seriously insufficient. Immediate measures such as bolt tightening and reinforcement and adding support components are required. Medium risk level: If the local damage potential index is greater than the damage potential index threshold and the fatigue life margin is greater than or equal to the fatigue life threshold, or if the local damage potential index is less than or equal to the damage potential index threshold and the fatigue life margin is greater than or equal to the fatigue life threshold, it indicates that there is a damage accumulation trend in the energy trap area, and the monitoring cycle needs to be shortened and parameter changes need to be continuously tracked. Low risk level: The local damage potential index is less than or equal to the damage potential index threshold and the fatigue life margin is greater than or equal to the fatigue life threshold, indicating that the damage potential of the energy trap area is low, the remaining life meets the design requirements, and routine maintenance (such as annual bolt inspection) is sufficient. The technical solution of this embodiment is as follows: Energy flow analysis is performed on the pre-energy trap area to obtain the energy flow intensity and energy divergence; energy trap areas within the pre-energy trap area are identified based on the energy flow intensity and energy divergence; by conducting energy flow analysis on the pre-energy trap area and identifying energy trap areas based on the energy flow intensity and divergence, key areas in the stadium roof light steel structure that truly have energy accumulation risks can be screened, providing a core targeting basis for proactive risk prevention of the structure; damage fatigue analysis is performed on the energy trap area to obtain the local damage potential index and fatigue life margin; a damage risk level judgment matrix is constructed based on the local damage potential index and fatigue life margin to determine the risk level of the energy trap area; this provides a core decision-making basis for differentiated priority reinforcement, operation and maintenance intervention, and full life-cycle safety management of the stadium roof light steel structure. Example 3:
[0022] Please see Figure 3 As shown, an intelligent monitoring method for light steel structures includes the following steps: Step 1: Using the temperature data from the grid monitoring units divided by the light steel structure of the stadium roof during the wind-temperature coupling period, gradient analysis is performed to obtain the temperature gradient field; Step 2: Collect the effective instantaneous wind pressure value of the grid monitoring unit during the wind-temperature coupling period, and combine it with the temperature gradient field to obtain the actual resultant stress value and identify local stress concentration units; Step 3: Perform stiffness and stress analysis on the bolt tightening data of each node of the local stress concentration element to obtain the actual comprehensive stiffness value of each node, and perform symmetrical stress analysis to determine the pre-energy trap region; Step 4: Determine the energy trap region within the pre-energy trap region by analyzing the energy flow intensity and energy divergence obtained from the energy flow analysis of the pre-energy trap region; Step 5: Perform damage and fatigue analysis on the energy trap area to obtain the local damage potential index and fatigue life margin, construct a damage risk level determination matrix, and determine the risk level of the energy trap area.
[0023] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.
Claims
1. An intelligent monitoring system for light steel structures, characterized in that: Includes the following modules: Gradient sensing module: The temperature gradient field is obtained by performing gradient analysis on the temperature data of the grid monitoring units divided by the light steel structure of the stadium roof during the wind-temperature coupling period. Coupled stress module: Collects the effective instantaneous wind pressure value of the grid monitoring unit during the wind-temperature coupling period, and obtains the actual resultant stress value by combining it with the temperature gradient field joint analysis, and identifies local stress concentration units; Stiffness Symmetrical Module: Performs stiffness stress analysis on the bolt tightening data of each node of the local stress concentration element to obtain the actual comprehensive stiffness value of each node, and performs symmetrical stress analysis to determine the pre-energy trap region; Energy trap identification module: Based on the energy flow intensity and energy divergence obtained from energy flow analysis in the pre-energy trap region, the energy trap region in the pre-energy trap region is determined; Damage rating module: Performs damage and fatigue analysis on the energy trap area, obtains the local damage potential index and fatigue life margin, constructs a damage risk level judgment matrix, and determines the risk level of the energy trap area.
2. The intelligent monitoring system for light steel structures according to claim 1, characterized in that: The process of performing the gradient analysis is as follows: The surface temperature of the component and the internal temperature of the light steel are obtained for each grid monitoring unit during the wind-temperature coupling period, and the difference is calculated to obtain the real-time temperature difference between the inside and outside. Obtain the real-time temperature difference between the inside and outside of all grid monitoring units, and calculate the temperature gradient difference by comparing the real-time temperature difference between the inside and outside of adjacent grid monitoring units. The temperature gradient field is obtained by arranging the temperature gradient differences of all grid monitoring units according to the actual spatial location of the grid.
3. The intelligent monitoring system for light steel structures according to claim 1, characterized in that: The method for identifying local stress concentration elements is as follows: Obtain the actual resultant stress value of the grid monitoring unit; For each grid monitoring unit, the actual resultant stress value is compared with the actual resultant stress values of all orthogonally adjacent grid monitoring units; If the actual resultant stress value of a grid monitoring unit is greater than the actual resultant stress value of each orthogonally adjacent grid monitoring unit, then the grid monitoring unit is determined to be a local stress concentration unit.
4. The intelligent monitoring system for light steel structures according to claim 3, characterized in that: The method for obtaining the actual resultant stress value of the grid monitoring unit is as follows: Obtain the effective instantaneous wind pressure value of the grid monitoring unit; obtain the thermal stress coefficient and wind pressure stress coefficient; The thermal stress value is calculated by multiplying the temperature gradient difference of the grid monitoring unit with the thermal stress coefficient, and the wind pressure stress value is calculated by multiplying the effective instantaneous wind pressure value of the grid monitoring unit with the wind pressure stress coefficient. The actual combined stress value is obtained by summing the thermal stress value and the wind pressure stress value.
5. The intelligent monitoring system for light steel structures according to claim 1, characterized in that: The method for performing the stiffness stress analysis is as follows: Obtain the equivalent preload at each node of the local stress concentration element; Obtain the axial stiffness coefficient of the bolt; calculate the ratio between the equivalent preload of each node and the axial stiffness coefficient of the bolt to obtain the axial tightening stiffness value of the bolt at that node. Obtain the friction coefficient of the contact surface of the light steel mesh node, the elastic modulus of the light steel component, the effective bearing area of the node contact surface, and the nominal bonding thickness of the contact surface; The bearing stiffness value of the node is calculated by multiplying the friction coefficient of the contact surface of the light steel mesh node, the elastic modulus of the light steel component, and the effective bearing area of the contact surface of the node. The result of the multiplication calculation is then compared with the nominal bonding thickness of the contact surface. The axial fastening stiffness value and the bearing stiffness value are summed to obtain the actual comprehensive stiffness value of each node.
6. The intelligent monitoring system for light steel structures according to claim 5, characterized in that: The method for obtaining the equivalent preload at each node of a local stress concentration element is as follows: Obtain the digital construction archive of the stadium roof project and retrieve the bolt tightening data of each node of all local stress concentration units; Obtain the nominal diameter of the bolt based on its specifications and model. The preload of a single bolt in each node is calculated by multiplying the actual tightening torque, the nominal diameter of the bolt, and the torque coefficient. The equivalent preload of the node is obtained by calculating the arithmetic mean of the preload of all single bolts within the node.
7. The intelligent monitoring system for light steel structures according to claim 1, characterized in that: The process of performing energy flow analysis is as follows: The vibration velocity during the wind-temperature coupling period is obtained and averaged to obtain the average vibration velocity. Measure the cross-sectional area of the rods within the pre-energy trap region and the surface area of the pre-energy trap region; The energy flow intensity is obtained by multiplying the actual resultant stress value, the average vibration velocity value and the cross-sectional area of the rod in the pre-energy trap region, and then comparing the result of the product calculation with the area of the region. The energy divergence is obtained by divergence-aggregation analysis based on the energy flow intensity in the pre-energy trap region.
8. The intelligent monitoring system for light steel structures according to claim 7, characterized in that: The process of performing divergent-aggregate analysis is as follows: The energy flow intensity of adjacent grid monitoring units in the pre-energy trap region is obtained, and the difference between the adjacent energy flow intensity in the pre-energy trap region and the energy flow intensity of adjacent grid monitoring units is calculated to obtain the adjacent energy difference. The energy divergence is obtained by averaging the adjacent energy differences between all adjacent grid monitoring units and the pre-energy trap region.
9. The intelligent monitoring system for light steel structures according to claim 1, characterized in that: The process of performing damage and fatigue analysis is as follows: Obtain the material fatigue limit value of the light steel components in the energy trap region; The stress concentration coefficient is obtained by calculating the ratio of the actual resultant stress value in the energy trap region to the material fatigue limit value. The stiffness-energy coupling coefficient is calculated by multiplying the discrete values of element stiffness in the energy trap region with the absolute value of energy divergence. The stress concentration coefficient and stiffness-energy coupling coefficient are dimensionless, and the local damage potential index is obtained by multiplying the stress concentration coefficient and stiffness-energy coupling coefficient. The fatigue life margin is obtained by fatigue life analysis based on the local damage potential index.
10. The intelligent monitoring system for light steel structures according to claim 9, characterized in that: The process of performing fatigue life analysis is as follows: The fatigue life attenuation coefficient is obtained by calculating the ratio of the local damage potential index to the material fatigue limit value. Obtain the baseline fatigue life; calculate the fatigue life margin by comparing the baseline fatigue life with the fatigue life attenuation coefficient.