A BIM-based construction method

Abstract

The application discloses a BIM-based construction method, and relates to computer technology: obtaining hot-state geometric point cloud data and surface temperature data of each point; calculating the shrink displacement amount of each point in the hot-state geometric point cloud data from the hot state to the cold state, and performing reverse temperature compensation on the hot-state geometric point cloud data by using the shrink displacement amount; establishing a cold-state BIM model according to cold-state reference point cloud data; obtaining structural deviation reflecting settlement, displacement and deformation of a target heat pipe; taking the structural deviation as an initial geometric condition, taking surface temperature data of the heat pipe as thermal load, and establishing a heat analysis model reflecting boundary constraints; calculating stress distribution of the target heat pipe according to the heat analysis model, and identifying a dangerous area with stress exceeding a preset threshold. The application restores the hot-state data to the cold-state reference and the like based on reverse temperature compensation according to the thermal expansion theory, and avoids misjudging normal thermal expansion as a structural defect.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a BIM-based construction method. Background Technology

[0002] As the main artery of the heating system, heating pipelines play a crucial role in heat energy transmission. However, operating under high temperature and high pressure conditions (supply water temperature 110-130°C, pressure 1.0-1.6MPa) for extended periods, coupled with factors such as geological subsidence, temperature cycling, and corrosion aging, various structural defects inevitably arise, including pipeline settlement, displacement, deformation, and stress concentration. In severe cases, these defects can lead to pipeline leaks or even ruptures, causing significant economic losses and safety accidents. Therefore, accurately detecting the true geometric state and stress distribution of pipelines during operation, and promptly identifying and addressing potential safety hazards, has become a major technical challenge that urgently needs to be addressed in the construction and maintenance of heating pipelines.

[0003] Traditional methods for inspecting and maintaining heating pipelines mainly rely on periodic shutdowns for inspection, with maintenance plans formulated based on manual measurements and experience. This method has several drawbacks: first, it necessitates shutdowns, each resulting in economic losses ranging from hundreds of thousands to millions of yuan, and disrupting normal heating for residents; second, the inspection cycle is long (usually 2-3 days), leading to low efficiency; third, manual measurements have poor accuracy (errors can reach ±50mm) and are heavily influenced by experience; and fourth, it is difficult to obtain information about the internal stress state of the pipeline, resulting in a lack of scientific basis for maintenance decisions.

[0004] In recent years, the rapid development of drone technology, laser scanning technology, infrared thermal imaging technology, and Building Information Modeling (BIM) technology has provided new technical means for the intelligent inspection and maintenance of heating pipelines. In particular, drones equipped with laser scanners and infrared thermal imagers can quickly acquire three-dimensional geometric data and surface temperature data of pipelines without interrupting operation, providing a rich data foundation for pipeline condition assessment. However, effectively applying these new technologies to the inspection and maintenance of heating pipelines still faces many technical challenges. Summary of the Invention

[0005] To address the problems in existing technologies where scanning data of operating thermal pipelines cannot be directly compared with cold-state design models due to the presence of thermal expansion, leading to the misjudgment of normal thermal expansion as structural defects and the masking of actual deviations, this application provides a BIM-based construction method. This method uses reverse temperature compensation based on thermal expansion theory to restore the thermal data to a cold-state baseline, thus avoiding the misjudgment of normal thermal expansion as structural defects.

[0006] This application provides a BIM-based construction method, including: S1, using a drone equipped with an infrared thermal imager to scan the operating target thermal pipeline, simultaneously acquiring thermal geometric point cloud data and surface temperature data of each point; S2, based on the temperature difference between the surface temperature data and the cold reference temperature, and combined with the linear expansion coefficient of the target thermal pipeline material, calculating the shrinkage displacement of each point in the thermal geometric point cloud data from the hot state to the cold state, and using the shrinkage displacement to perform reverse temperature compensation on the thermal geometric point cloud data to generate cold reference point cloud data that excludes the influence of thermal expansion; S3, based on the cold state... A cold-state BIM model is established using benchmark point cloud data. This cold-state BIM model is then overlaid and compared with the BIM design model of the target thermal pipeline to obtain structural deviations reflecting the settlement, displacement, and deformation of the target thermal pipeline. In step S4, using these structural deviations as initial geometric conditions, the surface temperature data of the thermal pipeline is obtained as a thermal load to establish a thermal analysis model reflecting boundary constraints. In step S5, the stress distribution of the target thermal pipeline is calculated based on the thermal analysis model, and hazardous areas where stress exceeds a preset threshold are identified. Based on the stress in these hazardous areas, the cold-state BIM model is marked, and a construction plan is generated.

[0007] The "hot state" refers to the state of a thermal pipeline under normal operating conditions. In this state, a high-temperature medium (steam or hot water) flows through the pipeline, and the pipeline expands due to heat, causing its geometric dimensions and spatial position to change compared to its initial state during installation. In the hot state, the pipeline length increases, its diameter expands, and it may undergo bending deformation under the constraints of supports.

[0008] The cold state refers to the state of a thermal pipeline at ambient temperature, before it is put into operation or after it has been cooled down. At this time, there is no high-temperature medium in the pipeline, the pipeline temperature is close to the ambient temperature, and there is no thermal expansion caused by temperature. The cold state is the benchmark state for pipeline design, installation, and inspection. Design drawings and BIM models usually reflect the cold-state geometry of the pipeline.

[0009] Surface temperature data refers to the temperature values ​​at various points on the outer surface of the pipe insulation layer, measured by an infrared thermal imager mounted on a drone. In this solution, these temperature data points correspond one-to-one with the spatial coordinates of the point cloud data, forming a point cloud dataset with temperature attributes. Due to the presence of the insulation layer, the surface temperature is typically lower than the pipe body temperature.

[0010] Cold reference temperature This refers to the reference temperature of the pipeline in a cold state, usually taken as the ambient temperature or the installation temperature specified in the design (such as 20℃). This is the benchmark value for calculating temperature difference and thermal expansion.

[0011] Reverse temperature compensation refers to calculating the displacement of each point in the hot point cloud data due to thermal expansion based on the measured surface temperature and the material's thermal expansion characteristics. This displacement is then subtracted from the hot coordinates to reconstruct the theoretical position of the point in the cold state. This process includes: calculating the axial contraction displacement. ; Calculate the radial contraction displacement: Subtracting the corresponding displacement from the hot coordinates yields the cold coordinates. Through reverse temperature compensation, the hot data collected during operation can be restored to cold data, allowing for accurate comparison with the cold design model and identification of the actual structural deviations.

[0012] Further, S2, generating cold-state baseline point cloud data that excludes the influence of thermal expansion includes: spatially matching surface temperature data with hot-state geometric point cloud data, associating temperature attributes with each point to form a point cloud dataset with temperature attributes; and calculating the temperature difference of each point in the point cloud dataset. ,in, For the temperature at the corresponding point, The cold reference temperature is used; the fixed support positions of the target thermal pipeline are identified, and a local coordinate system for the pipeline is established with the fixed support positions as the origin; the pipeline centerline is extracted based on the point cloud dataset; the cumulative arc length distance from each point in the point cloud dataset along the pipeline centerline to the fixed support position is calculated. Based on the linear expansion coefficient of the target thermal pipeline material Temperature difference and cumulative distance Calculate the cumulative axial shrinkage displacement at the corresponding point. According to temperature difference Coefficient of linear expansion and the outer diameter of the target heat pipe Calculate the radial contraction displacement at the corresponding point. Based on the cumulative axial shrinkage displacement and radial contraction displacement Correct the coordinates of each point in the point cloud dataset to obtain temperature-compensated coordinate data; transform the temperature-compensated coordinate data from the local coordinate system back to the global coordinate system to generate cold-state reference point cloud data.

[0013] Calculate the cumulative axial shrinkage displacement at the corresponding point. The formula is as follows: ;

[0014] Calculate the radial contraction displacement at the corresponding point. The formula is as follows: .

[0015] Specifically, on the one hand, existing technologies acquire thermal geometric data of pipelines under operating conditions, while design models describe the cold geometric state of pipelines during construction and installation. The geometric dimensions of the pipeline differ fundamentally between these two physical states. During operation, the medium temperature in a thermal pipeline can reach 100 to 300 degrees Celsius, differing from the cold reference temperature (typically 15 to 20 degrees Celsius) by tens to hundreds of degrees Celsius, inevitably resulting in significant thermal expansion.

[0016] On the other hand, when actual structural deviations (such as foundation settlement and support displacement) are superimposed with the thermal expansion effect, the two may cancel each other out or enhance each other, causing the actual deviation to be masked or exaggerated. For example, there may be a settlement defect of three centimeters in a certain location, but thermal expansion produces a lifting effect of two centimeters in that direction. The comparison result only shows a deviation of one centimeter, and the real problem is underestimated by two-thirds.

[0017] Therefore, this application uses physical modeling to restore thermal data to cold data, achieving a unified physical state. Specifically, through deep fusion of infrared thermal imaging data and laser point cloud data, each point in the point cloud acquires a temperature attribute. When the thermal expansion effect is correctly stripped away, the true structural deviations (such as foundation settlement, support displacement, and construction errors) are accurately revealed. Defects previously masked or obscured by thermal expansion can now be precisely quantified and located, significantly reducing the false negative rate.

[0018] Furthermore, the cumulative arc length distance from each point in the point cloud dataset along the pipe centerline to the fixed support location is calculated. The process includes: dividing the point cloud dataset into slices along the centerline of the target thermal pipeline; performing cylindrical surface fitting on the point cloud data within each slice to extract the center coordinates of the corresponding slice; connecting the center coordinates of all slices sequentially to form a discrete point sequence for the pipeline centerline; and performing B-spline curve fitting on the discrete point sequence to obtain the parameterized curve of the pipeline centerline. t∈[0,1], where t=0 corresponds to the fixed support position and t=1 corresponds to the pipe end position; for each point in the point cloud dataset Calculate the corresponding point to the parameterized curve The nearest projection point, whose parameter value on the parametric curve is denoted as . Among them, parameter values To make the point To parameterized curve The parameter value that minimizes the Euclidean distance represents the normalized relative position of the nearest projection point from the fixed support position to the pipe end position; this is achieved by analyzing the parameterized curve. In the parameter range Perform numerical integration to calculate the arc length. , , as the cumulative arc length distance of the corresponding point along the pipeline centerline to the fixed support position .

[0019] Among them, the parametric curve is a mathematical expression of the pipeline centerline obtained by fitting a B-spline curve. represents the corresponding three-dimensional space coordinates when the parameter is t . The complex three-dimensional pipeline centerline is described by a single parameter t. t = 0 represents the fixed support position (starting point), t = 1 represents the pipeline end (ending point), and 0 < t < 1 represents the intermediate position.

[0020] The nearest projection point refers to a point in the point cloud dataset on the parametric curve of the pipeline centerline The vertical projection point. By finding the nearest projection point, it is possible to determine which axial position of the pipeline the surface point belongs to, and further calculate its distance to the fixed support.

[0021] The parameter value is the parameter value corresponding to the nearest projection point of the i-th point in the point cloud on the parametric curve . represents the normalized relative position of the point along the pipeline centerline: , is near the fixed support; , is near the 30% position from the fixed support to the end; , is near the middle section of the pipeline; , is near the pipeline end.

[0022] The cumulative arc length distance refers to the actual curve length of the nearest projection point corresponding to the i-th point in the point cloud along the pipeline centerline from the fixed support position s(0) to this point.

[0023] In particular, the axial thermal expansion of the thermal pipeline is not an isolated local phenomenon, but a systematic effect that accumulates and superimposes along the entire length of the pipeline. When the pipeline is heated, each tiny segment expands, and the expansion amounts of these tiny segments accumulate gradually along the pipeline direction starting from the fixed support.

[0024] ​This application achieves precise quantification of the cumulative effect of thermal expansion through a three-level refinement strategy of slicing, fitting, and parameterization, and an arc-length integral method. First, the pipe is sliced ​​along its centerline and fitted with cylindrical surfaces to extract discrete centerline coordinate sequences. Second, B-spline curves are used to transform the discrete point sequence into a continuous parameterized curve. t∈[0,1], where t=0 corresponds to the expansion reference point of the fixed support; furthermore, for each point in the point cloud, calculate its nearest projection point to the parameterized curve and the corresponding parameter value. Finally, through numerical integration The cumulative arc length from the point to the fixed support along the actual path of the pipe is accurately calculated. The physical accumulation process of thermal expansion is transformed into a mathematical arc length integral, which fully describes the contribution of the entire path from the fixed support to any point to the expansion.

[0025] Furthermore, S3, obtain the structural deviations reflecting the settlement, displacement, and deformation of the target thermal pipeline, including: geometrically reconstructing the cold-state benchmark point cloud data, extracting pipeline geometric parameters, and establishing a cold-state BIM model; spatially registering the cold-state BIM model with the BIM design model of the target thermal pipeline; setting multiple feature sections along the pipeline centerline, calculating the center point coordinate deviation between the cold-state BIM model and the BIM design model at each feature section; decomposing the center point deviation into axial deviation, radial deviation, and settlement deviation to generate structural deviation data;

[0026] Furthermore, in S4, structural deviations are used as initial geometric conditions, and surface temperature data of the thermal pipeline is obtained as thermal load to establish a thermal analysis model reflecting boundary constraints. This includes: creating an initial geometric model for thermal analysis based on a cold-state BIM model; importing structural deviation data into the initial geometric model; applying axial deviation, radial deviation, and settlement deviation at characteristic section locations to generate a deformable geometric model containing the actual deformation state; and obtaining the internal medium temperature of the target thermal pipeline. and surface temperature data And calculate the temperature distribution along the pipe wall thickness. The temperature distribution along the pipe wall thickness is mapped onto a deformable geometric model. Multiple layers of elements are defined along the wall thickness, with each layer assigned a corresponding temperature value, forming a three-dimensional temperature field distribution. Boundary constraints are set according to the operating conditions of the target thermal pipeline. Thermal analysis parameters are set based on the material properties of the target thermal pipeline, including elastic modulus, Poisson's ratio, coefficient of linear expansion, and thermal conductivity. The deformable geometric model is meshed to generate a finite element mesh model, with at least three layers of elements along the wall thickness. A thermal analysis model is then established based on the finite element mesh model, the three-dimensional temperature field distribution, the boundary constraints, and the thermal analysis parameters.

[0027] Furthermore, the temperature distribution along the pipe wall thickness direction is calculated. The formula is as follows: Where r is the radial coordinate of the pipe, Let the inner radius be , The outer radius;

[0028] Furthermore, the boundary constraints include: applying fixed constraints at the fixed support location; applying axial freedom constraints at the sliding support location; and applying radial constraints at the guide support location.

[0029] Furthermore, based on the finite element mesh model, three-dimensional temperature field distribution, boundary constraints, and thermal analysis parameters, a thermal analysis model is established, including: applying the temperature values ​​in the three-dimensional temperature field distribution as nodal loads to each node of the finite element mesh model, and calculating the thermal strain of each element. ,in, Let be the coefficient of linear expansion, ΔT be the temperature change, and I be the unit tensor. Based on the boundary constraints, the degrees of freedom of the constrained nodes are set in the finite element mesh model: nodes at fixed supports are fully constrained with three translational and three rotational degrees of freedom; nodes at sliding supports are freed of axial translational degrees of freedom; and nodes at guide supports are freed of both axial translational and rotational degrees of freedom. Based on the thermodynamic analysis parameters, the element stiffness matrix is ​​established. The formula for calculating the element stiffness matrix is: ,in, The strain matrix, The elasticity matrix is ​​determined based on the elastic modulus and Poisson's ratio. The stiffness matrices of each element are assembled into a global stiffness matrix K, and the nodal forces generated by thermal strain and the constraint reactions generated by boundary constraints are assembled into a global load vector F. The thermoelasticity governing equations are then established. ,in, The thermoelasticity control equations, which serve as the nodal displacement vectors, constitute a thermodynamic analysis model.

[0030] Furthermore, S5 calculates the stress distribution of the target thermal pipeline based on the thermal analysis model, identifies dangerous areas where the stress exceeds a preset threshold, and marks the dangerous areas in the cold-state BIM model and generates a construction plan based on the stress in the dangerous areas. This includes: solving the thermoelasticity control equations to obtain the nodal displacement vectors; calculating the strain and stress of each element based on the nodal displacement vectors; calculating the equivalent stress of each element and identifying dangerous areas where the equivalent stress exceeds a preset threshold; marking the location and stress information of the dangerous areas in the cold-state BIM model; and generating a corresponding construction plan based on the stress state of the dangerous areas.

[0031] Compared to existing technologies, the advantages of this application are:

[0032] To address the problems in existing technologies where thermal pipelines must be shut down for geometric inspection, or where thermal geometric data obtained during operation cannot be directly compared with the cold-state design model due to thermal expansion, leading to the misjudgment of normal thermal expansion as structural defects, the masking of actual settlement and deformation by thermal expansion data, and consequently, inaccurate construction decisions and wasted maintenance costs, this application provides a BIM-based construction method. This innovative method uses UAV infrared thermal imaging to simultaneously acquire thermal geometric point clouds and surface temperatures at each point. Based on thermal expansion theory and fixed support constraints, it calculates axial cumulative shrinkage displacement and radial shrinkage displacement, and performs reverse temperature compensation on the thermal point cloud to restore it to a cold-state reference point cloud. This method systematically solves for the first time the problem of unifying the comparison benchmark between thermal scanning data and the cold-state design model, enabling accurate identification of actual structural deviations (settlement, displacement, deformation) without shutting down the pipeline. It avoids misjudging normal thermal expansion as structural defects. By combining the three-dimensional temperature field of the wall thickness temperature gradient and measured structural deviations, an accurate thermal analysis model is established, achieving precise stress calculation and accurate identification of hazardous areas in the operating pipeline. Attached Figure Description

[0033] This application will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0034] Figure 1 This is an exemplary flowchart of a BIM-based construction method according to some embodiments of this application;

[0035] Figure 2 This is an exemplary flowchart illustrating the generation of cold reference point cloud data according to some embodiments of this application;

[0036] Figure 3 This is an exemplary flowchart illustrating the calculation of cumulative arc length distance according to some embodiments of this application;

[0037] Figure 4 This is an exemplary flowchart illustrating the calculation of structural deviations according to some embodiments of this application;

[0038] Figure 5 This is an exemplary flowchart illustrating the construction of a thermal analysis model according to some embodiments of this application. Detailed Implementation

[0039] The methods and systems provided in the embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0040] like Figure 1As shown, a drone equipped with an infrared thermal imager scans the operating target thermal pipeline, simultaneously acquiring thermal geometric point cloud data and surface temperature data of each point. Based on the temperature difference between the surface temperature data and the cold reference temperature, and combined with the linear expansion coefficient of the target thermal pipeline material, the contraction displacement of each point in the thermal geometric point cloud data from the hot state to the cold state is calculated. The contraction displacement is used to perform reverse temperature compensation on the thermal geometric point cloud data to generate cold reference point cloud data that excludes the influence of thermal expansion. A cold BIM model is established based on the cold reference point cloud data. The cold BIM model is overlaid and compared with the BIM design model of the target thermal pipeline to obtain structural deviations reflecting the settlement, displacement, and deformation of the target thermal pipeline. Using the structural deviations as initial geometric conditions, the surface temperature data of the thermal pipeline is acquired as thermal load to establish a thermal analysis model reflecting boundary constraints. The stress distribution of the target thermal pipeline is calculated based on the thermal analysis model, and dangerous areas where the stress exceeds a preset threshold are identified. The stress in the dangerous areas is marked in the cold BIM model, and a construction plan is generated.

[0041] Thermal expansion occurs in pipes operating at high temperatures, leading to an increase in pipe length and outer diameter. By measuring the actual surface temperature of the pipe and combining this with the thermal expansion characteristics of the material, the amount of contraction displacement that should occur at each point when cooling from a hot state to a reference temperature can be accurately calculated. Applying this contraction displacement back to the hot point cloud data yields the theoretical geometry of the pipe in a cold state, thus achieving the goal of obtaining cold-state reference data without interrupting operation.

[0042] Specifically, S1, a drone equipped with an infrared thermal imager is used to scan the operating target thermal pipeline, and thermal geometric point cloud data and surface temperature data of each point are acquired simultaneously; wherein, the point cloud dataset contains N points (N is usually millions to tens of millions); the temperature data exists in the form of a thermal image sequence, with a total of M frames (M is usually thousands of frames).

[0043] like Figure 2 As shown in Figure S2, the surface temperature data and the thermal geometric point cloud data are spatially matched to associate temperature attributes with each point, forming a point cloud dataset with temperature attributes. Among them, based on the multi-view geometry principle and camera imaging model, the internal and external parameters of the infrared thermal imager are used to project the point cloud points in the three-dimensional space onto the two-dimensional thermal image plane, establish the correspondence between the three-dimensional points and the two-dimensional pixels, and thus extract the temperature information of each point.

[0044] Calculate the temperature difference of each point in the point cloud dataset. ,in, For the temperature at the corresponding point, The cold-state reference temperature is the reference temperature for thermal expansion compensation, and its selection directly affects the accuracy of the compensation calculation. Depending on the actual situation, one of the following options can be adopted: (1) Use the annual average ambient temperature of the area where the pipeline is located as the reference. (2) Use the installation reference temperature specified in the pipeline design documents. (3) Use the temperature measured under the actual shutdown condition of the pipeline.

[0045] Identify the fixed support locations of the target thermal pipeline and establish a local coordinate system for the pipeline with these locations as the origin. Specifically, select the center point of the first fixed support as the origin of the local coordinate system and record its coordinates in the global coordinate system. Axial direction (x-axis): Calculate the direction vector from the first support to the second support, normalize it, and use it as the unit direction vector of the x-axis. This direction represents the main extension direction of the pipeline. Vertical direction (z-axis): Use the opposite direction of gravity, i.e., a vertically upward unit vector. This direction is a fixed direction in the geodetic coordinate system. Lateral direction (y-axis): According to the right-hand rule, the y-axis direction is obtained by the cross product of the z-axis and the x-axis. This direction is perpendicular to the pipeline axis and horizontal.

[0046] Pipeline centerlines are extracted based on point cloud datasets. The pipeline centerline represents the geometric skeleton of the pipeline, signifying its spatial orientation. Accurate extraction of the centerline is fundamental for calculating axial distances and is also a crucial reference for subsequent thermal expansion compensation calculations. The centerline simplifies the complex three-dimensional pipeline surface into a one-dimensional spatial curve.

[0047] like Figure 3 As shown, the cumulative arc length distance from each point in the point cloud dataset along the pipe centerline to the fixed support position is calculated. The process includes: dividing the point cloud dataset into slices along the centerline of the target thermal pipeline; performing cylindrical surface fitting on the point cloud data within each slice, using either the least squares method or the RANSAC (Random Sample Consensus) algorithm. The RANSAC algorithm is robust to outliers and can eliminate the influence of interfering points such as support components. Cylindrical surface fitting yields the parameters of the circle corresponding to the slice, including the coordinates of the center in the yz plane and the radius. The center coordinates represent the center position of the pipeline at that cross-section, and the three-dimensional coordinates of the center are recorded (x-coordinate is the slice position, y and z coordinates are the fitted center coordinates). The center coordinates of all slices are arranged in ascending order of x-coordinate, forming a discrete point sequence along the pipeline's direction. This point sequence reflects the discrete sampling of the pipeline's centerline, with each point representing the center position of a cross-section.

[0048] By fitting a B-spline curve to the discrete point sequence, the parameterized curve of the pipe centerline is obtained. t∈[0,1], where t=0 corresponds to the fixed support position and t=1 corresponds to the pipe end position; for each point in the point cloud dataset Calculate the corresponding point to the parameterized curve The nearest projection point, whose parameter value on the parametric curve is denoted as . Among them, parameter values To make the point To parameterized curve The parameter value that minimizes the Euclidean distance represents the normalized relative position of the nearest projection point from the fixed support position to the pipe end position;

[0049] By using parameterized curves In the parameter range Perform numerical integration to calculate the arc length. As the cumulative arc length distance from the corresponding point along the pipe centerline to the fixed support position. .

[0050] Based on the linear expansion coefficient α of the target thermal pipeline material, the temperature difference ΔT, and the cumulative distance Calculate the cumulative axial shrinkage displacement at the corresponding point. , ;

[0051] Based on the temperature difference ΔT, the coefficient of linear expansion α, and the outer diameter of the target thermal pipe Calculate the radial contraction displacement at the corresponding point. The linear expansion coefficient α reflects the thermal expansion characteristics of the material. Radial expansion has no cumulative effect because radial deformation occurs independently at each cross-section and is not affected by distance.

[0052] Based on axial cumulative shrinkage displacement and radial contraction displacement Correct the coordinates of each point in the point cloud dataset to obtain temperature-compensated coordinate data; transform the temperature-compensated coordinate data from the local coordinate system back to the global coordinate system to generate cold-state reference point cloud data.

[0053] like Figure 4 As shown in Figure S3, the RANSAC algorithm is used to fit the cold reference point cloud data to a cylindrical surface, extracting the pipe's centerline axial direction, outer diameter, and elbow curvature radius. Specifically, based on the extracted geometric parameters, a parametric pipe model is created in BIM software to generate a cold BIM model. The cold BIM model includes the pipe centerline, pipe diameter, wall thickness, support location, and material properties. Commonly used BIM modeling software includes professional pipe design software such as Autodesk Revit, Bentley OpenPlant, and AVEVA E3D.

[0054] In the local coordinate system of the pipeline, the cold BIM model is spatially registered with the BIM design model of the target thermal pipeline. The registration method is to make the fixed support positions of the two models coincide and the main axis of the pipeline aligned.

[0055] Several characteristic sections are set along the centerline of the pipeline according to the principle of equal spacing. The spacing between the characteristic sections is set to 1 to 5 meters depending on the complexity of the pipeline.

[0056] For each characteristic section location, the three-dimensional coordinates of the pipe center point of that section are extracted from both the cold BIM model and the BIM design model. and ;

[0057] Calculate the displacement deviation vector Furthermore, in the local coordinate system of the pipeline, the displacement deviation vector ΔC is decomposed into: axial deviation along the pipeline axis. Radial deviation perpendicular to the pipe axis Vertical downward settlement deviation ;

[0058] The axial deviation, radial deviation, and settlement deviation of all characteristic sections are statistically analyzed to generate structural deviation distribution data;

[0059] In the cold BIM model, the values ​​and distribution locations of axial deviation, radial deviation, and settlement deviation are marked in the form of a color gradient map, where green indicates that the deviation is less than the design allowable value, yellow indicates that the deviation is close to the design allowable value, and red indicates that the deviation exceeds the design allowable value.

[0060] like Figure 5 As shown in Figure S4, extract the node number and coordinates of each node in the finite element mesh model to obtain the temperature value of each node in the three-dimensional temperature field distribution. ; Calculate the temperature change at each node ,in, For node temperature, The cold reference temperature is used; for each finite element element, the temperature change at the element nodes is used as the reference temperature. Calculate the thermal strain tensor of this element:

[0061] Where α is the linear expansion coefficient, I is the third-order unit tensor, represented as a diagonal matrix diag[1, 1, 1, 0, 0, 0], and the thermal strain tensor is... It is a six-dimensional column vector, with the first three components being the normal thermal strain and the last three components being the shear thermal strain with a value of zero;

[0062] Based on the boundary constraints, the degrees of freedom of the constraint nodes are set in the finite element mesh model: full constraints of three translational degrees of freedom and three rotational degrees of freedom are applied to the nodes at the fixed support position; the axial translational degree of freedom is released at the nodes at the sliding support position; and the axial translational degree of freedom and rotational degree of freedom are released at the nodes at the guide support position.

[0063] Based on the elastic modulus E and Poisson's ratio ν from the thermodynamic analysis parameters, establish the elastic matrix. The elasticity matrix is ​​a 6×6 matrix, represented as: ; where the diagonal element M[i, i] (i=1, 2, 3) of matrix M is (1-ν), the off-diagonal element M[i, j] (i≠j, i, j=1, 2, 3) is ν, and the shearing component M[k, k] (k=4, 5, 6) is (1-2ν) / 2;

[0064] The strain matrix [B] is established based on the spatial derivative of the element shape function. The strain matrix is ​​a 6×n matrix, where n is the product of the number of element nodes and the number of node degrees of freedom.

[0065] Based on the thermodynamic analysis parameters, establish the element stiffness matrix. The formula for calculating the element stiffness matrix is: The integral is calculated within the element domain using numerical integration methods, and the element stiffness matrix... It is an n×n symmetric positive definite matrix;

[0066] Based on the global node numbering of each element in the finite element mesh model, the stiffness matrix of each element is... The cumulative assembly forms the overall stiffness matrix K, which is an N×N sparse symmetric matrix, where N is the total number of degrees of freedom of the system.

[0067] Calculate the thermal load vector of each unit The thermal load vectors of each unit are assembled into an overall thermal load vector according to the global node number. The overall load vector F is an N-dimensional column vector, including the overall thermal load vector. and boundary loads;

[0068] Establish the governing equations of thermoelasticity ,in, Let N be the nodal displacement vector, representing the displacement degrees of freedom of all nodes. The thermoelasticity governing equations constitute the thermodynamic analysis model.

[0069] In step S5, the method for calculating the stress distribution of the target thermal pipeline based on the thermal analysis model, identifying dangerous areas where the stress exceeds a preset threshold, and marking the dangerous areas in the cold-state BIM model and generating a construction plan based on the stress in the dangerous areas includes:

[0070] Solve the thermoelasticity governing equations The nodal displacement vectors are calculated using a matrix solving algorithm. ;

[0071] Based on the nodal displacement vector and strain matrix Calculate the strain vector of each element. ,in, The element node displacement vector;

[0072] Based on the strain vector of each element Thermal strain calculation of three-dimensional temperature field distribution and elasticity matrix Calculate the stress vector of each element. ;

[0073] Convert the stress vectors of each element into principal stresses and calculate the first principal stress. Second principal stress and the third principal stress ;

[0074] Calculate the equivalent stress of each element. The equivalent stress is calculated using the von Mises criterion: The equivalent stress of each unit With preset threshold By comparing and identifying units whose equivalent stress exceeds a preset threshold, the areas where these units are located are marked as danger zones.

[0075] Spatial clustering of hazardous areas is performed to group spatially adjacent high-stress units into continuous hazardous sections.

[0076] In the cold BIM model, the location of the hazardous section on the centerline of the pipeline is marked, and the marking content includes the start and end positions of the hazardous section, the maximum equivalent stress value and the stress type.

[0077] Based on the location, stress value, and stress type of the hazardous area, a construction plan is generated. The construction plan includes:

[0078] For pipe sections with excessive stress, develop local reinforcement plans;

[0079] For support locations with stress concentration, develop a plan for support adjustment or replacement;

[0080] For areas at risk of fatigue, develop inspection cycles and maintenance plans;

[0081] For areas with excessive temperature gradient stress, develop an optimized insulation plan.

[0082] The foregoing illustrative description of the present application and its embodiments is not restrictive and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. The accompanying drawings are only one embodiment of the present application, and the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present application, such designs should fall within the scope of protection of this application. Furthermore, the word "comprising" does not exclude other elements or steps, and the word "a" preceding an element does not exclude the inclusion of "a plurality" of that element. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

Claims

1. A BIM-based construction method, characterized in that, include: S1 uses a drone equipped with an infrared thermal imager to scan the target thermal pipeline in operation, and simultaneously acquires thermal geometric point cloud data and surface temperature data of each point; S2, based on the temperature difference between the surface temperature data and the cold reference temperature, and combined with the linear expansion coefficient of the target thermal pipeline material, calculate the shrinkage displacement of each point in the hot geometric point cloud data from the hot state to the cold state, and use the shrinkage displacement to perform reverse temperature compensation on the hot geometric point cloud data to generate cold reference point cloud data that eliminates the influence of thermal expansion. S3, Establish a cold BIM model based on cold benchmark point cloud data; By overlaying and comparing the cold BIM model with the BIM design model of the target thermal pipeline, structural deviations reflecting the settlement, displacement and deformation of the target thermal pipeline can be obtained. S4. Using structural deviations as initial geometric conditions, the surface temperature data of the thermal pipe is obtained as the thermal load, and a thermal analysis model reflecting boundary constraints is established. S5 calculates the stress distribution of the target thermal pipeline based on the thermal analysis model, identifies dangerous areas where the stress exceeds the preset threshold, and marks the dangerous areas in the cold BIM model and generates a construction plan based on the stress in the dangerous areas.

2. The BIM-based construction method according to claim 1, characterized in that: S2 generates cold-state baseline point cloud data that excludes the effects of thermal expansion, including: Spatial matching of surface temperature data and thermal geometric point cloud data is performed to associate temperature attributes with each point, forming a point cloud dataset with temperature attributes. Calculate the temperature difference of each point in the point cloud dataset. ,in, For the temperature at the corresponding point, The cold reference temperature; Identify the location of the fixed support for the target thermal pipeline, and establish a local coordinate system for the pipeline with the location of the fixed support as the origin; Extracting the centerline of the pipeline based on point cloud dataset; Calculate the cumulative arc length distance from each point in the point cloud dataset along the pipe centerline to the fixed support location. ; Based on the linear expansion coefficient of the target thermal pipeline material Temperature difference and cumulative arc length Calculate the cumulative axial shrinkage displacement at the corresponding point. ; Based on temperature difference Coefficient of linear expansion and the outer diameter of the target heat pipe Calculate the radial contraction displacement at the corresponding point. ; Based on axial cumulative shrinkage displacement and radial contraction displacement Correct the coordinates of each point in the point cloud dataset to obtain temperature-compensated coordinate data; The temperature-compensated coordinate data is transformed from the local coordinate system back to the global coordinate system to generate cold-state reference point cloud data.

3. The BIM-based construction method according to claim 2, characterized in that: Calculate the cumulative axial shrinkage displacement at the corresponding point. The formula is as follows: ； Calculate the radial contraction displacement at the corresponding point. The formula is as follows: 。 4. The BIM-based construction method according to claim 2, characterized in that: Calculate the cumulative arc length distance from each point in the point cloud dataset along the pipe centerline to the fixed support location. ,include: The point cloud dataset is divided into slices along the centerline of the target thermal pipe; Cylindrical fitting is performed on the point cloud data within each slice to extract the center coordinates of the corresponding slice. The center coordinates of all slices are then connected sequentially to form a discrete point sequence of the pipeline centerline. By fitting a B-spline curve to the discrete point sequence, a parameterized curve of the pipe centerline is obtained. , t∈[0,1], where t=0 corresponds to the fixed support position and t=1 corresponds to the end position of the pipe; For each point in the point cloud dataset Calculate the corresponding point to the parameterized curve The nearest projection point, whose parameter value on the parametric curve is denoted as . Among them, parameter values To make the point To parameterized curve The parameter value that minimizes the Euclidean distance represents the normalized relative position of the nearest projection point from the fixed support position to the pipe end position; By using parameterized curves In the parameter range Perform numerical integration to calculate the arc length. , As the cumulative arc length distance from the corresponding point along the pipe centerline to the fixed support position. .

5. The BIM-based construction method according to claim 2, characterized in that: S3, obtain structural deviations reflecting the settlement, displacement, and deformation of the target thermal pipeline, including: Geometric reconstruction is performed on the cold reference point cloud data to extract pipeline geometric parameters and establish a cold BIM model; Spatial registration is performed between the cold BIM model and the BIM design model of the target thermal pipeline; Multiple feature sections are set along the centerline of the pipeline, and the coordinate deviation of the center point of the cold BIM model and the BIM design model at each feature section is calculated. The center point deviation is decomposed into axial deviation, radial deviation, and settlement deviation to generate structural deviation data.

6. The BIM-based construction method according to claim 5, characterized in that: S4, Establish a thermodynamic analysis model reflecting boundary constraints, including: An initial geometric model for thermal analysis is created based on a cold BIM model. Structural deviation data is imported into the initial geometric model, and axial deviation, radial deviation, and settlement deviation are applied at the characteristic section locations to generate a deformable geometric model that includes the actual deformation state. Obtain the internal medium temperature of the target thermal pipeline and surface temperature data And calculate the temperature distribution along the pipe wall thickness. ; The temperature distribution along the pipe wall thickness is mapped onto a deformable geometric model. Multiple layers of units are divided along the wall thickness, and each layer is assigned a corresponding temperature value to form a three-dimensional temperature field distribution. Set boundary constraints based on the operating conditions of the target thermal pipeline; Based on the material properties of the target thermal pipeline, thermal analysis parameters are set, including elastic modulus, Poisson's ratio, coefficient of linear expansion, and thermal conductivity. Mesh the deformable geometric model to generate a finite element mesh model, with at least 3 layers of elements in the wall thickness direction; A thermal analysis model is established based on the finite element mesh model, three-dimensional temperature field distribution, boundary constraints, and thermal analysis parameters.

7. The BIM-based construction method according to claim 6, characterized in that: Calculate the temperature distribution along the thickness of the pipe wall. The formula is as follows: Where r is the radial coordinate of the pipe, Let the inner radius be , Let be the outer radius.

8. The BIM-based construction method according to claim 7, characterized in that: Boundary constraints include: Apply a fixed constraint at the fixed support location; Apply axial freedom constraint at the sliding support location; Apply radial constraints at the guide support location.

9. The BIM-based construction method according to claim 7, characterized in that: Based on the finite element mesh model, three-dimensional temperature field distribution, boundary constraints, and thermodynamic analysis parameters, a thermodynamic analysis model is established, including: The temperature values ​​in the three-dimensional temperature field distribution are applied as nodal loads to each node of the finite element mesh model, and the thermal strain of each element is calculated. ,in, is the coefficient of linear expansion, ΔT is the temperature change, and I is the unit tensor; Based on the boundary constraints, the degrees of freedom of the constraint nodes are set in the finite element mesh model: full constraints of three translational degrees of freedom and three rotational degrees of freedom are applied to the nodes at the fixed support position; the axial translational degree of freedom is released at the nodes at the sliding support position; and the axial translational degree of freedom and rotational degree of freedom are released at the nodes at the guide support position. Based on the thermodynamic analysis parameters, establish the element stiffness matrix. The formula for calculating the element stiffness matrix is: ,in, The strain matrix; It is the elasticity matrix; The stiffness matrices of each element are assembled into an overall stiffness matrix K, and the nodal forces generated by thermal strain and the constraint reactions generated by boundary constraints are assembled into an overall load vector F. Establish the governing equations of thermoelasticity ,in, The thermoelasticity control equations, which serve as the nodal displacement vectors, constitute a thermodynamic analysis model.

10. The BIM-based construction method according to claim 9, characterized in that: S5 identifies hazardous areas where stress exceeds a preset threshold, including: Solve the thermoelasticity governing equations to obtain the nodal displacement vectors; Calculate the strain and stress of each element based on the nodal displacement vector; Calculate the equivalent stress of each unit and identify dangerous areas where the equivalent stress exceeds a preset threshold; Mark the location and stress information of hazardous areas in the cold BIM model; A corresponding construction plan is generated based on the stress state of the hazardous area.

Images