A method for evaluating the health state of existing buildings based on spatial deviation performance mapping and double-path diagnosis

Abstract

This invention relates to a method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis. The invention includes acquiring three-dimensional geometric data, surface texture data, and material information data of the existing building, fusing them to generate a current three-dimensional model, and establishing a baseline health status model; performing component-level correspondence and difference analysis on the two models, constructing deviation feature vectors, and mapping the deviations to the effects of building aging or structural performance; a building aging diagnosis path is used to identify and classify surface weathering, blistering and efflorescence, fading and cracking, bio-erosion, and coating aging; a structural risk diagnosis path is used to convert spatial deviations into initial defect parameters or equivalent mechanical parameters of the finite element model and perform disaster condition analysis; the diagnosis results are correlated to the model to generate a digital assessment file with risk distribution, intervention priorities, and sustainable updates. This invention improves the quantification, accuracy, and targeted intervention of the health status assessment of existing buildings.

Description

Technical Field

[0001] This invention relates to the field of digital inspection, assessment, and safety diagnosis of existing buildings, and in particular to a method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis. Existing buildings include, but are not limited to, historical buildings, ancient buildings, and old buildings, and the method is applicable to building status inspection, building condition assessment, structural safety analysis, maintenance decision-making, and continuous tracking of building health status. Background Technology

[0002] With the advancement of urban renewal and the protection of historical buildings, a large number of existing buildings need to undergo continuous status monitoring, risk assessment and maintenance management.

[0003] Existing buildings, due to their age, changes in their environment, and long-term loads, often differ in their actual condition from their initial state, exhibiting issues such as changes in geometric shape, material degradation, surface deterioration, and structural damage. However, some existing buildings lack complete design data, and current assessment methods often struggle to simultaneously consider the relationships between the building's actual spatial condition, material condition, and changes in structural performance.

[0004] Currently, the assessment of existing buildings mainly includes the following methods: I. Evaluation methods based on manual inspection.

[0005] Traditional building inspection mainly relies on professionals to conduct on-site observation, measurement, and experience-based judgment to analyze conditions such as cracks, deformation, and material deterioration.

[0006] This method has the following shortcomings: Test results are greatly influenced by personnel experience, making it difficult to form a unified quantitative evaluation. Detection data is usually stored in the form of independent reports, which makes it difficult to support continuous comparison between different time states; It has limited detection efficiency in high places, hidden areas, and complex spatial environments.

[0007] II. Evaluation methods based on BIM / HBIM models.

[0008] Building Information Modeling (BIM) and Historic Building Information Modeling (HBIM) have been applied to the protection and management of existing buildings in recent years. Existing methods typically create building models based on design data, historical drawings, or manual surveying results, and then perform information management, spatial analysis, or structural calculations based on these models.

[0009] However, after long-term use, the actual geometric dimensions, component condition, and material properties of existing buildings may have changed, and models based on the original state can hardly fully reflect the current real state of the building.

[0010] III. Evaluation methods based on 3D scanning and model comparison.

[0011] With the development of technologies such as 3D laser scanning and photogrammetry, some methods utilize point cloud data to spatially compare with BIM / HBIM models to obtain geometric differences between the current building and the model. However, existing model comparison methods mainly focus on geometric deviations, such as component displacement, tilt, and morphological changes, and typically treat the detected deviations directly as deformation results.

[0012] For apparent changes and material property variations caused by factors such as environmental erosion, material aging, and surface weathering, existing methods typically still rely on manual judgment and lack methods for classifying and processing the impact of deviations on building performance. Furthermore, existing methods often lack an effective conversion mechanism between spatial detection results and structural analysis models, making it difficult to directly use geometric deviations obtained from actual detections to correct structural calculation models.

[0013] IV. Structural evaluation methods based on finite element analysis.

[0014] The finite element method can be used to analyze the response of building structures under earthquakes, wind loads, and other forces. However, existing finite element models are usually built based on design dimensions and ideal material parameters, which differ to some extent from actual buildings.

[0015] For existing buildings, issues such as component deformation, cross-sectional changes, and material performance degradation usually require manual adjustment of model parameters, lacking a method for automatically correcting structural analysis models based on detection data.

[0016] Therefore, the existing technology still has the following problems: There is a lack of a method for systematic identification based on the differences between current building data and health status models; There is a lack of methods to import different deviations into the building aging analysis path and the structural risk analysis path according to the type of building performance impact corresponding to the spatial deviation; The lack of a method to convert spatial detection results into actual damage parameters in structural analysis models makes it difficult for structural assessment results to fully reflect the true state of existing buildings. Summary of the Invention

[0017] Therefore, the technical problem to be solved by this invention is to overcome the lack of a systematic difference identification mechanism between the current building data and the benchmark model in existing building assessment methods, and the difficulty in mapping different types of spatial deviations to different performance assessment paths. This invention provides a method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis. By integrating building geometric data, surface texture data, and material information data, a current three-dimensional model reflecting the actual building condition is established, and component-level difference analysis is performed between this model and the health status benchmark model. A mapping mechanism is established to convert spatial deviation characteristics into building performance impact types, transforming geometric, apparent, and material deviations into corresponding performance evaluation parameters. Based on the mapping results, either a building aging diagnosis path or a structural risk diagnosis path is automatically selected, enabling the differentiation between building appearance degradation and structural safety issues. Furthermore, a conversion relationship is established between spatial geometric deviations and structural analysis parameters, converting the differences in building condition obtained from actual detection into initial defect parameters or equivalent mechanical parameters in the structural analysis model, improving the consistency between structural risk assessment and the actual building condition.

[0018] To address the aforementioned technical problems, this invention provides a method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis, comprising: Acquire multi-source spatial data of the current status of existing buildings, including historical buildings, ancient buildings and old buildings. The multi-source spatial data includes three-dimensional geometric data, surface texture data and material information data. Perform spatial registration and fusion of the multi-source spatial data to generate a current three-dimensional model containing geometric information, texture information and material information. Based on one or more of the original design data, historical drawings, and on-site measurement data of the existing buildings, a health status benchmark model is constructed. The health status benchmark model includes geometric information, structural information, material information, and appearance information of building components. The existing 3D model is matched component by component with the health status benchmark model, and a difference analysis is performed to obtain geometric deviation data, apparent deviation data and material change data of each building component, and the deviation analysis results are generated. Based on the deviation analysis results, a component deviation feature vector is constructed. Based on the mapping relationship between the deviation feature vector and the building performance impact type, the performance impact category corresponding to each building component is determined. Based on the performance impact category, the deviation feature vector is used for diagnosis in the structural risk diagnosis path or the building aging diagnosis path. The deviation feature vector includes geometric deviation features, apparent deviation features, material state change features, and structural sensitivity features. In the building aging diagnosis path, based on appearance deviation data and material change data, the building aging type is identified and aging is classified, and building maintenance or repair suggestions are generated based on the aging classification results. In the structural risk diagnosis path, geometric deviation data is converted into initial defect parameters or equivalent mechanical parameters in the finite element analysis model, a structural analysis model containing the actual damage state is constructed, structural response analysis is performed based on the structural analysis model, the structural risk level is determined, and structural intervention suggestions are generated. The diagnostic results of the building aging diagnostic path and the structural risk diagnostic path are associated with the corresponding building components to generate the risk distribution results of existing buildings and graded intervention suggestions.

[0019] In one embodiment of the present invention, acquiring multi-source spatial data of the current status of existing buildings includes: Three-dimensional geometric data are acquired through one or more of the following methods: three-dimensional laser scanning, handheld three-dimensional scanning, or photogrammetry. Surface texture data is acquired using digital cameras or drone photogrammetry systems; Spatial registration and fusion of multi-source spatial data includes: The point cloud data obtained by 3D scanning is spatially registered with the image data obtained by photogrammetry, and the texture information is mapped onto the surface of the 3D geometric data to generate the current 3D model.

[0020] In one embodiment of the present invention, the current three-dimensional model is mapped component by component to the health status benchmark model, including: Using the component ID in the health status baseline model as an index, the corresponding component is found in the current 3D model; For regular components, automatic matching is performed based on the component's spatial location and size range; For irregular components or components that are difficult to match automatically, manual assistance is used to complete the matching. After matching is complete, the ID of each component in the health status baseline model is extended and associated with the corresponding mesh data in the current 3D model.

[0021] In one embodiment of the present invention, the method for obtaining the geometric deviation data includes: Using the component surface in the health status benchmark model as a reference surface, the signed distance from each sampling point on the mesh surface of the corresponding component in the current status 3D model to the reference surface is calculated. The sign of the signed distance is used to characterize the deviation direction. A positive value indicates that the component surface in the current status 3D model is convex outward relative to the reference surface, and a negative value indicates that the component surface in the current status 3D model is concave inward relative to the reference surface. Based on the signed distance corresponding to each sampling point on the surface of each component, the average deviation, maximum deviation, and standard deviation of each component are determined. For vertical components, calculate the offset and tilt angle of the axis of the vertical component relative to the corresponding reference position in the health status reference model, wherein the vertical component includes at least one of columns and walls; For horizontal members, calculate the deflection and end rotation change of the horizontal member relative to the health state reference model, wherein the horizontal member includes at least one of beams and slabs; The method for obtaining the apparent deviation data includes: The texture images and color information of each component surface are extracted from the current 3D model, and the reference color values ​​and reference texture features of the corresponding components are extracted from the health status benchmark model. The color difference ΔE between the current color and the reference color is calculated based on the color information and the reference color value. When the color difference ΔE is greater than the preset color difference threshold, it is determined that the corresponding component has an appearance deviation. The preset color difference threshold is determined based on the building material type and environmental exposure conditions of the corresponding component. The preset color difference threshold for outdoor exposed components is greater than the preset color difference threshold for indoor components or components in a protected state. The method for obtaining the material change data includes: The current texture features of each component surface in the current three-dimensional model are compared with the reference texture features of the corresponding component in the health status benchmark model to identify whether there are at least one material change signs among weathering, peeling, and blistering on the surface of the corresponding component. Based on the reflection intensity information and / or color information obtained from laser scanning, determine whether the material type of the corresponding component has changed relative to the health status benchmark model; When obtaining the material performance test data of the corresponding component, the material performance test data is used as an auxiliary basis for judging the material change. The material performance test data includes at least one of rebound value and moisture content. The material change determination results obtained based on the current texture features, the reflection intensity information, the color information, and the material performance test data are recorded as suspected changes for further confirmation in subsequent diagnostic paths.

[0022] In one embodiment of the present invention, a component deviation feature vector is constructed based on the deviation analysis results. Based on the mapping relationship between the deviation feature vector and the building performance impact type, the performance impact category corresponding to each building component is determined. Based on the performance impact category, the deviation feature vector is input into a structural risk diagnosis path or a building aging diagnosis path, including: Construct the component deviation feature vector according to the following formula: ; in, Indicates the first The component deviation feature vector of each building component G i Indicates the degree of geometric change. T i Indicates the degree of apparent change. M i Indicates the degree of change in the state of the material. S i Indicates the degree of structural sensitivity.

[0023] Based on the deviation analysis results, a system is constructed that includes the degree of geometric change. G i degree of apparent change T i Degree of change in material state M i and structural sensitivity S i Component deviation characteristics; The degree of geometric change G i degree of apparent change T i Degree of change in material state M i and structural sensitivity S i Each with a preset geometric deviation threshold T g Apparent deviation threshold T t Material state change threshold T m and structural sensitivity threshold T s Compare; when T i ≥T t or M i ≥T m When it is determined that the component has an impact on building aging, it is entered into the building aging assessment path; when G i ≥T g or S i ≥T s When it is determined that the component has an impact on structural performance, it enters the structural risk diagnosis path; When the component simultaneously meets the conditions for both building aging impact and structural performance impact, it is given priority to enter the structural risk diagnosis path.

[0024] In one embodiment of the present invention, based on apparent deviation data and material change data, the aging type of a building is identified and graded, and building maintenance or repair recommendations are generated based on the aging grading results, including: The types of aging include surface weathering, blistering or efflorescence caused by moisture, fading or cracking caused by sun exposure, biological erosion, and aging and peeling of surface coatings. The degree of aging is quantitatively graded based on one or more indicators, including deviation depth, affected area ratio, color difference value ΔE, degree of material change, and change in effective material properties. Based on the type and grade of aging, one or more maintenance or repair recommendations are generated, including surface cleaning, waterproofing, local repair, base treatment, component reinforcement, or component replacement.

[0025] In one embodiment of the present invention, converting geometric deviation data into initial defect parameters or equivalent mechanical parameters in a finite element analysis model includes: The actual cross-sectional geometric parameters of the component are determined based on the geometric deviation data, and the changes in the actual cross-sectional area, moment of inertia, section modulus, and / or torsional parameters relative to the design cross-sectional parameters are calculated. Based on the changes in the actual cross-sectional geometric parameters and material property parameters, the axial stiffness parameters, bending stiffness parameters, shear stiffness parameters and / or torsional stiffness parameters of the corresponding components in the finite element analysis model are corrected. Among them, for the axial force parameters, the axial stiffness-related parameters are corrected according to the ratio of the actual cross-sectional area to the designed cross-sectional area; For bending parameters, the bending stiffness-related parameters are corrected based on the ratio of the actual moment of inertia of the cross section to the design moment of inertia of the cross section. When material degradation is detected, the corresponding mechanical parameters are corrected based on the actual material elastic modulus and / or shear modulus.

[0026] The actual cross-sectional shape of the component is reconstructed based on the spatial deviation detection results, and the actual cross-sectional shape is converted into the cross-sectional mechanical parameters required by the finite element analysis model.

[0027] For components primarily subjected to axial forces, the actual cross-sectional area can be used. A actual With the design cross-sectional area A original ratio Parameters related to axial stiffness EA Make corrections.

[0028] For members primarily subjected to bending, the actual moment of inertia of the cross section can be used. I actual Moment of inertia of the design section I original ratio Parameters related to bending stiffness EI Make corrections.

[0029] When a component exhibits material aging, corrosion, deterioration, or defects, the actual elastic modulus obtained through testing or evaluation can also be used as a reference. E actual Actual shear modulus G actual Material property parameters are used to correct the corresponding mechanical parameters in the finite element analysis model.

[0030] In one embodiment of the present invention, a structural analysis model incorporating actual damage states is constructed, a preset disaster load is applied to perform structural response analysis, weak points and structural risk levels are determined, and structural intervention recommendations are generated, including: The aforementioned health status benchmark model is used as the basic model for structural analysis; Based on the deviation analysis results, the component parameters affected by the deviation are modified. The component parameters include at least one of cross-sectional dimensions, material properties, and node constraints, so that the modified component parameters characterize the actual state of the corresponding component. For non-structural components, it is determined whether to retain the corresponding non-structural components in the structural analysis model based on the structural analysis requirements. The non-structural components include at least one of infill walls and decorative components. The modified structural analysis model is subjected to a quality check to determine whether the input parameters of each component are correct and whether there are geometric conflicts or constraint errors in the structural analysis model. Establish the correlation between the modification information of each component parameter in the structural analysis model and the corresponding detection deviation data, and record the data source and basis for the modification of each component parameter, so that the parameter adjustment in the structural analysis model can be traced back to the corresponding detection deviation data; At least one preset disaster condition is applied to the structural analysis model that includes the actual damage state. The preset disaster conditions include earthquake conditions, wind load conditions, foundation settlement conditions, modification and new load conditions, and special load conditions. According to the preset disaster conditions, the structural analysis model is gradually loaded using elastic analysis or elastoplastic analysis methods, and the load application sequence and load combination are determined according to the most unfavorable conditions that the building may actually encounter, so as to obtain the structural response analysis results of the structure during the loading process. The structural risk level is determined based on the stress ratio, strain, displacement response, and stability evaluation results in the structural response analysis. Among them, when the stress ratio is less than 0.6, the displacement response meets the corresponding specification limit, and there are no signs of instability, the structural risk level is determined to be Level 1: low risk, no intervention required; When 0.6 ≤ stress ratio < 0.85 and the displacement response deviates from the corresponding specification limit within a preset range, the structural risk level is determined to be Level 2: medium risk, requiring continuous monitoring. When the stress ratio is ≥0.85, the displacement response exceeds the corresponding specification limit, or there is at least one of the following conditions, the structural risk level is determined to be Level 3: high risk, and reinforcement measures are taken.

[0031] In one embodiment of the present invention, generating risk distribution results and tiered intervention recommendations for existing buildings includes: The intervention priorities are generated by sorting the buildings according to their structural risk level, building aging level, and urgency, and then considering emergency intervention, planned intervention, monitoring and maintenance, or continuous observation.

[0032] In one embodiment of the present invention, it further includes: The existing 3D model, health status benchmark model, deviation analysis results, building aging diagnosis results, structural risk diagnosis results, and graded intervention recommendations are linked and stored to form an existing building health status assessment archive. The health status assessment file includes the health status benchmark model, the current three-dimensional model, the deviation analysis results, the building aging diagnosis results, the structural risk diagnosis results, and the graded intervention recommendations; When new existing 3D models or inspection data are acquired, the newly acquired existing 3D models or inspection data are compared and correlated with historical assessment data in the health status assessment archive, and the health status assessment results of the existing buildings are updated based on the correlation comparison results. The correlation comparison includes: Under a unified spatial reference, component-level geometric change analysis, apparent change analysis, and material change analysis are performed on the current 3D models acquired at different times to obtain multi-temporal change results for each component. Based on the multi-temporal change results, the state changes of each component of the existing building are determined, and the risk level and intervention priority of the existing building are updated based on the state changes.

[0033] The technical solution of the present invention has the following advantages compared with the prior art: This invention achieves quantitative analysis of changes in the condition of existing buildings by constructing a three-dimensional model of the current condition and identifying component-level differences with a health status benchmark model, thereby reducing the uncertainty caused by subjective judgment in traditional manual inspection processes.

[0034] This invention establishes a mapping mechanism from spatial deviation characteristics to building performance impact types, thereby converting geometric detection results into building performance status evaluation results. Based on the conversion results, the results are then entered into building aging diagnosis path and structural risk diagnosis path, respectively, enabling the differentiated assessment of building surface degradation problems and structural safety risk problems.

[0035] This invention establishes a conversion mechanism between spatial geometric deviations and structural analysis parameters, converting actual detected changes in building state into initial defect parameters or equivalent mechanical parameters in the structural model, enabling the structural analysis model to reflect the true state of the existing building.

[0036] This invention enables continuous updates to the health status of buildings by associating and storing building status models, difference analysis results, and diagnostic results, thus providing a data foundation for the long-term maintenance and risk management of existing buildings. Attached Figure Description

[0037] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0038] Figure 1 This is a schematic diagram of the overall process of the method of the present invention.

[0039] Figure 2 This is a schematic diagram of multi-source spatial data fusion and the construction of a current 3D model.

[0040] Figure 3 This is a schematic diagram showing the mapping between deviation characteristics and the types of impact on building performance.

[0041] Figure 4 This is a schematic diagram of the conversion of spatial deviation into structural analysis parameters. Detailed Implementation

[0042] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0043] In this invention, "several" means one or more, "multiple" means two or more, "greater than," "less than," "exceeding," etc., are understood to exclude the stated number; "above," "below," "within," etc., are understood to include the stated number. In the description of this invention, the terms "first" and "second" are used only to distinguish technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0044] Example 1 Reference Figures 1 to 4As shown, a method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis includes: S1. Obtain multi-source spatial data of the current status of existing buildings, including historical buildings, ancient buildings and old buildings, and the multi-source spatial data includes three-dimensional geometric data, surface texture data and material information data.

[0045] S2. Spatial registration and fusion of multi-source spatial data are performed to generate a current 3D model containing geometric, texture and material information.

[0046] In one embodiment, specifically including: (a) Acquisition and processing of three-dimensional geometric data.

[0047] Three-dimensional geometric data is acquired using a terrestrial 3D laser scanner. During scanning, scanning stations are set according to the building's size and site conditions. Appropriate overlap areas are established between scanning stations to ensure spatial registration of data from multiple stations. The scanning resolution is determined based on the required accuracy; for structural deformation analysis, the point cloud spacing should not exceed 5mm; for recording the overall building shape, the point cloud spacing can be increased to 10-20mm. During on-site scanning, at least three targets or control points are evenly distributed around the building to unify the coordinates of the multi-station scanning data and facilitate subsequent model registration.

[0048] For interior spaces, narrow passages, or areas where scanning is limited, handheld or gantry-based 3D scanners can be used to supplement data collection, ensuring the integrity of the existing 3D model. Multi-station scan data are registered using an iterative nearest-point algorithm; the registration accuracy is determined based on the evaluation target and detection accuracy requirements. The registered point cloud data undergoes denoising and thinning processes to reduce data redundancy while preserving geometric features.

[0049] Non-building point clouds such as vegetation, temporary facilities, and debris within the scanning range are removed through manual or automatic classification, retaining only the point cloud data of the building itself.

[0050] (ii) Collection and processing of texture data and material information.

[0051] Surface texture data and material information were acquired through photogrammetry. A digital camera or a photogrammetric system mounted on a drone was used to capture images of the building surface from multiple angles and with varying degrees of overlap. During image acquisition, the overlap between adjacent images was no less than 60%, and the overlap between adjacent flight strips was no less than 30%.

[0052] Photogrammetric images are used to extract and match feature points using a structure-of-motion (SOG) algorithm, generating a dense point cloud and texture mapping relationship for the building surface. After spatial coordinate registration between the dense point cloud generated by photogrammetry and the 3D laser scanning point cloud, the texture image is mapped onto the surface of the 3D geometric model, generating a current 3D model with realistic texture.

[0053] (III) Fusion of multi-source spatial data.

[0054] The fusion of 3D laser scanning point clouds and photogrammetric dense point clouds involves the following steps: Using the 3D laser scanning point cloud as the geometric reference, the photogrammetric point cloud and the laser scanning point cloud are precisely registered using the iterative nearest point algorithm; Based on the registration results, the texture image obtained by photogrammetry is applied to the surface of the geometric model constructed by laser scanning point cloud using a texture mapping algorithm; For areas that only have texture information but lack laser scanning data, geometric supplementation is performed using photogrammetric point clouds; In the fused model, each mesh patch is associated with its corresponding texture coordinates and color information.

[0055] (iv) Obtaining material information.

[0056] Material information can be obtained using one or more of the following methods in combination: Method 1: Automatic identification based on texture features. Utilizing machine learning or image recognition algorithms, this method classifies and identifies building surface materials based on texture images and color information in the existing 3D model. Identification types include, but are not limited to: brick, stone, wood, mortar, paint, and metal. Method 2: On-site inspection and recording. Professional technicians conduct an on-site inspection of the material types of each part of the building and record the inspection results to the corresponding components; Method 3: Matching with an existing material information database. Based on historical information such as the building's construction date, regional characteristics, and building type, the data is compared and matched with a material information database to obtain the estimated material type and performance parameter range for each component.

[0057] In the preferred embodiment, cross-validation is performed by combining Method 1 and Method 2. When the automatic identification result is inconsistent with the on-site investigation record, the on-site investigation record shall prevail, and the difference shall be recorded as "material doubtful part" for key attention during subsequent testing or evaluation.

[0058] Standardization of color information.

[0059] In current 3D models, color information is described and stored using a standardized color space. In a preferred embodiment, the CIELAB color space is used to record the color value of each pixel or each component surface, along with CIELAB color difference parameters (color difference values). ΔE Used for subsequent quantitative evaluation of color deviation.

[0060] When comparing color information with historical photographs or restoration records, the Pantone Matching System can be used as an auxiliary descriptive method. During implementation, a standard color chart is placed on-site for photography. By correcting the colors of the chart in the images, the image colors are unified to the standard color space, eliminating the influence of lighting conditions on color acquisition.

[0061] (v) Model accuracy verification After the current 3D model is constructed, the following methods are used to verify the model's accuracy: Select easily measurable parts of the building (such as door and window opening dimensions, floor height, column spacing, etc.), compare the geometric dimensions measured in the model with the manual measurement data on site to verify the geometric accuracy of the model; compare the color information of the texture image in the model with the standard color card on site to verify the accuracy and consistency of the color information.

[0062] In this embodiment, during the construction of the existing 3D model of a certain building, a 3D laser scanner was set up at 12 stations to acquire approximately 120 million point cloud data points; simultaneously, 320 images were acquired using oblique photography by a drone; after fusion processing, an existing 3D model containing complete geometric shape, texture information and material classification labels was generated. The geometric accuracy of the model was verified by on-site sampling, and the dimensional deviation was within ±5mm.

[0063] S3. Based on one or more of the original design data, historical drawings, and on-site measurement data of the existing building, construct a health status benchmark model. The health status benchmark model includes geometric information, structural information, material information, and appearance information of the building components.

[0064] Specifically, historical (original) design data includes original architectural design drawings, construction drawings, as-built drawings, and drawings from all previous renovations. For buildings with complete drawing data, a baseline model can be directly built based on the drawings. Historical drawings include historical photographs and documents, such as historical photos reflecting the original or intact state of the building, postcards, and textual descriptions in documents. When complete drawings are lacking, historical photographs can be used in conjunction with photogrammetry principles to extract building dimensions. On-site measurement data includes taking measurements of well-preserved components or parts of the building to obtain their geometric dimensions, material types, and surface conditions, which serve as a reference for judging other components. In addition, for parts that lack drawings and cannot be measured on-site, the determination is made by deduction based on similar building types, regional construction techniques, and architectural structural logic.

[0065] It should be noted that the "health status" in the health status benchmark model refers to the original state of the building when it was designed and constructed, or the target protection state achieved after renovation. The definition criteria for health status are as follows for different types of buildings: For buildings that are relatively well-preserved: the original design state of the building is taken as the healthy state; For buildings that have undergone multiple renovations: the condition after the most recent renovation with complete records is taken as the health status. For buildings lacking historical data: the condition of similar well-preserved components observed during on-site investigations is used as a reference to estimate their health status.

[0066] In this embodiment, the health status baseline model is established in Revit or a similar BIM platform. During modeling, a building information model is built for each component based on the aforementioned data sources. Information fields for each component are recorded in the health status baseline model, as shown in Table 1.

[0067] Table 1: Information Fields Recorded in the Health Status Benchmark Model

[0068] For components in the model that lack a precise data source, mark them as "presumed state" in the model attributes and note the basis for the presumption, such as reference to similar components or inference of form.

[0069] Furthermore, for buildings that have undergone multiple renovations or status updates, the health status baseline model can be set up with multiple versions. The initial version represents the original state of the building, and subsequent versions correspond to the target state after each renovation. Switching between different versions and comparing them allows analysis of the trajectory of changes in the building's health status.

[0070] S4. The existing three-dimensional model is matched with the health status benchmark model component by component, and a difference analysis is performed to obtain the geometric deviation data, appearance deviation data and material change data of each building component, and the deviation analysis results are generated.

[0071] Specifically, the current three-dimensional model is mapped component by component to the health status benchmark model, including: The component correspondence matching between the current 3D model (mesh model) and the health status baseline model (solid model) is achieved through the following steps: Using the component ID in the health status baseline model as an index, the corresponding component is found in the current 3D model; For regular components (columns, beams, slabs, walls), automatic matching is performed based on the component's spatial location and size range; For irregular components or components that are difficult to match automatically, manual assistance is used to complete the matching. After matching is complete, the ID of each component in the HBIM baseline model is extended and associated with the corresponding mesh data in the existing 3D model.

[0072] Specifically, the method for obtaining the geometric deviation data includes: Using the component surface in the health status benchmark model as a reference surface, the signed distance from each sampling point on the mesh surface of the corresponding component in the current status 3D model to the reference surface is calculated. The sign of the signed distance is used to characterize the deviation direction. A positive value indicates that the component surface in the current status 3D model is convex outward relative to the reference surface, and a negative value indicates that the component surface in the current status 3D model is concave inward relative to the reference surface. Based on the signed distance corresponding to each sampling point on the surface of each component, the average deviation, maximum deviation, and standard deviation of each component are determined. For vertical components, calculate the offset and tilt angle of the axis of the vertical component relative to the corresponding reference position in the health status reference model, wherein the vertical component includes at least one of columns and walls; For horizontal members, calculate the deflection and end rotation change of the horizontal member relative to the health state reference model, wherein the horizontal member includes at least one of beams and slabs; The method for obtaining the apparent deviation data includes: The texture images and color information of each component surface are extracted from the current 3D model, and the reference color values ​​and reference texture features of the corresponding components are extracted from the health status benchmark model. The color difference ΔE between the current color and the reference color is calculated based on the color information and the reference color value. When the color difference ΔE is greater than the preset color difference threshold, it is determined that the corresponding component has an appearance deviation. The preset color difference threshold is determined based on the building material type and environmental exposure conditions of the corresponding component. The preset color difference threshold for outdoor exposed components is greater than the preset color difference threshold for indoor components or components in a protected state. The method for obtaining the material change data includes: The current texture features of each component surface in the current three-dimensional model are compared with the reference texture features of the corresponding component in the health status benchmark model to identify whether there are at least one material change signs among weathering, peeling, and blistering on the surface of the corresponding component. Based on the reflection intensity information and / or color information obtained from laser scanning, determine whether the material type of the corresponding component has changed relative to the health status benchmark model; When obtaining the material performance test data of the corresponding component, the material performance test data is used as an auxiliary basis for judging the material change. The material performance test data includes at least one of rebound value and moisture content. The material change determination results obtained based on the current texture features, the reflection intensity information, the color information, and the material performance test data are recorded as suspected changes for further confirmation in subsequent diagnostic paths.

[0073] In this embodiment, a health status benchmark model is established based on existing building historical data, design data, on-site inspection data, or preset health status information. The health status benchmark model includes the following information for building components: geometric information, material information, appearance information, and structural information. This benchmark model represents a reference state of the building components under normal or target protection conditions. The existing 3D model is matched with the health status benchmark model to match corresponding components, and the difference information between corresponding components is calculated. This difference information includes geometric deviation, appearance deviation, and material change information. Geometric deviation includes component displacement, tilt, shape changes, and size changes; appearance deviation includes color changes, texture changes, and surface defect information; material change information includes changes in material type, surface performance changes, and detected material degradation information. For appearance deviation, a color difference value ΔE can be calculated based on a standardized color space, and the degree of texture change can be combined to determine the surface state change of the component. The color difference value ΔE is calculated using the following formula: ; in: , , These are the CIELAB spatial coordinates of the component surface color in the current 3D model. , , This refers to the CIELAB spatial coordinates of the reference color of the corresponding component in the health status benchmark model. When the ΔE value exceeds a preset threshold, it is determined that the corresponding component has an appearance deviation. The preset threshold is determined based on the type of building material, environmental exposure conditions, or statistical results of appearance data of similar components. Different material types correspond to different threshold ranges, and the specific value is set by technical personnel according to the actual situation. The material information is used to assist in determining whether the appearance abnormality is accompanied by changes in material properties.

[0074] S5. Construct component deviation feature vectors based on the deviation analysis results. Determine the performance impact category of each building component based on the mapping relationship between the deviation feature vectors and the building performance impact types. Based on the performance impact categories, diagnose the deviation feature vectors in the structural risk diagnosis path or building aging diagnosis path. The deviation feature vectors include geometric deviation features, apparent deviation features, material state change features, and structural sensitivity features.

[0075] Specifically, it includes: Construct the component deviation feature vector according to the following formula: ; in, Indicates the first The component deviation feature vector of each building component G i Indicates the degree of geometric change. T i Indicates the degree of apparent change. M i Indicates the degree of change in the state of the material. S i Indicates the degree of structural sensitivity.

[0076] It should be noted that the degree of geometric change G i This is used to characterize the degree of change in the actual spatial state of a component relative to a health state baseline model. In one embodiment, the degree of geometric change... G i It can be determined comprehensively based on the changes in component displacement, tilt, and cross-sectional area.

[0077] For example, for load-bearing column members, the following can be obtained separately: the change in horizontal displacement obtained from the member detection. d ; Inclination angle of component axis θ The ratio of the measured cross-sectional area to the cross-sectional area of ​​the reference model.

[0078] First, the various geometric change indicators are normalized: ; ; ; in, This indicates the preset maximum allowable displacement reference value; This indicates the preset maximum allowable tilt angle reference value; This represents the effective cross-sectional area of ​​the component obtained from the current status inspection; This represents the cross-sectional area of ​​the components in the health status baseline model. Further, in one implementation, a weighted method can be used to calculate the degree of geometric change: ; in, , , These represent the weighting coefficients corresponding to different geometric change indicators; the weighting coefficients can be adjusted according to the component type, structural form, and evaluation purpose.

[0079] For example, for structural components whose primary purpose is deformation analysis, the influence weight of displacement and tilt indices can be increased; for components with material erosion or cross-sectional loss, the influence weight of cross-sectional change indices can be increased.

[0080] The above calculation method is used to illustrate the construction method of geometric deviation vectorization features. Those skilled in the art can use other equivalent methods to determine the features according to different building types and test data formats. G i .

[0081] Based on the deviation analysis results, a system is constructed that includes the degree of geometric change. G i degree of apparent change T i Degree of change in material state M i and structural sensitivity S i Component deviation characteristics; The degree of geometric change G i degree of apparent change T i Degree of change in material state M i and structural sensitivity S i Each with a preset geometric deviation threshold T g Apparent deviation threshold T t Material state change threshold T m and structural sensitivity threshold T s Compare; when T i ≥T t or M i ≥T m When it is determined that the component has an impact on building aging, it is entered into the building aging assessment path; when G i ≥T g or S i ≥T s When it is determined that the component has an impact on structural performance, it enters the structural risk diagnosis path; When the component simultaneously meets the conditions for both building aging impact and structural performance impact, it is given priority to enter the structural risk diagnosis path.

[0082] Geometric deviation threshold T g Apparent deviation threshold T t Material state change threshold T m and structural sensitivity threshold T s The structural sensitivity is pre-defined or adjusted based on the component type, material type, component location, structural stress characteristics, and assessment objectives. S i The determination is based on one or more of the following factors: the force transmission relationship of the component in the overall structure, its connection relationship, its location, its load-bearing importance, and the scope of its failure impact. When the same component exhibits both apparent aging or changes in material condition, as well as changes in geometric shape or is located in a highly structurally sensitive position, the diagnosis should prioritize the impact on structural performance to avoid structural risks being masked by the building aging assessment results.

[0083] Path selection implementation. Calculate the deviation eigenvector for each component. ,Will The system is compared with the preset mapping rules. If it is determined to be "impact on apparent performance" or "impact on material performance", it enters track A (building aging diagnosis path); if it is determined to be "impact on structural performance", it enters track B (structural risk diagnosis path).

[0084] When the same component simultaneously meets the conditions for both building aging impact and structural performance impact, it is given priority in the structural risk diagnosis path; the building aging diagnosis results are recorded as auxiliary evaluation information during the structural risk diagnosis process for reference during comprehensive assessment.

[0085] Based on the deviation data obtained in step S4 and the judgment results in step S5, the building aging type is identified.

[0086] Track A: In the building aging diagnosis path, based on the appearance deviation data and material change data, the building aging type is identified and aging is classified, and building maintenance or repair suggestions are generated based on the aging classification results.

[0087] Specifically, it includes: The types of aging include surface weathering, blistering or efflorescence caused by moisture, fading or cracking caused by sun exposure, biological erosion, and aging and peeling of surface coatings. Specifically: Surface weathering: characterized by blurred surface texture, increased granularity, and lighter or grayer color, commonly found on brick and stone components and mortar surfaces; Blistering or efflorescence caused by dampness: manifested as localized surface bulging, white exudate, darkening of color, or water stains, commonly found at the base of walls and in components in damp environments; Fading or cracking caused by sun exposure: This manifests as reduced color saturation and fine cracks on the surface, commonly seen in components facing south or west. Biological erosion: manifested as traces of biological attachment such as moss, lichen, and mold on the surface, with green, black, or brown patches in color; Surface coating aging and peeling: This manifests as the paint or coating layer peeling and flaking off, exposing the base material, and obvious layering or color difference.

[0088] The identification of aging types can be accomplished through automatic classification algorithms, or it can be manually determined by professional technicians based on deviation data and on-site image data. In the preferred embodiment, automatic identification is the primary method, supplemented by manual verification.

[0089] The degree of aging is quantitatively graded based on one or more indicators, including deviation depth, affected area ratio, color difference value ΔE, degree of material change, and changes in the effective performance of the material. Based on the type and grade of aging, one or more maintenance or repair recommendations are generated, including surface cleaning, waterproofing, local repair, substrate treatment, component reinforcement, or component replacement. Details are as follows: For identified aging types, quantitative grading is performed based on one or more indicators, including deviation depth, affected area ratio, color difference value, degree of material change, and changes in the effective performance of the material. Grading standards are pre-set based on building type, material characteristics, and protection requirements.

[0090] For example, weathering of the surface of masonry components can be classified according to the following criteria: Level 1 (Slight): Deviation depth <3mm, affected area <10%, color difference ΔE <5. No significant change in material; Level 2 (Medium): Deviation depth 3-10mm, affected area 10-30%, color difference. ΔE The material varies slightly between 5 and 15. Level 3 (Severe): Deviation depth > 10mm, affected area > 30%, color difference ΔE >15, the material shows obvious changes or peeling.

[0091] The specific values ​​in the above grading standards can be adjusted according to the building type, material properties and protection requirements. Different grading thresholds can be used for different material types.

[0092] (iii) Generation of maintenance or repair recommendations Based on the aging type and grading results, corresponding maintenance or repair recommendations are generated, as shown in Table 2.

[0093] Table 2: Maintenance or Repair Recommendations Corresponding to Aging Type and Grading Results

[0094] Track B: In the structural risk diagnosis path, geometric deviation data is converted into initial defect parameters or equivalent mechanical parameters in the finite element analysis model, a structural analysis model containing the actual damage state is constructed, structural response analysis is performed based on the structural analysis model, the structural risk level is determined, and structural intervention suggestions are generated.

[0095] The actual cross-sectional shape of the component is reconstructed based on the spatial deviation detection results, and the actual cross-sectional shape is converted into the cross-sectional mechanical parameters required by the finite element analysis model.

[0096] The actual cross-sectional geometric parameters of the component are determined based on the geometric deviation data, and the changes in the actual cross-sectional area, moment of inertia, section modulus, and / or torsional parameters relative to the design cross-sectional parameters are calculated. Based on the changes in the actual cross-sectional geometric parameters and material property parameters, the axial stiffness parameters, bending stiffness parameters, shear stiffness parameters and / or torsional stiffness parameters of the corresponding components in the finite element analysis model are corrected. Among them, for the axial force parameters, the axial stiffness-related parameters are corrected according to the ratio of the actual cross-sectional area to the designed cross-sectional area; For bending parameters, the bending stiffness-related parameters are corrected based on the ratio of the actual moment of inertia of the cross section to the design moment of inertia of the cross section. When material degradation is detected, the corresponding mechanical parameters are corrected based on the actual material elastic modulus and / or shear modulus.

[0097] For components primarily subjected to axial forces, the actual cross-sectional area can be used. A actual With the design cross-sectional area A original ratio , for axial stiffness related parameters EA Make corrections.

[0098] For members primarily subjected to bending, the actual moment of inertia of the cross section can be used. I actual Moment of inertia of the design section I original ratio Parameters related to bending stiffness EI Make corrections.

[0099] When a component exhibits material aging, corrosion, deterioration, or defects, the actual elastic modulus obtained through testing or evaluation can also be used as a reference. E actual Actual shear modulusG actual Material property parameters are used to correct the corresponding mechanical parameters in the finite element analysis model.

[0100] Specifically, a structural analysis model incorporating actual damage conditions is constructed, pre-set disaster loads are applied to conduct structural response analysis, weak points and risk levels are identified, and structural intervention recommendations are generated, including: The aforementioned health status benchmark model is used as the basic model for structural analysis; Based on the deviation analysis results, the component parameters affected by the deviation are modified. The component parameters include at least one of cross-sectional dimensions, material properties, and node constraints, so that the modified component parameters characterize the actual state of the corresponding component. For non-structural components (such as infill walls, decorative components, etc.), it is determined whether to retain the corresponding non-structural components in the structural analysis model according to the structural analysis requirements. The non-structural components include at least one of infill walls and decorative components. The modified structural analysis model is subjected to a quality check to determine whether the input parameters of each component are correct and whether there are geometric conflicts or constraint errors in the structural analysis model. Establish the correlation between the modification information of each component parameter in the structural analysis model and the corresponding detection deviation data, and record the data source and basis for the modification of each component parameter, so that the parameter adjustment in the structural analysis model can be traced back to the corresponding detection deviation data; Apply at least one preset disaster condition to the structural analysis model that includes the actual damage state; According to the preset disaster conditions, the structural analysis model is gradually loaded using elastic analysis or elastoplastic analysis methods, and the load application sequence and load combination are determined according to the most unfavorable conditions that the building may actually encounter, so as to obtain the structural response analysis results of the structure during the loading process. The structural risk level is determined based on the stress ratio, strain, displacement response, and stability evaluation results from the structural response analysis.

[0101] In some embodiments, the preset disaster conditions include earthquake conditions, wind load conditions, foundation settlement conditions, retrofit load conditions, and special load conditions; wherein: Earthquake conditions: Apply the corresponding acceleration time history curve or response spectrum according to the seismic fortification intensity of the area where the building is located; Wind load condition: Calculate and apply wind load based on the basic wind pressure at the building location, building height, and shape coefficient; Foundation settlement condition: Apply support displacement load based on foundation settlement data obtained from on-site testing or estimated settlement. New load conditions during renovation: When a building is undergoing renovation or functional change, new floor loads, equipment loads, etc. are applied according to the renovation plan; Other load conditions: Special load conditions determined based on the specific building conditions (such as snow load, temperature load, construction load, etc.).

[0102] During loading, elastic or elastoplastic analysis methods are employed as needed to observe the entire response process of the structure from the elastic stage to the elastoplastic stage through gradual loading. The order and combination of loads are determined based on the most unfavorable working conditions that may actually be encountered.

[0103] Based on the structural response analysis results, the structural risk level of each component is determined. The structural risk level is determined according to the criteria in Table 3.

[0104] Table 3: Structural Risk Level Determination Criteria

[0105] When a single component has multiple failure modes, the structural risk level is the highest among all modes.

[0106] S6. Associate the diagnostic results of the building aging diagnostic path and the structural risk diagnostic path with the corresponding building components to generate existing building risk distribution results and graded intervention suggestions. Specifically, this includes: The intervention priorities are generated by sorting the buildings according to their structural risk level, building aging level, and urgency, and then considering emergency intervention, planned intervention, monitoring and maintenance, or continuous observation.

[0107] In this embodiment, the diagnostic results of track A and track B are correlated and overlaid onto the health status baseline model to generate building risk distribution results. The risk distribution results are presented in the model in a visual form: the diagnostic results of track A are represented by warm colors (yellow-orange-red) to indicate the degree of aging, with the color intensity corresponding to the aging level; the diagnostic results of track B are represented by cool colors (blue-purple-red) to indicate the structural risk level, with red indicating high-risk components; when the same component has both building aging and structural risk, the structural risk level is used as the dominant display color, and both diagnostic results are recorded in the component attributes.

[0108] For example, tiered intervention recommendations are generated based on diagnostic results, prioritized as follows: First priority (emergency intervention): For structural risk level 3 components, it is recommended to take reinforcement or replacement measures in the short term; Second priority (planned intervention): For components with a structural risk level of 2 and an aging level of 3, it is recommended to take repair or reinforcement measures in the medium term. Third priority (monitoring and maintenance): Components with an aging level of 2 or above are recommended to be included in the daily monitoring plan and maintained in a timely manner. Fourth priority (continuous observation): For other components with slight deviations, it is recommended to continue observation and not take any intervention measures for the time being.

[0109] Intervention recommendations are generated in units of components. Each component includes the following recommendations: type of intervention, recommended time window, priority level, and a brief description of the diagnostic criteria.

[0110] S7. Link and store the current three-dimensional model, health status benchmark model, deviation analysis results, building aging diagnosis results, structural risk diagnosis results, and graded intervention suggestions to form an existing building health status assessment archive. The health status assessment file includes the health status benchmark model, the current three-dimensional model, the deviation analysis results, the building aging diagnosis results, the structural risk diagnosis results, and the graded intervention recommendations; When new existing 3D models or inspection data are acquired, the newly acquired existing 3D models or inspection data are compared and correlated with historical assessment data in the health status assessment archive, and the health status assessment results of the existing buildings are updated based on the correlation comparison results. The correlation comparison includes: Under a unified spatial reference, component-level geometric change analysis, apparent change analysis, and material change analysis are performed on the current 3D models acquired at different times to obtain multi-temporal change results for each component. Based on the multi-temporal change results, the state changes of each component of the existing building are determined, and the risk level and intervention priority of the existing building are updated based on the state changes.

[0111] In this embodiment, the digital assessment file uses a health status benchmark model as its core carrier and employs the following data structure to store related information: each component extends the storage fields in the health status benchmark model, including: geometric deviation value, apparent deviation value, aging type and level, structural risk level, intervention recommendation type and priority; the current 3D model is stored as an independent data file and is associated with the health status benchmark model through spatial coordinate correspondence; deviation analysis results are stored in tabular form, with each row corresponding to a component and including fields such as deviation type, deviation value, and threshold judgment result; dual-path diagnostic results are stored in a structured data format, including diagnostic path identifier, diagnostic time, diagnostic basis, and output conclusion; cross-platform access can be achieved through a common model exchange format, such as IFC format for model data, JSON or XML format for attribute data, and CSV format for tabular data, ensuring data access and exchange between different software platforms.

[0112] When new existing 3D models or inspection data are acquired, the archive update is completed through the following steps: The newly acquired current 3D model is registered with the existing health status benchmark model under the same spatial benchmark. The new current 3D model is overlaid and compared with the historical current 3D model to calculate the differences in the geometric shape, appearance and material changes of the components over time. Based on the overlay comparison results, update the deviation analysis results and dual-path diagnostic results; The updated results are appended to the digital assessment archive, historical versions are retained, and a traceable record of status changes is created. When multi-temporal data accumulates to more than three times, trend analysis can be performed to identify the rate of change in state, providing a reference for adjusting the intervention time window.

[0113] It should be noted that when 3D laser scanning equipment is unavailable, photogrammetry can be used to acquire 3D geometric and texture data separately, constructing a current 3D model based solely on image data. In this variant, the geometric accuracy of the current 3D model is reduced, but structural deformation trends and apparent changes can still be identified by comparing it with the baseline model.

[0114] When the building is small or the accuracy requirement is low, the data collection process in step S1 can be simplified by using handheld scanning equipment or a combination of manual measurement and photogrammetry to obtain current data, thereby reducing data collection costs and time.

[0115] When a baseline health model is unavailable, a "presumed baseline model" can be constructed as a reference based on the condition of similar components preserved during on-site investigation. The presumed baseline model should be marked with the "presumed" label, and the uncertainties introduced by it should be noted in subsequent diagnostic results.

[0116] When digital assessment files are used on different software platforms, data can be exported and imported through common data exchange formats such as IFC and CityGML, enabling cross-platform information sharing.

[0117] When different areas of the same building have different protection levels or renovation needs, different mapping thresholds and diagnostic path selection conditions can be set for each area to achieve differentiated assessment standards within the same building.

[0118] The methods described in each step of this embodiment have been applied and verified in multiple existing building assessment projects. The preferred parameter ranges are based on practical application experience and can be adjusted according to the following factors in different implementation scenarios: building type and structural system (brick-wood structure, brick-concrete structure, reinforced concrete structure, etc.); material type and preservation status; assessment purpose (routine maintenance, pre-renovation assessment, structural safety appraisal, etc.); type and accuracy of available data sources; available computing resources and software environment.

[0119] Example 2: Track A Diagnosis The brick masonry components of the exterior wall of a historical building were analyzed component by component using a 3D model of the current condition and a baseline model of the building's health status. The resulting deviation data for each component are as follows: Geometric deviation data includes an average surface deviation of 2 mm and a maximum deviation of 4 mm, which are normalized to obtain the degree of geometric change. G i =18; Apparent deviation data includes color changes and texture sharpness changes, among which the color difference value is calculated based on the CIELAB color space. ΔE =12, the apparent degree of change was obtained through comprehensive calculation. T i =65; Material testing results show that the component has slight surface weathering, with a weathering depth of approximately 3mm, corresponding to the degree of material change. M i =45.

[0120] According to the preset deviation feature mapping rules, due to this component G i Below the threshold for structural risk assessment, T i and M i The building meets the criteria for aging assessment, therefore the component is determined to be mainly affected by its appearance and material properties, and is thus included in the building aging diagnosis pathway.

[0121] Further analysis is needed based on the depth of deviation, the proportion of affected area, and the color difference value. ΔE The degree of material change was quantitatively graded, and the surface weathering level of the component was determined to be level 2.

[0122] Based on the aging level, maintenance recommendations are generated: surface cleaning and waterproofing measures should be implemented, and the component should be included in the subsequent condition monitoring scope.

[0123] Example 3: Track B Diagnosis A wooden column on the ground floor of an existing building was analyzed using a 3D model of the current condition and a baseline model of its health status. The analysis revealed a horizontal displacement of 32mm at the top of the column, and the tilt angle of the column reached a preset structural deviation threshold. Furthermore, this wooden column is located at a corner of the main structure and bears a two-way beam load. The structural sensitivity was assessed to determine its degree of structural sensitivity. S i =85. According to the deviation characteristic mapping rule, the degree of geometric change of this component. G i and structural sensitivity S i All conditions were met for structural risk assessment; therefore, the component was determined to primarily affect structural performance and was entered into the structural risk diagnosis path. During the structural risk diagnosis process, the difference between the detected horizontal displacement at the column top and the allowable variation error was calculated to obtain the effective initial defect quantity. ; Based on the calculated initial defect parameters, the column node positions and initial geometric state in the finite element analysis model were corrected to establish a structural analysis model incorporating the actual deformation state. Further seismic loads were applied for structural response analysis, yielding a maximum stress response to material bearing capacity ratio of 0.82 for the wooden column. According to the preset risk grading rules, the structural risk level of this component was determined to be Level 2. Based on the risk level, intervention recommendations were generated: local reinforcement measures should be implemented, and further inspection and reinforcement treatment should be completed within 6 months.

[0124] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0128] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis, characterized in that, include: Acquire multi-source spatial data of the current status of existing buildings, including historical buildings, ancient buildings and old buildings. The multi-source spatial data includes three-dimensional geometric data, surface texture data and material information data. Perform spatial registration and fusion of the multi-source spatial data to generate a current three-dimensional model containing geometric information, texture information and material information. Based on one or more of the original design data, historical drawings, and on-site measurement data of the existing buildings, a health status benchmark model is constructed. The health status benchmark model includes geometric information, structural information, material information, and appearance information of building components. The existing 3D model is matched component by component with the health status benchmark model, and a difference analysis is performed to obtain geometric deviation data, apparent deviation data and material change data of each building component, and the deviation analysis results are generated. Based on the deviation analysis results, a component deviation feature vector is constructed. Based on the mapping relationship between the deviation feature vector and the building performance impact type, the performance impact category corresponding to each building component is determined. Based on the performance impact category, the deviation feature vector is used for diagnosis in the structural risk diagnosis path or the building aging diagnosis path. The deviation feature vector includes geometric deviation features, apparent deviation features, material state change features, and structural sensitivity features. In the building aging diagnosis path, based on appearance deviation data and material change data, the building aging type is identified and aging is classified, and building maintenance or repair suggestions are generated based on the aging classification results. In the structural risk diagnosis path, geometric deviation data is converted into initial defect parameters or equivalent mechanical parameters in the finite element analysis model, a structural analysis model containing the actual damage state is constructed, structural response analysis is performed based on the structural analysis model, the structural risk level is determined, and structural intervention suggestions are generated. The diagnostic results of the building aging diagnostic path and the structural risk diagnostic path are associated with the corresponding building components to generate the risk distribution results of existing buildings and graded intervention suggestions.

2. The method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis according to claim 1, characterized in that, Acquire multi-source spatial data of the existing building's current state, including: Acquire three-dimensional geometric data through one or more of the following methods: three-dimensional laser scanning, handheld three-dimensional scanning, or photogrammetry; Surface texture data is acquired using digital cameras or drone photogrammetry systems; Spatial registration and fusion of multi-source spatial data includes: The point cloud data obtained by 3D scanning is spatially registered with the image data obtained by photogrammetry, and the texture information is mapped onto the surface of the 3D geometric data to generate the current 3D model.

3. The method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis according to claim 1, characterized in that, The current 3D model is mapped component-by-component to the health status baseline model, including: Using the component ID in the health status baseline model as an index, the corresponding component is found in the current 3D model; For regular components, automatic matching is performed based on the component's spatial location and size range; For irregular components or components that are difficult to match automatically, manual assistance is used to complete the matching. After matching is complete, the ID of each component in the health status baseline model is extended and associated with the corresponding mesh data in the current 3D model.

4. The method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis according to claim 1, characterized in that, The method for obtaining the geometric deviation data includes: Using the component surface in the health status benchmark model as a reference surface, the signed distance from each sampling point on the mesh surface of the corresponding component in the current status 3D model to the reference surface is calculated. The sign of the signed distance is used to characterize the deviation direction. A positive value indicates that the component surface in the current status 3D model is convex outward relative to the reference surface, and a negative value indicates that the component surface in the current status 3D model is concave inward relative to the reference surface. Based on the signed distance corresponding to each sampling point on the surface of each component, the average deviation, maximum deviation, and standard deviation of each component are determined. For vertical components, calculate the offset and tilt angle of the axis of the vertical component relative to the corresponding reference position in the health status reference model, wherein the vertical component includes at least one of columns and walls; For horizontal members, calculate the deflection and end rotation change of the horizontal member relative to the health state reference model, wherein the horizontal member includes at least one of beams and slabs; The method for obtaining the apparent deviation data includes: The texture images and color information of each component surface are extracted from the current 3D model, and the reference color values ​​and reference texture features of the corresponding components are extracted from the health status benchmark model. The color difference ΔE between the current color and the reference color is calculated based on the color information and the reference color value. When the color difference ΔE is greater than the preset color difference threshold, it is determined that the corresponding component has an appearance deviation. The preset color difference threshold is determined based on the building material type and environmental exposure conditions of the corresponding component. The preset color difference threshold for outdoor exposed components is greater than the preset color difference threshold for indoor components or components in a protected state. The method for obtaining the material change data includes: The current texture features of each component surface in the current three-dimensional model are compared with the reference texture features of the corresponding component in the health status benchmark model to identify whether there are at least one material change signs among weathering, peeling, and blistering on the surface of the corresponding component. Based on the reflection intensity information and / or color information obtained from laser scanning, determine whether the material type of the corresponding component has changed relative to the health status benchmark model; When obtaining the material performance test data of the corresponding component, the material performance test data is used as an auxiliary basis for judging the material change. The material performance test data includes at least one of rebound value and moisture content. The material change determination results obtained based on the current texture features, the reflection intensity information, the color information, and the material performance test data are recorded as suspected changes for further confirmation in subsequent diagnostic paths.

5. The method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis according to claim 1, characterized in that, Based on the deviation analysis results, a component deviation feature vector is constructed. Based on the mapping relationship between the deviation feature vector and the building performance impact type, the performance impact category corresponding to each building component is determined. Based on the performance impact category, the deviation feature vector is input into the structural risk diagnosis path or the building aging diagnosis path, including: Based on the deviation analysis results, a system is constructed that includes the degree of geometric change. Gi degree of apparent change Ti Degree of change in material state Mi and structural sensitivity Si Component deviation characteristics; The degree of geometric change Gi degree of apparent change Ti Degree of change in material state Mi and structural sensitivity Si Each with a preset geometric deviation threshold Tg Apparent deviation threshold Tt Material state change threshold Tm and structural sensitivity threshold Ts Compare; when Ti≥Tt or Mi≥Tm When it is determined that the component has an impact on building aging, it is entered into the building aging assessment path; when Gi≥Tg or Si≥Ts When it is determined that the component has an impact on structural performance, it enters the structural risk diagnosis path; When the component simultaneously meets the conditions for both building aging impact and structural performance impact, it is given priority to enter the structural risk diagnosis path.

6. The method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis according to claim 1, characterized in that, Based on apparent deviation data and material change data, the aging type of buildings is identified and aging is classified. Based on the aging classification results, building maintenance or repair recommendations are generated, including: The types of aging include surface weathering, blistering or efflorescence caused by moisture, fading or cracking caused by sun exposure, biological erosion, and aging and peeling of surface coatings. The degree of aging is quantitatively graded based on one or more indicators, including deviation depth, affected area ratio, color difference value ΔE, degree of material change, and change in effective material properties. Based on the type and grade of aging, one or more maintenance or repair recommendations are generated, including surface cleaning, waterproofing, local repair, base treatment, component reinforcement, or component replacement.

7. The method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis according to claim 1, characterized in that, Converting geometric deviation data into initial defect parameters or equivalent mechanical parameters in the finite element analysis model includes: The actual cross-sectional geometric parameters of the component are determined based on the geometric deviation data, and the changes in the actual cross-sectional area, moment of inertia, section modulus, and / or torsional parameters relative to the design cross-sectional parameters are calculated. Based on the changes in the actual cross-sectional geometric parameters and material property parameters, the axial stiffness parameters, bending stiffness parameters, shear stiffness parameters and / or torsional stiffness parameters of the corresponding components in the finite element analysis model are corrected. Among them, for the axial force parameters, the axial stiffness-related parameters are corrected according to the ratio of the actual cross-sectional area to the designed cross-sectional area; For bending parameters, the bending stiffness-related parameters are corrected based on the ratio of the actual moment of inertia of the cross section to the design moment of inertia of the cross section. When material degradation is detected, the corresponding mechanical parameters are corrected based on the actual material elastic modulus and / or shear modulus.

8. The method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis according to claim 1, characterized in that, A structural analysis model incorporating the actual damage state is constructed. Based on this model, structural response analysis is performed to determine the structural risk level and generate structural intervention recommendations, including: The aforementioned health status benchmark model is used as the basic model for structural analysis; Based on the deviation analysis results, the component parameters affected by the deviation are modified. The component parameters include at least one of cross-sectional dimensions, material properties, and node constraints, so that the modified component parameters characterize the actual state of the corresponding component. For non-structural components, it is determined whether to retain the corresponding non-structural components in the structural analysis model based on the structural analysis requirements. The non-structural components include at least one of infill walls and decorative components. The modified structural analysis model is subjected to a quality check to determine whether the input parameters of each component are correct and whether there are geometric conflicts or constraint errors in the structural analysis model. Establish the correlation between the modification information of each component parameter in the structural analysis model and the corresponding detection deviation data, and record the data source and basis for the modification of each component parameter, so that the parameter adjustment in the structural analysis model can be traced back to the corresponding detection deviation data; At least one preset disaster condition is applied to the structural analysis model that includes the actual damage state. The preset disaster conditions include earthquake conditions, wind load conditions, foundation settlement conditions, modification and new load conditions, and special load conditions. According to the preset disaster conditions, the structural analysis model is gradually loaded using elastic analysis or elastoplastic analysis methods, and the load application sequence and load combination are determined according to the most unfavorable conditions that the building may actually encounter, so as to obtain the structural response analysis results of the structure during the loading process. The structural risk level is determined based on the stress ratio, strain, displacement response, and stability evaluation results in the structural response analysis. Among them, when the stress ratio is less than 0.6, the displacement response meets the corresponding specification limit, and there are no signs of instability, the structural risk level is determined to be Level 1: low risk, no intervention required; When 0.6 ≤ stress ratio < 0.85 and the displacement response deviates from the corresponding specification limit within a preset range, the structural risk level is determined to be Level 2: medium risk, requiring continuous monitoring. When the stress ratio is ≥0.85, the displacement response exceeds the corresponding specification limit, or there is at least one of the following conditions, the structural risk level is determined to be Level 3: high risk, and reinforcement measures are taken.

9. The method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis according to claim 1, characterized in that, Generate risk distribution results and tiered intervention recommendations for existing buildings, including: The intervention priorities are generated by sorting the buildings according to their structural risk level, building aging level, and urgency, and then considering emergency intervention, planned intervention, monitoring and maintenance, or continuous observation.

10. The method for assessing the health status of existing buildings based on spatial deviation performance mapping and dual-path diagnosis according to claim 1, characterized in that, Also includes: The existing 3D model, health status benchmark model, deviation analysis results, building aging diagnosis results, structural risk diagnosis results, and graded intervention recommendations are linked and stored to form an existing building health status assessment archive. The health status assessment file includes the health status benchmark model, the current three-dimensional model, the deviation analysis results, the building aging diagnosis results, the structural risk diagnosis results, and the graded intervention recommendations; When new existing 3D models or inspection data are acquired, the newly acquired existing 3D models or inspection data are compared and correlated with historical assessment data in the health status assessment archive, and the health status assessment results of the existing buildings are updated based on the correlation comparison results. The correlation comparison includes: Under a unified spatial reference, component-level geometric change analysis, appearance change analysis, and material change analysis are performed on the current 3D models acquired at different times to obtain multi-temporal change results for each component. Based on the multi-temporal change results, the state changes of each component of the existing building are determined, and the risk level and intervention priority of the existing building are updated based on the state changes.

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