BIM-based steel structure building sustainable maintenance method and system

Abstract

The application relates to the technical field of data processing, and discloses a steel structure building sustainable maintenance method and system based on BIM. The method comprises the following steps: integrating a steel structure digital library by collecting data, obtaining a health report by analyzing component information, obtaining a maintenance strategy by calculating resource consumption, setting repair parameters to form a construction guide, guiding and recording on site by importing the guide into a terminal, and establishing a case set by comparing data before and after maintenance. The application realizes scientific evaluation, accurate maintenance and resource optimization of steel structure buildings, thereby prolonging the service life of the buildings and reducing resource consumption and environmental impact.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a BIM-based method and system for sustainable maintenance of steel structure buildings. Background Technology

[0002] Steel structure buildings are widely used in modern architecture due to their advantages such as lightweight, high strength, short construction period, and large spatial span. However, as buildings age, steel structural components face various aging and damage problems, such as corrosion, fatigue, deformation, and loose connections. These issues affect the structural safety and performance of the building. Traditional steel structure maintenance methods mainly rely on manual inspections to identify problems, determine maintenance strategies based on experience, and perform repairs using traditional techniques. In recent years, with the development of Building Information Modeling (BIM) technology, digital and information-based methods have begun to be introduced into the field of steel structure maintenance. Some researchers have proposed BIM-based steel structure health monitoring methods, collecting structural operation data through sensor networks and visualizing it using a BIM model; other studies focus on the application of BIM in the entire building lifecycle management, integrating maintenance information into the BIM model to achieve information sharing and collaborative management.

[0003] However, existing technologies still have many shortcomings in the sustainable maintenance of steel structures. First, traditional steel structure maintenance often adopts a passive repair model, lacking scientific health assessment methods and preventative maintenance strategies, leading to problems such as untimely, over-maintenance, or insufficient maintenance. Second, the maintenance decision-making process lacks sustainability considerations, failing to comprehensively assess the resource consumption, environmental impact, and economic benefits of maintenance plans, making it difficult to achieve sustainable management throughout the entire life cycle of the steel structure. Third, current BIM applications focus primarily on the design and construction phases, with limited research on its application in the maintenance phase, particularly lacking effective methods for precisely integrating BIM with on-site maintenance construction. Fourth, the lack of systematic accumulation and intelligent application of maintenance knowledge and experience leads to the recurrence of similar problems and low maintenance efficiency. These problems severely restrict the sustainable development of steel structure buildings and the efficient use of resources. Summary of the Invention

[0004] This application provides a BIM-based sustainable maintenance method and system for steel structure buildings, which enables scientific assessment, precise maintenance and optimized resource utilization of steel structure buildings, thereby extending the building's service life and reducing resource consumption and environmental impact.

[0005] Firstly, this application provides a BIM-based sustainable maintenance method for steel structure buildings. The BIM-based sustainable maintenance method for steel structure buildings includes: collecting geometric data and material parameters of the steel structure through laser scanning and sensor networks; integrating the three-dimensional information of the steel structure to obtain a digital maintenance database for the steel structure; analyzing structural deformation and surface defects based on component information and monitoring data in the digital maintenance database to obtain a steel structure health assessment report; calculating the resource consumption and environmental impact of maintenance plans using key component data from the steel structure health assessment report to obtain a sustainable maintenance strategy for the steel structure; setting component repair parameters and configuring materials according to the sustainable maintenance strategy to obtain a steel structure maintenance construction guide; importing the steel structure maintenance construction guide into on-site terminal equipment for location guidance and operation identification to obtain a steel structure maintenance implementation record; and comparing the steel structure maintenance implementation record with the original state data before and after maintenance to verify the effect and obtain a steel structure maintenance case dataset.

[0006] Secondly, this application provides a BIM-based sustainable maintenance system for steel structure buildings, the BIM-based sustainable maintenance system for steel structure buildings comprising:

[0007] The integration module is used to collect geometric data and material parameters of steel structures through laser scanning and sensor networks, integrate the three-dimensional information of steel structures, and obtain a basic library for digital maintenance of steel structures.

[0008] The analysis module is used to perform structural deformation and surface defect analysis based on the component information and monitoring data in the steel structure digital maintenance base library, and to obtain a steel structure health assessment report.

[0009] The calculation module is used to calculate the resource consumption and environmental impact of the maintenance plan based on the key component data in the steel structure health assessment report, and to obtain a sustainable maintenance strategy for the steel structure.

[0010] The configuration module is used to set component repair parameters and configure materials according to the steel structure sustainable maintenance strategy to obtain a steel structure maintenance construction guide.

[0011] The import module is used to import the steel structure maintenance construction guide into the on-site terminal equipment, provide location guidance and operation identification, and obtain the steel structure maintenance implementation record.

[0012] The verification module is used to compare and verify the steel structure maintenance implementation records and original state data before and after maintenance, and obtain a steel structure maintenance case dataset.

[0013] A third aspect of the present invention provides a computer device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the computer device to execute the above-described BIM-based sustainable maintenance method for steel structure buildings.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described BIM-based sustainable maintenance method for steel structure buildings.

[0015] The technical solution provided in this application collects steel structure information through laser scanning and sensor networks to establish a digital maintenance database for steel structures. This enables precise mapping of the physical entity of the steel structure to the digital space, providing a comprehensive and accurate data foundation for health assessment and maintenance decisions, thus solving the problems of data gaps and inaccuracies in traditional maintenance. By analyzing structural deformation and surface defects using component information and monitoring data from the digital maintenance database, a steel structure health assessment report is generated, achieving scientific quantification and precise classification of the steel structure's health status, overcoming the subjectivity and uncertainty of traditional experience-based judgments. Furthermore, by combining the health assessment report data with calculations of resource consumption and environmental impact for maintenance plans, a sustainable maintenance strategy for steel structures is obtained, integrating the concept of sustainable development into the maintenance decision-making process. The process balances structural safety, resource utilization efficiency, and environmental protection requirements; based on sustainable maintenance strategies, it sets component repair parameters and configures materials, and formulates steel structure maintenance construction guidelines, achieving refined and standardized maintenance design and improving maintenance design efficiency and quality; the maintenance construction guidelines are imported into on-site terminal equipment for location guidance and operation identification, forming steel structure maintenance implementation records, and augmented reality technology enables accurate transmission of design intent to the construction site, solving the problems of large construction deviations and difficult quality control in traditional maintenance; the maintenance implementation records and original state data are compared before and after maintenance to verify the effect, establishing a steel structure maintenance case dataset, constructing a maintenance knowledge accumulation and experience inheritance mechanism, and improving the level of intelligent maintenance decision-making. Attached Figure Description

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

[0017] Figure 1 This is a schematic diagram of one embodiment of the BIM-based sustainable maintenance method for steel structure buildings in this application.

[0018] Figure 2 This is a schematic diagram of one embodiment of the BIM-based sustainable maintenance system for steel structure buildings in this application.

[0019] Figure 3 This is a schematic block diagram of the structure of the computer device in an embodiment of the present invention. Detailed Implementation

[0020] This application provides a BIM-based method and system for sustainable maintenance of steel structure buildings. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0021] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the BIM-based sustainable maintenance method for steel structure buildings in this application includes:

[0022] Step S101: Collect geometric data and material parameters of the steel structure through laser scanning and sensor network, integrate the three-dimensional information of the steel structure, and obtain the basic library of digital maintenance of steel structure.

[0023] Step S102: Based on the component information and monitoring data in the steel structure digital maintenance base library, perform structural deformation and surface defect analysis to obtain a steel structure health assessment report;

[0024] Step S103: Calculate the resource consumption and environmental impact of the maintenance plan based on the key component data in the steel structure health assessment report to obtain a sustainable maintenance strategy for the steel structure.

[0025] Step S104: Based on the sustainable maintenance strategy for steel structures, set the component repair parameters and configure the materials to obtain the steel structure maintenance construction guide;

[0026] Step S105: Import the steel structure maintenance construction guide into the on-site terminal equipment, perform location guidance and operation marking, and obtain the steel structure maintenance implementation record;

[0027] Step S106: Compare and verify the steel structure maintenance case dataset by recording the maintenance implementation and the original state data before and after maintenance.

[0028] It is understood that the implementing entity of this application can be a BIM-based sustainable maintenance system for steel structure buildings, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0029] Specifically, drones equipped with lidar are used to scan the building exterior from multiple angles to acquire macroscopic geometric shapes. Simultaneously, portable laser scanners are used to perform high-precision close-range scans of key nodes, capturing connection details. This scan data is processed into point cloud data to form a basic geometric model. Furthermore, parameters such as steel material, strength grade, and installation date are extracted from building archives to create a component basic information table. A sensor network, including strain sensors, tilt sensors, and displacement sensors, is deployed at key stress locations in the steel structure to record the dynamic stress state of the structure in real time. These heterogeneous data are integrated and correlated to construct a digital maintenance database for steel structures. For example, the three-dimensional coordinate point cloud data of the main steel beam of an office building is obtained through laser scanning. Combined with hourly dynamic deformation data recorded by strain sensors and Q345B steel parameter information extracted from the archives, a comprehensive digital representation of the main beam is formed, achieving a complete mapping from physical entities to digital information. The digital maintenance database for steel structures is used to conduct health status assessments. Sensor data is extracted for time-series analysis to identify structural deformation trends. After noise filtering of displacement sensor data, the maximum displacement change of a node over 24 hours is calculated. The measured data is compared with the design limit to obtain the deformation anomaly index. Simultaneously, surface images captured by high-definition cameras are processed, and surface defects such as rust and cracks are identified through edge detection, calculating the percentage of damaged area. For a steel column surface image, a pixel comparison algorithm identifies a rusted area of ​​200 square centimeters, accounting for 15% of the total surface area of ​​the component. Combined with rust depth information, the surface damage level of the component is determined to be Grade C. Using a comprehensive scoring method, the deformation anomaly index and surface damage are weighted to calculate the component's health score, which is divided into five levels from A to E. These levels are then marked with different colors in the BIM model, generating a steel structure health assessment report.

[0030] A sustainable maintenance strategy was developed based on the health assessment report. Class D and Class E components requiring maintenance were selected from the report, forming a maintenance list. For these components, applicable technologies were matched from a maintenance method database to generate preliminary maintenance scheme combinations. The required steel consumption, anti-corrosion coating consumption, and construction time for each scheme were calculated, resulting in a resource consumption data table. Based on the carbon emission coefficient of material production and construction energy consumption parameters, the carbon footprint of the entire maintenance process was calculated, generating environmental impact assessment data. Simultaneously, resource consumption was converted into economic costs using market unit prices. Three optional maintenance schemes for a damaged steel beam were analyzed: Scheme 1 requires replacing the entire component, Scheme 2 involves partial reinforcement, and Scheme 3 involves surface treatment. Through comprehensive calculations of resource consumption, environmental impact, and cost-effectiveness, Scheme 2 was determined to best meet sustainability requirements, forming a sustainable maintenance strategy for the steel structure. Detailed construction design was then developed based on the sustainable maintenance strategy. The geometric dimensions and damage characteristics of the components to be repaired were extracted, and the coordinates of the repair area were determined. The stress distribution in this area was analyzed to identify the weak points of the components, generating a stress diagram. Based on the damage characteristics and stress requirements, suitable steel grades, welding materials, and anti-corrosion coatings are selected, and the precise material usage is calculated. For a damaged steel beam connection node, mechanical calculations determine that an 8mm thick Q345B steel plate needs to be added for reinforcement, with a weld length of 300mm. E4303 welding rods are selected for welding, and epoxy zinc-rich primer is used for anti-corrosion coating. These parameters are converted into specific construction parameters such as cutting dimensions, welding specifications, and coating thickness, and operational procedures and quality control points are established to form a steel structure maintenance construction guideline.

[0031] Achieve precise on-site maintenance. Maintenance guidelines are imported into AR glasses or tablets and converted into on-site positioning parameters. Technicians scan the QR code labels on the steel structure using these devices to locate the components to be maintained. The system overlays the maintenance guidance information onto the actual components as a virtual image, marking the cutting lines, welding locations, and coating areas. Construction personnel follow the instructions, while the terminal devices record key quality parameters such as welding temperature, coating thickness, and bolt torque during the process. During the reinforcement of a steel beam node, the system monitored the welding temperature in real time, maintaining it within the range of 450℃ to 550℃, and ensuring the coating thickness reached the design requirement of 120 micrometers. It automatically recorded and associated location information, creating a steel structure maintenance implementation record. Sensors are used to re-monitor structural performance parameters after maintenance, extracting indicators such as deformation and vibration frequency, and comparing them with pre-maintenance data. The surface is re-scanned and imaged to assess the improvement in surface quality. For a reinforced steel beam, the maximum displacement decreased by 65% ​​after maintenance, approaching the design state; surface rust was completely removed, and the anti-corrosion coating was fully applied. The actual maintenance resource consumption is compared with the budget to evaluate the efficiency of the implementation plan. By integrating data on the pre-maintenance condition, adopted technologies, and maintenance results, a structured case record is established to form a steel structure maintenance case dataset.

[0032] In this embodiment, steel structure information is collected through laser scanning and sensor networks to establish a digital maintenance database for steel structures. This enables precise mapping of the physical entity of the steel structure to the digital space, providing a comprehensive and accurate data foundation for health assessment and maintenance decisions, thus solving the problems of data gaps and inaccuracies in traditional maintenance. By analyzing structural deformation and surface defects using component information and monitoring data from the digital maintenance database, a steel structure health assessment report is generated, achieving scientific quantification and precise grading of the steel structure's health status, overcoming the subjectivity and uncertainty of traditional experience-based judgments. Combining the health assessment report data with calculations of maintenance plan resource consumption and environmental impact yields a sustainable maintenance strategy for steel structures, integrating the concept of sustainable development into the maintenance decision-making process. This approach balances structural safety, resource utilization efficiency, and environmental protection requirements. Based on sustainable maintenance strategies, it sets component repair parameters and configures materials, developing a steel structure maintenance construction guideline. This achieves refined and standardized maintenance design, improving efficiency and quality. The maintenance construction guideline is imported into on-site terminal equipment for location guidance and operation identification, creating a steel structure maintenance implementation record. Augmented reality technology enables precise transmission of design intent to the construction site, solving the problems of large construction deviations and difficult quality control in traditional maintenance. By comparing maintenance implementation records with original state data before and after maintenance and verifying the effects, a steel structure maintenance case dataset is established, constructing a maintenance knowledge accumulation and experience transfer mechanism, and improving the intelligence level of maintenance decision-making.

[0033] In one specific embodiment, the process of performing step S101 may specifically include the following steps:

[0034] (1) The external shape of the steel structure is scanned from multiple angles by the lidar carried by the UAV to obtain the point cloud data of the geometric contour of the steel structure;

[0035] (2) Use a portable laser scanner to perform detailed scanning of the steel structure connection nodes and key parts, and collect detailed geometric feature data of the nodes;

[0036] (3) Extract steel specifications, strength grades, manufacturers and installation dates from steel structure building archives to establish a basic information table for steel components;

[0037] (4) Strain sensors, tilt sensors and displacement sensors are placed at key stress points of the steel structure to record the dynamic stress state data of the steel structure;

[0038] (5) Noise reduction processing is performed on the point cloud data to remove abnormal points generated during the acquisition process and form a clean point cloud of steel structure;

[0039] (6) The node detail geometric feature data is fused with the clean point cloud to generate complete steel structure geometric data;

[0040] (7) Add material and historical attributes to the complete steel structure geometric data based on the steel component foundation information table to construct the parametric information of the steel structure;

[0041] (8) Establish a correlation mapping between the dynamic stress state data of the steel structure and the parameterized information of the steel structure to obtain the basic library of digital maintenance of steel structure.

[0042] Specifically, a multi-angle scan of the steel structure's external morphology is performed using a lidar system mounted on a drone. LiDAR is an active remote sensing technology that acquires three-dimensional spatial information of a target object by emitting a laser beam and receiving its reflected signals. The drone platform can scan from multiple angles, avoiding the limited field of view and occlusion problems associated with ground-based scanning. In practice, the drone flies along a pre-set route, and the lidar collects data at a rate of hundreds of thousands of points per second, recording the three-dimensional coordinates (x, y, z) and reflection intensity of each reflection point, forming a point cloud data of the steel structure's geometric contour. This point cloud data records the macroscopic geometric features of the steel structure's external morphology, including information on the main frame and outer envelope. A portable laser scanner is then used to perform detailed scanning of the steel structure's connection nodes and key components. Portable laser scanners offer higher precision, typically reaching millimeter or even sub-millimeter levels, making them suitable for close-range acquisition of node details. Operators need to scan pre-determined key nodes, such as beam-column connections and support connections, to obtain high-precision geometric data for these areas, collecting detailed geometric feature data of the nodes to provide accurate geometric information for subsequent structural analysis.

[0043] Extracting steel specifications, strength grades, manufacturers, and installation dates from steel structure building archives to create a basic information table for steel components is a crucial way to obtain non-geometric information about steel structures. By reviewing original design drawings, construction records, and acceptance documents, material parameters for each component are extracted, including steel type (e.g., Q235B, Q345B), component dimensions, manufacturer, production batch, installation date, and historical maintenance records. This information is organized into a structured data table, assigning basic attribute information to the components for easy association in the BIM model. Strain sensors, tilt sensors, and displacement sensors are deployed at key stress points in the steel structure to record dynamic stress data. Strain sensors measure local strain changes in components, reflecting their stress state; tilt sensors monitor changes in the structure's tilt angle, used to assess overall structural stability; displacement sensors record structural deformation, reflecting the structure's response under load. The data collected by these sensors undergoes signal conversion, transmission, and processing to form time-series data, reflecting the dynamic performance of the steel structure in actual use.

[0044] The collected point cloud data undergoes noise reduction processing to remove outliers generated during the acquisition process, resulting in a clean point cloud for the steel structure. Point cloud noise reduction primarily targets outliers caused by equipment errors, environmental interference, and other factors, including isolated points, outliers, and noisy points. Statistical filtering methods are used to calculate the neighborhood characteristics of each point to determine if it is an outlier. For example, for a given point within a fixed radius, its distance distribution is calculated; if the average distance from a point to its neighbors is significantly greater than the average distance of the overall point cloud, it is considered an outlier and removed. Furthermore, a voxelization method is employed to divide the space into regular grids. Cluster analysis is performed on the points within each grid, retaining representative points and removing redundant points, thus reducing data volume while preserving geometric features. The nodal detail geometric feature data is then fused with the clean point cloud to generate complete steel structure geometric data. Since the two types of point cloud data have different resolutions and coverage areas, registration and fusion are necessary. First, a feature point matching algorithm is used to find corresponding points in the two sets of point clouds, and a coordinate transformation matrix is ​​calculated to transform the nodal detail point cloud into the coordinate system of the macro point cloud. Then, a multi-resolution fusion strategy is employed to retain high-precision nodal detail point cloud data in overlapping areas and macroscopic point cloud data in non-overlapping areas, generating comprehensive and detailed steel structure geometric data. Material and historical attributes are added to the complete steel structure geometric data based on the steel component basic information table, constructing parametric information for the steel structure. This step establishes the association between geometric data and attribute information. Through spatial location matching and component identifier association, attribute data from the steel component basic information table is mapped to the corresponding geometric model. For example, point cloud data is divided into different component units using a segmentation algorithm, and then a correspondence is established with the component IDs in the steel component basic information table, thereby attaching attribute information such as material strength, manufacturer, and installation date to the geometric model, forming a parametric model with rich semantic information. A mapping is established between the dynamic stress state data of the steel structure and the parametric information of the steel structure, resulting in a digital maintenance foundation library for the steel structure. This step combines real-time monitoring data with the static parametric model to form a dynamic digital twin system. Specifically, a data interface is set for each key node in the parametric model, connecting to the dynamic data stream collected by sensors, and establishing a mapping relationship between physical quantities and model positions. The database adopts a relational structure, constructing multiple data tables such as a steel structure component table, an attribute table, and a monitoring data table. The relationships between the tables are established through the component ID as the association key, ultimately forming a comprehensive database containing geometric, material, historical, and dynamic monitoring information.

[0045] For example, a drone-mounted LiDAR scanned the building from different heights and angles, acquiring raw point cloud data of approximately 20 million points, covering the building's external outline. Subsequently, technicians used a portable laser scanner to perform detailed scans of 40 key nodes in the building, acquiring approximately 500,000 high-precision point cloud data points for each node. By consulting building archives, material information for each component was extracted, including the specifications of Q345B steel used in the main frame, the type of high-strength bolts used in connection nodes, and historical maintenance records. A sensor network of 32 strain sensors, 16 tilt sensors, and 24 displacement sensors was installed at key structural locations, collecting data hourly. Noise reduction processing was performed on the raw point cloud data, eliminating approximately 1.2 million outliers while retaining effective geometric features. A registration algorithm was used to fuse the detailed point clouds of the nodes with the overall point cloud, generating a complete geometric model. Attribute data from the component basic information table was mapped onto the geometric model, forming a parametric information model. Real-time sensor data was correlated with the parametric model, constructing a basic library for digital maintenance of the steel structure.

[0046] In one specific embodiment, the process of performing step S102 may specifically include the following steps:

[0047] (1) Extract structural displacement sensor data from the steel structure digital maintenance base library, perform time series feature analysis, and obtain the steel structure deformation trend curve;

[0048] (2) Use a high-resolution camera to acquire images of the steel structure surface, and obtain the surface texture feature map of the steel structure through image enhancement processing;

[0049] (3) Based on the surface texture feature map of the steel structure, the surface rust area and crack direction are identified by the edge detection algorithm to form a steel structure defect distribution map;

[0050] (4) Compare the deformation trend curve of the steel structure with the theoretical stress calculation value, calculate the degree of structural deformation deviation, and generate the deformation anomaly index;

[0051] (5) Based on the steel structure defect distribution map, calculate the corrosion area ratio and crack density value of each component to form quantitative parameters of surface damage;

[0052] (6) The deformation anomaly index and surface damage quantitative parameters are weighted by a comprehensive scoring method to classify the health level of the components and obtain a steel structure health assessment report.

[0053] Specifically, historical data records from displacement sensors are read from the digital maintenance database to form a time series. The raw data is filtered to remove outliers and noise. Time series feature analysis includes trend analysis, periodic analysis, and abrupt change detection. Trend analysis extracts the long-term development trend of deformation using sliding window averaging or polynomial fitting methods; periodic analysis uses Fourier transform to identify the periodic characteristics of structural deformation; and abrupt change detection is used to discover abnormal changes in structural deformation. Using these analytical methods, trend curves representing the changes in steel structure deformation over different time periods are plotted, visually demonstrating the development law of structural deformation. High-resolution cameras are used to acquire surface images of the steel structure. Image enhancement processing to obtain surface texture feature maps of the steel structure is the starting point for surface condition analysis. The high-resolution camera should have sufficient pixel density to ensure it can capture millimeter-level surface details. Lighting conditions must be controlled during acquisition to avoid strong reflections or shadow interference. After acquiring the raw images, image enhancement processing is performed, including contrast adjustment, sharpening, and noise suppression. Contrast adjustment enhances image contrast by stretching the grayscale histogram; sharpening uses a high-pass filter to enhance image edges and details; noise suppression eliminates random noise through median or Gaussian filtering. These processing steps enhance subtle surface texture details in the original image, forming a surface texture feature map of the steel structure, providing a visual basis for subsequent defect identification.

[0054] Based on the surface texture feature map of the steel structure, edge detection algorithms are used to identify surface rust areas and crack directions, forming a defect distribution map of the steel structure. Edge detection algorithms include the Sobel operator, Canny operator, or LoG operator, used to identify areas with drastic grayscale changes in the image; these areas typically correspond to defect boundaries. For rust area identification, a color segmentation algorithm is also used, identifying rust areas based on their unique color characteristics. For crack identification, morphological operations are employed to extract elongated structural features. Through these algorithms, the surface texture feature map is converted into a binary defect distribution map, where different types of defects are represented by different colors or markers, visually displaying the distribution range of rust areas and the direction and length of cracks on the steel structure surface.

[0055] Comparing the deformation trend curve of the steel structure with the theoretical stress calculation value to calculate the degree of structural deformation deviation and generate a deformation anomaly index involves a quantitative assessment of the actual deformation degree of the structure. First, based on the structural theoretical model, the theoretical deformation value under given load conditions is calculated as a benchmark reference value. Then, the deviation between the measured deformation and the theoretical deformation is calculated, expressed as:

[0056] ,

[0057] in, This represents the Deformation Abnormality Index, where T is the length of the time series. Let be the actual deformation at time t. The calculated theoretical deformation value at time t. The time-weighted factor reflects the importance of recent data. A higher DAI value indicates a greater deviation between the actual deformation of the structure and theoretical expectations, suggesting potentially more abnormal structural performance. This index considers the relative magnitude of deformation deviations and their temporal distribution characteristics, providing a more comprehensive assessment of the degree of structural deformation anomalies.

[0058] Based on the steel structure defect distribution map, calculating the corrosion area ratio and crack density value of each component to form quantitative parameters of surface damage is a crucial step in quantitatively assessing the surface condition. The formula for calculating the corrosion area ratio is:

[0059] ,

[0060] in, This indicates the percentage of rusted area coverage. The area of ​​the rusted region. This represents the total surface area of ​​the component. The crack density value is calculated using the following formula:

[0061] ,

[0062] in, This represents the crack density factor. The number of cracks, Let be the length of the i-th crack. is the weighting coefficient for the i-th crack (related to crack width and depth). This represents the total surface area of ​​the component. Combining these two parameters forms a quantitative description of structural surface damage, intuitively reflecting the severity of the surface damage.

[0063] The final step in the health status assessment of steel structures is to calculate the weighted values ​​of the deformation anomaly index and quantitative parameters of surface damage using a comprehensive scoring method, classifying the health levels of the components and obtaining a health assessment report. The Comprehensive Health Index (CHI) is calculated using the following formula:

[0064] ,

[0065] in, and The weighting coefficients for deformation anomaly index, corrosion area ratio, and crack density factor are determined based on the importance of different types of components. A CHI value closer to 1 indicates a better component health condition. Based on the CHI value, component health conditions are classified into five levels, A to E: Level A (Excellent condition, 0.9≤CHI≤1.0), Level B (Good condition, 0.8≤CHI<0.9), Level C (Attention level, 0.7≤CHI<0.8), Level D (Warning level, 0.6≤CHI<0.7), and Level E (Dangerous condition, CHI<0.6). The health assessment report includes information on the health level, damage type, damage location, and development trend of each component, providing a basis for subsequent maintenance decisions.

[0066] Taking a steel structure factory roof truss as an example, displacement sensor data was extracted from the digital maintenance database to record the changes in the vertical displacement of nodes over 90 consecutive days. Time-series analysis revealed that the displacement of the mid-span node exhibited a fluctuating upward trend, with the peak value correlated with temperature changes. Time-series feature extraction was performed on the displacement sensor data to plot a deformation trend curve. Simultaneously, a high-resolution camera was used to photograph the truss surface, acquiring raw images. Contrast enhancement and sharpening processes made the surface rust marks and micro-cracks more prominent, forming a surface texture feature map. The Canny edge detection algorithm was applied to process the feature map, identifying the rusted area at the lower chord connection node and micro-cracks on the web members, generating a defect distribution map. Calculations showed that the actual maximum vertical displacement of this node was 22.5 mm, while the theoretical value was 15 mm. The deformation anomaly index (DAI) was calculated to be 0.35. From the defect distribution map, the corrosion area ratio (RAC) of the lower chord connection node was calculated to be 18%, and the crack density factor (CDF) of the web members was 0.015. Substituting these parameters into the comprehensive health index formula, the CHI value of the truss was calculated to be 0.73, corresponding to Grade C (Caution). The health assessment report indicated that attention should be paid to the corrosion problem at the lower chord connection nodes in the middle span and the crack development in the right web members, and recommended anti-corrosion treatment and enhanced monitoring.

[0067] In one specific embodiment, the process of executing step S103 may specifically include the following steps:

[0068] (1) Select the component information with health level D and E from the steel structure health assessment report and generate a list of components to be maintained;

[0069] (2) For each component in the list of components to be maintained, match the applicable maintenance technology from the maintenance method database to form a preliminary maintenance technology combination plan;

[0070] (3) Based on the preliminary maintenance technology combination scheme, calculate the amount of steel, anti-corrosion coating and construction time required for each scheme, and form a resource consumption data table;

[0071] (4) Based on the resource consumption data table, combined with the carbon emission coefficient of material production and construction energy consumption parameters, calculate the carbon footprint value of the entire maintenance process and generate environmental impact assessment data;

[0072] (5) Convert the material and labor data in the resource consumption data table according to the market unit price to form a maintenance economic cost budget;

[0073] (6) By comprehensively balancing the environmental impact assessment data and the maintenance economic cost budget, and comparing the maintenance benefits with the remaining service life of the building, a sustainable maintenance strategy for steel structures is obtained.

[0074] Specifically, the health assessment report filters out component information with health levels D and E to generate a list of components requiring maintenance. The health assessment report contains health level information for all components. According to a preset risk level classification standard, components at levels D (warning state) and E (hazardous state) exhibit significant structural performance degradation or surface damage, requiring priority maintenance intervention. The filtering process is implemented through data filtering operations, extracting records with health level field values ​​of D or E from the assessment report database, and extracting key information such as component number, location coordinates, damage type, and damage degree. These are then arranged according to priority to form a structured list of components requiring maintenance.

[0075] For each component in the list of components to be maintained, applicable maintenance technologies are matched from the maintenance method database to form a preliminary maintenance technology combination plan. The maintenance method database is a knowledge base containing various maintenance technologies for steel structures. Each method is marked with applicable damage types, applicable components, applicable environment, and other condition parameters. The matching process uses a multi-condition screening algorithm to retrieve maintenance technologies that meet the conditions based on multi-dimensional parameters such as component type, damage characteristics, and location conditions. For example, for steel columns with surface rust, technologies such as rust removal and anti-corrosion coating can be matched; for beam components with excessive deformation, reinforcement or replacement technologies are matched. For each Class D or Class E component, 2-3 optional maintenance technologies are usually selected and combined to form multiple preliminary maintenance plans for subsequent evaluation and selection.

[0076] Based on the preliminary maintenance technology combination plan, the required steel consumption, anti-corrosion coating consumption, and construction time for each plan are calculated to form a resource consumption data table. This step requires quantitative calculation of various resource requirements. The steel consumption is calculated using the following formula:

[0077] ,

[0078] in, This represents the total steel material quantity for scheme p. This represents the number of maintenance items involving steel in scheme p. Let the volume of the steel components required for the maintenance of the k-th item be _____. For the density of steel, To account for the correction factor for cutting losses, the formula for calculating the amount of anti-corrosion coating used is:

[0079] ,

[0080] in, This indicates the amount of protective coating used in scheme p. For the k-th maintenance, the coating area, For coating thickness, For coating density, A correction factor was added to account for paint wastage. Construction time was calculated based on standard work-hour quotas, combined with a construction difficulty coefficient. These calculation results were compiled into a resource consumption data table, providing a data foundation for subsequent evaluation.

[0081] Based on resource consumption data, combined with carbon emission coefficients from material production and construction energy consumption parameters, the carbon footprint value for the entire maintenance process is calculated, generating environmental impact assessment data. The full-process carbon footprint calculation employs a life cycle assessment approach, considering carbon emissions at each stage of material production, transportation, construction, and waste disposal. The calculation formula is:

[0082] ,

[0083] in, R represents the carbon footprint of scheme p, where R is the number of resource types. Let be the consumption of the r-th type of resource in scheme p. Let Q be the carbon emission factor per unit of this type of resource, and Q be the quantity of energy types. Let be the consumption of the q-th type of energy in scheme p. This represents the carbon emission factor for this type of energy. The carbon footprint value calculated using this formula serves as a core indicator in environmental impact assessment, quantifying the environmental impact of the maintenance plan.

[0084] The economic evaluation step involves converting material and labor data from the resource consumption data table into market unit prices to form a maintenance economic cost budget. Economic costs include direct costs (material costs, labor costs, equipment costs) and indirect costs (management fees, taxes, etc.). Material costs are calculated by multiplying the quantity of each material by its unit price; labor costs are calculated by multiplying labor hours by the labor unit price; and equipment costs are calculated by multiplying equipment hours by the equipment usage rate. Indirect costs are estimated as a percentage of direct costs. These cost items are then aggregated to form a complete economic cost budget, serving as the basis for evaluating the economic viability of the scheme. By comprehensively balancing the environmental impact assessment data and the maintenance economic cost budget, and comparing maintenance benefits with the remaining service life of the building, a sustainable maintenance strategy for the steel structure is derived. The comprehensive evaluation employs a multi-objective decision-making method, weighing multiple objectives such as environmental impact, economic costs, and maintenance benefits. First, the cost-effectiveness index of the scheme is calculated to reflect the input-output ratio; second, the extended service life of the structure after maintenance is estimated and compared with the remaining service life of the building to avoid over-maintenance; finally, based on the above analysis results, the optimal maintenance strategy is determined, including maintenance priority, technology selection, and implementation schedule.

[0085] Taking a steel structure office building as an example, a health assessment report identified 5 Class D columns and 2 Class E beams, which exhibited surface corrosion and joint deformation issues, generating a maintenance list of 7 components. For each component, applicable technologies were matched from a maintenance method database. For instance, for the corroded columns, two solutions were matched: mechanical rust removal + anti-corrosion coating and sandblasting + anti-corrosion coating. For the deformed beams, two solutions were matched: partial reinforcement and complete replacement, resulting in 4 sets of maintenance technology combinations. Taking Solution 1 (mechanical rust removal + partial reinforcement) as an example, the calculation showed that 42 square meters of anti-corrosion coating (area × thickness × density × 1.15 loss coefficient) and 0.8 tons of steel plate (volume × density × 1.1 loss coefficient) were needed, with a total construction time of 96 man-hours. Based on the carbon emission factors of material production (2.2 tons of CO2 / ton for steel, 3.5 kg of CO2 / liter for paint) and construction energy consumption (12 kWh of electricity per man-hour, 0.6 kg of CO2 per kWh), the carbon footprint of Scheme 1 is calculated to be 2.36 tons of CO2 equivalent. Based on the market price of materials (6000 yuan / ton for steel, 80 yuan / square meter for paint) and labor costs (100 yuan / man-hour), the economic cost budget for Scheme 1 is 22360 yuan. Considering the remaining service life of the building is 25 years, Scheme 1 can extend the service life of components by 30 years, offering better cost-effectiveness than other schemes. Therefore, it was ultimately determined to be the optimal choice for the sustainable maintenance strategy of the steel structure.

[0086] In one specific embodiment, the process of executing step S104 may specifically include the following steps:

[0087] (1) Based on the component information in the sustainable maintenance strategy of steel structure, extract the geometric dimension data and damage characteristics of the component to be repaired, and determine the coordinates of the component repair area and the boundary of the repair range;

[0088] (2) Based on the coordinates of the repair area, extract the force direction and stress distribution of the repair area from the mechanical analysis of the steel structure, analyze the weak points of the component, and form the component stress map;

[0089] (3) Based on the stress spectrum and damage characteristics of the component, select suitable steel type, welding material and anti-corrosion coating from the material database, calculate the material usage, and generate a repair material list;

[0090] (4) Perform mechanical property verification on the steel grades in the repair material list to ensure that the load-bearing capacity of the repaired components meets the design requirements, and formulate the geometric parameters and connection methods of the repaired components;

[0091] (5) Convert the geometric parameters of the repaired components into construction operation instructions, decompose them into cutting parameters, welding parameters, and painting parameters, and form a component repair process parameter table;

[0092] (6) Combine the component repair process parameter table with the maintenance operation process, add construction quality control points and safety precautions, draw construction guidance diagrams, and obtain the steel structure maintenance construction guide.

[0093] Specifically, the three-dimensional geometric data of the component to be repaired is retrieved from the BIM model database, including the component's spatial coordinates, geometric shape, and dimensional parameters. Simultaneously, damage characteristic information, such as the distribution of rusted areas, crack direction, and deformation, is extracted from the health assessment report. Using spatial analysis algorithms, the damage information is precisely matched with the geometric model to determine the precise coordinates of the areas on the component requiring repair. This process employs region segmentation technology, dividing the component surface into repair-required and non-intervention-required areas based on damage severity thresholds, and calculating the boundary contours of the repair areas to form closed repair boundaries, providing spatial positioning for subsequent precise maintenance. For the determined repair area coordinates, the stress state information of that area needs to be extracted from the steel structure mechanics analysis. This step uses finite element analysis to extract the component's stress boundary conditions from the BIM model, including support constraints, connection methods, and load transfer paths. These boundary conditions are applied to the numerical analysis model to calculate the stress distribution, strain distribution, and internal force distribution of the component under actual working conditions. The analysis results visually demonstrate the magnitude and direction of forces on various parts of the component, with particular attention to stress concentration areas, identifying the component's weak points. These weak points are often potential starting points for structural failure and need to be specially considered in maintenance design. The mechanical analysis results are displayed in the form of contour maps, forming a stress spectrum of the component, which intuitively expresses the mechanical performance state of the component.

[0094] The next crucial step is to select suitable repair materials from a materials database based on the component's stress diagram and damage characteristics. This database stores mechanical property parameters, chemical composition, processing performance, and durability data for various steel grades; strength grades, applicable steel types, and welding process parameters for various welding materials; and information on the protection level, operating environment, and coating process requirements for anti-corrosion coatings. The material selection process first considers the compatibility of the material's mechanical properties with the original component to ensure the material's strength meets stress requirements; secondly, it considers the material's weldability and connection compatibility to ensure a reliable connection between the repair area and the original component; and finally, it considers the material's durability and anti-corrosion performance to meet environmental requirements. Once the materials are selected, the required material quantity is precisely calculated based on the geometric dimensions and design parameters of the repair area, including steel plate area, welding rod length, and coating volume, resulting in a detailed repair material list.

[0095] Verifying the mechanical properties of the steel grades in the repair material list is a crucial step in ensuring maintenance quality. Based on structural mechanics principles, the verification process first establishes a mechanical model of the repaired component, analyzing the new material and the original component as a whole. The stress level of the repaired component under design loads is calculated to ensure it does not exceed the material's design strength; the stress transfer capacity of key connection nodes is calculated to verify the reliability of the connections; and the stability and deformation of the repaired component are checked to ensure they meet specification requirements. After successful verification, the geometric parameters of the repaired component are further optimized, including the thickness, width, and length of the reinforcing plate; the diameter, number, and spacing of the connecting bolts; and the type, location, and size of the welds. The final connection method, such as welding, bolting, or a hybrid connection, is then determined.

[0096] Translating the geometric parameters of the repair components into specific construction operation instructions is a crucial step in realizing the design intent. This process requires breaking down abstract design parameters into a series of concrete, executable operational parameters. Cutting parameters specify the precise location, size, and shape of the damaged parts of the original component to be removed or the reinforcement plate to be installed, including the coordinates of the cutting line, cutting depth, and cutting angle. Welding parameters specify process elements such as welding position, weld type, welding sequence, preheating temperature, welding current and voltage, and welding speed. Coating parameters include surface treatment grade, primer type and thickness, topcoat type and thickness, and coating temperature and humidity requirements. These process parameters are standardized to form a component repair process parameter table, facilitating precise execution by construction personnel.

[0097] This guide combines component repair process parameter tables with maintenance operation procedures to create a complete steel structure maintenance construction guide. Based on the process parameter tables, specific operation step sequences are designed, clearly defining the work content, operation methods, and process connections. Secondly, quality control points are set up in key processes, such as pre-weld inspection, post-weld flaw detection, and pre-painting surface treatment inspection, with corresponding testing standards and acceptance methods established. Thirdly, safety precautions are added for special processes or hazardous operations, such as requirements for high-altitude work protection, welding fire prevention measures, and toxic paint protection. Finally, these contents are presented in a visually appealing format, creating intuitive construction guidance diagrams, including 3D diagrams of the maintenance areas, flowcharts of construction steps, and key quality inspection points, forming a complete steel structure maintenance construction guide to guide precise on-site construction.

[0098] Taking the maintenance of a damaged steel column in a steel structure factory as an example, the geometric data of the steel column was extracted from the BIM model, identifying it as an H350×350×12×12 type steel column. Damage information was extracted from the health assessment report, revealing severe corrosion and slight deformation at the column base, with a corrosion area of ​​approximately 1200 square centimeters and a deformation of 8 millimeters. Spatial positioning determined that the area requiring repair was the entire cross-section within 0-500 millimeters from the ground at the column base. Mechanical analysis revealed that this area was subjected to a 270kN axial compressive force and a 15kN·m bending moment, with stress concentrated at the flange-web connection. Based on this information, Q345B steel plate was selected from the material database as the reinforcement material, E5015 welding rods as the welding material, and epoxy zinc-rich primer and polyurethane topcoat as the anti-corrosion coatings. Calculations determined that an 8mm thick steel plate was needed to fabricate a reinforcement sleeve around the column base, with dimensions of 360×360×500mm, a total welding rod length of approximately 6 meters, and a coating coverage area of ​​approximately 2 square meters. Mechanical calculations confirmed that the reinforcement increased the column base's load-bearing capacity by 30%, meeting design requirements. The design was translated into specific operational instructions: first, severely corroded areas were removed; then, reinforcing steel plates were cut to the dimensions shown in the drawings; 6mm double-sided fillet welds were used for connection; the welding current was 120-130A; the welding speed was 250-300mm / min; pre-treatment to Sa2.5 grade was required; the primer thickness was 80μm; and the topcoat thickness was 60μm. Finally, a construction guidance diagram incorporating the above parameters was drawn, and six quality control points and key safety tips were marked, forming a complete maintenance and construction guide.

[0099] In one specific embodiment, the process of executing step S105 may specifically include the following steps:

[0100] (1) Convert the spatial coordinate data in the steel structure maintenance construction guide into on-site positioning parameters and establish an electronic map of the maintenance location in the on-site terminal equipment;

[0101] (2) Based on the electronic map of the maintenance location, the steel structure components on site are matched and scanned using target recognition technology to confirm the actual location of the components to be maintained;

[0102] (3) Mark the actual location of the component to be maintained with virtual operation guidance, and project the maintenance process parameters intuitively onto the surface of the component to form a visual operation instruction;

[0103] (4) Guide construction personnel to complete component repair operations through visual operation instructions, and record key parameters during construction to generate construction process data stream;

[0104] (5) Monitor the welding temperature, coating thickness and bolt torque values ​​in the data stream during the construction process in real time, compare and verify them with the design standards, and record the construction quality data;

[0105] (6) Integrate construction quality data and component maintenance completion photos, associate construction location and technical parameter information, and generate steel structure maintenance implementation record.

[0106] Specifically, the spatial coordinate data in the steel structure maintenance construction guidelines is converted into on-site positioning parameters, and an electronic map of the maintenance location is created on the on-site terminal equipment. First, the global coordinate information of the component to be maintained is extracted from the BIM maintenance model, including the component's precise position and orientation parameters within the building. Then, combining the building's solid coordinate system with the mobile terminal's relative coordinate system, a coordinate transformation algorithm is used to convert the absolute coordinates in the BIM model into relative coordinates on-site. The converted spatial information is then displayed as two-dimensional or three-dimensional graphics on a mobile terminal (such as a tablet or AR glasses), forming an electronic map of the maintenance location. This map visually displays the location of the component to be maintained within the building, its surrounding environment, and access routes.

[0107] Based on the electronic map of the maintenance location, matching and scanning the steel structure components on site using target recognition technology to confirm the actual location of the components to be maintained is a crucial step in accurate positioning. Target recognition technology mainly includes two methods: QR code recognition and feature point matching. QR code recognition uses pre-attached QR code labels to the components, which are scanned by a mobile terminal to quickly identify the component. Feature point matching uses computer vision algorithms to compare real-time images captured by the terminal device's camera with the component features in the BIM model, identifying specific structural shapes, connection nodes, or surface features to determine the target component. Positioning accuracy can typically reach the centimeter level, meeting the precision requirements of maintenance construction. After successful matching, the terminal device automatically adjusts the viewing angle and display scale to precisely align the electronic map with the actual component. Marking the actual location of the component to be maintained with virtual operation guidance, visually projecting maintenance process parameters onto the component surface to form visual operation instructions, is a core application of augmented reality technology in maintenance. This process extracts process parameter information from the maintenance construction guidelines, including key operation points such as cutting line positions, welding positions, and painting ranges. Through an augmented reality rendering engine, this information is overlaid and displayed as virtual graphics on the real-time screen of the terminal device. When technicians observe actual components through AR glasses or tablets, they see virtual images superimposed on the component's surface, such as colored lines marking cutting positions, flashing arrows indicating welding directions, and semi-transparent shadows marking painting areas. Simultaneously, process parameters such as welding current, preheating temperature, and coating thickness are displayed in text or icon form near the corresponding locations, creating intuitive and easy-to-understand visual operation instructions.

[0108] Visual operation instructions guide construction workers through component repair operations, while key parameters are recorded, generating a construction process data stream – a crucial step in precision maintenance. Workers, wearing AR glasses or using tablets, follow virtual guidance for actual operations. During the process, sensor modules connected to terminal devices collect real-time data on key process parameters. For example, infrared thermometers monitor welding temperature, coating thickness gauges measure anti-corrosion coating thickness, and electronic torque wrenches record bolt tightening torque. This collected data is transmitted wirelessly to a data processing module, forming a time-series construction process data stream that comprehensively records parameter changes throughout the maintenance process. Real-time monitoring of welding temperature, coating thickness, and bolt torque values ​​in the construction process data stream, comparing them with design standards, and recording construction quality data are critical aspects of quality control. The data processing module receives the construction process data stream and compares each measured parameter in real-time with the standard ranges specified in the maintenance construction guidelines. For example, the measured welding temperature is compared with the required temperature range (e.g., 450-550℃), the measured coating thickness is compared with the designed thickness (e.g., 120 micrometers), and the actual bolt torque is compared with the specified torque (e.g., 120 N·m). When a parameter deviates from the standard range, the terminal equipment immediately issues a warning to guide the operator to make adjustments. The comparison results are simultaneously recorded as quality control data points, marked as qualified or unqualified, and accompanied by the actual measured value, standard value, and deviation value, forming structured construction quality data.

[0109] The final step in establishing maintenance records is to integrate construction quality data with completed component maintenance photos, linking construction location and technical parameter information to generate steel structure maintenance implementation records. After maintenance work is completed, high-definition cameras are used to take photos of the repaired components, recording the final state of each key part. Through data integration algorithms, construction quality data, completed photos, construction location coordinates, and technical parameter information are linked according to component IDs to form complete maintenance implementation records. These records are organized into a structured dataset, containing multiple dimensions such as maintenance object information, maintenance time information, maintenance location information, maintenance content information, quality control information, and acceptance result information. The final maintenance implementation records are uploaded to the BIM database, updating the component status information in the steel structure digital twin model, providing data support for subsequent building operation and maintenance management and effect evaluation.

[0110] In one specific embodiment, the process of executing step S106 may specifically include the following steps:

[0111] (1) Extract the component status parameters after maintenance from the steel structure maintenance implementation record, and at the same time retrieve the original component status data before maintenance from the steel structure digital maintenance basic library to form a data comparison table before and after maintenance;

[0112] (2) The displacement and strain data of key nodes of the steel structure after maintenance are re-collected through the sensor network, and compared with the structural monitoring data before maintenance to calculate the structural performance recovery rate.

[0113] (3) High-definition imaging scan of the surface of the steel structure after maintenance, extract surface features through image processing, compare with the surface defect distribution map before maintenance, and obtain the surface quality improvement index;

[0114] (4) Compare the structural performance recovery rate and surface quality improvement index with the expected targets of the maintenance plan to evaluate the effectiveness of the maintenance technology and generate technical evaluation results;

[0115] (5) Based on the actual resource consumption data in the maintenance implementation record, recalculate the economic cost and environmental impact of the maintenance, compare it with the budget value, and form a resource efficiency assessment;

[0116] (6) Integrate the technical evaluation results with the resource efficiency assessment, link the data on the pre-maintenance status, maintenance technology selection and maintenance effect, and establish a structural steel structure maintenance case dataset.

[0117] Specifically, it is necessary to extract the post-maintenance component status parameters from the steel structure maintenance implementation records, and simultaneously retrieve the original pre-maintenance component status data from the steel structure digital maintenance database, forming a pre- and post-maintenance data comparison table. The steel structure maintenance implementation records contain component status information after maintenance, such as geometric dimensions, surface condition, and connection status; while the steel structure digital maintenance database stores the original pre-maintenance status data. Through database queries, these two sets of data are extracted by component ID and organized into a structured comparison table, including three main parts: basic component information, pre-maintenance status parameters, and post-maintenance status parameters. This comparison table visually demonstrates the status changes of the same component before and after maintenance, providing a data foundation for subsequent assessments.

[0118] Re-collecting displacement and strain data at key nodes of the steel structure after maintenance using a sensor network and comparing them with pre-maintenance structural monitoring data to calculate the structural performance recovery rate is an important method for quantifying the maintenance effect. After maintenance, the sensor network installed at key locations on the steel structure is reactivated, and displacement and strain data are collected under the same load conditions. The formula for calculating the structural performance recovery rate is:

[0119] ,

[0120] in, This represents the structural performance recovery rate, where P is the number of monitoring points. Let p be the weight coefficient for the p-th monitoring point. To maintain the measured value of the p-th monitoring point, To maintain the measured value of the first p-th monitoring point, Let SPR be the design value for the p-th monitoring point. This formula considers the comprehensive performance of multiple monitoring points and assesses the recovery of structural performance by calculating the degree of improvement in the deviation between the measured value and the design value. The closer the SPR value is to 100%, the better the structural performance recovery. High-resolution imaging scans are performed on the surface of the steel structure after maintenance. Surface features are extracted through image processing and compared with the surface defect distribution map before maintenance to derive the surface quality improvement index, which is a method for assessing the improvement in surface condition. A high-resolution camera is used to image the surface of the maintained components to obtain clear surface images. Image processing techniques, including contrast enhancement, edge detection, and feature extraction, are used to analyze surface texture features and identify potential defect areas, such as residual rust and microcracks. The processed image is compared pixel-level with the surface defect distribution map before maintenance to calculate the reduction ratio of defect areas and the reduction ratio of defect severity, and a comprehensive surface quality improvement index is derived to quantify the degree of improvement in surface condition.

[0121] The technical evaluation step involves comparing the structural performance recovery rate and surface quality improvement index with the expected targets of the maintenance plan to assess the effectiveness of the maintenance technology and generate a technical evaluation result. The expected target values ​​of the maintenance plan, including the expected level of structural performance recovery and the expected degree of surface quality improvement, are extracted from the sustainable maintenance strategy for steel structures. The actual measured structural performance recovery rate and surface quality improvement index are compared with the expected target values ​​to calculate the target achievement rate. Based on the achievement rate and professional evaluation standards, a qualitative evaluation result of "Excellent," "Good," "Qualified," or "Unqualified" is given, along with specific quantitative indicators and analytical explanations, forming a comprehensive technical evaluation result. The economic and environmental evaluation step involves recalculating the economic cost and environmental impact of maintenance based on the actual resource consumption data in the maintenance implementation records and comparing it with the budgeted value to form a resource efficiency assessment. This step extracts resource data such as the actual consumption of steel, anti-corrosion coatings, and labor hours from the maintenance implementation records and calculates the actual economic cost based on the actual market unit price. Simultaneously, considering the environmental impact factors of materials and energy, the actual carbon footprint value and other environmental impact indicators are calculated. These actual values ​​are compared with the estimated values ​​from the maintenance plan budget phase to calculate the cost deviation rate and environmental impact deviation rate, assess the efficiency and accuracy of resource utilization, and generate a resource efficiency assessment report.

[0122] Integrating technical evaluation results with resource efficiency assessments, and linking pre-maintenance conditions, maintenance technology selection, and maintenance effect data to establish a structured steel structure maintenance case dataset is a crucial step in knowledge accumulation. Through data integration algorithms, technical evaluation results and resource efficiency assessments are linked by component ID, while simultaneously linking pre-maintenance condition data, maintenance technology selection information, and maintenance effect data to form a complete maintenance case record. These records are organized into a structured dataset, including multiple dimensions such as case background, problem description, solution, implementation process, effect evaluation, and experience summary. Through tagging and indexing, multi-dimensional search functions based on keywords, similar cases, or semantic understanding are achieved, facilitating reference for future similar maintenance projects.

[0123] The above describes the BIM-based sustainable maintenance method for steel structure buildings in the embodiments of this application. The following describes the BIM-based sustainable maintenance system for steel structure buildings in the embodiments of this application. Please refer to [link / reference]. Figure 2 One embodiment of the BIM-based sustainable maintenance system for steel structure buildings in this application includes:

[0124] The integration module is used to collect geometric data and material parameters of steel structures through laser scanning and sensor networks, integrate the three-dimensional information of steel structures, and obtain a basic library for digital maintenance of steel structures.

[0125] The analysis module is used to perform structural deformation and surface defect analysis based on the component information and monitoring data in the steel structure digital maintenance base library, and to obtain a steel structure health assessment report.

[0126] The calculation module is used to calculate the resource consumption and environmental impact of maintenance plans based on the key component data in the steel structure health assessment report, and to obtain a sustainable maintenance strategy for the steel structure.

[0127] The configuration module is used to set component repair parameters and configure materials according to the steel structure sustainable maintenance strategy to obtain a steel structure maintenance construction guide.

[0128] The import module is used to import the steel structure maintenance construction guide into the on-site terminal equipment, provide location guidance and operation identification, and obtain the steel structure maintenance implementation record.

[0129] The verification module is used to compare and verify the steel structure maintenance implementation records and original state data before and after maintenance, and obtain a steel structure maintenance case dataset.

[0130] Through the collaborative efforts of the aforementioned components, steel structure information is collected via laser scanning and sensor networks to establish a digital maintenance database for steel structures. This enables precise mapping of the physical entities of steel structures to the digital space, providing a comprehensive and accurate data foundation for health assessments and maintenance decisions, thus resolving the issues of data gaps and inaccuracies in traditional maintenance. By analyzing structural deformation and surface defects using component information and monitoring data from the digital maintenance database, a steel structure health assessment report is generated, achieving scientific quantification and precise grading of the steel structure's health status, overcoming the subjectivity and uncertainty of traditional experience-based judgments. Combining the health assessment report data with calculations of resource consumption and environmental impact for maintenance plans yields a sustainable maintenance strategy for steel structures, integrating the concept of sustainable development into maintenance decisions. The process balanced structural safety, resource utilization efficiency, and environmental protection requirements. Based on sustainable maintenance strategies, component repair parameters and material configurations were set, and a steel structure maintenance construction guideline was developed, achieving refined and standardized maintenance design and improving its efficiency and quality. The maintenance construction guideline was imported into on-site terminal equipment for location guidance and operation identification, forming a steel structure maintenance implementation record. Augmented reality technology enabled precise transmission of design intent to the construction site, solving the problems of large construction deviations and difficult quality control in traditional maintenance. Before-and-after comparisons and effect verification were performed between the maintenance implementation record and the original state data, establishing a steel structure maintenance case dataset and constructing a maintenance knowledge accumulation and experience transfer mechanism, thereby improving the intelligence level of maintenance decision-making.

[0131] Reference Figure 3 This invention also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0132] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0133] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0134] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0135] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0136] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0137] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A BIM-based sustainable maintenance method for steel structure buildings, characterized in that, The BIM-based sustainable maintenance method for steel structure buildings includes: By collecting geometric data and material parameters of steel structures through laser scanning and sensor networks, and integrating the three-dimensional information of steel structures, a basic library for digital maintenance of steel structures is obtained. Based on the component information and monitoring data in the aforementioned digital maintenance database for steel structures, structural deformation and surface defect analysis are performed to obtain a steel structure health assessment report. By analyzing the key component data in the steel structure health assessment report, we can calculate the resource consumption and environmental impact of the maintenance plan to obtain a sustainable maintenance strategy for the steel structure. Based on the aforementioned sustainable maintenance strategy for steel structures, component repair parameters are set and materials are configured to obtain a steel structure maintenance construction guide, including: Extracting the geometric dimensions and damage characteristics of the components to be repaired based on the component information in the sustainable maintenance strategy, and determining the coordinates of the repair area and the boundary of the repair range; extracting the force direction and stress distribution of the repair area from the steel structure mechanical analysis for the coordinates of the repair area, analyzing the weak points of the components, and forming a component stress map; selecting suitable steel types, welding materials, and anti-corrosion coatings from the material database based on the component stress map and damage characteristics, calculating the material usage, and generating a repair material list; verifying the mechanical properties of the steel types in the repair material list to ensure that the load-bearing capacity of the repaired components meets the design requirements, and formulating the geometric parameters and connection methods of the repaired components; converting the geometric parameters of the repaired components into construction operation instructions, decomposing them into cutting parameters, welding parameters, and coating parameters to form a component repair process parameter table; combining the component repair process parameter table with the maintenance operation process, adding construction quality control points and safety precautions, and drawing a construction guidance diagram to obtain the steel structure maintenance construction guide; The steel structure maintenance construction guide is imported into the on-site terminal equipment for location guidance and operation identification, resulting in a steel structure maintenance implementation record. The steel structure maintenance implementation records and original state data are compared and the effects are verified before and after maintenance to obtain a steel structure maintenance case dataset.

2. The BIM-based sustainable maintenance method for steel structure buildings according to claim 1, characterized in that, The process involves collecting geometric data and material parameters of the steel structure through laser scanning and sensor networks, integrating the three-dimensional information of the steel structure, and obtaining a basic library for digital maintenance of the steel structure, including: The external shape of the steel structure is scanned from multiple angles by a lidar mounted on a drone to obtain point cloud data of the geometric contour of the steel structure. A portable laser scanner was used to perform detailed scanning of steel structure connection nodes and key parts to collect detailed geometric feature data of the nodes. Extract steel specifications, strength grades, manufacturers, and installation dates from steel structure building archives to create a basic information table for steel components. Strain sensors, tilt sensors, and displacement sensors are deployed at key stress points of the steel structure to record dynamic stress state data of the steel structure. The point cloud data is subjected to noise reduction processing to remove abnormal points generated during the acquisition process, forming a clean point cloud of the steel structure; The node detail geometric feature data is fused with the clean point cloud to generate complete steel structure geometric data; Based on the steel component basic information table, material and historical attributes are added to the complete steel structure geometric data to construct parametric information of the steel structure. By establishing a correlation mapping between the dynamic stress state data of the steel structure and the parameterized information of the steel structure, a basic library for digital maintenance of steel structures is obtained.

3. The BIM-based sustainable maintenance method for steel structure buildings according to claim 1, characterized in that, The process involves analyzing structural deformation and surface defects based on component information and monitoring data from the steel structure digital maintenance database to obtain a steel structure health assessment report, including: Structural displacement sensor data is extracted from the aforementioned steel structure digital maintenance base library, and time-series characteristic analysis is performed to obtain the steel structure deformation trend curve. High-resolution cameras are used to acquire images of the steel structure surface, and image enhancement processing is used to obtain the surface texture feature map of the steel structure. Based on the surface texture feature map of the steel structure, the surface rust area and crack direction are identified by the edge detection algorithm to form a defect distribution map of the steel structure; The deformation trend curve of the steel structure is compared with the theoretical stress calculation value to calculate the degree of structural deformation deviation and generate a deformation anomaly index. Based on the steel structure defect distribution map, calculate the corrosion area ratio and crack density value of each component to form quantitative parameters of surface damage; By using a comprehensive scoring method to calculate the weights of the deformation anomaly index and the quantitative parameters of surface damage, the health level of the components is classified, and a steel structure health assessment report is obtained.

4. The BIM-based sustainable maintenance method for steel structure buildings according to claim 1, characterized in that, The process involves using key component data from the steel structure health assessment report to calculate the resource consumption and environmental impact of maintenance plans, resulting in a sustainable maintenance strategy for the steel structure, including: Component information with health levels D and E is selected from the steel structure health assessment report to generate a list of components to be maintained; For each component in the list of components to be maintained, applicable maintenance technologies are matched from the maintenance method database to form a preliminary maintenance technology combination scheme; Based on the aforementioned preliminary maintenance technology combination scheme, the required steel consumption, anti-corrosion coating consumption, and construction time for each scheme are calculated to form a resource consumption data table. Based on the resource consumption data table, combined with the carbon emission coefficient of material production and construction energy consumption parameters, the carbon footprint value of the entire maintenance process is calculated, and environmental impact assessment data is generated. The material and labor data in the resource consumption data table are converted according to the market unit price to form a maintenance economic cost budget; By comprehensively balancing the environmental impact assessment data and the maintenance economic cost budget, and comparing the maintenance benefits with the remaining service life of the building, a sustainable maintenance strategy for steel structures is obtained.

5. The BIM-based sustainable maintenance method for steel structure buildings according to claim 1, characterized in that, The process involves importing the steel structure maintenance construction guide into the on-site terminal equipment for location guidance and operation identification, resulting in a steel structure maintenance implementation record, including: The spatial coordinate data in the steel structure maintenance construction guide is converted into on-site positioning parameters, and an electronic map of the maintenance location is established in the on-site terminal equipment. Based on the electronic map of the maintenance location, the actual location of the steel structure components on site is confirmed by matching and scanning the target recognition technology. Virtual operation guidance is marked on the actual location of the component to be maintained, and the maintenance process parameters are intuitively projected onto the surface of the component to form a visual operation instruction. The visual operation instructions guide construction workers to complete component repair operations, while recording key parameters during construction and generating a construction process data stream. The welding temperature, coating thickness, and bolt torque values ​​in the construction process data stream are monitored in real time, compared with the design standards for verification, and construction quality data are recorded. By integrating the construction quality data and photos of completed component maintenance, and associating the construction location and technical parameter information, a steel structure maintenance implementation record is generated.

6. The BIM-based sustainable maintenance method for steel structure buildings according to claim 1, characterized in that, The steel structure maintenance implementation records and original state data are compared and the effects are verified before and after maintenance to obtain a steel structure maintenance case dataset, including: Extract the post-maintenance component status parameters from the steel structure maintenance implementation record, and simultaneously retrieve the original component status data before maintenance from the steel structure digital maintenance basic library to form a data comparison table before and after maintenance; Displacement and strain data of key nodes of the steel structure after maintenance are re-collected by sensor network and compared with the structural monitoring data before maintenance to calculate the structural performance recovery rate. High-definition imaging scans were performed on the surface of the steel structure after maintenance. Surface features were extracted through image processing and compared with the surface defect distribution map before maintenance to obtain the surface quality improvement index. The structural performance recovery rate and surface quality improvement index are compared with the expected targets of the maintenance plan to evaluate the effectiveness of the maintenance technology and generate technical evaluation results. Based on the actual resource consumption data in the maintenance implementation record, the economic cost and environmental impact of the maintenance are recalculated and compared with the budget value to form a resource efficiency assessment. By integrating the aforementioned technical evaluation results and resource efficiency assessments, and linking the pre-maintenance status, maintenance technology selection, and maintenance effect data, a structural steel structure maintenance case dataset is established.

7. A BIM-based sustainable maintenance system for steel structure buildings, used to implement the BIM-based sustainable maintenance method for steel structure buildings as described in any one of claims 1 to 6, characterized in that, The BIM-based sustainable maintenance system for steel structure buildings includes: The integration module is used to collect geometric data and material parameters of steel structures through laser scanning and sensor networks, integrate the three-dimensional information of steel structures, and obtain a basic library for digital maintenance of steel structures. The analysis module is used to perform structural deformation and surface defect analysis based on the component information and monitoring data in the steel structure digital maintenance base library, and to obtain a steel structure health assessment report. The calculation module is used to calculate the resource consumption and environmental impact of maintenance plans based on the key component data in the steel structure health assessment report, and to obtain a sustainable maintenance strategy for the steel structure. The configuration module is used to set component repair parameters and configure materials according to the steel structure sustainable maintenance strategy to obtain a steel structure maintenance construction guide. The import module is used to import the steel structure maintenance construction guide into the on-site terminal equipment, provide location guidance and operation identification, and obtain the steel structure maintenance implementation record. The verification module is used to compare and verify the steel structure maintenance implementation records and original state data before and after maintenance, and obtain a steel structure maintenance case dataset.

8. A computer device, characterized in that, The system includes a memory and a processor, the memory storing a computer program that can run on the processor, characterized in that, when the processor executes the computer program, it implements the BIM-based sustainable maintenance method for steel structure buildings as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, causing the processor to perform the BIM-based sustainable maintenance method for steel structure buildings as described in any one of claims 1 to 6.

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