Construction database building method for hydropower project based on numerical simulation

Abstract

The application provides a kind of water and electricity engineering construction database construction method based on numerical simulation, related to water and electricity engineering digital management technical field, by the water and electricity engineering project type is disassembled into construction process and is uniformly coded, collects multi-source construction data to construct learning sample set and numerical simulation model, the measured result and simulation result are weighted and fused, the construction index of each construction process is calibrated, and the corresponding project type and offer sub item are marked out, obtain the calibrated data, and after being audited, store in database, so as to realize the systematization collection, intelligent calibration and dynamic updating of water and electricity construction data, realize the fusion of measured result and simulation result, solve the problem that the existing water and electricity engineering construction measured result and simulation result lack fusion mechanism and cannot be applied to engineering cost preparation, the application is suitable for water and electricity engineering construction data integration and engineering cost preparation.

Description

Technical Field

[0001] This invention relates to the field of digital management technology for hydropower projects, specifically to a method for constructing a hydropower project construction database based on numerical simulation. Background Technology

[0002] Hydropower projects are characterized by complex construction techniques, numerous influencing factors, long production cycles, large investments, and the unique nature of their products. Hydropower project quotas are categorized by purpose into preliminary estimate quotas, budget quotas, and construction quotas; and by cost nature into building construction quotas, equipment installation quotas, and construction machinery hourly rate quotas. Traditional hydropower project construction quota compilation primarily relies on on-site observation and experience-based estimation methods, with the key to accurate construction efficiency data being obtaining on-site measurements. However, hydropower project construction conditions are complex and variable, and on-site measurements are costly, time-consuming, and have limited samples, making it difficult to cover various working conditions, resulting in insufficient accuracy and comprehensiveness of quota data.

[0003] In recent years, numerical simulation technology has been widely used in the field of hydropower engineering construction. Hydropower engineering construction simulation involves establishing mathematical models of complex construction processes such as excavation, filling (pouring), and transportation, and using the high-speed computing, large-capacity storage, and intuitive graphical display capabilities of computers to perform digital simulations, studying the impact of changes in simulation parameters on the simulation results. However, existing technologies lack an effective mechanism for integrating simulation results with measured data, the parameter calibration of simulation models lacks sufficient data support, and simulation results are difficult to directly apply to project cost estimation. Furthermore, while existing BIM-based hydropower engineering construction simulation methods have constructed construction process model libraries, construction resource model libraries, and construction constraint condition model libraries, they have failed to achieve systematic integration and dynamic calibration of measured and simulation results, and also lack data organization and management mechanisms for project cost estimation.

[0004] Currently, existing technologies have not yet formed a complete method for establishing a construction efficiency database oriented towards hydropower projects and based on numerical simulation, which cannot meet the core needs of enterprises in market competition. Summary of the Invention

[0005] The technical problem solved by this invention: This invention provides a method for constructing a hydropower engineering construction database based on numerical simulation, which solves the problem that the existing hydropower engineering construction measured results and simulation results lack a fusion mechanism, resulting in the inability to be used for engineering cost preparation.

[0006] The technical solution adopted by this invention to solve the above-mentioned technical problems is: a method for constructing a hydropower engineering construction database based on numerical simulation, comprising the following steps:

[0007] S1. Decompose the various construction project types of hydropower projects into construction procedures, assign a unique code to each construction procedure of each construction project type, and determine the unit price compilation measurement scope and construction indicators for each construction procedure.

[0008] S2. Based on the unit price of each construction process, compile the measurement caliber and construction indicators, collect and clean multi-source construction data, including construction processes and corresponding construction indicators, and label the working conditions. The data is then classified and archived according to construction project type, construction process and working conditions to construct a data learning sample set as the actual measurement result. Based on the data learning sample set, construct the numerical simulation model corresponding to each construction process.

[0009] S3. According to the construction procedure, select the corresponding numerical simulation model, import the geometric model of the construction object and apply the corresponding working conditions to simulate the entire process of each construction procedure and obtain the simulation results. The simulation results include the construction indicators corresponding to the construction procedure.

[0010] S4. Match the measured results with the simulation results according to the construction project type, construction procedure and working condition label. For each construction indicator that is successfully matched, dynamically allocate the fusion weight based on the quality level of the measured results and perform weighted fusion. Calibrate the construction indicators of each construction procedure and mark the corresponding project type and quotation item to obtain the calibrated data.

[0011] S5. Review the calibrated data. Once approved, store the data in the database and create a multi-dimensional composite index.

[0012] Furthermore, in S1, the construction project types include earthwork, rock excavation, masonry, concrete and formwork, shotcrete and anchor support, sand and gravel preparation, foundation treatment, and other projects, as well as electromechanical installation and metal structure fabrication and installation projects; earthwork excavation projects can be broken down into excavation and loading, transportation, unloading, and empty return; rock excavation projects can be broken down into drilling, blasting, excavation and loading, transportation, unloading, empty return, and site clearing; masonry projects can be broken down into fill material transportation, spreading, and compaction; concrete pouring... The construction project is broken down into concrete mixing, transportation, pouring, vibration, and curing; the shotcrete and anchor support project is broken down into drilling, hole cleaning, cement mortar mixing, anchor rod fabrication and installation, grouting, shotcrete, and steel mesh binding; the sand and gravel preparation project is broken down into crushing, screening, washing, and transportation; the foundation treatment project is broken down into drilling, water pressure testing, grouting, grouting, and hole sealing; the electromechanical installation project is broken down into equipment hoisting, wiring, commissioning, and trial operation; and the metal structure fabrication and installation project is broken down into material cutting, welding, hoisting, installation, and commissioning.

[0013] Furthermore, in S1, the unit price compilation measurement scope includes the commencement and completion acceptance standards of construction procedures, engineering quantity measurement rules, work content, and work boundaries; the construction indicators include time indicators, output indicators, resource consumption indicators, work efficiency indicators, and working condition indicators; the time indicators include single cycle time, work table hours, daily effective working hours, and waiting time; the output indicators include daily advance, daily excavation, pouring, grouting volume, and monthly strength; the resource consumption indicators include labor consumption, machinery consumption, and material consumption; the work efficiency indicator is the ratio of qualified engineering quantity to actual working time; the working condition indicators include surrounding rock type, tunnel diameter, cross-section, burial depth, seepage flow, temperature control conditions, and blasting parameters.

[0014] Furthermore, in S2, the working condition labels include geological labels, engineering labels, process labels, resource labels, and quotation-related labels; the geological labels include surrounding rock type, rock mass integrity, weathering degree, and seepage flow; the engineering labels include tunnel diameter, cross-sectional dimensions, burial depth, slope ratio, and lining thickness; the process labels include drill-and-blast method, tunneling machine, pumping, normal operation, grouting method, and temperature control measures; the resource labels include equipment model, drilling rig type, mixing plant model, and work team configuration; and the quotation-related labels include the project type, corresponding quotation item, and measurement caliber.

[0015] Furthermore, in S2, the multi-source construction data includes at least design data, construction process record data, real-time monitoring data, geological survey data, historical project data, and industry benchmarking data.

[0016] Furthermore, in S3, corresponding numerical simulation models are selected according to the construction process, including: the excavation process adopts a mechanical and construction progress coupled simulation model, the grouting process adopts a seepage and grout diffusion coupled simulation model, the concrete pouring process adopts a temperature and stress coupled simulation model, and the multi-process cross-operation adopts a discrete event simulation model.

[0017] Furthermore, S3 also includes generating different groups of orthogonal working conditions using factors affecting construction indicators as variables, conducting simulations, and obtaining the construction indicators corresponding to the construction procedures under each working condition.

[0018] Furthermore, the factors affecting construction indicators include the type of surrounding rock, cross-sectional area, seepage conditions, equipment, and processes.

[0019] Furthermore, in S5, the multi-dimensional composite index includes a combination index of quotation sub-items, project types, construction procedures, surrounding rock categories, and equipment.

[0020] Furthermore, the method also includes: using the measured results after the completion of the new project to supplement the data learning sample set, and optimizing the simulation model parameters.

[0021] The beneficial effects of this invention are:

[0022] (1) This invention establishes a standardized construction procedure classification system by decomposing and uniformly coding the types of hydropower engineering construction projects, providing a unified data organization framework for the collection, archiving and retrieval of multi-source data, and solving the problems of scattered and inconsistent formats of hydropower engineering construction data.

[0023] (2) This invention constructs a data learning sample set and establishes a numerical simulation model, and uses numerical simulation technology to generate construction index data covering multiple working conditions, thus making up for the defects of limited field measurement results and insufficient working condition coverage.

[0024] (3) This invention matches measured results and simulation results by project type, construction procedure and working condition label, and performs weighted fusion based on the quality level of measured results. This achieves complementary advantages between measured results and simulation results, which fully utilizes the authenticity and reliability of measured results and leverages the comprehensive coverage of simulation results, thereby improving the accuracy and robustness of calibration results.

[0025] (4) By establishing a multi-dimensional composite index and a dynamic update mechanism, this invention enables rapid retrieval and continuous optimization of construction data. As new project data continues to flow back, the coverage and accuracy of the database continue to improve, providing reliable data support for the preparation of hydropower engineering costs. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a method for constructing a hydropower engineering construction database based on numerical simulation, provided by the present invention. Detailed Implementation

[0027] This invention addresses the problem that existing hydropower engineering construction data lacks a fusion mechanism between measured and simulated results, making it unsuitable for engineering cost estimation. It provides a method for constructing a hydropower engineering construction database based on numerical simulation. This method involves breaking down hydropower engineering construction project types into construction procedures and uniformly encoding them. Multi-source construction data is collected to construct a learning sample set and a numerical simulation model. The measured and simulated results are then weighted and fused. Construction indicators for each procedure are calibrated, and the corresponding project type and pricing items are labeled. The calibrated data is obtained, reviewed, and then stored in the database. This achieves systematic collection, intelligent calibration, and dynamic updating of hydropower construction data, enabling the fusion of measured and simulated results and ensuring that the construction data in the database is suitable for engineering cost estimation.

[0028] like Figure 1 As shown, the present invention provides a method for constructing a hydropower engineering construction database based on numerical simulation, comprising the following steps:

[0029] S1. Decompose the various construction project types of hydropower projects into construction procedures, assign a unique code to each construction procedure of each construction project type, and determine the unit price compilation measurement scope and construction indicators for each construction procedure.

[0030] Specifically, the construction project types include earthwork, rock excavation, masonry, concrete and formwork, shotcrete and anchor support, aggregate preparation, foundation treatment, and other projects, as well as electromechanical installation and metal structure fabrication and installation projects; earthwork excavation projects can be broken down into excavation, loading, transportation, unloading, and empty return; rock excavation projects can be broken down into drilling, blasting, excavation, loading, transportation, unloading, empty return, and site clearing; masonry projects can be broken down into fill material transportation, spreading, and compaction; concrete pouring projects... The project is broken down into concrete mixing, transportation, pouring, vibration and curing; the shotcrete and anchor support project is broken down into drilling, hole cleaning, cement mortar mixing, anchor rod fabrication and installation, grouting, shotcrete and steel mesh binding; the sand and gravel preparation project is broken down into crushing, screening, washing and transportation; the foundation treatment project is broken down into drilling, water pressure testing, slurry preparation, grouting and hole sealing; the electromechanical installation project is broken down into equipment hoisting, wiring, commissioning and trial operation; and the metal structure fabrication and installation project is broken down into material cutting, welding, hoisting and installation and commissioning.

[0031] The unit price compilation measurement scope includes the commencement and completion acceptance standards of construction procedures, engineering quantity measurement rules, work content, and work boundaries; the construction indicators include time indicators, output indicators, resource consumption indicators, work efficiency indicators, and working condition indicators; the time indicators include single cycle time, work table hours, daily effective working hours, and waiting time; the output indicators include daily advance, daily excavation, pouring, grouting volume, and monthly strength; the resource consumption indicators include labor consumption, machinery consumption, and material consumption; the work efficiency indicator is the ratio of qualified engineering quantity to actual working time; the working condition indicators include surrounding rock type, tunnel diameter, cross-section, burial depth, seepage flow, temperature control conditions, and blasting parameters.

[0032] S2. Based on the unit price of each construction process, compile the measurement caliber and construction indicators, collect and clean multi-source construction data, including construction processes and corresponding construction indicators, and label them with working condition tags. Classify and archive the data according to construction project type, construction process and working condition, and construct a data learning sample set as the actual measurement result. Based on the data learning sample set, construct the numerical simulation model corresponding to each construction process.

[0033] Specifically, the operating condition tags include geological tags, engineering tags, process tags, resource tags, and quotation-related tags; the geological tags include surrounding rock type, rock mass integrity, weathering degree, and seepage flow; the engineering tags include tunnel diameter, cross-sectional dimensions, burial depth, slope ratio, and lining thickness; the process tags include drill-and-blast method, tunneling machine, pumping, normal operation, grouting method, and temperature control measures; the resource tags include equipment model, drilling rig type, mixing plant model, and work team configuration; and the quotation-related tags include the project type, corresponding quotation item, and measurement scope.

[0034] The multi-source construction data includes at least design data, construction process record data, real-time monitoring data, geological survey data, historical project data, and industry benchmarking data.

[0035] S3. Based on the construction sequence, select the corresponding numerical simulation model, import the geometric model of the construction object and apply the corresponding working conditions to simulate the entire process of each construction sequence and obtain the simulation results. The simulation results include the construction indicators of the construction sequence.

[0036] Specifically, based on the construction process, the corresponding numerical simulation model is selected, including: the excavation process uses a mechanical and construction progress coupled simulation model, the grouting process uses a seepage and grout diffusion coupled simulation model, the concrete pouring process uses a temperature and stress coupled simulation model, and the multi-process cross-operation uses a discrete event simulation model.

[0037] S3 also includes generating orthogonal working conditions for different groups using factors affecting construction indicators as variables, conducting simulations, and obtaining the construction indicators corresponding to the construction procedures under each working condition. The factors affecting construction indicators include surrounding rock type, cross-sectional area, seepage conditions, equipment, and process.

[0038] S4. Match the measured results with the simulation results according to the project type, construction procedure and working condition label. For each construction indicator that is successfully matched, dynamically allocate the fusion weight based on the quality level of the measured results and perform weighted fusion. Calibrate the construction indicators of each construction procedure and mark the corresponding project type and quotation item to obtain the calibrated data.

[0039] S5. Review the calibrated data. Once approved, store the data in the database and create a multi-dimensional composite index.

[0040] Specifically, the multi-dimensional composite index includes a combination index of quotation sub-items, project types, construction procedures, surrounding rock categories, and equipment.

[0041] The present invention provides a method for constructing a hydropower engineering construction database based on numerical simulation, which also includes supplementing the data learning sample set with the measured results after the completion of a new project, and optimizing the simulation model parameters.

[0042] Example 1 illustrates the implementation process of the method of the present invention by taking the rock excavation process of a water diversion tunnel in a hydropower project as an example.

[0043] Step 1: Decompose and code the construction procedures and determine the measurement standards and construction indicators.

[0044] The hydropower project type "rock excavation project" is broken down into drilling, blasting, excavation and loading, transportation, unloading, empty return, and site clearing construction procedures. Each procedure is assigned a unique code. For example, the drilling procedure code is "EXC-DRL-001", where "EXC" is the rock excavation project type code, "DRL" is the drilling procedure code, and "001" is the extension code.

[0045] The unit price for the drilling process is determined by the following measurement standards: the acceptance criteria are that the drilling position, depth, and direction meet the blasting design requirements; the engineering quantity measurement rules are based on the designed drilling length; the work content includes drilling positioning, drilling rig placement, drilling, and hole cleaning; the work boundary is from the drilling rig placement to the drilling acceptance.

[0046] Determine the construction indicators for the drilling process: Time indicators include single-cycle drilling time and drilling rig operating hours; Production indicators include daily footage; Resource consumption indicators include labor consumption (drillers, assistants), machinery consumption (drill rig operating hours), and material consumption (drill bits, drill rods); Efficiency indicators are the ratio of the final blast volume to the actual drilling operation time; Working condition indicators include surrounding rock type, rock grade, tunnel diameter, cross-sectional dimensions, and tunnel inclination.

[0047] Step 2: Data acquisition, cleaning, labeling, sample set construction, and simulation model establishment.

[0048] Collect multi-source construction data: obtain design data such as surrounding rock type, rock grade, tunnel diameter, cross-sectional dimensions and tunnel inclination from design documents; obtain construction record data such as drilling time, drilling length and manpower configuration from construction logs; obtain geological survey data such as rock mass integrity and weathering degree from geological survey reports.

[0049] The collected data is cleaned by standardizing the data format and unifying time data from different sources into hourly units; outliers are removed, such as abnormally long records caused by equipment failure; and missing auxiliary working hour data is interpolated and supplemented.

[0050] Label the working conditions: Geological label indicates the surrounding rock type (e.g., Class III surrounding rock), rock grade (e.g., Class IX rock), and rock mass integrity; Engineering label indicates the tunnel diameter (e.g., 10m) and cross-sectional dimensions; Process label indicates the drill-blast method; Resource label indicates the drilling rig type (e.g., hydraulic drilling rig) and team configuration (e.g., 8 people / shift); Quotation association label indicates that the project type is "rock excavation", the corresponding quotation item is "rock tunnel excavation", and the measurement scope is "measured according to the design blasting volume".

[0051] Data learning sample sets are constructed by classifying and archiving the data according to a three-level structure: project type (rock excavation), construction procedure (drilling), and working conditions (Class III surrounding rock, Class IX rock, tunnel diameter 10m, hydraulic drilling rig, tunnel inclination ≤6°).

[0052] Based on the measured results in the data learning sample set, a numerical simulation model of the drilling process is established. A coupled simulation method of mechanics and construction progress is adopted, using surrounding rock type, rock grade, cross-sectional dimensions, drilling rig parameters, and tunnel inclination as inputs, and drilling speed, drilling time, and drill bit consumption as outputs to establish a mechanical model of the interaction between the drilling rig and the surrounding rock. The model parameters are then calibrated using the learning sample set.

[0053] Step 3: Numerical simulation.

[0054] Select the mechanical and construction progress coupled simulation model corresponding to the drilling process, import the geometric model of the tunnel cross section, apply working conditions such as mechanical parameters of Class III surrounding rock and Class IX rock, 10m tunnel diameter, and geometric constraints of tunnel inclination ≤6°, simulate the entire drilling process, and obtain simulation results, including simulated drilling speed, simulated drilling time, and simulated drill bit consumption.

[0055] Using the surrounding rock type (Class II, Class III, Class IV), rock grade (VIII, IX, X), tunnel diameter (8m, 10m, 12m), and drilling rig type (hydraulic drilling rig, pneumatic drilling rig) as variables, 54 working condition combinations were generated using orthogonal experimental design, and simulations were performed to obtain the drilling process construction indicators under each working condition.

[0056] Step 4: Matching, fusing and calibrating the measured results and simulation results.

[0057] Match measured results with simulation results according to project type (rock excavation), construction procedure (drilling), and working condition label (Class III surrounding rock, Class IX rock, 10m tunnel diameter, hydraulic drilling rig).

[0058] For successfully matched drilling operations, the resource consumption (labor consumption, drilling rig hours, drill bit consumption) and efficiency are dynamically weighted based on the quality level of the measured results. Assuming there are 15 measured samples for this condition, with data from construction logs and real-time monitoring, and a quality level of B, the measured weight is assigned 0.7 and the simulation weight 0.3. The weighted average of the measured values ​​and the simulation values ​​are calculated, and then weighted and fused according to their respective weights. The standard labor consumption, standard drilling rig hours, standard drill bit consumption, and standard efficiency for this condition are then calibrated, and the corresponding project type "rock excavation" and quotation item "rock tunnel excavation" are labeled.

[0059] Step 5: Review and entry into the database.

[0060] Verify the rationality of the calibration results: compare with historical data of similar projects to determine whether the calibration value is within a reasonable range; verify the trend consistency with the calibration values ​​of adjacent working conditions (such as Class II and Class IV surrounding rock); and verify the deviation with industry quota standards.

[0061] After verification, construction technology experts conduct a technical review of the rationality of the drilling process, and cost engineers conduct a cost review of the rationality of the pricing data. Once the review is passed, the calibration data is stored in the database, and a composite index is established for quotation items, project types, construction procedures, surrounding rock categories, rock grades, and equipment models.

[0062] Step Six: Dynamic Updates and Optimization.

[0063] After the hydropower project is completed, the measured results of the drilling process collected during actual construction will be fed back into the data learning sample set. Once the sample set reaches the preset size, the parameters of the numerical simulation model for the drilling process will be recalibrated and verified to optimize the model accuracy.

[0064] Example 2: This example uses the concrete pouring process of a hydropower dam as an example to further illustrate the implementation process of the method of the present invention.

[0065] Step 1: Decompose and code the construction procedures and determine the measurement standards and construction indicators.

[0066] The project type "Concrete Pouring Project" is broken down into construction procedures such as concrete mixing, transportation, pouring, vibration, and curing. The pouring procedure is assigned the code "CON-PLACE-001", where "CON" is the concrete pouring project type code, "PLACE" is the pouring procedure code, and "001" is the extension code.

[0067] The unit price calculation method for the pouring process is determined as follows: the acceptance standard is that the concrete is poured densely, the surface is flat, and there are no honeycomb pits; the engineering quantity measurement rule is to measure the volume as shown in the design drawings; the work content includes formwork inspection, concrete placement, leveling, and vibration; the work boundary is from the time the concrete is placed to the completion of vibration.

[0068] Determine the construction indicators for the pouring process: Time indicators include pouring cycle time and pouring time; output indicators include daily pouring intensity and monthly pouring intensity; resource consumption indicators include labor consumption (concrete workers, formwork workers), machinery consumption (vibrator, pump truck time), and material consumption (concrete, admixtures); efficiency indicators are the ratio of qualified pouring volume to actual pouring operation time; working condition indicators include temperature control conditions, pouring layer thickness, and placement method.

[0069] Step 2: Data acquisition, cleaning, labeling, sample set construction, and simulation model establishment.

[0070] Collect multi-source construction data: obtain design data such as temperature control requirements and pouring compartment scheme from design documents; obtain data such as pouring time, pouring volume, and vibration time from construction records; obtain real-time monitoring data such as concrete entry temperature and pouring temperature from temperature monitoring system.

[0071] Label the working conditions: the engineering label indicates the thickness of the pouring layer (e.g., 1.5m) and the dimensions of the slab surface; the process label indicates pumped concrete and normal concrete; the resource label indicates the model of the mixing plant, the model of the pump truck, and the team configuration.

[0072] Data is archived by project type (concrete pouring), construction procedure (pouring), and working conditions (layer thickness 1.5m, pumping into the silo) to build a data learning sample set.

[0073] Based on the learning sample set, a numerical simulation model of the concrete pouring process is established. The temperature and stress coupled simulation method is adopted, with the pouring temperature, pouring layer thickness, and pouring speed as inputs, and the pouring time, concrete temperature change, and stress distribution as outputs, to establish a simulation model of the entire concrete pouring process.

[0074] Step 3: Numerical simulation.

[0075] A temperature and stress coupled simulation model was selected, the geometric model of the dam surface was imported, and working conditions such as temperature control and pouring layer thickness were applied to simulate the entire pouring process and obtain results such as simulated pouring time, simulated temperature change curve, and simulated stress distribution.

[0076] Based on the temperature at which the material enters the silo (10℃, 15℃, 20℃), the thickness of the poured layer (1.0m, 1.5m, 2.0m), and the silo surface area (100m²), the following parameters are considered: 2 150m 2 200m 2The pouring speed (5m³ / h, 10m³ / h, 15m³ / h) was used as a variable to generate 81 sets of orthogonal working conditions for simulation.

[0077] Step 4: Matching, fusing and calibrating the measured results and simulation results.

[0078] The measured results and simulation results are matched according to the project type (concrete pouring), construction procedure (pouring), and working condition labels (layer thickness 1.5m, surface area, pumping into the sump, and sump temperature 15℃). Based on the quality level of the measured results, the fusion weight is dynamically assigned and weighted, and the standard labor consumption, machine hour consumption, material consumption, and work efficiency are calibrated.

[0079] Step 5: Review and entry into the database.

[0080] The calibration results undergo rationality verification, including comparison with historical data, trend consistency verification, and deviation verification from industry quota standards. After passing the verification, the results are reviewed by technical and cost departments and then stored in the database.

[0081] Step Six: Dynamic Updates and Optimization.

[0082] The measured results after the completion of a new project are fed back to supplement the learning sample set, optimize the simulation model parameters, and achieve continuous updates to the database.

[0083] Example 3: Taking the construction simulation of multiple overlapping operations as an example, this invention is applied in complex construction scenarios.

[0084] Step 1: Decompose and code the construction procedures and determine the measurement standards and construction indicators.

[0085] For construction scenarios involving multiple overlapping project types such as excavation, support, and concrete pouring, the work processes are broken down and coded according to each project type. The excavation process is coded as "EXC-XXX-XXX", the support process is coded as "SUP-XXX-XXX", and the concrete pouring process is coded as "CON-XXX-XXX".

[0086] Determine the unit price compilation measurement scope and construction indicators for each process to ensure clear work boundaries and coordinated measurement rules between processes.

[0087] Step 2: Data acquisition, cleaning, labeling, sample set construction, and simulation model establishment.

[0088] Multi-source construction data for each process were collected separately, each labeled with working condition tags, and archived according to project type, construction process, and working condition. Data learning sample sets and numerical simulation models for each process were then constructed.

[0089] Step 3: Numerical simulation.

[0090] For multi-process overlapping operations, a coupled simulation model is established based on the single-process simulation model using the discrete event simulation method. This model takes the duration, resource requirements, and logical relationships between processes (such as support can only be carried out after excavation is completed, and concrete pouring can only be carried out after support is completed) of each single process as input, and the total construction period, resource utilization rate, and process connection time as output, to simulate the entire process of multi-process overlapping operations.

[0091] Using process duration, resource allocation scheme, and process overlap method as variables, multiple sets of working conditions are generated for simulation to obtain construction indicators under each working condition.

[0092] Step 4: Matching, fusing and calibrating the measured results and simulation results.

[0093] The measured results and simulation results of each process are matched separately according to project type, construction procedure, and working condition label. Based on the quality level of the measured results, the fusion weight is dynamically assigned and weighted fusion is performed to calibrate standard labor consumption, machine hour consumption, material consumption, and work efficiency.

[0094] Step 5: Review and entry into the database.

[0095] The calibration results of each process are verified and reviewed for reasonableness, and stored in the database after approval.

[0096] Step Six: Dynamic Updates and Optimization.

[0097] The measured results after the completion of a new project are fed back to supplement the learning sample set, optimize the simulation model parameters, and achieve continuous updates to the database.

[0098] The hydropower engineering construction database constructed using the above method can be applied to the following scenarios:

[0099] (1) Project cost preparation: Based on the project type, construction procedures and working conditions of the project, the corresponding standard resource consumption and efficiency are quickly retrieved through multi-dimensional composite indexes to serve as the data basis for unit price preparation.

[0100] (2) Construction scheme optimization: Using the simulation model in the database, different construction schemes are compared and selected to optimize resource allocation and process arrangement.

[0101] (3) Construction progress prediction: Based on the efficiency standards in the database and combined with specific working conditions, predict the construction duration of each process and compile a construction progress plan.

[0102] (4) Cost control: Compare the actual resource consumption during construction with the standard values ​​in the database, identify deviations, and issue timely warnings and adjustments.

Claims

1. A method for constructing a hydropower engineering construction database based on numerical simulation, characterized in that, Includes the following steps: S1. Decompose the various construction project types of hydropower projects into construction procedures, assign a unique code to each construction procedure of each construction project type, and determine the unit price compilation measurement scope and construction indicators for each construction procedure. S2. Based on the unit price of each construction process, compile the measurement caliber and construction indicators, collect and clean multi-source construction data, including construction processes and corresponding construction indicators, and label the working conditions. The data is then classified and archived according to construction project type, construction process and working conditions to construct a data learning sample set as the actual measurement result. Based on the data learning sample set, construct the numerical simulation model corresponding to each construction process. S3. According to the construction procedure, select the corresponding numerical simulation model, import the geometric model of the construction object and apply the corresponding working conditions to simulate the entire process of each construction procedure and obtain the simulation results. The simulation results include the construction indicators corresponding to the construction procedure. S4. Match the measured results with the simulation results according to the construction project type, construction procedure and working condition label. For each construction indicator that is successfully matched, dynamically allocate the fusion weight based on the quality level of the measured results and perform weighted fusion. Calibrate the construction indicators of each construction procedure and mark the corresponding project type and quotation item to obtain the calibrated data. S5. Review the calibrated data. Once approved, store the data in the database and create a multi-dimensional composite index.

2. The method for constructing a hydropower engineering construction database based on numerical simulation according to claim 1, characterized in that, In S1, the construction project types include earthwork, rock excavation, masonry, concrete and formwork, shotcrete and anchor support, sand and gravel preparation, foundation treatment, and other projects, as well as electromechanical installation and metal structure fabrication and installation projects; earthwork excavation projects can be broken down into excavation and loading, transportation, unloading, and empty return; rock excavation projects can be broken down into drilling, blasting, excavation and loading, transportation, unloading, empty return, and site clearing; masonry projects can be broken down into fill material transportation, spreading, and compaction; concrete pouring projects... The project is broken down into concrete mixing, transportation, pouring, vibration and curing; the shotcrete and anchor support project is broken down into drilling, hole cleaning, cement mortar mixing, anchor rod fabrication and installation, grouting, shotcrete and steel mesh binding; the sand and gravel preparation project is broken down into crushing, screening, washing and transportation; the foundation treatment project is broken down into drilling, water pressure testing, slurry preparation, grouting and hole sealing; the electromechanical installation project is broken down into equipment hoisting, wiring, commissioning and trial operation; and the metal structure fabrication and installation project is broken down into material cutting, welding, hoisting and installation and commissioning.

3. The method for constructing a hydropower engineering construction database based on numerical simulation according to claim 1, characterized in that, In S1, the unit price compilation measurement scope includes the commencement and completion acceptance standards of construction procedures, engineering quantity measurement rules, work content, and work boundaries; the construction indicators include time indicators, output indicators, resource consumption indicators, work efficiency indicators, and working condition indicators; the time indicators include single cycle time, work table hours, daily effective working hours, and waiting time; the output indicators include daily advance, daily excavation, pouring, grouting volume, and monthly strength; the resource consumption indicators include labor consumption, machinery consumption, and material consumption; the work efficiency indicator is the ratio of qualified engineering quantity to actual working time; the working condition indicators include surrounding rock type, tunnel diameter, cross-section, burial depth, seepage flow, temperature control conditions, and blasting parameters.

4. The method for constructing a hydropower engineering construction database based on numerical simulation according to claim 1, characterized in that, In S2, the working condition labels include geological labels, engineering labels, process labels, resource labels, and quotation-related labels; the geological labels include surrounding rock type, rock mass integrity, weathering degree, and seepage flow; the engineering labels include tunnel diameter, cross-sectional dimensions, burial depth, slope ratio, and lining thickness; the process labels include drill-and-blast method, tunneling machine, pumping, normal operation, grouting method, and temperature control measures; the resource labels include equipment model, drilling rig type, mixing plant model, and work team configuration; and the quotation-related labels include the project type, corresponding quotation item, and measurement scope.

5. The method for constructing a hydropower engineering construction database based on numerical simulation according to claim 1, characterized in that, In S2, the multi-source construction data includes at least design data, construction process record data, real-time monitoring data, geological survey data, historical project data, and industry benchmarking data.

6. The method for constructing a hydropower engineering construction database based on numerical simulation according to claim 1, characterized in that, In S3, the corresponding numerical simulation model is selected according to the construction process, including: the excavation process adopts the mechanical and construction progress coupled simulation model, the grouting process adopts the seepage and grout diffusion coupled simulation model, the concrete pouring process adopts the temperature and stress coupled simulation model, and the multi-process cross-operation adopts the discrete event simulation model.

7. The method for constructing a hydropower engineering construction database based on numerical simulation according to claim 1, characterized in that, S3 also includes generating different groups of orthogonal working conditions by using factors affecting construction indicators as variables, conducting simulations, and obtaining the construction indicators corresponding to the construction procedures under each working condition.

8. The method for constructing a hydropower engineering construction database based on numerical simulation according to claim 7, characterized in that, The factors affecting construction indicators include the type of surrounding rock, cross-sectional area, seepage conditions, equipment, and processes.

9. The method for constructing a hydropower engineering construction database based on numerical simulation according to claim 1, characterized in that, In S5, the multi-dimensional composite index includes a combination index of quotation sub-items, project types, construction procedures, surrounding rock categories, and equipment.

10. The method for constructing a hydropower engineering construction database based on numerical simulation according to claim 1, characterized in that, The method further includes: using the measured results after the completion of the new project to supplement the data learning sample set, and optimizing the simulation model parameters.

Images