BIM and GIS based whole-process design decision support system

Abstract

This invention relates to the field of construction data processing technology, specifically to a full-process design support decision-making system based on BIM and GIS, solving the technical problem that existing technologies cannot meet the needs of engineering projects for full-process quality control and long-term risk prediction. The system includes: a data acquisition unit for acquiring construction data and generating daily incremental datasets based on the construction data; the construction data includes daily topographic data and compaction equipment process data during construction; the daily incremental dataset includes new load fields, compaction work fields, and compression deformation fields; a feature diagnosis unit for extracting internal structural feature data of the fill body based on historical cumulative compaction work fields and the daily incremental dataset; the historical cumulative compaction work field is determined based on the daily cumulative compaction work fields within a historical period and is used to characterize the state of the fill body up to the end of construction on the previous day; and a risk assessment unit for performing risk assessments based on the daily internal structural feature data of the fill body.

Description

Technical Field

[0001] This invention relates to the field of construction data processing technology, specifically to a full-process design support decision-making system based on BIM and GIS. Background Technology

[0002] In the construction of large-scale infrastructure projects such as roads and water conservancy, the construction quality of large-scale artificial fill directly determines the long-term service performance of the project. The core requirement is to effectively control the internal structural state of the fill and anticipate long-term settlement risks in advance. With the development of construction technology, the industry now widely uses drones for three-dimensional terrain scanning to obtain geometric change data of the fill surface. At the same time, intelligent compaction equipment is used to record key parameters during the construction process, forming high-precision construction data such as building information modeling (BIM) and geographic information system (GIS).

[0003] However, existing technologies largely rely on localized, point-to-point compliance checks, only capable of judging compliance with indicators such as compaction degree at individual measuring points, but unable to effectively integrate construction data from different sources and with varying properties. Digital elevation models acquired by drones only reflect surface geometric changes, and compaction equipment data only reflects energy input intensity; these two are disconnected, failing to reveal the transmission law of compaction energy and deformation response mechanism within non-uniform fill bodies. When problems such as long-term uneven settlement occur, managers struggle to trace the root cause, unable to distinguish whether it's due to insufficient local compaction or internal stress redistribution issues such as soil arching caused by differences in stiffness across different areas. These structural defects are often the core triggers for long-term settlement risks. Therefore, existing technologies still have significant limitations in practical applications, failing to meet the needs of engineering projects for comprehensive quality control and long-term risk prediction. Summary of the Invention

[0004] To address the technical problem that existing technologies cannot meet the needs of engineering projects for full-process quality control and long-term risk prediction, the purpose of this invention is to provide a full-process design auxiliary decision-making system based on BIM and GIS. The specific technical solution adopted is as follows: This application provides a full-process design support decision-making system based on BIM and GIS, including: The data acquisition unit is used to acquire construction data and generate daily incremental datasets based on the construction data. The construction data includes daily topographic data and compaction equipment process data during the construction process. The daily incremental dataset includes the new load field, compaction work field, and compression deformation field. The new load field is used to characterize the load distribution generated by the self-weight of the newly added fill on the day, the compaction work field is used to characterize the energy distribution of the compaction construction input on the day, and the compression deformation field is used to characterize the compression amount distribution generated under the compaction action on the day. The feature diagnosis unit is used to extract the internal structural feature data of the fill body based on the historical cumulative compaction field and the daily incremental dataset; the historical cumulative compaction field is determined based on the daily compaction field accumulation within the historical period and is used to characterize the state of the fill body up to the end of construction on the previous day. The risk assessment unit is used to conduct risk assessments based on daily data on the internal structural characteristics of the fill.

[0005] In one possible implementation, the data acquisition unit includes: The spatial alignment module is used to spatially align topographic data and compaction equipment process data in construction data. The load field determination module is used to perform incremental calculations based on two consecutive days of spatially aligned terrain data to determine the new load field. The compaction field determination module is used to extract compaction energy and determine the compaction field based on the spatially aligned process data of the compaction equipment. The compression deformation field determination module is used to determine the compression deformation field based on topographic data from two consecutive days after spatial alignment and the designed loose paving thickness.

[0006] In one possible implementation, the feature diagnosis unit includes: The total compaction field construction module is used to add the historical cumulative compaction field to the compaction field in the daily incremental dataset to construct the total compaction field for the day; the total compaction field for the day is used to characterize the stiffness state of the fill body after including the impact of construction on the day. The transfer path simulation module is used to construct the mapping relationship between compaction energy input and compression deformation response based on the total compaction work field of the day through the optimal transfer algorithm, and generate the action transfer path field; the action transfer path field is used to characterize the action transfer path of the virtual potential energy of compaction energy input inside the fill body; The feature extraction module is used to extract internal structural feature data based on the action transmission path field and the total compaction work field of the day; The status update module is used to update the total compaction work area for the current day to the historical cumulative compaction work area for the next day.

[0007] In one possible implementation, the state update module is also used for: Before the first operation of the BIM and GIS-based full-process design support decision-making system, the historical accumulated compaction field is initialized as a zero-value field.

[0008] In one possible implementation, the path simulation module includes: The cost matrix construction submodule is used to construct the action transfer cost matrix based on the total compaction field of the day. The transfer cost value between any two grid cells in the action transfer cost matrix is ​​positively correlated with the geometric distance between the two grid cells and the average stiffness characterization value on the path between the two grid cells determined based on the total compaction field of the day. The optimal transport calculation submodule is used to perform optimal transport calculations with the first probability distribution obtained after normalizing the compaction work field in the daily incremental dataset as the source distribution and the second probability distribution obtained after normalizing the compression deformation field as the target distribution, and with the action transport cost matrix as a constraint, to obtain the action transport scheme matrix; the action transport scheme matrix is ​​used to describe the action transport scheme from the source distribution to the target distribution. The path field generation submodule is used to calculate the weighted average flow direction of the action of each grid cell based on the action transmission scheme matrix, and generate the action transmission path field.

[0009] In one possible implementation, the internal structural feature data includes raster data for identifying high- and low-stiffness coupling regions; the feature extraction module is specifically used for: Identify high-stiffness and low-stiffness regions from the total compaction field of the day; high-stiffness regions are those with a higher energy threshold than the first preset energy threshold in the total compaction field of the day, and low-stiffness regions are those with a lower energy threshold than the second preset energy threshold in the total compaction field of the day; the first preset energy threshold is greater than or equal to the second preset energy threshold. Region marking is performed based on the path direction of the action transmission path field within the low stiffness region, generating raster data to identify high and low stiffness coupling regions.

[0010] In one possible implementation, the internal structural feature data also includes action propagation path tortuosity data; the feature extraction module is specifically used for: For each action transmission path in the action transmission path field, calculate the ratio of the length of the action transmission path to the straight-line distance between the start and end points of the action transmission path, and use it as the tortuosity of the action transmission path of the starting grid cell of the action transmission path. Action transmission path tortuosity data is generated based on the tortuosity of the starting grid cell of each action transmission path.

[0011] In one possible implementation, the risk assessment unit includes: The cumulative calculation module is used to perform weighted cumulative calculations based on the daily internal structural characteristics data of the fill body to generate a structural defect cumulative index field; the structural defect cumulative index field is used to characterize the degree of long-term settlement risk at different spatial locations of the fill body.

[0012] In one possible implementation, the cumulative calculation module is specifically used for: For each grid cell, a weighted cumulative calculation is performed based on the cumulative number of times the grid cell is marked as a high-low stiffness coupling region in the daily grid data used to identify high-low stiffness coupling regions and the tortuosity of the action transmission path of the grid cell in the daily data. The structural defect cumulative index field is then generated based on the calculation results of each grid cell.

[0013] In one possible implementation, the risk assessment unit further includes: The visualization and traceability module is used to integrate and display the cumulative index field of structural defects in the form of a heat map with the three-dimensional building information model.

[0014] The present invention has the following beneficial effects: Given the technical challenges of existing technologies in meeting the needs of engineering projects for full-process quality control and long-term risk prediction, this application provides a full-process design auxiliary decision-making system based on BIM and GIS. Through the collaborative work of data acquisition units, feature diagnosis units, and risk assessment units, a full-process system is constructed, from standardized processing of construction data to internal structure diagnosis and long-term risk assessment. The data acquisition unit can acquire construction data and generate daily incremental datasets based on the construction data, solving the problem of heterogeneous fusion of multi-source construction data and providing structured data with a unified benchmark for subsequent analysis. The feature diagnosis unit can extract internal structural feature data of the fill body based on historical cumulative compaction field and daily incremental datasets. By embedding the historical construction cumulative effect, it can accurately identify internal structural defects of the fill body, breaking through the limitations of traditional point-to-point quality assessment. The risk assessment unit can conduct risk assessment based on the daily internal structural feature data of the fill body, establishing a direct correlation between the construction process and long-term risks. This application effectively solves the technical problems of data fragmentation, inability to identify systemic structural defects, and difficulty in predicting long-term risks in existing technologies, providing comprehensive and objective data support for engineering quality control and decision-making, and meeting the needs of engineering for full-process quality control and long-term risk prediction. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a system architecture diagram of a full-process design auxiliary decision-making system based on BIM and GIS, provided as an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a data acquisition unit provided in one embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a feature diagnosis unit provided in one embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a transmission path simulation module provided in one embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a risk assessment unit provided in one embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of another risk assessment unit provided in an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the BIM and GIS-based full-process design auxiliary decision-making system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] Given the technical challenges of existing technologies in meeting the needs of engineering projects for full-process quality control and long-term risk prediction, this application provides a full-process design auxiliary decision-making system based on BIM and GIS. Through the collaborative work of data acquisition units, feature diagnosis units, and risk assessment units, a full-process system is constructed, from standardized processing of construction data to internal structure diagnosis and long-term risk assessment. The data acquisition unit can acquire construction data and generate daily incremental datasets based on the construction data, solving the problem of heterogeneous fusion of multi-source construction data and providing structured data with a unified benchmark for subsequent analysis. The feature diagnosis unit can extract internal structural feature data of the fill body based on historical cumulative compaction field and daily incremental datasets. By embedding the historical construction cumulative effect, it can accurately identify internal structural defects of the fill body, breaking through the limitations of traditional point-to-point quality assessment. The risk assessment unit can conduct risk assessment based on the daily internal structural feature data of the fill body, establishing a direct correlation between the construction process and long-term risks. This application effectively solves the technical problems of data fragmentation, inability to identify systemic structural defects, and difficulty in predicting long-term risks in existing technologies, providing comprehensive and objective data support for engineering quality control and decision-making, and meeting the needs of engineering for full-process quality control and long-term risk prediction.

[0020] The specific solution of the full-process design auxiliary decision-making system based on BIM and GIS provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Please see Figure 1 The diagram illustrates a system architecture of a BIM and GIS-based full-process design auxiliary decision-making system 10 provided in an embodiment of the present invention. The BIM and GIS-based full-process design auxiliary decision-making system 10 includes: a data acquisition unit 20, a feature diagnosis unit 30, and a risk assessment unit 40.

[0022] It should be noted that the BIM and GIS-based full-process design auxiliary decision-making system 10 provided in this application operates in a backend analysis mode, where data processing and risk assessment are based on complete data collected after the end of each day's construction. Specifically, the BIM and GIS-based full-process design auxiliary decision-making system 10 operates in the backend analysis mode. After the day's construction is completed, obtain the final topographic data and the entire process data of the compaction equipment; on the following day (i.e., the... Within the daily analysis period, based on the acquired completion data and combined with historical cumulative status, daily incremental calculations, feature diagnosis, and risk assessment are performed. The results are used to guide subsequent construction decisions and risk management, thereby achieving closed-loop management of the fill body status and risks.

[0023] The data acquisition unit 20 is used to acquire construction data and generate daily incremental datasets based on the construction data.

[0024] The construction data includes daily topographic data and compaction equipment process data during construction. The daily incremental dataset includes the newly added load field, compaction work field, and compression deformation field.

[0025] The newly added load field is used to characterize the load distribution generated by the self-weight of the newly added fill on the same day. It can be determined by the vertical stress increment corresponding to the thickness of the newly added fill on the same day, and is directly related to the unit weight of the fill.

[0026] The compaction energy field is used to characterize the energy distribution of the compaction process input on a given day. It can be obtained by spatially accumulating the process data of the compaction equipment. The energy input intensity is positively correlated with the stiffness of the fill body.

[0027] The compression deformation field is used to characterize the distribution of compression generated under compaction on the same day, reflecting the direct deformation result of the combined effect of the initial state of the fill (such as moisture content and loose thickness) and the compaction process.

[0028] It should be noted that construction data is the foundation of system analysis. Its sources are diverse and heterogeneous. Data acquisition unit 20 can standardize these raw data and transform them into a structured dataset under a unified spatiotemporal benchmark, providing reliable input for subsequent feature diagnosis.

[0029] For example, terrain data can be generated by UAV 3D scanning, including raw point clouds or triangular mesh models, to reflect the geometric changes on the surface of the fill. Compaction equipment process data can be recorded by intelligent compaction equipment, including parameters such as compaction meter value (CMV) with geographic coordinates, to characterize the energy input intensity of the compaction operation.

[0030] The daily incremental dataset is a quantitative representation of the daily construction activities and the incremental state of the fill body. It includes three core data fields: the new load field, the compaction work field, and the compression deformation field. Each data field is generated based on a unified analysis reference grid. For example, the analysis reference grid can be set to the CGCS2000 coordinate system, with a grid cell size of 0.5 meters × 0.5 meters, which can be adjusted according to the engineering accuracy requirements.

[0031] Feature diagnosis unit 30 is used to extract internal structural feature data of fill bodies based on historical cumulative compaction field and daily incremental dataset.

[0032] Among them, the historical cumulative compaction field is determined based on the daily cumulative compaction field within the historical period and is used to characterize the state of the fill body up to the end of construction on the previous day.

[0033] The feature diagnosis unit 30 can establish a mathematical model of the transmission of internal forces within the fill body by integrating historical construction influences with current construction data, thereby achieving quantitative diagnosis of invisible internal structural features and breaking through the limitation of traditional technologies that can only analyze surface conditions.

[0034] Among them, the historical cumulative compaction field is a key basic data characterizing the state of the fill body. It is determined based on the daily cumulative compaction field within a historical period and is used to characterize the overall stiffness state of the fill body up to the end of construction the previous day. It can reveal structural defects such as soil arching that lead to long-term settlement risks. This historical cumulative compaction field can be updated daily as construction progresses, ensuring that the diagnostic analysis of the day is based on a complete construction history.

[0035] Risk assessment unit 40 is used to conduct risk assessment based on daily data on the internal structural characteristics of the fill.

[0036] The risk assessment unit 40 can transform daily instantaneous structural feature diagnosis results into a spatial assessment of long-term settlement risk, thereby establishing a quantitative correlation between the construction process and long-term risks, and providing an intuitive and traceable basis for engineering decisions. In this application, the risk assessment unit 40 can accumulate and analyze daily internal structural feature data to form quantitative indicators that reflect the severity of defects in different spatial locations, thereby achieving accurate positioning and tracing of the causes of long-term uneven settlement risks.

[0037] Based on the above technical solutions, this application constructs a complete system from standardized processing of construction data to internal structure diagnosis and long-term risk assessment through the collaborative work of data acquisition unit 20, feature diagnosis unit 30, and risk assessment unit 40. Data acquisition unit 20 acquires construction data and generates daily incremental datasets, solving the problem of heterogeneous fusion of multi-source construction data and providing structured data with a unified benchmark for subsequent analysis. Feature diagnosis unit 30 extracts internal structural feature data of the fill body based on historical cumulative compaction field and daily incremental datasets. By embedding the historical construction cumulative effect, it achieves accurate identification of internal structural defects in the fill body, breaking through the limitations of traditional point-to-point quality assessment. Risk assessment unit 40 performs risk assessment based on daily internal structural feature data of the fill body, establishing a direct link between the construction process and long-term risks. This application effectively solves the technical problems of fragmented data, inability to identify systemic structural defects, and difficulty in predicting long-term risks in existing technologies, providing comprehensive and objective data support for engineering quality control and decision-making, and meeting the needs of engineering for full-process quality control and long-term risk prediction.

[0038] As one possible embodiment of this application, to further improve the accuracy and reliability of construction data processing, this application can further refine the data acquisition unit 20 to achieve more comprehensive and standardized data acquisition, combined with... Figure 1 ,like Figure 2 As shown, the data acquisition unit 20 includes: a spatial alignment module 21, a load field determination module 22, a compaction work field determination module 23, and a compression deformation field determination module 24.

[0039] The spatial alignment module 21 is used to spatially align the terrain data and the compaction equipment process data in the construction data.

[0040] It should be noted that, due to differences in the source, coordinate system, and data structure of BIM / GIS data such as UAV terrain data and compaction equipment process data, direct use of these data for joint analysis can lead to spatial misalignment and affect calculation accuracy. Therefore, this application can unify the spatial benchmark through the spatial alignment module 21, ensuring that all data are correlated under the same raster system, thus laying a spatial consistency foundation for subsequent calculations of various data fields.

[0041] For example, this application can predefine an analysis reference grid covering the entire project area, specifying the coordinate system (such as CGCS2000), spatial range, and grid cell size (e.g., 0.5m × 0.5m, 1m × 1m, etc., set according to the project scale and accuracy requirements). After the daily construction is completed, the original point cloud or triangulation model generated by the UAV is processed through a digital photogrammetry workflow, resampled to the analysis reference grid, and a digital elevation model for the day (i.e., spatially aligned terrain data) is generated, represented as follows: ,in, For date indexing, The coordinates of the grid cells are used; at the same time, the set of data points with geographic coordinates recorded by the compaction equipment is traversed, and each data point is mapped to a unique cell of the reference grid according to the coordinates to form the spatially aligned process data of the compaction equipment.

[0042] The load field determination module 22 is used to perform incremental calculations based on the topographic data of two consecutive days after spatial alignment to determine the new load field.

[0043] The self-weight of the newly added fill is the core load driving the compaction deformation and long-term settlement of the fill. The load field determination module 22 can quantify the spatial distribution of this load to provide a basis for subsequent analysis of the stress and deformation response of the fill.

[0044] For example, this application can obtain spatially aligned terrain data for the current day (denoted as...). ) and the previous day's topographic data (denoted as Simultaneously, the design unit weight of the fill material for the current construction layer is extracted from the engineering BIM model. (The unit weight of fill soil is usually 18-20 kN / m) 3 (The specific value is determined by the design documents based on the type of filler). The newly added load field satisfies the following formula: in, Date index The new load field, Date index grid cells Topographic data, Date index grid cells Topographic data, For the design unit weight, the numerical value of each grid cell in this new load field represents the vertical stress increment at the corresponding location caused by the filling on that day.

[0045] The compaction field determination module 23 is used to extract compaction energy and determine the compaction field based on the spatially aligned process data of the compaction equipment.

[0046] The input intensity of compaction energy directly affects the compaction effect and stiffness characteristics of the fill. To assess the energy input intensity and its spatial distribution uniformity during the compaction process on a given day, it is necessary to spatially accumulate the process parameters recorded by each compaction device. The compaction energy field determination module 23 can quantify the spatial distribution of compaction energy on a given day by spatially accumulating the process data of the compaction devices, providing a basis for subsequent characterization of the stiffness state of the fill.

[0047] For example, this application uses spatially aligned compaction equipment process data as input and selects CMV, which is directly related to the increase in soil stiffness, as the energy characterization parameter. Specifically, a temporary grid field with the same initial value as the analysis reference grid is first created. Each data point in the compaction equipment process data is traversed, and the CMV values ​​of all data points within the same grid cell are accumulated to obtain the compaction energy of that grid cell. The compaction energy of each grid cell in the compaction energy field is calculated using the following formula: in, For grid cells In date index The compaction energy, For grid cells The set of data points consisting of all data points in , For the data point set with index value CMV of data points.

[0048] The compression deformation field determination module 24 is used to determine the compression deformation field based on the topographic data of two consecutive days after spatial alignment and the designed loose paving thickness.

[0049] The actual compression of the newly laid soil layer is the direct result of the combined effect of the initial state of the fill (such as moisture content and loose thickness) and the compaction process. It can reflect the actual effect of the compaction operation on the day. The compression deformation field determination module 24 provides a basis for establishing the correlation between compaction and deformation response by quantifying the spatial distribution of this compression.

[0050] For example, this application can query the design loose fill thickness from the daily construction plan or BIM model (for example, according to construction specifications, the loose fill thickness is usually 20-30cm, the specific thickness is determined by the construction plan), and obtain the daily physical fill thickness increment based on the topographic data of two consecutive days after spatial alignment. At the same time, it obtains the BIM / GIS polygon area representing the boundary of the actual construction area on the day. For the grid cells covered by the boundary of the actual construction area on the day, it calculates the compression deformation by the difference between the design loose fill thickness and the actual thickness increment. For example, the compression deformation satisfies the following formula: in, For grid cells In date index The amount of compression deformation, To design the loose-lay thickness, Date index Topographic data, Date index Terrain data, This is a function to maximize the value. In practical engineering, due to topographic survey errors, data registration deviations, or local surface disturbances, the following may occur: This situation results in negative compression deformation, which contradicts the physical nature of the compaction process. To ensure the physical validity of the data, this application can... The function applies a non-negative constraint to the compressive deformation; that is, when the calculated value is negative, it is set to 0, indicating that no effective compressive deformation was detected at that location on that day. This method avoids physical inconsistencies caused by data noise while maintaining the spatial continuity of the deformation field data, thus conforming to engineering practice.

[0051] It should be noted that for grid cells outside the actual construction area boundary on the day, this application can set the corresponding values ​​of the grid cells to zero or invalid values, ultimately forming the compression deformation field for that day, denoted as... .

[0052] Based on the above technical solution, this application achieves refined processing of construction data from spatial alignment to the generation of various data fields by modularizing and refining the data acquisition unit 20. The spatial alignment module 21 ensures the spatial consistency of multi-source data, providing a guarantee for the accuracy of subsequent calculations. The load field determination module 22, compaction work field determination module 23, and compression deformation field determination module 24 quantify the daily construction behavior and fill body state increment from three core dimensions: load, energy, and deformation, respectively. The generated daily increment dataset has clear physical meaning and a unified spatiotemporal reference. The above solution further improves the efficiency and accuracy of data processing, provides high-quality input data for the accurate calculation of the subsequent feature diagnosis unit 30, and further enhances the reliability and practicality of the system.

[0053] As one possible embodiment of this application, in order to achieve accurate diagnosis of the internal structural features of the fill body, this application can further refine the feature diagnosis unit 30, combined with... Figure 1 ,like Figure 3 As shown, the feature diagnosis unit 30 includes: a total compaction work field construction module 31, a transfer path simulation module 32, a feature extraction module 33, and a state update module 34.

[0054] The total compaction field construction module 31 is used to add the historical cumulative compaction field to the compaction field in the daily incremental data set to construct the total compaction field for the day.

[0055] Among them, the total compaction field of the day is used to characterize the stiffness state of the fill body after including the impact of the construction on the day.

[0056] The stiffness state of the embankment is a core influencing factor in the transmission of compaction action, and this stiffness state is the cumulative result of all historical compaction actions and the current day's compaction action. This module integrates historical and current compaction work data to construct a total compaction work field that accurately reflects the current stiffness distribution of the embankment, providing a crucial foundation for subsequent simulations of action transmission paths.

[0057] For example, this application can call the historical cumulative compaction work field (representing the stiffness state up to the end of construction the previous day) and the daily compaction work field (representing the energy input of the day) in the daily incremental dataset, and obtain the total compaction work field of the day through the addition operation at the grid level, as shown in the following formula: in, For grid cells In date index The cumulative compaction energy, i.e., the total compaction work field of the day. For grid cells In date index Cumulative compaction energy (i.e., date index) Historical accumulation of compaction in grid cells (cumulative compaction energy) For grid cells In date index The compaction energy. In the total compaction energy field of the day, the greater the cumulative compaction energy of the grid cell, the stronger the compaction action experienced at the corresponding location, and the higher the relative stiffness of the soil.

[0058] The transfer path simulation module 32 is used to construct the mapping relationship between compaction energy input and compression deformation response based on the total compaction work field of the day and the optimal transfer algorithm, and generate the action transfer path field.

[0059] Among them, the action transmission path field is used to characterize the action transmission path of the virtual potential energy of the compaction energy input within the fill body.

[0060] Since the transmission path of construction action within a non-uniform fill body cannot be directly observed, traditional techniques struggle to establish the correlation between compaction and deformation response. However, this application transforms the engineering problem into a mathematical model through an optimal transmission algorithm, constructing a mapping relationship between energy sources and deformation response. This reveals a virtual potential energy transmission tendency, providing a core basis for revealing internal structural characteristics.

[0061] For example, this application takes the total compaction work field of the day as the core constraint, takes the compaction action corresponding to the compaction work field of the day as the source distribution, and the deformation response corresponding to the compression deformation field as the target distribution. By constructing the transfer cost constraint, the optimal transmission problem is solved to obtain the transfer scheme of the action from the source to the target, and then transforms it into a visualized action transfer path field. This path field can intuitively reflect the flow direction and distribution law of the action inside the fill body.

[0062] The feature extraction module 33 is used to extract internal structural feature data based on the action transmission path field and the total compaction work field of the day.

[0063] Among them, the action transmission path field reflects the dynamic process of virtual potential energy transmission, and the total compaction work field of the day reflects the static stiffness distribution of the fill body. The joint analysis of the two can further extract structural features that cannot be reflected by a single data field, thereby achieving accurate identification of hidden dangers inside the fill body.

[0064] For example, this application can extract structural features with clear engineering significance by analyzing the matching relationship between the direction and shape of the action transmission path field and the stiffness distribution in the total compaction work field on that day. For example, it can identify the action flow area caused by stiffness difference (high and low stiffness coupling area), quantify the degree of deviation of the action path caused by uneven stiffness (torsional degree of action transmission path), etc., to form structured internal structural feature data.

[0065] The status update module 34 is used to update the total compaction work area of ​​the day to the historical cumulative compaction work area of ​​the next day.

[0066] The status update module 34 can accumulate the stiffness state changes generated during the day's construction into historical data, ensuring that the diagnostic analysis for the next day is based on the latest fill state, thus achieving dynamic synchronization between the construction process and the analysis results. For example, after all feature diagnostic calculations for the day are completed, the status update module 34 can update the total compaction field generated by the total compaction field construction module 31 for the day. The accumulated work fields from history are stored in the database and recorded as follows: This serves as the input data for the feature diagnosis unit 30 for the next day, completing the iterative update of the state.

[0067] In some embodiments, the status update module 34 is further configured to initialize the historical cumulative compaction field to a zero value field before the first operation of the BIM and GIS-based full-process design auxiliary decision system 10.

[0068] It should be noted that no construction history data is generated during the initial system run. Without an initial state definition, there will be no valid data available for the historical cumulative compaction site, which will prevent the subsequent construction of the total compaction site and feature diagnosis from proceeding normally. Therefore, the state update module 34 can also provide basic data support for the initial system run by setting a reasonable initial state.

[0069] Based on the above technical solutions, this application can construct a complete process from stiffness state characterization, action path simulation, structural feature extraction to state update by modularizing and refining the feature diagnosis unit 30. The total compaction work field construction module 31 realizes the deep integration of historical construction influence and current construction data, ensuring the accuracy of stiffness state characterization. The transfer path simulation module 32 realizes the analysis of the internal action transfer path of the virtual potential energy of compaction energy input through the optimal transfer algorithm. The feature extraction module 33 realizes the accurate identification of structural defects through the joint analysis of the action transfer path field and the total compaction work field of the day. The state update module 34 ensures the dynamic iteration capability of the system. The above solutions can effectively identify systemic defects such as soil arching effect that cannot be detected by traditional technologies, further improving the core analysis capability and engineering application value of the system.

[0070] As one possible embodiment of this application, to improve the accuracy of action transmission path simulation, this application can further refine the transmission path simulation module 32, combining... Figure 3 ,like Figure 4 As shown, the transmission path simulation module 32 includes: a cost matrix construction submodule 321, an optimal transmission calculation submodule 322, and a path field generation submodule 323.

[0071] The cost matrix construction submodule 321 is used to construct the action transfer cost matrix based on the total compaction field of the day.

[0072] Among them, the transfer cost value between any two grid cells in the action transfer cost matrix is ​​positively correlated with the geometric distance between the two grid cells and the average stiffness characterization value on the path between the two grid cells determined based on the total compaction work field of the day.

[0073] When the cost is transferred within the fill material, it is hindered by both geometric distance and soil stiffness. Traditional solutions struggle to account for the transfer characteristics of non-uniform media. This cost matrix construction submodule 321 constructs a non-uniform transfer cost matrix by coupling geometric distance and soil stiffness, making the cost constraints more aligned with engineering realities.

[0074] For example, this application uses the total compaction field of the day as input to construct... The role of the transfer cost matrix ( To analyze the total number of raster cells in the reference raster, (For raster cell indexes). Action transfer cost matrix. any element The representative will transfer the unit action from the grid cell. Passed to The effective cost satisfies the following formula: in, Date index Time unit action from grid cell Passed to Effective cost, For grid cells and grid cells The Euclidean distance between the center points. Date index Time indicates connection of grid cells and The average stiffness along the path, for example, can be determined using the Bresenham straight line algorithm to determine the connecting grid cells. and The discrete grid path at the center point is used to extract the total compaction work of all grid cells on the discrete grid path on that day. The cumulative compaction energy in the sample is calculated, and its arithmetic mean is used to obtain the average stiffness. For example, the stiffness influence weighting coefficients are... The value can be calibrated in the engineering test section by burying objects with known stiffness differences. The value is usually in the range of 0.1-0.5 to ensure that the impact of stiffness factor on cost is within a reasonable range. Normalization functions (such as maximum and minimum value normalization) are used to normalize the calculation results to a range between 0 and 1.

[0075] This reflects the impact of geometric distance on transmission difficulty; the greater the distance, the higher the cost. It is a stiffness correction term. A larger value indicates higher soil stiffness along the path, greater resistance transmission, a larger correction term, and a higher cost. Multiplying these two values ​​couples the geometric distance and stiffness, thus improving the effective cost. It can accurately reflect the transmission resistance in non-uniform media.

[0076] The optimal transmission calculation submodule 322 is used to perform optimal transmission calculation with the first probability distribution obtained after normalizing the compaction work field in the daily incremental dataset as the source distribution and the second probability distribution obtained after normalizing the compression deformation field as the target distribution, and with the action transmission cost matrix as the constraint, to obtain the action transmission scheme matrix.

[0077] The action transfer scheme matrix describes the action transfer scheme from the source distribution to the target distribution.

[0078] The optimal transport algorithm is used to find the lowest-cost transport scheme from the source distribution to the target distribution under a given cost constraint. This application transforms the compaction action into a source distribution and the deformation response into a target distribution, and combines this with a non-uniform cost matrix to ensure that the optimal transport solution reflects the actual transport relationship from compaction action to deformation response, providing a mathematical basis for simulating the transport path. Since the input to the optimal transport algorithm is two discrete probability distributions that sum to 1, this application requires normalization of the compaction work field and the compression deformation field.

[0079] For example, in the first probability distribution, the grid cell The probability value at a given location satisfies the following formula: in, Date index Grid cells in the first probability distribution The probability value at that location. For grid cells In date index The compaction energy, For all grid cells In date index The sum of the compaction energy. This is a compaction work noise field, where all elements are extremely small positive numbers, such as... Its value is much smaller than the typical value of normal compaction work, so as to avoid significantly affecting the original distribution.

[0080] In the second probability distribution, the grid cell The probability value at a given location satisfies the following formula: in, Date index Grid cells in the second probability distribution The probability value at that location. For grid cells In date index The absolute value of the compressive deformation, For all grid cells In date index The sum of the absolute values ​​of the compressive deformation. This is a compressible deformation noise field, where each element is a very small positive number, such as... Its value is much smaller than the typical value of normal compressive deformation to avoid significantly affecting the original distribution. The above formula applies a non-negative correction to the amount of compressive deformation to ensure that the amount of compressive deformation of all grid cells is greater than or equal to zero.

[0081] It should be noted that, regarding the above or When the value is 0, it indicates that there was no compaction work or no effective deformation on that day. In this case, the first probability distribution can be defined. Or the second probability distribution All elements in the array are 0 or invalid values ​​to avoid meaningless calculations.

[0082] For example, this application may invoke the Sinkhorn algorithm based on entropy regularization, with a first probability distribution. Source distribution, second probability distribution Target distribution and action transfer cost matrix Given the cost matrix, solve the optimal transport problem to obtain... The role of the transmission scheme matrix , action transmission scheme matrix Matrix elements Characterization from grid cells Passed to grid cell The percentage of the effect.

[0083] The path field generation submodule 323 is used to calculate the weighted average flow direction of the action of each grid cell based on the action transmission scheme matrix, and generate the action transmission path field.

[0084] It should be noted that the action transmission scheme matrix is ​​high-dimensional discrete data, which is difficult to intuitively reflect the spatial distribution of the action transmission path. This application can make the action transmission path visualized and analyzable by converting the data of the action transmission scheme matrix into a vector field of action transmission path field.

[0085] For example, this application can, for each grid cell, use that grid cell as the starting grid cell and determine the ending grid cell based on the action transfer scheme matrix, thereby generating an action transfer path field. The position corresponding to the ending grid cell satisfies the following formula: in, For grid units The position of the endpoint grid cell corresponding to the action propagation path obtained from the starting grid cell. Date index In the time-acting transfer scheme matrix from the grid cell Passed to grid cell The proportion of the effect, For grid cells The corresponding position can be represented by a grid cell. The location of the center point is indicated. For a very small positive number, such as This is used to avoid the denominator being zero. In this way, this application can determine the corresponding position of the endpoint grid cell in the action propagation path obtained with each grid cell as the starting grid cell. Then, for each set of starting and ending grid cells, based on the action propagation cost matrix... The minimum cost path corresponding to each set of starting and ending grid cells is calculated using a shortest path search algorithm (such as A* algorithm or Dijkstra algorithm) and used as the action transmission path, ultimately forming an action transmission path field covering the entire field.

[0086] Based on the above technical solutions, this application achieves accurate calculation of action transmission path simulation by refining the transmission path simulation module 32. The cost matrix construction submodule 321 couples geometric distance with soil stiffness, solving the problem that the traditional uniform cost model does not match the actual non-uniform medium. The optimal transmission calculation submodule 322 transforms the engineering problem into a standard mathematical problem through normalization processing, and the optimal transmission algorithm ensures the feasibility of the calculation. The path field generation submodule 323 transforms the discrete matrix into a visual vector field. The above technical solutions can accurately reflect the law of action transmission in non-uniform fill, providing core technical support for identifying internal structural defects, and further improving the system's analytical accuracy and engineering value.

[0087] In addition, the internal structural feature data in this application mainly includes two dimensions: stiffness coupling features and path tortuosity features. The feature extraction of the above two dimensions will be further explained below.

[0088] As one possible embodiment of this application, the internal structural feature data includes grid data for identifying high and low stiffness coupling regions.

[0089] The feature extraction module 33 is specifically used to: identify high-stiffness regions and low-stiffness regions from the total compaction field of the day, and mark the regions based on the path direction of the action transmission path field in the low-stiffness region, and generate raster data for identifying high- and low-stiffness coupling regions.

[0090] The high-stiffness region is defined as the region within the total daily compaction energy field that exceeds a first preset energy threshold, while the low-stiffness region is defined as the region within the total daily compaction energy field that falls below a second preset energy threshold. The first preset energy threshold is greater than or equal to the second preset energy threshold.

[0091] The setting of the first and second preset energy thresholds should be combined with the actual engineering and construction specifications. For example, they can be determined by statistical analysis of test section data: collect compaction work data of different compaction quality areas of the test section, set the minimum compaction work corresponding to meeting the design compaction requirements (such as compaction degree ≥ 95%) as the first preset energy threshold, and set the maximum compaction work corresponding to not meeting the minimum compaction standard (such as compaction degree < 90%) as the second preset energy threshold.

[0092] It should be noted that the high-low stiffness coupling region corresponds to the area where the soil arching effect occurs in engineering. Its core characteristic is that the effects of the low-stiffness region flow around to the surrounding high-stiffness region. The above scheme can identify this flow-around characteristic by analyzing the direction of action transmission within the low-stiffness region, thus achieving accurate labeling of the coupling region.

[0093] For example, for each identified low-stiffness region, this application can analyze the vector direction of the action transmission path field within it. If the low-stiffness region is surrounded by a high-stiffness region, and the vectors within the low-stiffness region exhibit a divergence pattern from the center to the surrounding high-stiffness regions (i.e., the action is transmitted from the low-stiffness region to the surrounding high-stiffness regions), then it is determined that the low-stiffness region and the surrounding high-stiffness region together constitute a high-low stiffness coupling region. This application can mark the high-low stiffness coupling region using a binary value raster mask. For example, if the raster cells within the coupling region are assigned a value of 1, and the remaining raster cells are assigned a value of 0, the raster mask is the raster data identifying the high-low stiffness coupling region.

[0094] As another possible embodiment of this application, the internal structural feature data also includes action transmission path tortuosity data.

[0095] The feature extraction module 33 is specifically used to: calculate the ratio of the length of the action transmission path to the straight-line distance between the starting point and the ending point of the action transmission path for each action transmission path field, and use this ratio as the tortuosity of the action transmission path of the starting grid cell of the action transmission path, and generate action transmission path tortuosity data based on the tortuosity of the action transmission path of the starting grid cell of each action transmission path.

[0096] The tortuosity of the action transmission path directly reflects the influence of soil stiffness inhomogeneity on the transmission process. Greater tortuosity indicates a longer path for the action to bypass high-stiffness obstacles, and a more significant structural inhomogeneity. When the stiffness inside the fill is uniform, the action is usually transmitted in a straight line, and the length of the action transmission path is relatively close to the straight-line distance between its start and end points. The tortuosity of the action transmission path is close to 1. When high-stiffness obstacles exist, the action is transmitted around them, increasing the length of the action transmission path and the tortuosity. Furthermore, the greater the deviation from the path, the greater the tortuosity of the action transmission path.

[0097] As one possible embodiment of this application, combined with Figure 1 ,like Figure 5 As shown, the risk assessment unit 40 includes: a cumulative calculation module 41.

[0098] The cumulative calculation module 41 is used to perform weighted cumulative calculation based on the daily internal structural feature data of the fill body to generate a structural defect cumulative index field.

[0099] The structural defect accumulation index field is used to characterize the degree of long-term settlement risk at different spatial locations of the fill. Long-term settlement risk is the result of the continuous accumulation of structural defects during construction. The structural characteristics of a single day cannot reflect the cumulative effect of the risk. Therefore, this application can convert instantaneous defects into quantitative indicators of long-term risk by weighting and accumulating the structural characteristic data of each day, thereby achieving accurate location and degree assessment of the risk.

[0100] In some embodiments, based on the internal structural feature data including grid data for identifying high and low stiffness coupling regions and action transmission path tortuosity data, the cumulative calculation module 41 is specifically used to: for each grid cell, perform weighted cumulative calculation based on the cumulative number of times the grid cell is marked as a high and low stiffness coupling region in the daily grid data for identifying high and low stiffness coupling regions and the tortuosity of the action transmission path of the grid cell in the daily, and generate a structural defect cumulative index field based on the calculation results of each grid cell.

[0101] For example, the weighted cumulative calculation satisfies the following formula: in, Date index Time grid cell The calculation results Used to indicate grid units In date index Whether it is located in the high-low stiffness coupling region, Represents grid cells Index up to date The cumulative number of times a raster data used to identify high- and low-stiffness coupling regions is marked as such each day. For grid cells In date index The effect of time on the tortuosity of the transmission path These are weighting coefficients used to adjust the contribution of cumulative coupling region and cumulative tortuosity to the calculation results. They can be set by engineering designers based on experience or calibration results from test sections. For example, since the high and low stiffness coupling region (soil arching effect) has a greater impact on long-term settlement, a weighting coefficient can be set. It is 0.6. The value is 0.4 (the sum of the weighting coefficients is 1 to ensure the normalization property of the exponent). This is a scaling factor, which can be determined through data statistics. It is used to make the typical numerical ranges of the two summed terms similar, for example, it can be 10.

[0102] The more times the accumulation occurs, the more likely the grid cell location will repeatedly exhibit soil arching effect defects, and the greater the contribution index value. The greater the cumulative effect of tortuosity, the more likely the grid cell location will have poor structural uniformity over a long period, and the greater the contribution index value. The total cumulative index is obtained by weighted summation of the two parts, which comprehensively reflects the long-term cumulative effect of the two types of defects. The larger the value, the more severe the structural defects accumulated in the location of the grid cell during the construction history, and the higher the risk of long-term uneven settlement.

[0103] Furthermore, this application can also achieve intuitive presentation of risk assessment results and trace the causes through visualization, thereby improving the system's decision-making practicality.

[0104] As one possible embodiment of this application, combined with Figure 5 ,like Figure 6 As shown, the risk assessment unit 40 also includes a visualization and traceability module 42.

[0105] The visualization and traceability module 42 is used to integrate and display the structural defect cumulative index field in the form of a heat map with the three-dimensional building information model.

[0106] For example, the visualization and traceability module 42 can render the latest structural defect cumulative index field generated by the cumulative calculation module 41 onto the surface of the 3D BIM model in the form of a heat map. A color gradient is used to represent the index magnitude; for example, the color range is set from blue (low index, low risk) to red (high index, high risk), with higher indices resulting in more prominent colors. Simultaneously, the BIM model is linked to GIS geographic information, overlaying the geographical coordinates of the engineering area, construction zones, road red lines, and other information onto the model to achieve precise geographic location of risk areas.

[0107] Furthermore, the visualization and traceability module 42 also provides interactive query functionality. When a manager clicks on any high-risk grid cell in the 3D BIM model, the system automatically pops up an information window displaying the cell's "defect evolution history graph." The horizontal axis represents the construction date, and the vertical axis is divided into two parts: one part uses a bar chart to show whether the cell is marked as a high-low stiffness coupling area each day (a bar height of 1 indicates yes, 0 indicates no); the other part uses a line graph to show the daily deviation of the action transmission path tortuosity for the cell. Through this graph, managers can intuitively view key information such as when defects in high-risk areas began to appear, whether they recurred, and the trend of tortuosity deviation.

[0108] Furthermore, the visualization and traceability module 42 can also automatically query the construction management database based on the key dates and locations obtained from defect history tracing, and extract construction process information for the corresponding time period and area, including construction teams, batches of fillers used, compaction equipment numbers, and daily construction process parameters (such as the number of compaction passes and driving speed), providing a complete data chain for analyzing the causes of defects and defining quality responsibilities.

[0109] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0110] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A full-process design support decision-making system based on BIM and GIS, characterized in that, include: The data acquisition unit is used to acquire construction data and generate a daily incremental dataset based on the construction data. The construction data includes daily topographic data and compaction equipment process data during the construction process. The daily incremental dataset includes a new load field, a compaction work field, and a compression deformation field. The new load field is used to characterize the load distribution generated by the self-weight of the newly added fill on the day. The compaction work field is used to characterize the energy distribution of the compaction construction input on the day. The compression deformation field is used to characterize the compression amount distribution generated under compaction on the day. The feature diagnosis unit is used to extract the internal structural feature data of the fill body based on the historical cumulative compaction field and the daily incremental dataset; the historical cumulative compaction field is determined based on the daily compaction field accumulation within the historical period and is used to characterize the state of the fill body up to the end of construction on the previous day. The risk assessment unit is used to conduct risk assessments based on daily data on the internal structural characteristics of the fill.

2. The full-process design support decision-making system based on BIM and GIS according to claim 1, characterized in that, The data acquisition unit includes: The spatial alignment module is used to spatially align the terrain data and compaction equipment process data in the construction data. The load field determination module is used to perform incremental calculations based on the topographic data of two consecutive days after spatial alignment to determine the newly added load field; The compaction work field determination module is used to extract compaction energy based on the spatially aligned compaction equipment process data to determine the compaction work field. The compression deformation field determination module is used to determine the compression deformation field based on topographic data from two consecutive days after spatial alignment and the designed loose paving thickness.

3. The full-process design support decision-making system based on BIM and GIS according to claim 1, characterized in that, The feature diagnosis unit includes: The total compaction field construction module is used to add the historical cumulative compaction field to the compaction field in the daily incremental dataset to construct the total compaction field for the day; the total compaction field for the day is used to characterize the stiffness state of the fill body after including the impact of construction on the day. The transfer path simulation module is used to construct a mapping relationship between compaction energy input and compression deformation response based on the total compaction work field of the day through an optimal transfer algorithm, and generate an action transfer path field; the action transfer path field is used to characterize the action transfer path of the virtual potential energy of compaction energy input inside the fill body; The feature extraction module is used to extract the internal structural feature data based on the action transmission path field and the total compaction work field of the day; The status update module is used to update the total compaction work area of ​​the day to the historical cumulative compaction work area of ​​the next day.

4. The full-process design support decision-making system based on BIM and GIS according to claim 3, characterized in that, The status update module is also used for: Before the first operation of the BIM and GIS-based full-process design auxiliary decision-making system, the historical cumulative compaction field is initialized to a zero value field.

5. The full-process design support decision-making system based on BIM and GIS according to claim 3, characterized in that, The transmission path simulation module includes: The cost matrix construction submodule is used to construct an action transfer cost matrix based on the total compaction field of the day; the transfer cost value between any two grid cells in the action transfer cost matrix is ​​positively correlated with the geometric distance between the two grid cells and the average stiffness characterization value on the path between the two grid cells determined based on the total compaction field of the day. The optimal transmission calculation submodule is used to perform optimal transmission calculation with the first probability distribution obtained after normalizing the compaction work field in the daily incremental dataset as the source distribution and the second probability distribution obtained after normalizing the compression deformation field as the target distribution, and with the action transmission cost matrix as the constraint, to obtain the action transmission scheme matrix; the action transmission scheme matrix is ​​used to describe the action transmission scheme from the source distribution to the target distribution. The path field generation submodule is used to calculate the weighted average flow direction of the action of each grid cell based on the action transmission scheme matrix, and generate the action transmission path field.

6. The full-process design support decision-making system based on BIM and GIS according to claim 3, characterized in that, The internal structural feature data includes grid data used to identify high and low stiffness coupling regions; the feature extraction module is specifically used for: Identify high-stiffness regions and low-stiffness regions from the total compaction field of the day; the high-stiffness regions are those regions in the total compaction field of the day that are greater than a first preset energy threshold, and the low-stiffness regions are those regions in the total compaction field of the day that are less than a second preset energy threshold. The first preset energy threshold is greater than or equal to the second preset energy threshold; Based on the path direction of the action transmission path field within the low stiffness region, region marking is performed to generate raster data for identifying high and low stiffness coupling regions.

7. The full-process design support decision-making system based on BIM and GIS according to claim 6, characterized in that, The internal structural feature data also includes data on the tortuosity of the action transmission path; the feature extraction module is specifically used for: For each action transmission path in the action transmission path field, the ratio of the length of the action transmission path to the straight-line distance between the start and end points of the action transmission path is calculated, and used as the action transmission path tortuosity of the starting grid cell of the action transmission path. The tortuosity data of the action transmission path is generated based on the tortuosity of the starting grid cell of each action transmission path.

8. The full-process design support decision-making system based on BIM and GIS according to claim 7, characterized in that, The risk assessment unit includes: The cumulative calculation module is used to perform weighted cumulative calculations based on the daily internal structural feature data of the fill body to generate a structural defect cumulative index field; the structural defect cumulative index field is used to characterize the degree of long-term settlement risk at different spatial locations of the fill body.

9. The full-process design support decision-making system based on BIM and GIS according to claim 8, characterized in that, The cumulative calculation module is specifically used for: For each grid cell, a weighted cumulative calculation is performed based on the cumulative number of times the grid cell is marked as a high-low stiffness coupling region in the daily grid data used to identify high-low stiffness coupling regions and the tortuosity of the action transmission path of the grid cell in the daily, and the structural defect cumulative index field is generated based on the calculation results of each grid cell.

10. The full-process design support decision-making system based on BIM and GIS according to claim 8, characterized in that, The risk assessment unit also includes: The visualization and traceability module is used to integrate and display the structural defect cumulative index field in the form of a heat map with the three-dimensional building information model.

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