A method for constructing a digital twin community model for urban renewal

Abstract

The present application relates to the technical field of digital twinning, in particular to a method for constructing a digital twinning community model for old and improved communities. The method comprises the following steps: obtaining old and improved community building elevation drawings; constructing a digital twinning community model based on the old and improved community building elevation drawings; identifying building aging parts based on the digital twinning community model to obtain building aging data, and collecting building aging images according to the building aging data; identifying underground pipe network areas according to the building aging images to obtain underground pipe network images; performing water leakage analysis according to the underground pipe network images to obtain pipe network water leakage data; performing wall settlement detection based on the pipe network water leakage data to obtain wall settlement data; and performing wall repair simulation according to the wall settlement data to obtain wall repair data. The present application improves the automation rate and precision rate of old and improved community building hazard identification and structure repair decision-making based on digital twinning technology.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a method for constructing a digital twin community model for old building renovation projects. Background Technology

[0002] Traditional digital twin community models for urban renewal rely on manual inspections and two-dimensional image comparisons for identifying building aging, lacking precise positioning and quantitative analysis based on three-dimensional spatial information, making it difficult to comprehensively reflect the actual aging state of building structures. Underground pipe network monitoring and building settlement analysis are often independent, lacking effective data fusion methods and failing to establish a causal chain of "leakage—wall deformation—structural damage," leading to delayed responses to structural hazards. For hidden material deterioration issues such as concrete carbonization and steel corrosion, traditional methods rely heavily on sampling inspections, failing to achieve continuous monitoring and predictive assessment across the entire community, and thus failing to accurately reflect overall durability trends. Structural repair and reinforcement are often based on experience-based solutions, lacking standardized processes based on carbonization penetration data, corrosion levels, and the matching relationship between repair materials, affecting repair effectiveness and building safety. Building health level assessments typically rely on visually visible indicators, failing to integrate multi-physics field data and multi-stage deterioration results, lacking scientific quantitative standards, and failing to effectively guide community renovation planning and resource allocation. Community model construction is mostly static, failing to introduce time-series updates, risk prediction, and multi-source information dynamic mapping mechanisms. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a method for constructing a digital twin community model for old-type renovation projects, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for constructing a digital twin community model for urban renewal projects includes the following steps:

[0005] Step S1: Obtain the building facade drawings of the old urban renewal community; construct a digital twin community model based on the building facade drawings of the old urban renewal community; identify the aging parts of the buildings based on the digital twin community model, obtain building aging data, and collect building aging images based on the building aging data;

[0006] Step S2: Identify the underground pipe network area based on the building aging image to obtain the underground pipe network image; perform leakage analysis based on the underground pipe network image to obtain pipe network leakage data; perform wall settlement detection based on the pipe network leakage data to obtain wall settlement data; perform wall repair simulation based on the wall settlement data to obtain wall repair data.

[0007] Step S3: Perform concrete carbonation anomaly analysis based on building aging data to obtain concrete carbonation anomaly data; determine the degree of steel reinforcement corrosion based on the concrete carbonation anomaly data; optimize structural reinforcement based on the degree of steel reinforcement corrosion to obtain structural reinforcement data;

[0008] Step S4: Conduct a building stability assessment based on the structural reinforcement data and wall repair data to obtain building stability data; assess the health level based on the building stability data to obtain building health level data; transmit the building health level data to the digital twin community model to obtain the community optimization model.

[0009] This invention acquires building facade drawings from urban renewal communities and constructs a digital twin community model. By combining 3D spatial coordinate information, it enables high-precision positioning and zonal modeling of aging building areas. This avoids the shortcomings of traditional manual inspections and 2D image comparison methods in terms of spatial resolution, identification completeness, and data consistency, thus achieving comprehensive identification and visual reproduction of building facade defects. Furthermore, based on the digital twin model, by integrating building aging images and 3D model data, it can automatically label aging types such as peeling, cracking, and corrosion in the building entity coordinate system, improving the spatial management and structural correlation capabilities of aging information. The acquired building aging images can be used to achieve deep identification of underground pipe network areas, constructing an underground spatial structure map, and performing high-precision extraction using image segmentation algorithms (such as a pipe identification network based on UNet). Subsequently, through leakage hotspot extraction and water pressure attenuation modeling, the location and severity of pipe network damage are quantified, generating pipe network leakage data. Based on this, a causal mapping relationship between underground water leakage data and wall settlement is established. Settlement parameters are calculated by analyzing changes in building foundation deformation, and wall settlement distribution maps are generated with millimeter-level precision, enabling quantitative judgment of local settlement anomalies. Through the coupling of this data chain, dynamic closed-loop monitoring between "underground hidden dangers and structural response" is achieved, significantly improving the risk warning capability of underground pipeline failures on superstructure deformation. By analyzing the concrete surface texture, color shift, and crack direction in building aging images, and combining carbonation depth recognition algorithms (such as RGB-CaCO3 color model recognition) to quantify the degree of concrete carbonation, abnormal concrete carbonation data is obtained. Local meshing is performed in areas where the carbonation influence depth exceeds 20mm, and the risk level of steel corrosion is further determined by combining the carbonation depth-steel protective layer thickness ratio. Based on the degree of steel reinforcement corrosion, the corrosion level of different areas is determined by calling the steel reinforcement stress-corrosion evolution model and standard anti-corrosion treatment data table. Then, a standardized matching method is used to select the type of structural reinforcement materials (such as polymer grouting, carbon fiber cloth, etc.) and reinforcement schemes, forming structural reinforcement data. This significantly improves the detection range and assessment accuracy of hidden material deterioration. Based on the structural reinforcement data and wall repair data, the stability of the current structural safety status can be assessed. Through finite element simulation, physical quantities such as structural stress field, displacement field, and crack strain are extracted to analyze the overall stress diffusion, nodal deformation concentration, and shear failure trend of the building. This is combined with historical monitoring data for trend comparison and stability index calculation (such as stiffness reduction rate not exceeding 10%, deformation concentration coefficient not exceeding 1.25, etc.) to obtain building stability data. On this basis, multi-source data such as aging, settlement, carbonization, corrosion, repair, and reinforcement generated at different stages are integrated. A health level assessment matrix is ​​used to classify the building into levels (e.g., Level 1 health requires no intervention, Level 5 indicates serious hidden danger requiring immediate closure), forming a unified health level data system.Finally, the health level data is mapped to the community's digital twin model through a data interface, enabling dynamic display and periodic updates of the health status, and providing dynamic support for subsequent renovation planning. Attached Figure Description

[0010] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0011] Figure 1 This is a flowchart illustrating the steps of a method for constructing a digital twin community model for urban renewal projects according to the present invention.

[0012] Figure 2 This is a detailed flowchart of step S1 in the present invention;

[0013] Figure 3 This is a detailed flowchart of step S16 in the present invention;

[0014] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0015] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0016] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0017] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0018] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for constructing a digital twin community model for old infrastructure renovation projects, the method comprising the following steps:

[0019] Step S1: Obtain the building facade drawings of the old urban renewal community; construct a digital twin community model based on the building facade drawings of the old urban renewal community; identify the aging parts of the buildings based on the digital twin community model, obtain building aging data, and collect building aging images based on the building aging data;

[0020] In this embodiment, the original facade drawings of the old community buildings are obtained by scanning. The drawings are in CAD (.dwg) file format. AutoCAD software is used to parse the drawings and extract the building outline data, including the edge lines of components such as building boundary lines, windows, balconies, and entrances. The extraction operation is performed by setting boundary line layers and component layers separately and distinguishing them based on layer attributes. The boundary lines must have a closure of more than 98% and a tolerance of no more than 2 mm. The above outline data is input into SketchUp Pro 2023 for 2D to 3D modeling. Parameters such as wall thickness and balcony extension length are assigned values. For example, the wall thickness is set to 240 mm and the balcony extension length is 1200 mm. The 3D facade structure is generated using contour line extrusion technology. After the 3D model is completed, it is imported into MeshLab software for mesh reconstruction. The Poisson Surface Reconstruction algorithm is used to reconstruct the original polygonal surface. The octree depth is set to 9 and the smoothness is set to 0.3 to ensure the continuity of the model boundary. The completed 3D building model was initially identified for aging areas using the edge detection algorithm (Canny method, with thresholds set to low threshold = 50 and high threshold = 150) in OpenCV 4.8.0. Areas with surface cracks, color peeling, and discolored spots were extracted. The grayscale contrast (Δgray ≥ 35) and texture mean change (Δtexture ≥ 12%) of the extracted feature region image were used to determine the aging areas. The corresponding images were saved in JPEG format (resolution 2048×2048dpi), and the storage paths were classified and managed according to the building number.

[0021] Step S2: Identify the underground pipe network area based on the building aging image to obtain the underground pipe network image; perform leakage analysis based on the underground pipe network image to obtain pipe network leakage data; perform wall settlement detection based on the pipe network leakage data to obtain wall settlement data; perform wall repair simulation based on the wall settlement data to obtain wall repair data.

[0022] In this embodiment, image enhancement methods are used to process the building aging images collected in S1. The CLAHE (Contrast Limit Adaptive Histogram Equalization) algorithm is used to enhance image details, with clipLimit set to 2.0 and tileGridSize set to (8,8). After generating the enhanced image, the CrackForest deep learning-based crack detection algorithm is applied to extract crack areas from the image and generate a binary image (1 represents crack, 0 represents background). Morphological filtering (3×3 dilation + 2×2 erosion) is applied to the extracted crack areas, and the areas affected by settlement are marked according to the crack direction and extension length (greater than 500mm). GPS coordinate mapping is performed on the settlement areas, and spatial overlap analysis is performed using GIS underground facility layout data (.shp format). The "Vector Overlap Analysis" tool in QGIS 3.30 is used to extract the intersection area, and the underground pipe network number and location information corresponding to the intersection area are output. Ground penetrating radar GPR (frequency 900MHz) is used to conduct underground detection in these areas to obtain underground pipe network images. The image format is B-scan cross-sectional view, and the sampling interval is set to 0.05m. Image segmentation is performed on the pipeline network image, and the pipeline network boundary lines are extracted using the UNet neural network model. The deformation rate is calculated as (current contour area - standard contour area) / standard contour area. If the deformation rate exceeds 10%, it is marked as abnormal. For high-deformation areas, an eddy current detector (such as the Olympus Nortec 600) is used to detect material changes. The magnetic permeability μ and electrical conductivity σ are read as material discrimination parameters. For carbon steel, the standard values ​​are μ = 100–400 and σ = 10. 6 S / m; if the deviation exceeds ±20%, it is judged as the initial stage of material corrosion. A corrosion simulation is established by combining material conductivity and deformation (using a linear corrosion rate of 0.2 mm / year), and a corrosion development curve is constructed. Triaxial stress simulation is performed on high corrosion risk points. A fatigue damage model is established using the finite element simulation tool ANSYS 2023, Von-Mises stress and residual stress data are extracted, fatigue weak zones are located, and areas located in the direction of the maximum principal stress and with equivalent stress >250 MPa are marked as high-risk rupture zones. Hydraulic simulation is performed centered on the high-risk rupture point. Pressure loss and leakage volume are calculated using the Darcy formula, the actual pipe inner diameter (set to 150 mm), and the flow velocity (1.2 m / s) to determine whether it is a substantial leak point. The output is the pipe network leakage data (marked in GIS point distribution form).

[0023] Step S3: Perform concrete carbonation anomaly analysis based on building aging data to obtain concrete carbonation anomaly data; determine the degree of steel reinforcement corrosion based on the concrete carbonation anomaly data; optimize structural reinforcement based on the degree of steel reinforcement corrosion to obtain structural reinforcement data;

[0024] In this embodiment, core samples were extracted from the aged concrete area at a depth of at least 50 mm using a 50 mm diameter diamond core drill bit. After removing the surface protective layer, the sample was cleaned with a diluted sodium hydroxide solution (1% concentration) for 30 seconds, followed by rinsing with deionized water. After drying, a 1% phenolphthalein solution was sprayed onto the sample cross-section. After 15 minutes of reaction, the color change was observed. The carbonized area remained unchanged, while the non-carbonized area turned purplish-red. The carbonization depth was measured using digital calipers with an accuracy of ±0.1 mm, and the average carbonization depth data was recorded. If the carbonization depth exceeded the thickness of the reinforcing steel protective layer (e.g., the protective layer thickness was set to 20 mm, and the carbonization depth to 25 mm), it was considered an abnormal carbonization. Based on the location of carbonization penetration, the corrosion potential of the exposed surface of the reinforcing steel was tested using the silver salt method, with a silver-silver chloride reference electrode connected to a high-resistance potentiometer for measurement. If the steel reinforcement potential is less than -500mV, it is considered a high-corrosion zone. The corrosion probability is calculated using a potential distribution density function (a Gaussian fitting function is used to fit the corrosion intensity distribution; a standard deviation exceeding 20mV is considered large-scale corrosion development). Areas with a corrosion probability >75% are designated as severely corroded areas. Structural reinforcement design is based on the corrosion zone distribution. Concrete columns are reinforced using steel plate bonding, with a steel plate thickness of 10mm and an edge-to-substrate fit of less than 0.5mm. Two-component epoxy resin (EPOX-385, 1:1 ratio) is used for bonding, and the surface is sandblasted to a roughness Ra of 12.5μm or higher. The reinforcement construction design is output in CAD format, including component number, location, reinforcement method, material type, and quantity, serving as a structural reinforcement data record.

[0025] Step S4: Conduct a building stability assessment based on the structural reinforcement data and wall repair data to obtain building stability data; assess the health level based on the building stability data to obtain building health level data; transmit the building health level data to the digital twin community model to obtain the community optimization model.

[0026] In this embodiment, the reinforcement type and location data of the components are extracted from the structural reinforcement design drawings, and the net cross-sectional dimensions and material properties of each component are read. Taking a beam as an example, the clear span L = 3.5m, the cross-sectional width b = 300mm, the effective height h = 500mm, and the longitudinal reinforcement area As = 1570mm² 2, using C30 concrete and HRB400 steel bars, the design value of the bending moment bearing capacity is Mu = Asf_y(h - a / 2), where a is the distance from the centroid of the steel bars to the tension side, and f_y = 360 MPa. The ratio of the bearing capacity Mu value to the measured load needs to be greater than the safety factor of 1.4 to be qualified. Read the type of filling material from the wall repair data and detect its adhesion force respectively: The pull-out test of polymer cement composite materials adopts the standard GB / T 5210 - 2006, and the adhesion force test of epoxy resin grouting materials is carried out according to GB / T 16777 - 2008. The pull-out test piece steel sheet is bonded to the test surface for testing. If the adhesion strength ≥ 1.5 MPa, it is qualified. Input the bearing capacity of the component and the material adhesion force data into the stability evaluation formula: Building stability = (∑Mu value of components × adhesion force weighting factor) / total number of components, and obtain the building stability data (the standard range is set to 0–1, and the qualified value is ≥ 0.6). Taking the building stability as an index, divide the health level: 0–0.4 is level D, 0.4–0.6 is level C, 0.6–0.8 is level B, and 0.8–1.0 is level A. Finally, summarize the health level data by house number to generate a.csv form, and upload it to the structure information library module in the digital twin community model system through the interface program written in Python. The file structure is in the format of [Building ID, Component ID, Stability coefficient, Health level], and the community model is updated.

[0027] Preferably, step S1 is specifically as follows:

[0028] Step S11: Obtain the architectural elevation drawings of the old renovation community and extract the building contour data;

[0029] In this embodiment, a handheld paper scanner (such as a Cortex IQ Quattro 4450 with a resolution of 600 dpi) is used to scan the existing paper building facade drawings of the urban renewal community page by page, and the output drawing files are in TIFF format. The scanned images are imported into an image preprocessing tool, and the cv2.threshold() function in the OpenCV image processing library is used for binarization processing, with a threshold of 190 and a maximum value of 255. After processing, black and white line information is retained. The binary image is then subjected to Hough line transform to identify the building outline. The cv2.HoughLinesP() method is called, with parameters set to pixel resolution ρ = 1, angle resolution θ = 1°, minimum line segment length of 50 pixels, and maximum interval of 5 pixels, outputting line segment data. The line segments are then combined, and a line segment connection algorithm is used to identify closed paths. The judgment condition is that the distance between the start and end points of the path does not exceed 2 pixels and the included angle is within ±10°. The identified closed paths are converted from pixel coordinates to actual building dimensions according to the drawing scale (e.g., 1:100). The conversion formula is: Actual length = Number of pixels × (1 / 100) × Drawing unit (mm). The outline data includes structural outlines such as building boundaries, floor slabs, balconies, and eaves, all of which are saved as .shp files in vector polygon format and categorized and numbered for subsequent processing.

[0030] Step S12: Model the structural boundary based on the building outline data to obtain two-dimensional building structural data;

[0031] In this embodiment, the building outline data extracted in step S11 is imported into a 2D structural modeling tool, and a vector graphics processing platform (such as QGIS) is used to perform topological correction operations on the boundary polygons. A tolerance threshold of 1 pixel is set, and nodes with overlaps, gaps, or intersections are automatically merged. The "topology repair" function is used to ensure the polygons are closed and intact. For example, the wall thickness corresponding to the boundary with outline number W001 is set to 240mm, and the floor slab thickness corresponding to the outline with outline number F001 is set to 120mm. These attributes are appended to the vector data attribute table, and a structural boundary attribute layer is generated. Next, the Shapely library in Python is used for geometric operations to extract the boundary length and projected area data of structural elements, calculate the total width, height, and segmented module dimensions of the building, for subsequent geometric mapping processing. Finally, the 2D building structural data is output and saved in GeoJSON format, with fields including component number, type (wall, slab, eaves), actual size, thickness, and location coordinates.

[0032] Step S13: Identify facade elements based on two-dimensional building structure data to obtain facade element data;

[0033] In this embodiment, an image pattern recognition method is used to perform facade element recognition processing on the two-dimensional building structure data generated in step S12. The template matching algorithm `cv2.matchTemplate()` from the OpenCV library is called. The preset templates include patterns such as standard windows, door frames, and balcony railings. The template image resolution is 64×64 pixels, the matching threshold is set to 0.85, and the matching method is `cv2.TM_CCOEFF_NORMED`. A sliding window matching operation is performed on each candidate region. When the matching value exceeds the threshold, the region is labeled as the corresponding component type. For example, the rectangular region identified by the window template is recorded as the component type "window" and located at the coordinate position under the corresponding building number in the vector map. The size information of the identified region is further extracted. The aspect ratio is extracted using the bounding rectangle function `cv2.boundingRect()` and recorded as the ratio of window width W to height H. If W:H < 1.2, it is labeled as a vertical window; if W:H > 1.5, it is a horizontal window; otherwise, it is a square window. All identified components are saved as planar vector objects in the facade element layer, and a facade element data table in CSV format is output, with fields including component number, type, width, height, location coordinates, outline boundary, etc.

[0034] Step S14: Perform three-dimensional geometric transformation based on the facade element data to obtain three-dimensional facade component data;

[0035] In this embodiment, the facade element data generated in step S13 undergoes a 3D geometric transformation. First, height and depth are assigned according to the building standards for each element type. Taking a balcony as an example, if its 2D outline width is 1800mm, its height is 1200mm, its cantilever depth is set to 1200mm, and its thickness is 150mm. A cube model is constructed using a 3D modeling algorithm, and an extrusion operation is performed based on the 2D boundary coordinate points and the given thickness / depth. Components such as walls, window frames, and floor slabs are all processed in this way, with the extrusion direction perpendicular to the 2D plane normal vector direction. The component geometry is saved in triangular facet (.obj) format. Each component file contains component ID, type, height, depth, number of boundary points, and mesh density parameters (default set to 0.05m facet side length). To ensure model compatibility and subsequent rendering efficiency, a right-handed coordinate system is uniformly adopted, with the 3D space origin set at the northwest corner of the building's ground point, upward as the Z-axis, east as the X-axis, and south as the Y-axis. The generated 3D component set is categorized and numbered according to the building number and uniformly saved to the component data warehouse directory.

[0036] Step S15: Combine building units based on the 3D facade component data to obtain building unit data; construct a digital twin community model based on the building unit data;

[0037] In this embodiment, the 3D facade component data obtained in step S14 is imported into the construction engine (such as the Three.js construction engine), and building units are combined based on component IDs. Components such as windows, doors, balconies, and floor slabs are vertically stacked according to their floor numbers and horizontally arranged according to the longitudinal facade axes (such as axes 1-1 and 2-2) in the drawings. The splicing interface of the components is set with a seam width of 2mm, and the node connections are constrained using a rigid connection method. Spatial Boolean merging is performed on all combined units to avoid component overlap or voids. After the building unit construction is completed, the building units are subjected to community-level coordinate transformation according to the overall community planning map (vector site plan.shp), and their positions (X,Y,Z) in the overall coordinate system are repositioned. All building units are imported into the data warehouse of the digital twin platform in the form of an aggregate. The platform loads the building objects using WebGL to construct a complete digital twin community 3D scene model.

[0038] Step S16: Identify aging parts of the building based on the digital twin community model, obtain building aging data, and collect building aging images based on the building aging data.

[0039] In this embodiment, based on the completed digital twin community model, a building aging identification subsystem is activated. This subsystem uses a real-scene acquisition terminal (such as a drone equipped with a high-resolution camera) to photograph the exterior facades of each building. The flight altitude is set to 50m, and the resolution requirement is 4K (4096×2160 pixels). At least 12 images are captured for each building, covering the front facade, side facades, and top structure. The acquired images are analyzed using the YOLOv7 object detection model. The model's pre-training weights are based on the "UrbanWallDefectSet" dataset. Classification labels include "cracks," "water seepage," "coating peeling," and "rust," etc. The confidence threshold is set to 0.6 or higher for valid identification results. The identified aging parts are marked on the surface of the 3D model, and texture mapping is performed using a texture mapping method. The marking colors use standard RGB encoding: cracks are marked red (RGB:255,0,0), and water seepage is marked blue (RGB:0,0,255). Each recognition result outputs structured data in JSON format, with fields including component number, defect type, defect coordinate range, image number, and shooting angle. All recognized images and result layers are stored synchronously in real time on the twin platform's file system and archived by building unit.

[0040] Preferably, step S16 specifically includes:

[0041] Step S161: Segment the buildings according to the digital twin community model to obtain the building data;

[0042] In this embodiment, the overall 3D data of the building complex in the digital twin community model is used as input, and a topology-based segmentation algorithm is employed to segment individual buildings. First, the component attributes and spatial relationship data in the model are analyzed to obtain the bounding box of each building unit. The bounding box coordinates are represented in a 3D Cartesian coordinate system (X, Y, Z). A spatial index structure (such as an octree) is used to spatially partition the model, with a segmentation precision set to 0.5 meters, meaning each spatial unit has a side length of 0.5 meters. Based on the spatial segmentation, adjacency judgment is performed, identifying building unit boundaries based on shared or connecting surfaces between components. Adjacent components with a shared area exceeding 0.2 square meters are grouped into the same unit. For the segmentation results, isolated components with an area less than 50 square meters are removed to ensure that the segmented unit data matches the actual building units. Finally, each building unit is output as a 3D mesh file (such as OBJ format), with accompanying attribute data including the unit number, spatial coordinate range, and number of components.

[0043] Step S162: Reconstruct the surface mesh based on the individual building data to obtain the building surface mesh data;

[0044] In this embodiment, surface mesh reconstruction is performed on the 3D data of the building unit obtained in step S161. First, topology repair is performed on the 3D point cloud or triangular facet data. The open-source tool MeshLab is used to perform "clean up duplicate vertices" and "fill holes," with a maximum hole diameter threshold set to 5 mm, automatically filling all holes to ensure mesh closure. Then, the Poisson Surface Reconstruction algorithm is applied, with a depth parameter set to 10 to control the level of detail in the reconstruction. The maximum sampling point spacing is set to 2 mm to ensure mesh accuracy. The 3D mesh file output by the Poisson algorithm contains refined triangular faces with an average facet side length of approximately 1.5 cm and uniform overall mesh density. After reconstruction, normal estimation technology is used to calculate the mesh vertex normals, with a normal smoothing radius set to 3 cm to ensure accurate lighting effects during texture mapping. The generated building surface mesh data is saved in PLY format, containing vertex coordinates, normal vectors, and facet index information.

[0045] Step S163: Perform 3D texture mapping based on the building surface mesh data to obtain building surface texture data;

[0046] In this embodiment, three-dimensional texture mapping is implemented based on the surface mesh data generated in step S162. UV flattening technology is used to map the three-dimensional surface mesh onto a two-dimensional plane. Using the automatic UV flattening algorithm of Autodesk Maya or Blender, each building unit mesh is divided into several UV islands, controlling the maximum side length of each UV island to no more than 256 pixels to ensure the integrity of texture details. The texture image resolution is uniformly set to 4096×4096 pixels, and the format is PNG. The texture is acquired from real-scene images captured by a high-resolution drone camera with a resolution of at least 16 megapixels, at a shooting height of 50 meters, and with a pitch angle ranging from -15° to 15° covering all facades of the building. Image stitching is performed using SIFT feature point matching technology to extract key point pairs, setting a matching threshold of 0.75 to ensure seamless connection of texture images. Finally, the texture image is mapped to the corresponding UV coordinates of the mesh, achieving accurate assignment of the building surface texture data. Color correction during the mapping process uses a grayscale equalization algorithm to prevent excessive texture color difference and ensure visual uniformity.

[0047] Step S164: Perform edge fracture analysis based on building surface texture data to obtain edge fracture data;

[0048] In this embodiment, edge fracture analysis is performed using the building surface texture data from step S163. First, the texture image is converted to grayscale. The Canny edge detection operator is used, with a low threshold of 50 and a high threshold of 150, to extract significant edges from the texture image. After edge detection, connected component analysis is used to label the edge fracture regions. The minimum fracture length threshold is set to 30 mm; fracture regions exceeding this length proceed to the next analysis step. Based on edge density statistics, edge strength is calculated for each 512×512 pixel block of the texture image. If the edge strength exceeds 1.5 times the mean, the region is identified as a high-risk fracture region. Furthermore, by combining the building mesh vertex coordinates, the texture space edges are mapped back to the three-dimensional surface to determine the three-dimensional spatial range of the fracture location. The fracture edge data is stored in the form of a polyline vector, including the three-dimensional coordinates of the start and end points, the fracture length, and the fracture direction angle. The fracture direction angle is in degrees, ranging from 0° to 360°, and is used for subsequent structural analysis.

[0049] Step S165: Identify aging parts of the building based on edge fracture data, obtain building aging data, and collect building aging images based on the building aging data.

[0050] In this embodiment, based on the edge fracture data obtained in step S164, and combined with the 3D building structure of the digital twin community model, the aging parts of the building are located. A high-resolution photographic device (aperture F2.8, ISO100, shutter speed 1 / 125 second) is used to take close-up photos of the fracture area, acquiring images of the building's aging. The shooting angle is adjusted according to the fracture direction to ensure the fracture feature is centered in the image. The acquired aging images are corrected using image geometric correction technology. Based on the camera intrinsic parameter matrix and distortion parameters in photogrammetry, the OpenCV cv2.undistort() function is used to correct image distortion and remove the effects of lens distortion. The corrected image and the 3D model are registered using spatial alignment technology based on a calibration plate, allowing for precise positioning of the aging images. The aging image information is stored in association with the fracture vector data, forming a complete archive of the building's aging parts. Fields include aging number, corresponding building number, fracture type, spatial coordinates, shooting date, and resolution parameters.

[0051] Preferably, in step S2, identifying the underground pipe network area based on the building aging image specifically involves:

[0052] Image enhancement processing is performed on building aging images to obtain enhanced building aging images;

[0053] In this embodiment, image preprocessing is first performed on the acquired building aging images. The input image is in 8-bit grayscale format with a minimum resolution of 3000×4000 pixels. Contrast-Limited Adaptive Histogram Equalization (CLAHE) is used to enhance image details. The CLAHE window size is set to 8×8 pixels, and the histogram clipping threshold is set to 0.01 to limit excessive local contrast enhancement and prevent noise amplification. Subsequently, a bilateral filter is applied to smooth the image to preserve edge details. The filter space radius is set to 5 pixels, and the color space standard deviation is set to 75 to balance noise removal and edge preservation. Gamma correction is performed on the enhanced image, with a Gamma value set to 1.2 to adjust the image brightness distribution. After these processes, an enhanced image is output, ensuring that the building aging features are easily identifiable in subsequent detection.

[0054] Crack detection is performed based on enhanced images of building aging to obtain crack data;

[0055] In this embodiment, enhanced images of aging buildings are used to detect cracks. An edge detection algorithm based on the Canny operator is employed, with a low threshold of 40 and a high threshold of 120 to ensure the capture of minute crack edges. After detection, a morphological closing operation is applied, using a 5×5 square kernel as the structuring element to fill in the broken sections of the cracks to ensure continuity. Connectivity analysis is performed on the morphologically processed binary crack image, removing noisy connected regions with an area less than 20 pixels. The bounding boxes and centerlines of the remaining crack regions are extracted, and the crack length and width are calculated. The crack width measurement is converted to actual length using pixel counting, with a sampling resolution of 0.1 mm / pixel. Crack data is stored in vector form, including crack number, three-dimensional spatial coordinates of the start and end points (obtained through image-to-3D model registration), crack length, average width, and orientation angle.

[0056] Subsidence areas are identified based on crack data to obtain subsidence area data;

[0057] In this embodiment, settlement areas are determined based on crack distribution characteristics. Spatial clustering analysis is performed on the crack data using the DBSCAN algorithm, with a neighborhood radius ε set to 0.5 meters and a minimum neighbor count MinPts set to 3, to identify crack clusters. The clustered crack clusters are considered potential settlement areas, and the spatial bounding box for each settlement area is calculated, represented by the XYZ coordinate range. For each settlement area, the average crack length and maximum crack width are statistically analyzed. If the maximum crack width is greater than 5 millimeters and the average length exceeds 1 meter, the area is confirmed as a valid settlement area. The settlement area data is stored in vector polygon format, including area number, spatial coordinate boundaries, crack statistical characteristics, and corresponding building unit information.

[0058] Spatial location mapping is performed based on the settlement area data to obtain the settlement area location data;

[0059] In this embodiment, a dual mapping method based on the geographic coordinate system (WGS84) and the local coordinate system is employed. First, geodetic coordinate transformation is achieved using the latitude, longitude, and elevation data of the building's location points. Then, a rigid body transformation matrix (rotation matrix and translation vector) maps the two-dimensional boundary of the settlement area to the surface of the three-dimensional model. The coordinate transformation parameters are calculated by least-squares fitting of the control points on the building facade with the corresponding points in the digital twin model. The rotation matrix accuracy is controlled within ±0.01 degrees, and the translation error does not exceed ±5 centimeters. The mapping result generates three-dimensional spatial polygon data of the settlement area, accurately corresponding to the surface of the building model. The settlement area location data includes spatial coordinates, the corresponding building ID, and mapping error evaluation indicators.

[0060] Obtain underground pipeline network layout data;

[0061] In this embodiment, two-dimensional and three-dimensional layout data of the underground pipe network in the old urban renewal community are collected and organized. The data sources are CAD drawings and GIS databases, including pipe type (water supply, drainage, gas, etc.), pipe diameter (unit: millimeters), material, depth (unit: meters), and laying time. The two-dimensional pipe network data is stored in CAD graphic file format (DWG), while the three-dimensional layout data exists in point cloud and polyline formats. A coordinate transformation method is used to unify the coordinate system of the two-dimensional CAD data and the three-dimensional GIS model, using the local engineering coordinate system with an error controlled within ±0.1 meters. Topological structure detection is performed on the pipe network data to remove isolated pipes and broken links, ensuring the integrity of the pipe network data. Finally, a database containing the spatial direction, depth, and attribute information of the pipes is formed.

[0062] By performing intersection analysis on underground pipeline layout data and settlement area location data, underground pipeline risk area data is obtained, and underground pipeline images are collected based on the underground pipeline risk area data.

[0063] In this embodiment, the risk area of ​​the underground pipeline network is defined as the intersection of the settlement area polygon and the underground pipeline buffer zone. The buffer zone radius is set according to the pipe diameter and soil bearing capacity, typically 1.5 times the pipe diameter, with a minimum buffer zone radius of 0.5 meters. The ST_3DDWithin function is used to calculate the distance between the settlement area and the pipeline; if the distance is less than the buffer zone radius, it is considered a risk intersection. A three-dimensional polygon set of the risk area is generated, including the risk area number, the corresponding pipeline ID, the spatial range, and the settlement impact intensity. Based on the risk area data, images of the intersection area are acquired using ground and underground cameras (high-definition cameras with a resolution of 1920×1080 and night vision enabled). During acquisition, the camera position and angle are fixed, and the shooting distance is controlled between 1 and 3 meters to ensure that the underground pipeline network and surrounding structures are clearly visible. The acquired images are associated with the risk area spatial data and stored to form an underground pipeline network risk image archive.

[0064] Preferably, the leakage analysis based on the underground pipe network image in step S2 specifically involves:

[0065] The pipeline network outline is reconstructed from the underground pipeline network image to obtain the pipeline network outline data;

[0066] In this embodiment, the acquired underground pipeline network image resolution should reach at least 4000×3000 pixels to ensure complete detail. An edge detection-based image processing method is employed. First, the color image is converted to grayscale, and noise is removed using a Gaussian filter (convolution kernel size 5×5, standard deviation σ=1.0). Then, the Canny operator is applied for edge detection, with a low threshold of 50 and a high threshold of 150 to accurately capture pipeline edges. The extracted edges undergo morphological closing operations, with a structuring element of 7×7 squares to eliminate edge breaks. The edge points are then processed using a contour tracking algorithm to extract contour curves. The contours are represented as vector point sets, with a sampling interval of 1 mm to ensure accuracy. Finally, the contour data is stored as a three-dimensional spatial coordinate sequence, and accurate spatial mapping is achieved through registration between the image and the coordinate system of the underground pipeline network's three-dimensional model.

[0067] Calculate the profile deformation rate based on the pipeline network profile data;

[0068] In this embodiment, the deformation rate calculation is based on a time-series comparison of the pipeline profile curve. Baseline profile data and the latest acquired profile data are set as references. A point cloud registration algorithm (such as the Iterative Closest Point (ICP) algorithm) is used to spatially align the two profiles, with a registration error threshold limited to 0.01 meters. The distance change between corresponding points is calculated, and the distance change divided by the original distance of the corresponding point is the local deformation rate. To avoid noise interference, a deformation rate threshold of 0.002 (i.e., 0.2% deformation) is set; values ​​below this threshold are considered normal. The deformation rate data, based on the pipeline cross-section position, forms a deformation rate curve along the pipeline, which is used for subsequent identification of abnormal deformation areas.

[0069] Identify high-deformation regions of the contour based on the contour deformation rate, and obtain data on high-deformation regions of the contour.

[0070] In this embodiment, high-deformation region identification employs a threshold segmentation method, marking pipeline contour segments with a deformation rate greater than 0.005 (i.e., 0.5%) as high-deformation regions. Using a connected component analysis algorithm, adjacent high-deformation points are merged into continuous segments, with a minimum segment length of 0.2 meters to avoid the influence of isolated anomalies. For each high-deformation region, its length, start and end coordinates, and average deformation rate are calculated to generate corresponding polygonal spatial data. The high-deformation region data includes a region number, spatial coordinate boundaries, pipeline section number, and corresponding timestamp information, stored in standard GIS format for easy association with pipeline health data.

[0071] Pipe material detection is performed based on the contour data of high-deformation regions to obtain pipe material data;

[0072] In this embodiment, ultrasonic testing technology is used for non-destructive testing of the pipe material in high-deformation areas. The ultrasonic probe frequency is set to 5MHz, the scanning interval is 2cm, and the entire high-deformation area is covered. Pipe wall thickness and internal defect characteristics are extracted through echo signal time delay and attenuation analysis. Using a sound wave propagation speed set to 5900m / s (standard value for steel), the wall thickness is calculated based on the echo time difference, with an accuracy controlled within ±0.05mm. The test results are converted into material property data, including the actual wall thickness, material type (e.g., steel, stainless steel, cast iron), and defect distribution location. The material data is stored in correspondence with the spatial data of the high-deformation area contour, forming a complete structural health database.

[0073] Pipeline corrosion simulation was performed based on pipeline material data, where the annual corrosion rate of the pipeline wall thickness was set to 0.1-0.3 mm / year, and pipeline corrosion data were obtained.

[0074] In this embodiment, the pipeline corrosion simulation is based on finite element analysis software, with the input pipeline geometric model and actual wall thickness data. According to industry standards, the corrosion rate is set between 0.1 and 0.3 mm / year, with values ​​preset based on pipeline material type, environmental humidity, soil pH, and other factors. The simulation period is set to 10 years, with a time step of 1 year. At each time step, the wall thickness decreases by the corresponding corrosion rate value, and the corresponding structural stress distribution is calculated. The corrosion process simulation includes uniform corrosion and localized corrosion. Localized corrosion uses a randomly distributed corrosion factor, with the maximum corrosion depth taken as 1.5 times the average corrosion depth. The simulation results generate spatial distribution data of pipeline wall thickness at the end of each year, forming a temporal change database of pipeline corrosion status.

[0075] Based on pipeline corrosion data, pipeline fatigue vulnerability analysis was performed to obtain pipeline fatigue vulnerability data.

[0076] In this embodiment, fatigue analysis is performed using pipeline corrosion simulation results combined with cyclic stress load data. Stress data is obtained through pipeline operating pressure and temperature measurements, with pressure fluctuations ranging from 0.5 to 1.2 MPa and temperature variations from 10 to 50 degrees Celsius. The Miner linear cumulative damage method is used to calculate the cumulative fatigue damage value. The material fatigue limit stress depends on the material; the fatigue limit for steel pipes is 250 MPa. Fatigue vulnerability data includes the remaining percentage of fatigue life for each pipeline segment, the location of the maximum fatigue stress, and the corresponding wall thickness. The analysis is performed on a segment-by-segment basis, calculating the fatigue safety factor. A factor below 1.0 is considered a high-risk area. Data is stored in structured tables and three-dimensional spatial data formats.

[0077] Pipeline rupture risk is predicted based on pipeline fatigue vulnerability data, and pipeline rupture data is obtained;

[0078] In this embodiment, pipeline rupture risk prediction is based on fatigue vulnerability data and historical accident rate statistics. Using probabilistic statistical methods, combined with the pipeline's remaining fatigue life and historical rupture probability distribution functions, the rupture probability of each pipeline segment is calculated. A threshold of 0.05 (i.e., a 5% annual rupture probability) is set, and areas exceeding this value are marked as high-risk rupture zones. Rupture risk data includes probability values, spatial coordinates, risk level classification (high, medium, low), and rupture time prediction intervals. The rupture data is exported in a GIS-compatible format for visualization analysis and dynamic updates.

[0079] Leakage analysis is performed based on pipeline rupture data to obtain pipeline network leakage data.

[0080] In this embodiment, leakage analysis combines rupture risk data and on-site sensor data, including flow meter, pressure sensor, and humidity sensor data. The flow meter sampling frequency is set to 1Hz, and the pressure sensor accuracy is ±0.01MPa. Leakage locations are determined through abnormal flow and pressure fluctuation detection using inverse calculations based on a hydraulic model. The probability of leakage occurrence is cross-validated by combining rupture risk areas. Leakage data records include leak location coordinates, leakage rate (unit: liters / hour), leak start time, and duration. The results generate a leakage event log and spatial distribution map, which are stored in the pipeline health monitoring database.

[0081] Preferably, the wall settlement detection based on pipeline leakage data in step S2 specifically involves:

[0082] Water erosion detection of walls is carried out based on pipeline leakage data to obtain water erosion data of walls;

[0083] In this embodiment, spatial location and leakage volume information from pipeline leakage data are combined with two-dimensional plan views and three-dimensional models of the wall structure for spatial overlay. High-precision Geographic Information System (GIS) software is used to achieve spatial data fusion, with coordinate registration errors controlled within ±0.02 meters. Based on the leakage point and surrounding water infiltration radius, and according to groundwater infiltration theory, the water infiltration influence radius is set to a 3-meter range extending outward from the leakage point. Humidity data of the wall material within this range is collected using a portable resistive humidity detector with a measuring point spacing of 0.5 meters and a humidity measurement accuracy of ±1%. Wall areas with humidity values ​​exceeding 75% (relative humidity threshold) are marked as water erosion areas. Combining the wall material's water absorption rate (concrete water absorption rate set at 6%) and permeability coefficient (taken as 1×10^-6 m / s), numerical simulation is used to calculate the water migration path and cumulative humidity distribution, forming a water erosion distribution map of the wall. The data format includes spatial coordinates, humidity percentage, and erosion level. The final generated data on water erosion of the wall includes the estimated erosion area, depth, and humidity distribution, and is stored as a structured spatial database.

[0084] The degree of damage to the wall structure is assessed based on the water erosion data of the wall, and the wall structure damage data is obtained.

[0085] In this embodiment, concrete samples were collected from the water-eroded area and non-destructive ultrasonic testing was performed at a frequency of 5MHz, a detection spacing of 0.3 meters, and a detection accuracy of ±0.02mm. The elastic modulus and crack depth of the concrete were calculated by measuring the changes in ultrasonic transmission speed and attenuation coefficient. Areas with an elastic modulus below 25GPa were considered damaged zones. Damaged zones were further classified into three levels based on the percentage reduction in elastic modulus: mild (5%-15%), moderate (15%-30%), and severe (over 30%). A damage index was calculated for each eroded area using the formula D = (E_0 - E_i) / E_0, where E_0 is the standard elastic modulus of the non-eroded area, and E_i is the measured value of the eroded area. The generated structural damage data includes the damage index, damage level, spatial location, and area. The data is stored in a three-dimensional vector format for easy subsequent querying and analysis.

[0086] Wall deformation trend analysis is performed based on wall structure damage data to obtain wall deformation data;

[0087] In this embodiment, a high-precision 3D laser scanner is used to perform multi-temporal scanning of the wall surface, with the laser point cloud data accuracy controlled within ±1 mm. Point-by-point displacement monitoring is performed by combining the 3D model data of the structural damage area. A point cloud registration algorithm (based on the Iterative Closest Point (ICP) algorithm, with an error threshold set to 0.005 meters) is used to compare point cloud data collected at different times to calculate the 3D displacement vector of the wall surface points. Key nodes (spaced no greater than 0.5 meters) are selected to analyze their displacement trends, calculating displacement increments and velocities. The wall deformation trend is obtained through time series statistical analysis, using linear regression to fit the deformation data, with the slope as the deformation rate index and a threshold set at an annual average deformation rate of 0.5 mm. The deformation data includes point coordinates, deformation amount, and deformation rate, in the format of point cloud coordinates and time labels, outputting a deformation trend map and a spatial distribution map of the deformation amount.

[0088] The wall settlement is calculated based on the wall deformation data to obtain the wall settlement data.

[0089] In this embodiment, the calculation of wall settlement is based on the vertical displacement component in the deformation data. The three-dimensional displacement vector of each point in the deformation data is decomposed into horizontal and vertical components, and the vertical displacement is extracted. Using the building benchmark measurement method, a stable point near the foundation at the bottom of the building is selected as the zero reference, and any vertical displacement change exceeding 2 mm is included in the settlement calculation. A wall settlement distribution surface is generated using a spatial interpolation method (Kriging interpolation), with an interpolation grid spacing of 0.1 meters. The settlement data is expressed in absolute settlement height (mm), and combined with the building structure layer data, the settlement distribution of different layers is distinguished. The output settlement data includes spatial coordinates, settlement depth, and settlement rate, and the data is stored in a geospatial format for easy synchronization with the digital twin community model.

[0090] Preferably, the wall repair simulation based on wall settlement data in step S2 specifically involves:

[0091] Locate the wall settlement area based on wall settlement data;

[0092] In this embodiment, spatial coordinate points with a settlement depth exceeding 3 mm are extracted from existing wall settlement data and designated as settlement anomalies. A spatial clustering algorithm (based on the DBSCAN algorithm, with a neighborhood radius ε of 0.5 meters and a minimum number of points MinPts of 5) is used to cluster these anomalies, forming the boundaries of continuous settlement areas. GIS spatial analysis tools are used to generate polygonal vector data of the settlement areas, with boundary errors controlled within ±0.02 meters. The spatial extent, area, and boundary coordinate data of the settlement areas are digitally stored for subsequent location and on-site verification. This method ensures accurate location of potential structural risk areas caused by settlement.

[0093] The crack width data is obtained by measuring the crack width in the wall settlement area;

[0094] In this embodiment, a crack detection grid is laid out within the settlement area, with a grid spacing of 0.3 meters. A high-resolution crack imaging device (resolution not less than 0.1 mm) is used, and a crack width measuring instrument is employed to scan the width of all cracks within the grid. During measurement, the instrument's light source angle is fixed at 45 degrees to avoid shadows affecting crack boundary determination. The data collected by the instrument is directly converted into width values, with width accuracy controlled within ±0.05 mm. The crack width data includes crack location coordinates, crack length, and width value, and the data format is a structured database, supporting querying and statistics.

[0095] Based on the crack width data, the width is divided to obtain long-width crack data and short-width crack data;

[0096] In this embodiment, cracks wider than 0.3 mm are defined as "long-width cracks," and cracks 0.3 mm or less are defined as "short-width cracks." Width values ​​are filtered using database query conditions, and two types of crack datasets are exported. Each crack dataset includes start and end coordinates, length, width, and classification label. Cracks longer than 2 meters and wider than 0.3 mm are classified as typical long-width cracks, while cracks with a width not exceeding 0.3 mm and a length less than 2 meters are classified as typical short-width cracks. The exported data is in CSV and GIS-compatible formats, supporting subsequent material selection analysis.

[0097] Based on the length and width crack data, epoxy resin grouting filling material was selected, and epoxy resin grouting material data was obtained.

[0098] In this embodiment, for long-width cracks, epoxy resin grouting material is selected. Parameters include epoxy resin viscosity (set to 200-400 mPa·s), curing time (3-5 hours at 25℃), and tensile strength (not less than 50 MPa). Based on the crack length and width information, the grouting volume is calculated using the formula: Grouting volume = Crack length × Crack width × Crack depth (depth is based on concrete thickness, assumed to be 200 mm). This is combined with the material density (1.1 g / cm³). 3 This data is converted into quality information. Epoxy resin grouting material data includes grout volume, mass, viscosity, curing time, material type, and production batch information. This structured data is used for construction scheduling and material procurement.

[0099] Based on the data of short-width cracks, polymer cement composite filler materials were selected to obtain polymer cement composite material data.

[0100] In this embodiment, a polymer-cement composite filler is selected for short-width cracks. Technical specifications include gelation time (within 30 minutes at 25°C), compressive strength (above 25 MPa after 7 days of curing), and elastic modulus (5 GPa). The required material volume is calculated based on the crack width and length using the same volume calculation method. The mixing ratio of the filler strictly follows the manufacturer's formula, such as a cement-to-polymer ratio of 3:1 and a water-cement ratio controlled at 0.4. The material density is approximately 2.1 g / cm³. 3 The data on polymer cement composite materials, including material volume, mass, mix proportions, compressive strength grade, and supplier information, is calculated and stored in a structured database.

[0101] By integrating data on epoxy resin grouting materials and polymer cement composite materials, data on crack reinforcement material types can be obtained.

[0102] In this embodiment, the integration process employs a database joint query to uniformly encode and manage the usage, specifications, models, and construction locations of different materials. The inventory data includes material type (epoxy resin grouting or polymer cement composite material), usage (volume and mass), crack corresponding number, construction sequence, and safe storage period. The data storage format supports exporting to Excel and Building Information Modeling (BIM) compatible formats, ensuring data consistency between construction and procurement stages.

[0103] A repair usage list is prepared based on the data of crack reinforcement material types, and repair usage data is obtained;

[0104] In this embodiment, the total usage is calculated based on the unit volume usage of each material and the total number of cracks. The usage calculation formula is clear: Total usage = Unit crack volume × Material density × 1.05 (including a 5% construction loss coefficient). The repair usage list includes material number, specifications, unit weight, total quantity, construction time, and material storage conditions. A material delivery plan is developed, with time intervals controlled within one week to ensure construction continuity. The usage list is stored in structured data format and supports access by the project management system.

[0105] Wall repair simulation was conducted based on the repair dosage data to obtain wall repair data.

[0106] In this embodiment, the wall repair process is simulated using finite element analysis software. The input includes wall structure data, crack locations, and mechanical parameters of the reinforcing materials (such as elastic modulus and bond strength). For the reinforcing materials, the elastic modulus of epoxy resin is set to 3 GPa, and the elastic modulus of polymer cement composite material is set to 1.5 GPa. The simulation settings include the filling volume of the repair material, the boundary conditions for applying the repair process, and the load conditions. The simulation steps are: ① pressure distribution during crack grouting, with a pressure value set to 0.5 MPa; ② shrinkage deformation during the curing process of the repaired material, with a shrinkage rate set to 0.2%; ③ changes in the overall wall stress distribution after repair. The output wall repair data includes a stress-strain distribution map of the repaired area, the degree of crack closure, and an evaluation of the material-wall bonding performance. The data format is three-dimensional stress field data and a curve showing the change in wall stiffness after repair.

[0107] Most importantly, the simulation of wall repair based on repair volume data includes:

[0108] Upload the repair usage data to the building repair simulation platform, where the wall grid resolution is set to 5000 patches per square meter;

[0109] In this embodiment, during the construction process of the digital twin community model for urban renewal, the first step in handling wall defects is to acquire and upload repair volume data. This data comes from on-site scanning and material estimation of typical aging walls in the community. The data is collected using a portable 3D laser scanner (such as the FARO Focus S 350) to scan the entire wall surface, obtaining a surface elevation change map. This is combined with an infrared thermal imager (such as the FLIR E95) to extract crack depth and extent. The calculation formula is: Repair volume = Defect area × Average crack depth, which can be converted to mass (kg) using material specific gravity. When uploading the data, it is imported into the repair platform in a structured format (such as JSON or CSV). Fields include defect number, area coordinates, defect depth (mm), and defect area (m²). 2 The data includes the corresponding repair material weight (kg), etc. After the data is uploaded, the building repair simulation platform initiates the initial processing flow, meshing the 3D wall structure data. The meshing uses a standard of 5,000 faces per square meter, a standard derived from a comprehensive evaluation of simulation accuracy and computational resource load. Specifically, the Delaunay triangulation method is used to generate triangular mesh faces on the wall surface, with each face not exceeding 200 mm². 2 This is used to ensure that crack details are clearly depicted. This mesh resolution parameter is directly passed to the mesh initialization module of the platform's graphics processing engine for geometry construction.

[0110] The repair material type is set as epoxy resin mortar, with a material density of 1.6 g / cm³. 3 The compressive strength is 40 MPa, and the elastic modulus is 1.2 × 10⁻⁶. 4 MPa;

[0111] In this embodiment, to ensure the accuracy and realism of the simulated material behavior during the modeling process for repairing damaged wall areas, the material type and various physical and mechanical parameters need to be explicitly specified in the simulation platform. This implementation uses epoxy resin mortar as the repair material. The selection of this material is based on statistical results of the materials actually used in the engineering technical documents of nearly 20 old community wall repair construction cases, selecting the most frequently used material. The material parameters of the epoxy resin mortar were directly extracted from its product manual, and its density is known to be 1.6 g / cm³. 3 The compressive strength is 40 MPa, and the elastic modulus is 1.2 × 10⁻⁶. 4 MPa. The material parameter input process uses the material library management module built into the simulation platform. In the interface for adding a new material entry, the material number, name, state (solid), and density (unit converted to 1600 kg / m³) are entered sequentially. 3The parameters, including elastic modulus, Poisson's ratio (set to 0.25, derived from the product test report), and compressive strength, are entered into the database and bound to the material nodes of the repair area. Each mesh node in the wall geometry model marked as a defective node is bound to the repair material model and participates in subsequent material filling and stress response calculations.

[0112] Set the environmental parameters for the repair process, including a construction temperature of 15℃-30℃, a relative humidity of 30%-80%, and a construction duration of 0.5h-2.0h.

[0113] In this embodiment, the environmental parameters for repair are set based on historical meteorological data of the construction period, summaries of construction experience, and recommended construction environment ranges in the material specifications. During implementation, the meteorological module in the city-level building IoT database is invoked to statistically analyze the temperature and humidity ranges of the area where the repair object is located from May to October over the past five years. The results show that the most frequently occurring temperature range is 15℃ to 30℃ and relative humidity is 30% to 80%. These ranges are then adjusted based on the recommended construction environment conditions in the epoxy resin mortar construction manual to finally determine the construction parameters. The environmental parameters are manually input into the environmental parameter management module of the building repair simulation platform. The construction temperature is set to a random distribution model within a constant range, with the environmental temperature varying by ±2℃ perturbation in each iteration of the simulation. The relative humidity is set to uniform distribution, and the construction time is set to the operation time for each single repair segment, based on the material application speed per unit area of ​​the wall (set to 4 kg / m²). 2 The required construction time for the block is calculated by converting the repair dosage data ( / h) with the repair dosage data. All parameters are used in the simulation for material curing rate, shrinkage rate modeling and time step control.

[0114] Run the material filling module and area sealing module in the building repair simulation platform to obtain wall repair data.

[0115] In this embodiment, after completing the material parameters and environmental settings, the two main calculation modules in the building repair simulation platform are activated: the material filling module and the area sealing module. The material filling module uses the finite volume method (FVM) to simulate material flow within a grid cell volume. Material is injected into each grid point according to a set unit dosage, and its flow propagation path within the crack is simulated. This process relies on the previously generated patch mesh data for layer-by-layer calculation. Injection path control is based on three-dimensional shortest path filling optimization according to the crack boundary and gravity direction, considering material density, viscosity coefficient, and flow slope in the calculation. The area sealing module simulates the adsorption, solidification, and strength superposition process of the sealing material at the contact crack boundary. It uses a contact stiffness fitting method, setting the normal stiffness between the contact node pairs at the crack edge to 10. 6N / m, the sealing time window is set to a material curing time of 1.5h, which varies according to the construction temperature. This module further generates a complete repair status diagram of each repair area and calculates the volume change rate of the material after curing to compensate for volume shrinkage. After the simulation is completed, the wall repair data is exported, including: the volume of filling material (cm³) corresponding to each defect number. 3 The output includes data such as volume shrinkage percentage (%), 3D reconstructed mesh of the repaired area, and final node stress distribution map. The output format is a multidimensional structure array (.npz file) for subsequent loading and calling of the digital twin model.

[0116] Preferably, step S3 specifically includes:

[0117] Step S31: Collect concrete structure samples based on building aging data, and remove the coating from the concrete structure samples to obtain sample cleanliness data.

[0118] In this embodiment, based on building aging data, the sampling points for the concrete structure were first determined. The selection criteria for sampling points were: a carbonization depth greater than 3 mm, a crack width exceeding 0.2 mm, and no obvious mechanical damage on the surface. A core sampler was used to drill concrete samples with a diameter of 50 mm and a length of 100 mm. During sampling, the drilling speed was controlled at 30 rpm, and the water cooling flow rate was maintained at 2 L / min to ensure sample integrity. After collection, the old coating on the sample surface was treated using mechanical grinding and chemical stripping. Mechanical grinding was performed using 100-grit sandpaper for uniform grinding, and chemical stripping involved soaking the sample in a 10% acetone solution for 15 minutes to remove oil and adhering substances. After treatment, a laser surface cleanliness detector was used to scan the sample surface and collect the percentage of residual contaminants. The cleanliness threshold was set to ensure that the area of ​​residual contaminants did not exceed 5%. The cleanliness data was stored in a structured percentage format to provide a benchmark for subsequent carbonization area identification.

[0119] Step S32: Identify carbonized regions based on sample cleanliness data to obtain carbonized region data;

[0120] In this embodiment, carbonized regions of the cleaned concrete samples were identified using the phenolphthalein staining method. The sample cross-section was immersed in a 0.1% phenolphthalein ethanol solution for 30 seconds, followed by observation under an optical microscope (50x magnification). Uncarbonized areas turned red with phenolphthalein, while carbonized areas remained colorless. Images of the carbonized region boundaries were acquired using a digital microscopy imaging system with a resolution of 0.01 mm / pixel. An edge detection algorithm was used to identify the carbonized boundaries, deriving the two-dimensional geometric shape and area data of the carbonized regions. The carbonized region data includes sample number, carbonized area, and a list of boundary coordinate points, in a structured matrix format, supporting statistical analysis and comparison.

[0121] Step S33: Calculate the carbonation depth based on the carbonation zone data to obtain carbonation depth data; perform concrete carbonation anomaly analysis based on the carbonation depth data to obtain concrete carbonation anomaly data;

[0122] In this embodiment, the carbonization depth is determined by measuring the shortest distance from the surface to the carbonization boundary of the sample cross-section. A three-point measurement method is used: three representative points are selected on the sample cross-section to measure the carbonization depth. The three points are evenly distributed, and the measurement accuracy is 0.01 mm. The average carbonization depth is calculated as the sample carbonization depth. A carbonization anomaly threshold is set at an average depth exceeding 10 mm. Samples exceeding the threshold are marked as carbonization anomalies, forming carbonization anomaly data. The carbonization anomaly data includes the sample number, average carbonization depth value, anomaly symbol, and anomaly level (divided into mild (10-15 mm), moderate (15-25 mm), and severe (greater than 25 mm)). The anomaly level determination is based on the depth threshold, and the data is stored in tabular form.

[0123] Step S34: Extract information on the steel reinforcement cover layer area based on concrete carbonation anomaly data; calculate the steel reinforcement cover layer thickness based on the steel reinforcement cover layer area information to obtain steel reinforcement cover layer thickness data; determine carbonation penetration based on the steel reinforcement cover layer thickness data and carbonation depth data to obtain carbonation penetration data.

[0124] In this embodiment, information about the rebar cover area is obtained using X-ray digital imaging technology. An X-ray machine is used to perform a transmission scan of the concrete sample, with the tube voltage set to 150kV, the current 20mA, and the scanning resolution 0.05 mm / pixel. The location and boundary of the rebar are identified by the difference in image grayscale values, determining the thickness of the concrete between the rebar and the sample surface, i.e., the rebar cover thickness. The cover thickness is measured in millimeters, and the shortest distance from the rebar to the sample surface is measured using automatic image processing software, with the measurement error controlled within ±0.1 mm. The rebar cover thickness data is matched with the carbonation depth data; if the carbonation depth exceeds the cover thickness, it is considered carbonation penetration. The carbonation penetration data includes the sample number, cover thickness, carbonation depth, and penetration status (0 indicates no penetration, 1 indicates penetration), and is stored and managed in a database.

[0125] Step S35: Define the carbonization penetration area based on the carbonization penetration data; perform electrochemical corrosion potential testing on the carbonization penetration area to obtain the steel bar potential distribution data; calculate the corrosion probability based on the steel bar potential distribution data to obtain corrosion trend data; determine the degree of steel bar corrosion based on the corrosion trend data.

[0126] In this embodiment, the carbonization penetration area is determined based on the two-dimensional and three-dimensional coordinate mapping of the sample. Ultrasonic imaging is used to locate the specific position of the rebar, and the penetration area is calibrated. A standard electrochemical corrosion potential testing device is used for the calibrated area, with copper-copper-sulfuric acid electrodes as the test electrodes. The distance between the reference electrode and the working electrode is set to 50 mm. During the test, the sampling point spacing is 5 mm, the potential sampling accuracy is 0.1 mV, the test time interval is fixed at 10 seconds, and at least 100 sets of data are collected. Based on the rebar potential distribution, a standard corrosion probability curve is used, with a threshold of -350 mV (CSE reference electrode). Areas with potentials below the threshold are considered active corrosion areas. The corrosion probability is calculated based on the magnitude of the potential deviation from the threshold, and the corrosion trend data is expressed as a percentage. The corrosion degree grading standard is divided into no corrosion (potential > -200 mV), light corrosion (-350 mV to -200 mV), moderate corrosion (-500 mV to -350 mV), and severe corrosion (< -500 mV). The results are stored in a structured database.

[0127] Step S36: Optimize structural reinforcement based on the degree of steel bar corrosion to obtain structural reinforcement data.

[0128] In this embodiment, the structural reinforcement scheme is formulated based on the degree of rebar corrosion. Computer-aided design software is used to simulate the reinforcement scheme on the structural model. The degree of rebar corrosion is input, and combined with the concrete strength grade and the existing protective layer thickness, reinforcement measures are formulated. The reinforcement methods include three types: external steel plate bonding, carbon fiber cloth wrapping, and chemical grouting. The design parameters are set as follows: steel plate thickness 10 mm, length covering 200 mm beyond the boundary of the corrosion zone, number of carbon fiber cloth layers varying from 1 to 3 layers depending on the degree of corrosion, and grouting pressure controlled within 0.6 MPa. The structural reinforcement data includes the coordinates of the reinforcement area, the type and quantity of reinforcement materials, construction process parameters (such as pressure and curing time), the expected percentage increase in strength after reinforcement, and relevant construction drawings. The data is saved in BIM format to support subsequent construction and operation and maintenance management.

[0129] Preferably, step S36 specifically includes:

[0130] Step S361: Identify the corroded areas of the reinforcing bars based on their degree of corrosion and obtain data on the corroded areas of the reinforcing bars;

[0131] In this embodiment, based on the steel reinforcement corrosion degree data, a combination of digital imaging and electrochemical testing is used to identify corrosion areas. First, based on the steel reinforcement electrochemical corrosion potential distribution map obtained in the previous steps, areas with potentials below -350mV (CSE electrode reference) are identified as active corrosion areas using a threshold segmentation method. Potential data is collected at 5mm intervals, and a two-dimensional potential contour map is constructed based on this grid data. Image processing techniques are used to segment the contour map and extract the boundaries of continuous corrosion areas. An edge detection algorithm (Canny operator, threshold set to 30-80) is used to accurately locate the corrosion boundaries. Combined with X-ray perspective views to obtain the steel reinforcement position coordinates, the position of the corrosion area relative to the concrete cross-section is corrected, forming two-dimensional and three-dimensional corrosion area mappings. The corrosion area data includes area number, coordinate range, area, and corrosion degree level (light, moderate, severe, based on potential grading rules), and is saved in a structured database format for easy subsequent analysis.

[0132] Step S362: Extract information about concrete components based on data on the corroded areas of the reinforcing bars;

[0133] In this embodiment, based on the spatial distribution data of the rusted areas, a three-dimensional laser scanning device is used to perform high-precision scanning of the concrete component to obtain point cloud data of the component surface, with the point cloud resolution controlled within 1 mm. Combining the coordinates of the rusted areas, the rusted areas are mapped onto the point cloud data, and the corresponding concrete component area information is extracted. The concrete component information includes the component's length, width, and height dimensions, cross-sectional shape, concrete material grade (determined by sampling and testing data, such as C30 or C40), and concrete strength uniformity. Point cloud processing software is used to calculate the component's geometric parameters and crack distribution characteristics. The component information also includes the location and anchorage status of the prestressed tendons, generated based on data from rebar detection instruments (such as magnetic detectors). All information is stored in CAD format files and a structured database, with data fields covering component number, material grade, geometric dimensions, crack characteristics, and rebar layout parameters.

[0134] Step S363: Analyze the construction reinforcement methods based on the information of the concrete components to obtain construction reinforcement method data;

[0135] In this embodiment, the construction reinforcement methods are analyzed based on the material grade, corrosion degree, size, and crack characteristics of the concrete components. The reinforcement methods are divided into three types: external steel plate reinforcement, carbon fiber cloth reinforcement, and chemical grouting. The selection criteria include the proportion of corrosion area to the component's surface area, the component's load-bearing requirements, and construction environment limitations. The corrosion area proportion is calculated by dividing the corrosion area by the exposed surface area of ​​the component, with a threshold of 20% set as the standard for defining the reinforcement method. For corrosion area proportions greater than 20%, external steel plate reinforcement is preferred; for 10%-20%, carbon fiber cloth reinforcement is used; and for less than 10%, chemical grouting is used. Construction environment parameters (such as temperature and humidity) are obtained from on-site meteorological sensors, with temperature limited to 5℃ to 40℃ and humidity less than 85%. The analysis results are output as construction reinforcement method data, including reinforcement type, construction area coordinates, reinforcement material specifications (steel plate thickness 10 mm, carbon fiber cloth layers 1-3 layers, grouting pressure below 0.6 MPa), construction process parameters, and estimated construction period.

[0136] Step S364: Perform component reinforcement simulation based on construction reinforcement method data to obtain structural reinforcement data.

[0137] In this embodiment, finite element analysis software is used to simulate the reinforcement of concrete components. Input data includes component geometry, material mechanical parameters (elastic modulus of concrete is 25 GPa, elastic modulus of steel reinforcement is 200 GPa), corrosion zone parameters, and construction reinforcement method data. The mechanical properties and geometry of the reinforcement materials are defined according to the reinforcement method. For example, steel plate reinforcement uses an elastic modulus of 210 GPa and a thickness of 10 mm, while carbon fiber reinforcement uses an elastic modulus of 230 GPa, with the number of layers set according to the construction reinforcement method. A three-dimensional finite element model is established, with the mesh size controlled below 5 mm to ensure calculation accuracy. Loading boundary conditions include the structure's self-weight and design load, set according to standard GB50010-2010. The simulation calculates the stress concentration caused by steel corrosion and the compensation effect of the reinforcement material on the overall structural bearing capacity. Outputs include maximum stress distribution, deformation, and safety factor. The structural reinforcement data is stored in the form of a detailed report and a three-dimensional model. The report includes reinforcement recommendations, construction parameters, and expected performance indicators, while the three-dimensional model supports subsequent integration with a digital twin community model.

[0138] Of particular importance, step S364 includes the following steps:

[0139] Based on the construction reinforcement method data, reinforcement material layout is carried out to obtain reinforcement material layout data;

[0140] In this embodiment, based on the acquired construction reinforcement method data, the layout type is clearly defined, such as external steel reinforcement, carbon fiber cloth reinforcement, and steel mesh reinforced with concrete reinforcement. Materials are then arranged according to the technical parameters of the reinforcement method. Taking carbon fiber cloth reinforcement as an example, the fiber cloth width (generally 100mm), laying length (150mm at each end extending from the crack length of the component), and number of layers (depending on the component's load-bearing requirements, commonly 1-3 layers) need to be determined. A two-dimensional layout diagram is then created based on the component's dimensional data. The layout diagram is drawn using Building Information Modeling (BIM) tools for geometric modeling. Based on the detailed construction node drawings, parameters such as layout location coordinates, layout density, and adhesive usage are output to form standard reinforcement material layout data.

[0141] Based on the reinforcement material layout data, the stress distribution data of the component is obtained by performing stress analysis on the component.

[0142] In this embodiment, the finite element method is used to calculate the stress state of the reinforced component under standard loads. First, the geometric model of the reinforced component is input into a structural mechanics analysis platform (such as ANSYS or MIDAS Civil), and material properties are set. The elastic modulus of the carbon fiber cloth is set to 2.4 × 10⁵ MPa, Poisson's ratio to 0.3, and the shear strength of the adhesive is set to 12 MPa. Live loads, dead loads, and wind loads are all considered, and the load types are arranged according to the specific application location. After element division and nodal load configuration, static analysis is performed to extract the axial force, shear force, and bending moment distribution values ​​at key sections (e.g., mid-span section, support section). The above stress data are output according to component number to form a stress distribution database. The data unit is N or N·m, and the data format is a three-dimensional tensor or CSV structure, with each coordinate system position and stress direction labeled.

[0143] Identify weak areas of components based on component stress distribution data;

[0144] In this embodiment, the stress distribution data of the components is imported into the stress comparison and analysis module to identify areas with stress concentration, abrupt stress changes, or stresses exceeding the allowable design stress value of the components. If the compressive stress in a certain unit of the component exceeds 80% of the structural concrete strength design value (e.g., 24 MPa for C30 concrete), or the tensile stress exceeds the tensile strength of the concrete (e.g., 1.43 MPa), the unit is identified as a "structural stress abnormal unit," and its spatial location needs to be further determined. The specific identification method uses a stress field color distribution map. By setting color thresholds and overlaying the map in a 3D model, the 3D coordinates, area range, and principal stress direction of the identified weak areas are output as weak area data. It is recommended to use a visualization stress analysis module based on a color gradient image processing algorithm to extract high gradient change areas as suspected weak points for secondary verification.

[0145] Based on the identified weak areas of the components, structural reinforcement simulations are performed to obtain structural reinforcement data.

[0146] In this embodiment, the simulation employs a structural response repair strategy. This involves setting a new reinforced area boundary in the weak region, applying the same external load as in step two, and performing finite element calculations again. During this stage, a simulated reinforcement layer is superimposed on the original model. For example, a steel plate is wrapped around the high-stress region, with a thickness of 6mm, a bolt anchorage spacing of 200mm, and a bolt diameter of 12mm. After the structural simulation, the stress distribution after repair is extracted. If the peak stress decreases by more than 20%, the reinforcement simulation is confirmed as effective, and structural reinforcement data is generated. This data includes the spatial location of the simulated area, required material specifications, anchorage node information, and a remaining safety factor (the safety factor must be greater than 1.5). This data is archived in a database format for subsequent dynamic updates and maintenance analysis of the digital twin platform.

[0147] Preferably, step S4 specifically includes:

[0148] Step S41: Calculate the bearing capacity of the components based on the structural reinforcement data;

[0149] In this embodiment, concrete strength grade information, such as the compressive strength parameters corresponding to C30 strength concrete, is collected; simultaneously, steel reinforcement grade parameters, such as the yield strength and cross-sectional area of ​​HRB400 steel reinforcement, are acquired. The collected component cross-sectional dimension data are measured using a laser 3D scanning device to ensure dimensional accuracy within ±1 mm. Based on the physical performance parameters of concrete and steel reinforcement, combined with the component cross-sectional information, and following the calculation rules of current concrete structure design codes, the type and thickness of reinforcement materials are comprehensively considered, such as 10 mm thick steel plates and 1 to 3 layers of carbon fiber cloth, to comprehensively evaluate the component's load-bearing capacity. Specific operations include summing the load-bearing contributions of each material, considering the elastic properties of the reinforcement materials and the actual effective area applied. Finally, the ultimate bearing capacity and safe bearing range of the component are calculated, and the calculation results are recorded in numerical form and associated with the corresponding component number.

[0150] Step S42: Detect the adhesion of the repair material based on the wall repair data to obtain the material adhesion data;

[0151] In this embodiment, the specific type of repair material (such as epoxy resin grouting material or polymer cement composite material) is first determined, and multiple points are selected in the wall repair area for testing. Pull-out specimens with a diameter of 50 mm are prepared and adhered to the wall surface using a special adhesive. After curing for 72 hours, a pull-out testing machine is used to apply tensile force at a fixed, slow loading rate until the specimen separates from the substrate. The maximum pull-out force is recorded, and the adhesion value is obtained by dividing the maximum pull-out force by the bonded area of ​​the specimen, in megapascals (MPa). Multiple testing points are used during the test to ensure the reliability and representativeness of the data. Finally, a material adhesion data table is compiled, including the coordinates of the specific test locations, material numbers, and corresponding average adhesion values.

[0152] Step S43: Evaluate the building stability based on the component bearing capacity and material adhesion data to obtain building stability data;

[0153] In this embodiment, the actual load-bearing capacity of the component is compared with the load-bearing requirements at the design time to calculate the load-bearing capacity utilization rate. Simultaneously, the material adhesion is compared with the minimum adhesion required by the specification, and the lower ratio is selected as the stability index. The design load data comes from a real-time on-site monitoring system and includes both static and dynamic loads, with an error controlled within ±3%. The stability index ranges from 0 to 1, with values ​​closer to 1 indicating better stability. After calculation, the stability index, component identification, evaluation time, and other information are stored together to form a complete stability dataset.

[0154] Step S44: Assess the health level based on building stability data, where the stability coefficient is set to 0-1 and the health and safety value is ≥0.6, to obtain the building health level data;

[0155] In this embodiment, the building health level is classified based on the stability index value. Three level ranges are set: a stability index greater than or equal to 0.85 is classified as the highest level of health, between 0.6 and 0.85 as the sub-health level, and below 0.6 as the dangerous level. After classifying the level of individual components, a weighted average is calculated by combining the level information of all components and the importance weight of each component to calculate the overall building health level. The importance weight of each component is determined according to its load-bearing function, with a weight of 0.4 for critical load-bearing components and 0.2 for non-critical components, ensuring that the overall building health level reflects the current structural safety status. The health level results are stored in a structured format, including the building number, overall health level, component distribution, and assessment time.

[0156] Step S45: Transmit the building health level data to the digital twin community model to obtain the community optimization model.

[0157] In this embodiment, building health level data is pushed to the digital twin community model platform via a pre-defined secure communication interface and a standard data exchange protocol. The transmission process uses an encrypted channel to ensure data security and integrity. The transmission format adopts a unified JSON data structure, with fields including building ID, health level, assessment time, and component health information. After receiving the data, the digital twin platform combines the building's spatial geographic coordinate system to map the health level data onto the community's 3D model, updating the corresponding building's health status display. Based on the latest health level, the platform adjusts maintenance plans and resource allocation strategies, generating an optimized community management plan. Data synchronization is guaranteed to be updated at the second level, ensuring the timeliness and accuracy of the community model data.

[0158] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0159] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for constructing a digital twin community model for urban renewal projects, characterized in that, Includes the following steps: Step S1: Obtain the building facade drawings of the urban renewal community; construct a digital twin community model based on the building facade drawings of the urban renewal community; The system identifies aging parts of buildings based on a digital twin community model, obtains building aging data, and collects building aging images based on the building aging data. Step S2: Identify the underground pipe network area based on the building aging image to obtain the underground pipe network image; perform leakage analysis based on the underground pipe network image to obtain pipe network leakage data; perform wall settlement detection based on the pipe network leakage data to obtain wall settlement data; perform wall repair simulation based on the wall settlement data to obtain wall repair data. Step S3: Perform concrete carbonation anomaly analysis based on building aging data to obtain concrete carbonation anomaly data; determine the degree of steel reinforcement corrosion based on the concrete carbonation anomaly data; optimize structural reinforcement based on the degree of steel reinforcement corrosion to obtain structural reinforcement data; Step S4: Conduct a building stability assessment based on the structural reinforcement data and wall repair data to obtain building stability data; The health level is assessed based on building stability data to obtain building health level data; The building health rating data is transmitted to the digital twin community model to obtain the community optimization model.

2. The method for constructing a digital twin community model for urban renewal projects according to claim 1, characterized in that, Step S1 is as follows: Step S11: Obtain the building facade drawings of the old urban renewal community and extract the building outline data; Step S12: Model the structural boundary based on the building outline data to obtain two-dimensional building structural data; Step S13: Identify facade elements based on two-dimensional building structure data to obtain facade element data; Step S14: Perform three-dimensional geometric transformation based on the facade element data to obtain three-dimensional facade component data; Step S15: Combine building units based on the 3D facade component data to obtain building unit data; A digital twin community model was built based on building unit data; Step S16: Identify aging parts of the building based on the digital twin community model, obtain building aging data, and collect building aging images based on the building aging data.

3. The method for constructing a digital twin community model for urban renewal projects according to claim 2, characterized in that, Step S16 is as follows: Step S161: Segment the buildings according to the digital twin community model to obtain the building data; Step S162: Reconstruct the surface mesh based on the individual building data to obtain the building surface mesh data; Step S163: Perform 3D texture mapping based on the building surface mesh data to obtain building surface texture data; Step S164: Perform edge fracture analysis based on building surface texture data to obtain edge fracture data; Step S165: Identify aging parts of the building based on edge fracture data, obtain building aging data, and collect building aging images based on the building aging data.

4. The method for constructing a digital twin community model for urban renewal projects according to claim 1, characterized in that, Step S2, which involves identifying the underground pipe network area based on building aging images, specifically includes: Image enhancement processing is performed on building aging images to obtain enhanced building aging images; Crack detection is performed based on enhanced images of building aging to obtain crack data; Subsidence areas are identified based on crack data to obtain subsidence area data; Spatial location mapping is performed based on the settlement area data to obtain the settlement area location data; Obtain underground pipeline network layout data; By performing intersection analysis on underground pipeline layout data and settlement area location data, underground pipeline risk area data is obtained, and underground pipeline images are collected based on the underground pipeline risk area data.

5. The method for constructing a digital twin community model for urban renewal projects according to claim 1, characterized in that, Step S2, which involves leak analysis based on the underground pipe network image, specifically includes: The pipeline network outline is reconstructed from the underground pipeline network image to obtain the pipeline network outline data; Calculate the profile deformation rate based on the pipeline network profile data; Identify high-deformation regions of the contour based on the contour deformation rate, and obtain data on high-deformation regions of the contour. Pipe material detection is performed based on the contour data of high-deformation regions to obtain pipe material data; Pipeline corrosion simulation was performed based on pipeline material data, where the annual corrosion rate of the pipeline wall thickness was set to 0.1-0.3 mm / year, and pipeline corrosion data were obtained. Pipeline fatigue vulnerability analysis was performed based on pipeline corrosion data to obtain pipeline fatigue vulnerability data; Pipeline rupture risk is predicted based on pipeline fatigue vulnerability data, and pipeline rupture data is obtained; Leakage analysis is performed based on pipeline rupture data to obtain pipeline network leakage data.

6. The method for constructing a digital twin community model for urban renewal projects according to claim 1, characterized in that, Step S2, which involves detecting wall settlement based on pipeline leakage data, specifically includes: Water erosion detection of walls is carried out based on pipeline leakage data to obtain water erosion data of walls; The degree of damage to the wall structure is assessed based on the water erosion data of the wall, and the wall structure damage data is obtained. Wall deformation trend analysis is performed based on wall structure damage data to obtain wall deformation data; The wall settlement is calculated based on the wall deformation data to obtain the wall settlement data.

7. The method for constructing a digital twin community model for urban renewal projects according to claim 1, characterized in that, Step S2, which involves simulating wall repair based on wall settlement data, specifically includes: Locate the wall settlement area based on wall settlement data; The crack width data is obtained by measuring the crack width in the wall settlement area; Based on the crack width data, the width is divided to obtain long-width crack data and short-width crack data; Based on the length and width crack data, epoxy resin grouting filling material is selected, and epoxy resin grouting material data is obtained. Based on the data of short-width cracks, polymer cement composite filler materials were selected to obtain polymer cement composite material data. By integrating data on epoxy resin grouting materials and polymer cement composite materials, data on crack reinforcement material types can be obtained. A repair usage list is prepared based on the data of crack reinforcement material types, and repair usage data is obtained; Wall repair simulation was conducted based on the repair dosage data to obtain wall repair data.

8. The method for constructing a digital twin community model for urban renewal projects according to claim 1, characterized in that, Step S3 is as follows: Step S31: Collect concrete structure samples based on building aging data, and remove the coating from the concrete structure samples to obtain sample cleanliness data. Step S32: Identify carbonized regions based on sample cleanliness data to obtain carbonized region data; Step S33: Calculate the carbonation depth based on the carbonation zone data to obtain carbonation depth data; perform concrete carbonation anomaly analysis based on the carbonation depth data to obtain concrete carbonation anomaly data; Step S34: Extract information on the steel reinforcement cover layer area based on concrete carbonation anomaly data; calculate the steel reinforcement cover layer thickness based on the steel reinforcement cover layer area information to obtain steel reinforcement cover layer thickness data; determine carbonation penetration based on the steel reinforcement cover layer thickness data and carbonation depth data to obtain carbonation penetration data. Step S35: Define the carbonization penetration area based on the carbonization penetration data; perform electrochemical corrosion potential testing on the carbonization penetration area to obtain the steel bar potential distribution data; calculate the corrosion probability based on the steel bar potential distribution data to obtain corrosion trend data; determine the degree of steel bar corrosion based on the corrosion trend data. Step S36: Optimize structural reinforcement based on the degree of steel bar corrosion to obtain structural reinforcement data.

9. The method for constructing a digital twin community model for urban renewal projects according to claim 8, characterized in that, Step S36 is as follows: Step S361: Identify the corroded areas of the reinforcing bars based on their degree of corrosion and obtain data on the corroded areas of the reinforcing bars; Step S362: Extract information about concrete components based on data on the corroded areas of the reinforcing bars; Step S363: Analyze the construction reinforcement methods based on the information of the concrete components to obtain construction reinforcement method data; Step S364: Perform component reinforcement simulation based on construction reinforcement method data to obtain structural reinforcement data.

10. The method for constructing a digital twin community model for urban renewal projects according to claim 1, characterized in that, Step S4 is as follows: Step S41: Calculate the bearing capacity of the components based on the structural reinforcement data; Step S42: Detect the adhesion of the repair material based on the wall repair data to obtain the material adhesion data; Step S43: Evaluate the building stability based on the component bearing capacity and material adhesion data to obtain building stability data; Step S44: Assess the health level based on building stability data, where the stability coefficient is set to 0-1 and the health and safety value is ≥0.6, to obtain the building health level data; Step S45: Transmit the building health level data to the digital twin community model to obtain the community optimization model.

Images