BIM-based digital management method for construction process of water conservancy and hydropower project

Abstract

This invention discloses a BIM-based digital management method for the construction process of water conservancy and hydropower projects, including: establishing a dedicated building information model component library for hydraulic structures, covering typical hydraulic components such as dam galleries and tunnel linings; constructing a dynamic simulation module for construction diversion to simulate water flow paths and flood season water level changes during different diversion periods; developing a coupled temperature and stress field simulation engine to simulate temperature field evolution and crack risk warning for large-volume concrete, and performing sequential coupled iterative solutions; establishing an intelligent decision-making model for construction sequence priorities, comprehensively considering constraints such as construction period, resources, flood season, temperature control, and spatial interference, and optimizing the construction sequence through a genetic algorithm; and realizing the dynamic correlation between BIM and simulation results, mapping inundation risk, temperature stress, and planned progress data to model component units. This invention solves the technical problems of lacking targeted models for simulation and multi-factor coupled decision-making, achieving visualized simulation and intelligent optimization of the construction process.

Description

Technical Field

[0001] This application belongs to the field of intelligent management of water conservancy and hydropower construction projects using computer technology, and specifically involves a digital management method for the construction process of water conservancy and hydropower projects based on BIM. Background Technology

[0002] Building Information Modeling (BIM) technology, as a core tool for digital management throughout the entire lifecycle of engineering projects, has been widely applied in the design, construction, and operation and maintenance phases of building engineering by constructing multi-dimensional data models that encompass geometric, physical, and functional information. This has effectively improved construction management efficiency and the scientific nature of decision-making. Water conservancy and hydropower projects, as crucial infrastructure for national flood control, disaster reduction, water resource allocation, and clean energy development, urgently require enhanced informatization and intelligentization in their construction management.

[0003] However, compared to general construction projects, water conservancy and hydropower projects have significant unique characteristics and complexities. Hydraulic structures mainly include typical hydraulic structures such as dams, water diversion tunnels, spillways, gate chambers, pressure pipelines, and aqueducts. These structures face unique working conditions during construction, including construction diversion, flood control, and foundation pit drainage. The construction environment is significantly affected by hydrological conditions. Furthermore, dams and other hydraulic structures typically employ large-volume concrete pouring techniques. The temperature field evolution and stress field distribution caused by the heat of concrete hydration directly affect the structural quality. Improper temperature control can easily lead to temperature cracks. Therefore, precise control of key parameters such as initial pouring temperature, pouring layer thickness, interval time, and cooling water flow is necessary during construction.

[0004] Existing construction management technologies based on Building Information Modeling (BIM) are primarily developed for the building engineering field, with their technical architecture and functional modules built around the characteristics of industrial and civil buildings. Firstly, existing technologies lack a dedicated BIM component library for hydraulic structures in water conservancy and hydropower projects. They cannot provide detailed geometric models and engineering attribute information for typical hydraulic components such as dam galleries, tunnel linings, and gate chambers, resulting in a lack of accurate data support for construction simulation. Secondly, existing technologies fail to fully consider the dynamic impacts of unique construction conditions such as diversion and flood control in water conservancy projects. They lack the ability to simulate water flow paths, construction site divisions, and flood control water level changes during different diversion periods, making it difficult for construction managers to dynamically assess the impact of diversion on the construction area and the construction safety risks during the flood season. Thirdly, existing technologies lack the ability to couple the temperature field evolution and stress field distribution of large-volume hydraulic concrete, and cannot simulate temperature field evolution and provide early warning of crack risks based on the pouring sequence of dam sections. Furthermore, existing technologies struggle to comprehensively consider multiple factors such as schedule constraints, resource allocation constraints, flood control requirements, and temperature control constraints to intelligently optimize the priority of construction procedures.

[0005] The aforementioned technical issues result in a lack of targeted model support for the construction simulation of hydraulic structures using existing technologies. This fails to meet the unique construction management needs of water conservancy and hydropower projects, severely hindering the improvement of the informatization and intelligentization level of construction management in these projects. Therefore, it is urgent to develop a dedicated building information model (BIM)-based dynamic construction management method tailored to the characteristics of hydraulic structures in water conservancy and hydropower projects, in order to achieve visualized simulation and intelligent decision optimization of the construction process. Summary of the Invention

[0006] The purpose of this invention is to provide a BIM-based digital management method for the construction process of water conservancy and hydropower projects, which can effectively solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The BIM-based digital management method for the construction process of water conservancy and hydropower projects includes the following specific steps: Establish a dedicated building information model component library for hydraulic structures. The library covers typical hydraulic components such as dam corridors, tunnel linings, gate chambers, pressure pipelines, spillways, and aqueducts, and the geometric accuracy of the typical hydraulic components reaches the LOD3.0 geometric accuracy level. A dynamic simulation module for construction diversion was constructed to dynamically simulate the water flow path, construction site division, and flood season water level changes during different diversion periods. A temperature and stress field coupled simulation engine was developed to simulate the temperature field evolution and provide early warning of crack risk for large-volume hydraulic concrete. The temperature and stress field coupled simulation engine performs sequential coupled iterative solution of temperature field and stress field. Establish an intelligent decision-making model for construction sequence priority, and optimize the intelligent decision-making of construction sequence priority by comprehensively considering constraints such as construction period, resource allocation, flood season, temperature control, and spatial construction interference. To achieve dynamic association between the building information model and simulation results, the flooding risk data from the construction diversion dynamic simulation module, the temperature and stress field data from the temperature and stress field coupling simulation engine, and the planned progress data output by the intelligent decision-making model for construction process priority are mapped to the corresponding component units of the building information model through the attribute dynamic update interface.

[0008] Furthermore, a dedicated building information model (BIM) component library for hydraulic structures will be established, including: A parameterized hydraulic component template system is constructed using a constraint-based parametric modeling method. Key geometric feature points of hydraulic components are selected as constraint control nodes, and a mapping relationship between node coordinates and engineering dimension parameters is established. A series of components are automatically derived based on the input key dimension parameters through a parameter-driven engine. The design target for the reusability of a single component template is over 80%, enabling a single component template to cover over 80% of engineering variations of the same type of component through parameter adjustments.

[0009] Furthermore, establishing a dedicated building information model component library for hydraulic structures also includes: Establish a geometric accuracy level standard and verification mechanism, using LOD3.0 accuracy level as the benchmark, stipulating that the linear dimension error of the geometric model of hydraulic components shall not exceed ±50mm, the angle error shall not exceed ±0.5 degrees, and the area error shall not exceed ±2%; An automatic comparison algorithm based on nearest point iteration is used to verify geometric accuracy. The surface of the geometric model generated by the component template is discretely sampled, and the sampled point cloud dataset is extracted. The standard reference model is discretized into a reference point cloud dataset. The residual vector between the sampled point cloud and the reference point cloud is calculated through iterative registration. The maximum error value is statistically analyzed. If the maximum error exceeds the geometric accuracy level standard, the current component is marked as unqualified.

[0010] Furthermore, establishing a dedicated building information model component library for hydraulic structures also includes: Collect and organize the physical property information of hydraulic components, which covers mechanical properties, thermal properties and hydraulic properties, including at least concrete strength grade, elastic modulus, elastic modulus temperature decay coefficient, thermal expansion coefficient, thermal conductivity, specific heat capacity and hydraulic roughness coefficient. A mechanism for linking physical property information and geometric models is established, employing a dual-database storage structure of an attribute database and a geometric model library. The unique identifier of the component is used as the primary key. The attribute database adopts a relational database management system, while the geometric model library uses the IFC4 international standard format to store parametric geometric kernel files. The precise mapping between physical property information and geometric models is achieved through the unique identifier of the component.

[0011] Furthermore, a dynamic simulation module for construction diversion is constructed, including: The three-dimensional model of the diversion structure is obtained from the special building information model component library of hydraulic structures through the secondary development interface, and the geometric dimensions and hydraulic parameters are read. An algorithm for dividing the diversion period was developed. The multi-year monthly average flow sequence data of the watershed where the project is located was read. Statistical analysis methods were used to identify the time distribution characteristics of the high-water season, normal-water season and low-water season. The diversion period was divided according to the design flood standard determined by the project grade and the building level. A water flow path tracing model was established, and a two-dimensional shallow water equation numerical solution method was adopted. The two-dimensional shallow water equation includes a continuity equation and a momentum equation, which describe the mass conservation and momentum conservation laws of water flow on the free surface. The Manning formula was used to calculate the riverbed shear stress. The water flow path tracking model is spatially discretized and time-step controlled. The spatial discretization adopts the finite volume method, which divides the computational domain into unstructured grid cells. The time step is dynamically adjusted according to the Courant number stability condition. Output the flow velocity distribution, water depth distribution, and inundation duration distribution data for each grid node, and map the water depth distribution data to the construction site model in the 3D scene. Display the inundation risk level through color coding and present the spatiotemporal evolution of the inundation risk in the construction area in the form of a timeline animation.

[0012] Furthermore, a coupled simulation engine for temperature and stress fields was developed, including: A nonlinear transient temperature field solution algorithm is implemented, which uses the finite element method to solve the transient heat conduction partial differential equation. The heat source intensity in the transient heat conduction partial differential equation consists of the heat release from concrete hydration and external heat sources. The relationship between the heat release rate from hydration and age is described by an exponential decay model. The temperature field solution is meshed and the time step is controlled. The mesh is refined on the concrete surface and joint surfaces, and the time step is dynamically adjusted according to the heat release law of concrete hydration. A two-way coupled model of temperature field and stress field is constructed. The two-way coupled model of temperature field and stress field considers the nonlinear characteristics of the elastic modulus of concrete changing with temperature, and introduces a temperature stress correction coefficient to account for the influence of concrete creep and relaxation aging characteristics. The sequential coupling iterative solution of temperature field and stress field is performed. First, the temperature field is solved to obtain the temperature distribution at each time. Then, the stress field is solved based on the temperature field results to calculate the temperature stress. The convergence criterion for the sequential coupling iterative solution of temperature field and stress field is set as follows: the residual heat flux of temperature field is less than a preset threshold and the residual stress of stress field is less than 0.01 MPa.

[0013] Furthermore, the development of a coupled temperature and stress field simulation engine also includes: A crack risk level assessment method based on a crack resistance safety factor is developed, wherein the crack resistance safety factor is defined as the ratio of the ultimate tensile strength of concrete to the temperature stress obtained by sequential coupling and iterative solution of the temperature field and stress field; The crack risk level is classified according to the crack resistance safety factor: when the crack resistance safety factor is greater than or equal to 2.0, it is judged as low risk; when the crack resistance safety factor is greater than or equal to 1.5 and less than 2.0, it is judged as medium risk; when the crack resistance safety factor is greater than or equal to 1.0 and less than 1.5, it is judged as high risk; and when the crack resistance safety factor is less than 1.0, it is judged as dangerous. The crack risk assessment results are visualized in a 3D model in the building information model, and color coding is used to highlight the dam areas at each risk level in the 3D scene.

[0014] Furthermore, an intelligent decision-making model for prioritizing construction procedures is established, including: A multi-objective optimization mathematical model is constructed, with the optimization objectives being the shortest total construction period, the lowest construction cost, and the lowest construction risk. The multi-objective problem is transformed into a single-objective problem using the weighted summation method. The comprehensive objective function is composed of the weighted summation of the normalized total construction period, total construction cost, and total construction risk. The constraints include resource supply constraints, flood season constraints, temperature control constraints, and spatial construction interference constraints. The flood season constraints stipulate that the concrete must be completed before the flood season. The temperature control constraints stipulate that the concrete pouring temperature must not exceed the maximum allowable temperature and that the interval between adjacent pouring blocks must meet the temperature control requirements. A sequence solving engine based on genetic algorithm was developed. The construction process sequence was encoded using binary encoding. The initial population size, crossover probability, mutation probability and maximum number of iterations were set. The convergence condition was that the change in the comprehensive objective function value of the best individuals in several consecutive generations was less than a preset threshold. The optimal construction sequence was then solved.

[0015] Furthermore, establishing an intelligent decision-making model for prioritizing construction procedures also includes: Establish a dynamic weight adjustment mechanism for constraints, and automatically determine the current construction stage based on the construction stage identification algorithm and trigger the corresponding weight adjustment. When entering the pre-flood season warning period, the weighting coefficient corresponding to the flood control constraint will be increased to 2 to 3 times the original value; When the high-temperature pouring season arrives, the weighting coefficient corresponding to the temperature control constraint will be increased to 2 to 2.5 times the original value. The weight adjustment adopts a smooth transition strategy, gradually changing the weight coefficient values ​​by linear interpolation within the transition time window.

[0016] Furthermore, achieving dynamic correlation between building information models and simulation results also includes: Establish a construction schedule deviation calculation model, calculate the schedule deviation by comparing the planned schedule and the actual schedule, and output three core indicators: schedule deviation percentage, critical path deviation days, and milestone achievement rate. A three-level early warning mechanism for schedule deviation is set up: a yellow warning is triggered when the percentage of schedule deviation exceeds 5% to 10%, an orange warning is triggered when the percentage of schedule deviation exceeds 10% to 20%, and a red warning is triggered when the percentage of schedule deviation exceeds 20%. Risk warning information is spatially associated with the 3D model. The unique component identifier is used as the association key to link the risk warning information to the building information model component unit corresponding to the unique component identifier. The risk level distribution is displayed in the 3D visualization environment through color coding.

[0017] In summary, this application includes at least one of the following beneficial technical effects: 1. This invention establishes a dedicated building information model component library for hydraulic structures in water conservancy and hydropower projects, breaking through the technical bottleneck of existing technologies lacking targeted model support in the construction simulation of hydraulic structures. The component library covers typical hydraulic components such as dam corridors, tunnel linings, gate chambers, pressure pipelines, spillways, and aqueducts, with geometric accuracy reaching the preset geometric accuracy level, providing an accurate data foundation for construction simulation and effectively solving the problem of construction simulation being unable to obtain accurate geometric and attribute data.

[0018] 2. This invention constructs a dynamic simulation module for construction diversion, which realizes dynamic simulation of water flow path, construction site division and flood season water level changes during different diversion periods. It breaks through the technical limitations of existing technologies that cannot dynamically simulate the unique working conditions such as construction diversion. It can assess the impact of construction diversion on the construction area and the construction safety risks during the flood season in real time, and provide a scientific basis for construction decision-making.

[0019] 3. This invention develops a temperature and stress field coupled simulation engine to simulate the evolution of temperature field and provide early warning of crack risk in large-volume hydraulic concrete. It overcomes the shortcomings of existing technologies that lack digital simulation capabilities for temperature control of hydraulic concrete. By quantitatively assessing the crack risk level through the crack resistance safety factor, it provides technical support for temperature control during construction.

[0020] 4. This invention establishes an intelligent decision-making model for construction sequence priority, which comprehensively considers multiple factors such as construction period constraints, resource allocation, flood control requirements, and pouring temperature control to optimize the construction sequence. It breaks through the technical bottlenecks of existing technologies that cannot comprehensively optimize the construction sequence based on multiple factors and lack the ability to handle the unique constraints of hydraulic engineering. The sequence solving engine based on genetic algorithm can quickly generate the optimal construction sequence. Attached Figure Description

[0021] Figure 1 A schematic diagram of the overall technical solution for a BIM-based digital management method for the construction process of water conservancy and hydropower projects; Figure 2 This is a schematic diagram illustrating the core principle of a temperature-stress-field coupling simulation engine. Figure 3 A schematic diagram of the logic flow of the construction diversion dynamic simulation module; Figure 4 A schematic diagram of the multi-level interaction relationships and data flow of the intelligent decision-making model for construction process priority; Figure 5 A visual representation of the dynamic association between building information models and simulation results. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the following description is provided in conjunction with the appendix. Figure 1 To be continued Figure 5 The present invention will be further described in detail below with reference to specific embodiments.

[0023] This invention provides a BIM-based digital management method for the construction process of water conservancy and hydropower projects, including the following core steps: establishing a dedicated building information model component library for hydraulic structures, constructing a dynamic simulation module for construction flow guidance, developing a coupled temperature and stress field simulation engine, establishing an intelligent decision-making model for construction sequence priorities, and realizing the dynamic correlation between the building information model and simulation results. The following details each step.

[0024] Step S1: Establish a dedicated building information model component library for hydraulic structures, which is carried out in the following sub-steps.

[0025] Step S101: Construct a parametric template system for hydraulic engineering components. Using a constraint-based parametric modeling method, key geometric feature points of the hydraulic engineering components are selected as constraint control nodes, establishing a mapping relationship between node coordinates and engineering dimensional parameters. Taking a dam gallery as an example, with a cross-sectional shape resembling a city gate, the template system defines the top arch radius of the gallery cross-section. Side wall height Base plate thickness Set as an adjustable parameter.

[0026] The specific calculation method for the coordinate sequence of the cross-section contour line is as follows: A local coordinate system is established with the center point of the cross-section base plate as the origin. The contour line of the base plate is defined from left to right as starting from point... Time The horizontal line segment, where the width of the base plate Through parameters Functional relationship Determined, usually Pick to The outlines of the two side walls start from point ; and points Extend vertically upwards to the point and points .

[0027] The outline of the arch is a circular arc, with its center located on the vertical axis of symmetry of the cross-section at a height of [height missing from base plate]. The position of the arc is the apex of the left wall. The endpoint is the apex of the right wall. The radius of the arc is equal to the radius of the top arch. The coordinates of the center of the circle are The position of the center of the circle is obtained by solving the geometric constraints of the arc passing through the vertices of the two walls.

[0028] The parameter-driven engine calculates the parameters according to the above rules, based on the input. , , The parameter values ​​are automatically calculated to determine the coordinates of each control point on the cross-sectional profile, and then the model is stretched along the corridor axis to generate a three-dimensional geometric model. Using parametric modeling, a series of components can be automatically derived simply by adjusting key dimensional parameters. For example, when the inner diameter of the pressure pipe in the spillway is adjusted from 8 meters to 10 meters, the parameter-driven engine automatically updates all relevant dimensions of the pipe cross-section to generate a component geometric model that conforms to the new specifications.

[0029] The design target for the reusability of a single component template is over 80%, meaning that the same template can cover over 80% of the engineering variations of the same type of component through parameter adjustments.

[0030] Step S102: Determine the classification system and capacity of the component library. Based on the professional classification standards for water conservancy and hydropower engineering, typical hydraulic components are divided into five major categories: water-retaining structures, water-discharge structures, water-diversion structures, water-transfer structures, and powerhouse structures. Each category contains several typical component types. The component library's design capacity covers all typical component types within the above five categories, and each typical component type includes at least three variations in engineering specifications. The total number of component templates is no less than 200 standard units.

[0031] Step S101 provides the parametric generation rules and coordinate calculation methods for a single component, and step S102 specifies the overall classification framework and lower limit of the component library. Together, they ensure that the component library meets the needs of subsequent simulation in terms of geometric generation operability and type coverage completeness.

[0032] Step S103: Establish geometric accuracy level standards and verification mechanisms. Based on the LOD3.0 accuracy level, it is stipulated that the linear dimension error of the geometric model of hydraulic components shall not exceed ±50mm, the angle error shall not exceed ±0.5 degrees, and the area error shall not exceed ±2%. The geometric accuracy of each type of hydraulic component in the component library must meet the above control standards.

[0033] Geometric accuracy verification employs an automatic comparison algorithm based on nearest-point iteration. The specific execution process is as follows: First, the surface of the geometric model generated from the component template is discretely sampled to extract the sampled point cloud dataset. The sampling density is no less than 100 sampling points per square meter; the second step is to discretize the standard reference model into a reference point cloud dataset. The third step is to analyze the sampling point cloud. Each point in In the reference point cloud Search for the point closest to it in Euclidean distance. Establish a point-to-point set .

[0034] The fourth step involves calculating the rigid body transformation matrix based on the set of point pairs, and then solving for the rotation matrix using the singular value decomposition method. Translation vector This makes the transformed point cloud With reference point cloud Overall registration error between Minimize; Step 5: Iterate through steps 3 and 4 until the registration error converges. The convergence criterion is that the change in the overall registration error between two adjacent iterations is less than 1. m; Step 6: Calculate the registered point cloud point by point. With reference point cloud The residual vectors between the two are statistically analyzed, the magnitude distribution of the residual vectors is statistically analyzed, the maximum error value is calculated and an error distribution cloud map is generated, and the maximum error statistical value is output. If the maximum error exceeds the above accuracy standard, the current component is marked as unqualified.

[0035] Step S104: Collect and organize the physical property information of hydraulic components, covering mechanical properties, thermal properties and hydraulic properties.

[0036] Concrete strength grade information is expressed in terms of the compressive strength value of a standard cylinder. Commonly used grades include C15, C20, C25, C30, C35, and C40, with corresponding standard compressive strengths of 15MPa, 20MPa, 25MPa, 30MPa, 35MPa, and 40MPa, respectively.

[0037] The elastic modulus parameter is determined based on the concrete strength grade and age. For example, the elastic modulus of C30 concrete at 28 days is taken as... MPa. The relationship between the elastic modulus and temperature is described by a nonlinear constitutive model, and the specific mathematical expression of the constitutive model is as follows: ,in For temperature The elastic modulus at time t, in MPa; Reference temperature The following is a reference value for the elastic modulus, in MPa; For reference temperature, 20℃ is used; The elastic modulus and temperature decay coefficient are for hydraulic concrete. Values ​​in to The specific value is determined by fitting concrete mix design test data; when test data is unavailable, the default value is taken. ; The current temperature of the concrete is expressed in °C.

[0038] The coefficient of thermal expansion is defined as the relative change in length of concrete for every 1°C increase in temperature. The coefficient of thermal expansion for hydraulic concrete is taken from... to The specific values ​​are determined based on the concrete mix proportions.

[0039] Thermal conductivity is defined as the heat flow rate through a unit area per unit time under a unit temperature gradient. The thermal conductivity of hydraulic concrete is taken from... to Specific heat capacity is defined as the amount of heat absorbed by a unit mass of concrete when its temperature rises by 1°C. The specific heat capacity of hydraulic concrete is taken as a value between [value missing]. to between.

[0040] The hydraulic roughness coefficient is determined based on the roughness characteristics of the surface of hydraulic components. The roughness coefficient of the concrete lining surface is taken as 0.012 to 0.015, and the roughness coefficient of the bedrock exposed surface is taken as 0.035 to 0.045.

[0041] Step S105: Establish the association mechanism between physical attribute information and geometric model. A dual-database storage structure of attribute database and geometric model database is adopted.

[0042] The attribute database adopts a relational database management system. The database table structure is designed as follows: the unique identifier of the component is used as the primary key field, the data type is VARCHAR(64), and the encoding rule is project code, building type code, and component serial number; the geometric parameter field group includes the top arch radius. Side wall height Base plate thickness For field names, data types, and units of the same size parameters, the data type should be uniformly set to FLOAT, and the unit should be uniformly set to meters.

[0043] The physical parameter field group includes concrete strength grade and reference value for elastic modulus. Elastic modulus and temperature decay coefficient The parameters include coefficient of thermal expansion, thermal conductivity, specific heat capacity, and roughness coefficient. Each parameter field contains a field name, data type, unit, and value range constraints. The material parameter field group includes density, Poisson's ratio, tensile strength, and compressive strength. All fields are of type FLOAT and the unit is indicated.

[0044] The geometric model library is stored in a parametric geometric kernel file format, specifically the IFC4 international standard format. Each component template is stored as an independent IFC file, and the file name corresponds to the component's unique identifier.

[0045] The association between attribute information and geometric model is precisely mapped through the identification code field. When a component is called, this method queries the attribute database with the component's unique identification code as the primary key to extract all physical parameter records corresponding to the called component. At the same time, it locates the corresponding IFC file in the geometric model library based on the identification code and sends the extracted physical parameters and the loaded geometric model into the simulation calculation module.

[0046] The dedicated building information model component library for hydraulic structures established in step S1 provides parametric, high-precision geometric models and corresponding generation algorithms. It binds thermal, mechanical, and hydraulic properties to components through defined constitutive relations and database table structure design. This provides a calculable and queryable data foundation for subsequent dynamic simulation of construction diversion, coupled simulation of temperature and stress fields, and intelligent decision-making on construction process priorities, enabling the entire digital management method to be closely connected and operate collaboratively.

[0047] For step S2, a dynamic simulation module for construction diversion is constructed, which is carried out in the following sub-steps.

[0048] Step S201: Obtain diversion structure data from the Building Information Modeling (BIM) component library. Call the 3D model of the diversion structure from the component library created in Step S1 via a secondary development interface. The diversion structures include diversion channels, diversion tunnels, cofferdams, and intercepting embankments.

[0049] The geometric dimensions and hydraulic parameters of each diversion structure were directly read from the component library. The roughness coefficient of the diversion channel was taken as 0.012 to 0.015 corresponding to the concrete lining surface, and the roughness coefficient of the diversion tunnel was also taken as 0.012 to 0.015 corresponding to the concrete lining surface. The roughness coefficient of the cofferdam and the intercepting embankment was taken as 0.035 to 0.045 corresponding to the bedrock exposed surface, depending on the type of filling material, in order to ensure the integrity and consistency of the simulation data.

[0050] Step S202: Develop a diversion period segmentation algorithm. This algorithm reads multi-year monthly average flow sequence data of the watershed where the project is located, uses statistical analysis methods to calculate the mean, coefficient of variation, and coefficient of deviation of the monthly flow, and identifies the temporal distribution characteristics of the high-water season, normal-water season, and low-water season. The flood control standard is determined according to the project's classification and structure level, following relevant specifications, and the corresponding design flood hydrograph is extracted.

[0051] Taking a certain project as an example, the high-water season in the basin is from June to September, and the low-water season is from November to March of the following year. The design flood standard adopts a 50-year flood. The diversion period is divided as follows: the initial diversion period corresponds to the low-water season, spanning from November of the current year to March of the following year; the middle diversion period corresponds to the transition stage of the normal water season, spanning from April to May; the later diversion period corresponds to the pre-flood season, spanning from October; and the flood season is from June to September, with the strictest flood control constraints.

[0052] Step S203: Determine the upstream water level boundary conditions and downstream discharge capacity constraints for each diversion period. The upstream water level boundary conditions are determined by calculating the water level-discharge relationship curves for each period based on the inflow process and reservoir scheduling rules, and these curves serve as the boundary inputs for the simulation calculations.

[0053] Downstream discharge capacity constraints are determined based on the flow capacity of the diversion structures: during the initial diversion period, downstream cofferdams primarily retain water, and discharge capacity mainly relies on the diversion tunnels; during the mid-term diversion period, part of the dam body already possesses water-retaining capabilities, and temporary flood discharge facilities are activated; during the later diversion period, the dam body is raised, and permanent flood discharge structures are gradually put into use. The transition time window between adjacent periods is determined according to the actual project progress, with a transition period of 5 to 15 days, during which preparations for the conversion of the diversion method are completed.

[0054] Step S204: Establish a water flow path tracing model. A two-dimensional shallow water equation numerical solution method is adopted. The shallow water equation consists of a continuity equation and a momentum equation, which describe the mass and momentum conservation laws of water flow on a free surface.

[0055] The expression for the continuity equation is: in, Water depth, in meters (m). Time, in seconds; The vertical average velocity in the x-direction is expressed in m / s. The vertical average velocity in the y-direction is expressed in m / s. and These are spatial coordinates, in meters (m).

[0056] The momentum equation in the x-direction is expressed as: The momentum equation in the y-direction is expressed as: in, The acceleration due to gravity is taken as 9.81 m / s². 2 ; This refers to the water level, measured in meters (m). For the density of water, take 1000 kg / m³. 3 ; This represents the component of the riverbed shear stress in the x-direction, with units of N / m. 2 ; This represents the component of the riverbed shear stress in the y-direction, with units of N / m. 2 .

[0057] The shear stress in the riverbed is calculated using Manning's formula, and the expression is: in, This represents the shear stress vector of the riverbed, in N / m. 2 ; is the Manning roughness coefficient, determined based on the roughness characteristics of the construction site surface, and is dimensionless.

[0058] Step S205 involves spatial discretization and time step control of the flow path tracing model. Spatial discretization employs the finite volume method, dividing the computational domain into unstructured triangular or quadrilateral grid cells with a grid size ranging from 5 to 20 meters. The grid size is further refined to 2 to 5 meters in the upstream and downstream areas of the cofferdam and the inlet and outlet areas of the diversion tunnel. The time step is dynamically adjusted based on the Courand number stability condition. The Courand number is calculated using the following formula: Or it can be written in the equivalent form: in, The Courant number is dimensionless. The time step is expressed in seconds (s). This refers to the mesh feature size, in meters (m). The maximum permissible Courant number is set between 0.5 and 0.8. When the Courant number exceeds... When the time step is reduced, numerical stability is ensured; when the Courant number is much smaller than 1, the time step is reduced. In this case, the time step can be increased to improve computational efficiency.

[0059] Step S206: Output the water flow path tracing results. The water flow path tracing model outputs three types of data for each grid node within the construction area: velocity distribution, water depth distribution, and inundation duration distribution. The velocity distribution data is used to assess the risk of water flow scouring to construction personnel and equipment; the water depth distribution data is used to determine the inundation range of the construction site; and the inundation duration distribution is used to assess the impact of different inundation depths on construction activities.

[0060] The model outputs data in a standard grid data file format, which interfaces with the 3D visualization engine to support the dynamic display of water flow evolution in a 3D scene.

[0061] Step S207 visualizes the spatiotemporal evolution of flooding risk in the construction area. The water depth distribution data at each moment output by the water flow path tracing model is mapped onto the construction site model in the 3D scene, and the flooding risk level is displayed using color coding.

[0062] The color coding scheme is as follows: areas with a water depth of less than 0.5 meters are displayed in green, indicating low-risk areas; areas with a water depth between 0.5 meters and 2 meters are displayed in yellow, indicating medium-risk areas; areas with a water depth between 2 meters and 5 meters are displayed in orange, indicating higher-risk areas; and areas with a water depth greater than 5 meters are displayed in red, indicating high-risk areas. The spatiotemporal evolution animation is presented using a timeline control method, supporting pause, drag, and playback operations.

[0063] The construction diversion dynamic simulation module constructed in step S2 takes the geometric and hydraulic data of the diversion structure provided by the component library as input, determines the hydraulic boundary conditions of different construction stages by dividing the diversion time period, and then obtains the velocity field and water depth field by numerical solution of two-dimensional shallow water equation. Finally, it outputs the spatiotemporal distribution of flooding risk in the form of three-dimensional color cloud map and time axis animation.

[0064] The construction diversion dynamic simulation module directly provides quantitative basis for flood control constraints for the intelligent decision-making model of construction procedure priority, and also prepares structured simulation data for the dynamic correlation between the subsequent building information model and simulation results.

[0065] The next step is step S3, which involves developing a coupled simulation engine for temperature and stress fields, and will be carried out in the following sub-steps.

[0066] Step S301: Implement the algorithm for solving the nonlinear transient temperature field. Using the finite element method, the temperature field governing equations are derived based on Fourier's law of heat conduction and the principle of mass conservation. The transient heat conduction partial differential equation is as follows: in, Temperature, in °C; Time, in seconds; This refers to the density of concrete, expressed in kg / m³. 3 ; This refers to the specific heat capacity of concrete, expressed in J / (kg·K). The thermal conductivity of concrete is expressed in W / (m·K). Heat intensity per unit volume, expressed in W / m³ 3 .

[0067] Heat source intensity It consists of two parts: the heat of hydration of concrete and an external heat source. The relationship between the heat of hydration release rate and age is described by an exponential decay model: in, Age The rate of heat release from hydration at any given time is expressed in W / m³. 3 ; This refers to the total heat of hydration released by concrete, expressed in W / m³. 3 The proportions are determined based on concrete mix design tests. This is the hydration heat attenuation coefficient, with units of 1 / d, determined based on concrete mix proportion tests. The concrete age is expressed in days (d).

[0068] Step S302 involves mesh generation and time step control for the temperature field solution. A hybrid meshing strategy dominated by hexahedrons is employed, with mesh refinement at locations with drastic temperature gradients, such as concrete surfaces and joints. The minimum element size ranges from 0.5 to 1.0 meters, and a gradual transition strategy is used for mesh transitions between adjacent regions. Spatial interpolation of the temperature field utilizes the isoparametric element method, with shape functions constructed based on element node coordinates.

[0069] The time step is dynamically adjusted according to the heat release law of concrete hydration. In the early stage of pouring, the heat release rate of hydration is relatively large and the temperature field changes drastically, so the time step is 1 to 2 hours; in the later stage of pouring, the heat release rate of hydration tends to be slow and the temperature field changes less, so the time step is increased to 6 to 12 hours.

[0070] The dynamic time step adjustment algorithm is based on temperature change rate monitoring. When the temperature change rate between adjacent time steps exceeds a preset threshold, the time step subdivision is automatically triggered. The convergence criterion for temperature field calculation of a single dam section is that the maximum temperature difference between adjacent iteration steps is less than 0.1℃.

[0071] Step S303: Construct a two-way coupled model of the temperature and stress fields. The coupled model considers the nonlinear characteristics of the change in the elastic modulus of concrete with temperature. The relationship between the elastic modulus and temperature is expressed as: in, For temperature The elastic modulus of concrete at time t, expressed in MPa; Reference temperature The elastic modulus is given below, in MPa, and is taken as the reference value of elastic modulus defined in step S104; For reference temperature, 20℃ is used; This is the temperature coefficient of elastic modulus, with units of 1 / ℃, determined based on concrete mix proportion tests. The current temperature of the concrete is expressed in °C.

[0072] Introducing a temperature stress correction factor To account for the effects of aging characteristics such as concrete creep and relaxation, the revised formula for calculating temperature stress is as follows: in, This refers to temperature stress, measured in MPa. This is a dimensionless temperature stress correction factor. For early-age concrete, it is taken as 0.5 to 0.7 to account for creep effects, and for later-age concrete, it is taken as 0.8 to 1.0 to reflect the enhanced elastic response. This is the elastic modulus at the current temperature, expressed in MPa. The coefficient of thermal expansion of concrete is expressed in 1 / ℃, and is taken from the coefficient of thermal expansion defined in step S104. This represents the change in temperature, expressed in °C.

[0073] Step S304 involves performing a sequential coupled iterative solution for the temperature and stress fields. First, the temperature field is solved to obtain the temperature distribution at each time step; then, the stress field is solved based on the temperature field results to calculate the temperature stress. The temperature and stress fields are linked through material properties and thermal boundary conditions.

[0074] The convergence criterion for coupled solutions is set as follows: the residual heat flux in the temperature field is less than... W; The residual stress in the stress field is less than 0.01 MPa. When both criteria are met simultaneously, the coupled solution is considered to have converged at the current moment, and the calculation proceeds to the next moment.

[0075] Step S305: Develop a crack risk level assessment method based on the crack resistance safety factor. Crack resistance safety factor. Defined as the ratio of the ultimate tensile strength of concrete to the temperature stress: in, The crack resistance safety factor is dimensionless. This is the ultimate tensile strength of concrete, expressed in MPa, and is determined based on the concrete strength grade. The temperature stress calculated in step S304 is expressed in MPa.

[0076] Crack risk level is based on crack resistance safety factor Division: When When it is judged as low risk; when It was determined to be of medium risk at that time; It is judged as high risk at that time; when It was deemed dangerous at that time.

[0077] Step S306 involves visualizing the crack risk assessment results in a 3D model within the Building Information Model (BIM). Areas of the dam body at each risk level are highlighted in the 3D scene, using color coding for differentiation: green for low-risk areas, yellow for medium-risk areas, orange for high-risk areas, and red for hazardous areas. Construction managers can click on any area to view the calculated temperature and stress fields, providing data support for the development of temperature control measures.

[0078] By constructing a coupled temperature and stress field simulation engine, the system starts with solving the transient temperature field, then obtains the temperature and stress distribution through sequential temperature-stress coupling iteration. Crack risk levels are then classified according to the crack resistance safety factor and presented in a color-coded manner in the 3D scene. The coupled simulation engine provides a quantitative basis for temperature control constraints for the intelligent decision-making model of construction process priorities. Its output temperature field, stress field, and crack risk level data are directly used as structured inputs to the dynamic update interface of the building information model attributes in step S5.

[0079] For step S4, an intelligent decision-making model for prioritizing construction procedures is established, which is carried out in the following sub-steps.

[0080] Step S401: Construct a multi-objective optimization mathematical model. The multi-objective optimization problem takes the shortest total construction period, the lowest construction cost, and the lowest construction risk as optimization objectives, and resource supply constraints, flood control constraints, temperature control constraints, and spatial construction disturbance constraints as optimization conditions.

[0081] The multi-objective problem is transformed into a single-objective problem by using the weighted summation method. The comprehensive objective function expression is as follows: in, The value of the comprehensive objective function is dimensionless. The normalized total construction period is dimensionless. The normalized total construction cost is dimensionless. The normalized total construction risk is dimensionless. , , Let be the corresponding weight coefficients, which are dimensionless and satisfy . .

[0082] The quantification of the project duration target uses the critical path method, with the total construction period as the duration indicator. The quantification of the cost target includes both direct and indirect costs. Direct costs include labor costs, material costs, and machinery usage fees, while indirect costs include management fees, camp expenses, and equipment rental fees. The quantification of the risk target uses a weighted summation method based on risk indices, assessing various construction risks according to their probability of occurrence and severity of loss to obtain a comprehensive risk index.

[0083] Step S402: Set constraints. Constraints include four categories: resource supply constraints, which stipulate the upper limit of available manpower, materials, and machinery resources for each construction period, and the resource demand of any construction sequence shall not exceed the upper limit of available resources; flood control constraints, which stipulate the physical appearance that must be completed before the flood control node, including the removal of cofferdams and the pouring of dam body to the flood control elevation.

[0084] Temperature control constraints stipulate that the concrete pouring temperature must not exceed the maximum allowable temperature, and the interval between adjacent pouring blocks must meet temperature control requirements; spatial construction interference constraints stipulate that mutually interfering construction activities cannot be carried out simultaneously in the same construction area, including the simultaneous pouring of concrete in adjacent dam sections and the construction of galleries.

[0085] Steps S401 and S402 complete the mathematical modeling of the optimization problem. First, the three optimization objectives of schedule, cost, and risk and the comprehensive objective function are determined. Then, four types of constraints are established to form a feasible search space.

[0086] Step S403: Develop a sequence solving engine based on a genetic algorithm. The construction process sequence is encoded using binary encoding. The construction process sequence is mapped to a binary string, the string length of which equals the total number of construction activities. Each bit corresponds to one construction activity, and the bit value indicates whether the corresponding construction activity is scheduled for the current position.

[0087] The initial population size is a random sequence of 100 to 200 individuals. The crossover probability is set to 0.7 to 0.9 to control the proportion of individuals in the population that undergo crossover.

[0088] The mutation probability is set to 0.01 to 0.05 to maintain population diversity. The maximum number of iterations is set to 500 to 1000. The algorithm convergence condition is set to the overall objective function value of the best individuals changing by less than 0.1% over 50 consecutive generations.

[0089] Step S404: Establish a dynamic weight adjustment mechanism for constraints. The weight adjustment strategy is based on a construction stage identification algorithm, which automatically determines the current construction stage and triggers corresponding weight adjustments. The construction stage identification algorithm takes the construction schedule and the current date as input, compares the current date with the time nodes of each stage in the construction schedule, and increases the weight coefficient corresponding to the flood control constraint to 2 to 3 times the original value when entering the pre-flood warning period.

[0090] When the high-temperature pouring season arrives, the weighting coefficient of the temperature control constraint will be increased to 2 to 2.5 times the original value; when key equipment arrives or special materials are delivered, the weighting coefficient of the corresponding resource constraint will be increased.

[0091] The weight adjustment adopts a smooth transition strategy, that is, the weight coefficient values ​​are gradually changed by linear interpolation within the transition time window to avoid oscillation of optimization results caused by sudden weight changes.

[0092] Step S405: Output the optimized construction sequence results. The output includes the optimal construction sequence, critical path, Gantt chart, and resource allocation plan. The optimal construction sequence is presented as a sequence of construction activity numbers, clearly indicating the order of each construction activity.

[0093] The critical path identifies the sequence of key construction activities that affect the overall construction period. The Gantt chart displays the planned schedule of each construction activity in a timeline format, supporting comparative analysis with actual progress. The resource allocation plan lists the required quantities of labor, materials, and machinery for each construction period, providing a basis for resource allocation and scheduling.

[0094] The intelligent decision-making model for prioritizing construction procedures established in the above steps takes schedule, cost, and risk as optimization objectives. Under the constraints of resources, flood control, temperature control, and spatial interference, it generates the optimal construction sequence through a genetic algorithm sequence solving engine and a dynamic weight adjustment mechanism.

[0095] The optimal construction sequence and resource allocation plan output by the intelligent decision-making model for construction process priority directly provide the planned progress benchmark data for the construction progress deviation calculation model in step S5. The flood season constraint and temperature control constraint are respectively provided by the construction diversion dynamic simulation module in step S2 and the temperature field stress field coupled simulation engine in step S3.

[0096] Finally, step S5 is to realize the dynamic association between the building information model and the simulation results, which is carried out in the following sub-steps.

[0097] Step S501: Develop a dynamic update interface for Building Information Model (BIM) attributes. The interface adopts an open data exchange standard, supporting data exchange formats including IFC, COBie, and IFC4 international standard formats, as well as customized extended formats for the water conservancy and hydropower industry.

[0098] The interface's data reading function automatically retrieves temperature field data, stress field data, and schedule deviation data from the simulation engine and maps this data to the attribute fields of the corresponding component units in the Building Information Model. Temperature field data is mapped to the component's temperature analysis result attribute group, including the current temperature, highest temperature, lowest temperature, and temperature gradient fields.

[0099] Stress field data is mapped to the stress analysis result attribute group of the component, including principal stress, temperature stress, and crack resistance safety factor fields; schedule deviation data is mapped to the schedule management attribute group of the component, including planned completion percentage, actual completion percentage, and schedule deviation percentage fields.

[0100] The attribute update frequency is determined according to the data type: the update cycle of the simulation results of temperature field and stress field is determined according to the simulation calculation time, ranging from 1 to 4 hours; the update cycle of construction progress deviation data is determined according to the frequency of on-site data collection, ranging from daily to weekly.

[0101] The interface supports two modes: incremental update and full update. Incremental update only transmits the data fields that have changed, while full update transmits the complete attribute dataset. The update mode is automatically selected based on the data volume and network conditions.

[0102] Step S502: Establish a construction schedule deviation calculation model. The schedule deviation is calculated by comparing the planned schedule and the actual schedule. The planned schedule data comes from the construction schedule plan determined by the construction organization design, and is based on the work breakdown structure, decomposed hierarchically into four levels: individual project, sub-project, itemized project, and construction procedure.

[0103] The actual progress data comes from data collection at the construction site, including completion status reported manually and quantity data collected by automated monitoring equipment.

[0104] The schedule variance calculation model outputs three core metrics: schedule variance percentage, critical path deviation days, and milestone achievement rate. The formula for calculating the schedule variance percentage is: in, This represents the percentage of schedule deviation. Percentage of plan completed; This represents the actual percentage completed.

[0105] The critical path deviation days are calculated based on the critical path identification results, by summing the differences between the actual duration and the planned duration of the currently completed critical path activities. Milestone achievement rate is defined as the ratio of the number of milestones completed on schedule to the total number of milestones.

[0106] Step S503: Set up a three-level early warning mechanism for schedule deviation. When the percentage of schedule deviation exceeds the first early warning threshold, a yellow early warning is triggered. The first early warning threshold is 5% to 10%, indicating that the schedule is slightly behind but still within a controllable range. When the percentage of schedule deviation exceeds the second early warning threshold, an orange early warning is triggered. The second early warning threshold is 10% to 20%, indicating that the schedule is more seriously behind and requires close attention.

[0107] A red alert is triggered when the percentage deviation from the schedule exceeds the third warning threshold. The third warning threshold is set at 20%, indicating a serious delay that requires immediate corrective action. Warning signals are sent to relevant personnel via push notifications, SMS messages, and email alerts.

[0108] Step S504: Implement the spatial association mapping between risk warning signals and the 3D model. A unique component identifier is used as the association key; each component unit in the building information model is assigned a globally unique identifier, in the format of project code-building type code-component serial number.

[0109] Risk warning information includes fields for warning type, warning level, warning content, and recommended measures, and is linked to the building information model (BIM) via an identification code field. After the association mapping is completed, the warning information is attached to the corresponding BIM component unit.

[0110] Step S505: Display construction progress deviations and risk warnings in a 3D visualization environment. The risk level distribution is displayed using a color-coding scheme: green indicates no or low risk, yellow indicates medium risk, orange indicates higher risk, and red indicates high risk.

[0111] Color coding is applied to color block rendering on 3D model surfaces and progress bar display on project progress dashboards. When a component is selected, a property panel pops up, displaying the component's analysis data and warning information, supporting drill-down to query detailed lower-level data. Through rotation, scaling, and translation operations of the 3D scene, the spatial distribution characteristics of construction risks can be observed from any angle.

[0112] The dynamic association between the building information model and simulation results achieved in step S5 maps the flooding risk data output by the construction diversion dynamic simulation module in step S2, the temperature and stress field data output by the temperature and stress field coupling simulation engine in step S3, and the planned progress data output by the construction procedure priority intelligent decision-making model in step S4 to the building information model component units through the attribute dynamic update interface. Combined with the construction progress deviation calculation and the three-level early warning mechanism, the construction progress deviation and risk warning information are comprehensively presented in the three-dimensional visualization environment in the form of color coding and attribute panel.

[0113] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects.

[0114] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A BIM-based digital management method for the construction process of water conservancy and hydropower projects, characterized in that: include: Establish a dedicated building information model component library for hydraulic structures. The library covers typical hydraulic components such as dam corridors, tunnel linings, gate chambers, pressure pipelines, spillways, and aqueducts, and the geometric accuracy of the typical hydraulic components reaches the LOD3.0 geometric accuracy level. A dynamic simulation module for construction diversion was constructed to dynamically simulate the water flow path, construction site division, and flood season water level changes during different diversion periods. A temperature and stress field coupled simulation engine was developed to simulate the temperature field evolution and provide early warning of crack risk for large-volume hydraulic concrete. The temperature and stress field coupled simulation engine performs sequential coupled iterative solution of temperature field and stress field. Establish an intelligent decision-making model for construction sequence priority, and optimize the intelligent decision-making of construction sequence priority by comprehensively considering constraints such as construction period, resource allocation, flood season, temperature control, and spatial construction interference. To achieve dynamic association between the building information model and simulation results, the flooding risk data from the construction diversion dynamic simulation module, the temperature and stress field data from the temperature and stress field coupling simulation engine, and the planned progress data output by the intelligent decision-making model for construction process priority are mapped to the corresponding component units of the building information model through the attribute dynamic update interface.

2. The BIM-based digital management method for the construction process of water conservancy and hydropower projects according to claim 1, characterized in that, Establish a dedicated building information model (BIM) component library for hydraulic structures, including: A parameterized hydraulic component template system is constructed using a constraint-based parametric modeling method. Key geometric feature points of hydraulic components are selected as constraint control nodes, and a mapping relationship between node coordinates and engineering dimension parameters is established. A series of components are automatically derived based on the input key dimension parameters through a parameter-driven engine. The design target for the reusability of a single component template is over 80%, enabling a single component template to cover over 80% of engineering variations of the same type of component through parameter adjustments.

3. The BIM-based digital management method for the construction process of water conservancy and hydropower projects according to claim 2, characterized in that, The establishment of a dedicated building information model component library for hydraulic structures also includes: Establish a geometric accuracy level standard and verification mechanism, using LOD3.0 accuracy level as the benchmark, stipulating that the linear dimension error of the geometric model of hydraulic components shall not exceed ±50mm, the angle error shall not exceed ±0.5 degrees, and the area error shall not exceed ±2%; An automatic comparison algorithm based on nearest point iteration is used to verify geometric accuracy. The surface of the geometric model generated by the component template is discretely sampled, and the sampled point cloud dataset is extracted. The standard reference model is discretized into a reference point cloud dataset. The residual vector between the sampled point cloud and the reference point cloud is calculated through iterative registration. The maximum error value is statistically analyzed. If the maximum error exceeds the geometric accuracy level standard, the current component is marked as unqualified.

4. The BIM-based digital management method for the construction process of water conservancy and hydropower projects according to claim 3, characterized in that, The establishment of a dedicated building information model component library for hydraulic structures also includes: Collect and organize the physical property information of hydraulic components, which covers mechanical properties, thermal properties and hydraulic properties, including at least concrete strength grade, elastic modulus, elastic modulus temperature decay coefficient, thermal expansion coefficient, thermal conductivity, specific heat capacity and hydraulic roughness coefficient. A mechanism for linking physical property information and geometric models is established, employing a dual-database storage structure of an attribute database and a geometric model library. The unique identifier of the component is used as the primary key. The attribute database adopts a relational database management system, while the geometric model library uses the IFC4 international standard format to store parametric geometric kernel files. The precise mapping between physical property information and geometric models is achieved through the unique identifier of the component.

5. The BIM-based digital management method for the construction process of water conservancy and hydropower projects according to claim 1, characterized in that, Construct a dynamic simulation module for construction diversion, including: The three-dimensional model of the diversion structure is obtained from the special building information model component library of hydraulic structures through the secondary development interface, and the geometric dimensions and hydraulic parameters are read. An algorithm for dividing the diversion period was developed. The multi-year monthly average flow sequence data of the watershed where the project is located was read. Statistical analysis methods were used to identify the time distribution characteristics of the high-water season, normal-water season and low-water season. The diversion period was divided according to the design flood standard determined by the project grade and the building level. A water flow path tracing model was established, and a two-dimensional shallow water equation numerical solution method was adopted. The two-dimensional shallow water equation includes a continuity equation and a momentum equation, which describe the mass conservation and momentum conservation laws of water flow on the free surface. The Manning formula was used to calculate the riverbed shear stress. The water flow path tracking model is spatially discretized and time-step controlled. The spatial discretization adopts the finite volume method, which divides the computational domain into unstructured grid cells. The time step is dynamically adjusted according to the Courant number stability condition. Output the flow velocity distribution, water depth distribution, and inundation duration distribution data for each grid node, and map the water depth distribution data to the construction site model in the 3D scene. Display the inundation risk level through color coding and present the spatiotemporal evolution of the inundation risk in the construction area in the form of a timeline animation.

6. The BIM-based digital management method for the construction process of water conservancy and hydropower projects according to claim 1, characterized in that, Develop a coupled simulation engine for temperature and stress fields, including: A nonlinear transient temperature field solution algorithm is implemented, which uses the finite element method to solve the transient heat conduction partial differential equation. The heat source intensity in the transient heat conduction partial differential equation consists of the heat release from concrete hydration and external heat sources. The relationship between the heat release rate from hydration and age is described by an exponential decay model. The temperature field solution is meshed and the time step is controlled. The mesh is refined on the concrete surface and joint surfaces, and the time step is dynamically adjusted according to the heat release law of concrete hydration. A two-way coupled model of temperature field and stress field is constructed. The two-way coupled model of temperature field and stress field considers the nonlinear characteristics of the elastic modulus of concrete changing with temperature, and introduces a temperature stress correction coefficient to account for the influence of concrete creep and relaxation aging characteristics. The sequential coupling iterative solution of temperature field and stress field is performed. First, the temperature field is solved to obtain the temperature distribution at each time. Then, the stress field is solved based on the temperature field results to calculate the temperature stress. The convergence criterion for the sequential coupling iterative solution of temperature field and stress field is set as follows: the residual heat flux of temperature field is less than a preset threshold and the residual stress of stress field is less than 0.01 MPa.

7. The BIM-based digital management method for the construction process of water conservancy and hydropower projects according to claim 6, characterized in that, The development of a temperature-stress-field coupled simulation engine also includes: A crack risk level assessment method based on a crack resistance safety factor is developed, wherein the crack resistance safety factor is defined as the ratio of the ultimate tensile strength of concrete to the temperature stress obtained by sequential coupling and iterative solution of the temperature field and stress field; The crack risk level is classified according to the crack resistance safety factor: when the crack resistance safety factor is greater than or equal to 2.0, it is judged as low risk; when the crack resistance safety factor is greater than or equal to 1.5 and less than 2.0, it is judged as medium risk; when the crack resistance safety factor is greater than or equal to 1.0 and less than 1.5, it is judged as high risk; and when the crack resistance safety factor is less than 1.0, it is judged as dangerous. The crack risk assessment results are visualized in a 3D model in the building information model, and color coding is used to highlight the dam areas at each risk level in the 3D scene.

8. The BIM-based digital management method for the construction process of water conservancy and hydropower projects according to claim 1, characterized in that, Establish an intelligent decision-making model for prioritizing construction procedures, including: A multi-objective optimization mathematical model is constructed, with the optimization objectives being the shortest total construction period, the lowest construction cost, and the lowest construction risk. The multi-objective problem is transformed into a single-objective problem using the weighted summation method. The comprehensive objective function is composed of the weighted summation of the normalized total construction period, total construction cost, and total construction risk. The constraints include resource supply constraints, flood season constraints, temperature control constraints, and spatial construction interference constraints. The flood season constraints stipulate that the concrete must be completed before the flood season. The temperature control constraints stipulate that the concrete pouring temperature must not exceed the maximum allowable temperature and that the interval between adjacent pouring blocks must meet the temperature control requirements. A sequence solving engine based on genetic algorithm was developed. The construction process sequence was encoded using binary encoding. The initial population size, crossover probability, mutation probability and maximum number of iterations were set. The convergence condition was that the change in the comprehensive objective function value of the best individuals in several consecutive generations was less than a preset threshold. The optimal construction sequence was then solved.

9. The BIM-based digital management method for the construction process of water conservancy and hydropower projects according to claim 8, characterized in that, The establishment of an intelligent decision-making model for prioritizing construction procedures also includes: Establish a dynamic weight adjustment mechanism for constraints, and automatically determine the current construction stage based on the construction stage identification algorithm and trigger the corresponding weight adjustment. When entering the pre-flood season warning period, the weighting coefficient corresponding to the flood control constraint will be increased to 2 to 3 times the original value; When the high-temperature pouring season arrives, the weighting coefficient corresponding to the temperature control constraint will be increased to 2 to 2.5 times the original value. The weight adjustment adopts a smooth transition strategy, gradually changing the weight coefficient values ​​by linear interpolation within the transition time window.

10. The BIM-based digital management method for the construction process of water conservancy and hydropower projects according to claim 1, characterized in that, Achieving dynamic correlation between building information models and simulation results also includes: Establish a construction schedule deviation calculation model, calculate the schedule deviation by comparing the planned schedule and the actual schedule, and output three core indicators: schedule deviation percentage, critical path deviation days, and milestone achievement rate. A three-level early warning mechanism for schedule deviation is set up: a yellow warning is triggered when the percentage of schedule deviation exceeds 5% to 10%, an orange warning is triggered when the percentage of schedule deviation exceeds 10% to 20%, and a red warning is triggered when the percentage of schedule deviation exceeds 20%. Risk warning information is spatially associated with the 3D model. The unique component identifier is used as the association key to link the risk warning information to the building information model component unit corresponding to the unique component identifier. The risk level distribution is displayed in the 3D visualization environment through color coding.

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