Construction carbon emission dynamic simulation and optimization decision-making method based on BIM and digital twinning

Abstract

The invention provides a construction carbon emission dynamic simulation and optimization decision-making method based on BIM and digital twinning, relates to the technical field of building construction carbon emission management and control, and solves the problem of limitation of an existing management and control process in the aspects of accounting precision, response speed, emission reduction effect and economical efficiency. The method comprises the following steps: firstly, establishing a multi-dimensional BIM model, constructing a digital twinborn body linked with a physical construction site in real time based on the model, and mapping real-time energy consumption and material data; and then monitoring and calculating the real-time carbon emission intensity of the whole construction process in combination with a dynamic carbon emission factor library, further performing multi-target optimization solution by adopting a mixed integer programming algorithm based on the intensity value and an optimization decision, outputting a low-carbon construction optimization scheme, and guiding field execution. Through BIM, digital twinning and dynamic optimization full-chain technology integration, real-time monitoring, accurate accounting, intelligent optimization and closed-loop management and control of building construction carbon emission are smoothly realized.

Description

Technical Field

[0001] This invention relates to the field of carbon emission control technology in building construction, specifically to a dynamic simulation and optimization decision-making method for construction carbon emissions based on BIM and digital twins. Background Technology

[0002] With the global climate change problem becoming increasingly severe, the construction industry, as one of the major sources of carbon emissions, faces urgent pressure to reduce emissions. This industry accounts for nearly 40% of global carbon emissions, and the effectiveness of carbon management during construction is crucial to achieving emission reduction targets. Currently, traditional construction carbon emission management suffers from significant technical bottlenecks, hindering the precise implementation of emission reduction measures. Static accounting methods generally rely on quotas or empirical coefficients for ex-post calculations, making it difficult to capture real-time emission fluctuations during the dynamic construction process. Typical scenarios, such as idle machinery and sudden surges in energy consumption caused by work process conflicts, lack effective quantitative means, leading to systematic deviations between actual and theoretical emissions.

[0003] Existing technologies also suffer from inherent data fragmentation. Building Information Models (BIMs) typically only contain geometric and topological information, while key elements such as material carbon footprint data and construction machinery energy consumption parameters rely on manual input and maintenance. When design changes occur, carbon emission calculations lag significantly behind adjustments to the project schedule; studies have confirmed that this can lead to carbon emission deviations during the construction phase exceeding 12% of the estimated value. Furthermore, current optimization strategies exhibit isolated decision-making characteristics, with carbon emission control measures often focusing on localized improvements in single aspects, failing to form a synergistic optimization mechanism with construction schedule plans and cost control targets.

[0004] To address the aforementioned issues, while carbon accounting plugins based on Building Information Modeling (BIM) estimate carbon emissions from building materials through additional databases, they fail to integrate dynamic energy consumption data from construction machinery operation, resulting in persistently high calculation error rates. Some projects deploy IoT monitoring platforms that collect machinery fuel consumption data via sensors; however, this data is only used for visual dashboard displays and fails to achieve deep integration and closed-loop linkage with the construction process simulation model. While offline optimization algorithms theoretically offer solutions, the lack of real-time data makes them prone to failure in complex actual construction environments.

[0005] While digital twin technology has achieved mature applications in manufacturing, it still faces multiple limitations in the field of construction carbon emission management. The primary obstacle lies in the bottleneck of integrating and processing multi-source heterogeneous data. It requires simultaneously coordinating component-level geometric attributes from Building Information Modeling (BIM), second-level time-series operational data from IoT devices, and unstructured text information from the supply chain; existing platforms suffer from excessive data throughput latency. Secondly, the dynamic factor coupling mechanism has not yet been mastered. Time-varying parameters such as real-time fluctuations in grid carbon emission intensity and concrete carbonization processes present fundamental compatibility barriers with traditional static model architectures. More critically, real-time optimization is constrained by computational power. When using algorithms such as mixed-integer programming to solve multi-objective collaborative optimization problems, the computation time far exceeds the minute-level response threshold required on construction sites, severely restricting the timeliness of engineering decisions. Summary of the Invention

[0006] The purpose of this invention is to address the limitations of existing carbon emission control processes in construction, particularly in terms of calculation accuracy, response speed, emission reduction effectiveness, and economic efficiency. Therefore, it proposes a dynamic simulation and optimization decision-making method for construction carbon emissions based on BIM and digital twins. This invention successfully achieves real-time monitoring, accurate calculation, intelligent optimization, and closed-loop management of construction carbon emissions through the integration of BIM, digital twins, and dynamic optimization technologies across the entire technology chain.

[0007] The present invention employs the following technical solutions to achieve its objective: A dynamic simulation and optimization decision-making method for construction carbon emissions based on BIM and digital twins includes the following steps: S1. Establish a multi-dimensional BIM model that includes geometric information of building materials, a material carbon footprint database, and construction process parameters. This model provides static basic data support for simulation optimization. S2. Based on a multi-dimensional BIM model, construct a digital twin that is linked with the physical construction site in real time. This twin continuously maps and updates the real-time energy consumption and material data of the site. S3. Based on real-time energy consumption and material data updated by mapping, combined with a dynamic carbon emission factor library, monitor and calculate the real-time carbon emission intensity throughout the construction process, providing a quantitative basis for optimization decisions. S4. Based on the calculated real-time carbon emission intensity and optimization decision instructions, a mixed integer programming algorithm is used to solve multi-objective optimization problems, outputting a low-carbon construction optimization scheme to guide on-site implementation.

[0008] Preferably, in step S1, the components of the multi-dimensional BIM model adopt LOD 400 level component details, and each component is associated with a dynamic carbon footprint label, whose label data is automatically updated as the design changes. The event-driven engine of the multi-dimensional BIM model platform listens for the triggering conditions of design changes, including: when basic design parameters change, material usage recalculation is triggered; when construction plans are modified, mechanical carbon emission factors are updated; and when supply chain data is pushed and updated, transportation carbon emission factors are corrected. Independently track the carbon footprint of each component in the multi-dimensional BIM model, and use the multi-dimensional BIM model to provide feedback on optimization decisions corresponding to design changes.

[0009] Preferably, in step S2, the digital twin collects real-time energy consumption and material data of the physical construction site through an IoT sensor network; the real-time energy consumption and material data include: the fuel or electricity consumption rate of construction machinery, the material arrival time and transportation route, and the energy efficiency of temporary facilities on site. Edge devices with computing capabilities are deployed at the locations corresponding to the data sources on the physical construction site to perform localized preprocessing before real-time energy consumption and material data are uploaded to the cloud or central platform. Localized preprocessing includes data cleaning, missing data compensation, feature extraction, and protocol conversion.

[0010] Specifically, in step S3, the dynamic carbon emission factor library contains the following: grid carbon emission factors updated based on geographical location, concrete carbonization absorption coefficient that changes over time, and nonlinear relationship curves between load and emissions of construction machinery. The dynamic carbon emission factor library uses blockchain technology for version management and achieves trusted control over the relevant parameters of carbon emission data throughout the entire declaration lifecycle through distributed ledgers and smart contracts.

[0011] Furthermore, in step S3, while monitoring and calculating real-time carbon emission intensity, a carbon emission scenario is simulated using a discrete event simulation engine; based on the comparison and evaluation between the simulated scenario and the actual carbon emission intensity, an optimization decision is formed; in the simulated scenario, the following simulation is performed: The construction process is broken down into a discrete sequence of events, the carbon emission intensity triggered by each event is dynamically calculated, and the probability distribution of the impact of material supply delay risk on carbon emissions is assessed by integrating a Monte Carlo module. Establish a carbon emission fingerprint database for various types of construction machinery, including carbon emission curves for various types of construction machinery at different time scales and energy consumption difference coefficients under different modes; The UWB positioning system tracks the movement path of construction machinery in real time and compares the movement path with the optimal path plan simulated in the multi-dimensional BIM model to provide early warning of deviation. A spatiotemporal traceability module for material carbon footprint is constructed. Reinforcement learning algorithms are used to simulate and optimize transportation batches and vehicle loading rates for off-site prefabricated components in multi-dimensional BIM models, thereby reducing carbon emissions per unit weight of material during transportation.

[0012] Preferably, in step S3, when calculating real-time carbon emission intensity, a carbon emission responsibility sharing model is constructed, the responsible entities are divided according to hierarchical dimensions, and the carbon responsibility sharing ratio of each participant is preset, including: the decision weight of the general contractor in the selection of construction machinery, the influence coefficient of the subcontractor team on the implementation efficiency of the construction site, and the responsibility ratio of the design institute for high-carbon building materials; at the same time, a concrete curing carbon absorption calculation submodule is constructed to determine the carbon dioxide absorption kinetic equation of different grades of concrete, as well as the correction factor of environmental temperature and humidity on the carbonization rate, and update it to the dynamic carbon emission factor library.

[0013] Specifically, in step S4, the mixed integer programming algorithm simultaneously processes continuous and discrete variables in the construction scheme optimization, and outputs the optimal low-carbon construction scheme that satisfies the preset multiple constraints represented by the optimization decision through mathematical modeling. Among them, the preset multiple constraints include: minimizing carbon emissions, construction period deviation not exceeding the first preset percentage of the baseline plan, and cost increase controlled within the second preset percentage of the budget.

[0014] Preferably, in step S4, after outputting the low-carbon construction optimization scheme, a carbon emission knowledge graph corresponding to the construction project is constructed, and a graph neural network is used to predict potential carbon emission hotspots for similar newly started projects. When carrying out actual on-site construction based on the low-carbon construction optimization scheme, a carbon emission early warning system based on digital twins is constructed, and multi-level early warning and emergency emission reduction are implemented through the system; During the actual construction process on site, the carbon budget allocation is dynamically adjusted using a rolling time-domain control method, and the low-carbon construction optimization scheme is optimized and updated according to a preset cycle. When outputting low-carbon construction optimization solutions, we match them with special carbon emission optimization strategies for prefabricated buildings, including: prefabricated component hoisting sequence and mechanical coordination strategies, the spatiotemporal distribution of carbon emissions from node grouting operations, and the reuse strategy of modular temporary support systems.

[0015] Preferably, the method further includes the following steps: S5. Establish a digital credit system for construction carbon emissions. Based on the cumulative carbon emission intensity throughout the construction process and the emission reduction performance after the implementation of low-carbon construction optimization schemes, determine the amount of tradable carbon credits to be obtained, and automatically execute carbon credit settlement through smart contracts preset in blockchain technology.

[0016] Preferably, the method further includes the following steps: S6. Establish a verification mechanism for carbon emission scenario simulation. In the simulation scenario, a prediction model is used to predict future carbon emissions. The predicted data is then compared with the corresponding real data using KS verification. When the error exceeds a preset threshold, the parameters of the prediction model are calibrated.

[0017] In summary, due to the adoption of this technical solution, the beneficial effects of this invention are as follows: This invention improves the efficiency of carbon emission management during building construction, enabling refined carbon control throughout the construction cycle. By linking building information models with dynamic carbon footprint labels, a component-level carbon emission calculation system is constructed, effectively controlling the calculation error range. Integrating a dynamically updated carbon emission factor library automatically corrects parameter deviations caused by regional and temporal differences. When design changes or supply chain adjustments occur, the system automatically triggers a real-time carbon accounting synchronization mechanism, effectively reducing delays caused by manual intervention.

[0018] In this invention, a monitoring and optimization system based on digital twins integrates IoT edge computing technology to achieve near real-time data acquisition and processing. Combined with a dynamic simulation engine, it performs pre-analysis of the carbon emission impacts of multiple construction scenarios, significantly improving prediction accuracy. After constructing a database of mechanical operation characteristics and optimizing equipment scheduling strategies, energy consumption curve analysis effectively reduces emissions generated by ineffective paths.

[0019] This invention also employs a multi-objective collaborative optimization algorithm to simultaneously process key factors such as carbon emissions, construction schedule, and cost control, generating a globally optimal solution within the project decision-making timeframe. When monitoring data indicates a risk of exceeding carbon emission standards, the system automatically activates an emergency control mechanism. A dedicated optimization module developed specifically for the characteristics of prefabricated buildings can further enhance the emission reduction effect during on-site construction.

[0020] This invention simultaneously establishes a trusted data storage system based on a distributed ledger, and uses smart contracts to automate the confirmation and settlement of carbon assets, effectively improving the transparency of carbon trading and reducing verification costs. Based on the calculated carbon credits, a digital credit system is applied, transforming emission reductions into tradable assets, which incentivizes enterprises to actively adopt low-carbon technologies.

[0021] This invention features a self-calibrating model maintenance mechanism that automatically adjusts key parameters when prediction deviations exceed allowable limits, ensuring the long-term reliability of the model's predictions. By fully utilizing deep learning technology to mine a knowledge base of low-carbon patterns from historical engineering projects, the design efficiency and construction adaptability of emission reduction schemes for new projects can be significantly improved. Attached Figure Description

[0022] The present invention will be further described in detail with reference to the following figures, which specifically include two figures as follows: Figure 1 This is a schematic diagram illustrating the overall process of the method of the present invention; Figure 2 Detailed illustrations of the components of the method of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0025] A dynamic simulation and optimization decision-making method for construction carbon emissions based on BIM and digital twins. Figure 1 This document provides a brief overview of the overall process of the method, which can be viewed concurrently. The key steps of the method can be summarized as follows: S1. Establish a multi-dimensional BIM model that includes geometric information of building materials, a material carbon footprint database, and construction process parameters. This model provides static basic data support for simulation optimization. S2. Based on a multi-dimensional BIM model, construct a digital twin that is linked with the physical construction site in real time. This twin continuously maps and updates the real-time energy consumption and material data of the site. S3. Based on real-time energy consumption and material data updated by mapping, combined with a dynamic carbon emission factor library, monitor and calculate the real-time carbon emission intensity throughout the construction process, providing a quantitative basis for optimization decisions. S4. Based on the calculated real-time carbon emission intensity and optimization decision instructions, a mixed integer programming algorithm is used to solve multi-objective optimization problems, outputting a low-carbon construction optimization scheme to guide on-site implementation.

[0026] This implementation method will describe in detail the specific details and preferred methods of the above steps. Key components of each step can be referred to in the accompanying text. Figure 2 The illustration.

[0027] In step S1 of this embodiment, the multi-dimensional BIM model serves as the core data carrier, providing static foundational data support for subsequent carbon emission simulation and optimization analysis. All components of the model adopt the LOD 400 precision standard, which requires components to contain detailed information directly usable for factory manufacturing, such as the precise placement of reinforcing bars in concrete components and the bolt hole positioning of steel structure connection nodes, ensuring complete consistency between geometric information and actual construction. In this embodiment, each component is associated with a dynamic carbon footprint tag, whose data can be automatically updated synchronously with design changes.

[0028] At the component detail level, in addition to geometric information, the model also embeds data on the physical composition of materials. Taking concrete components as an example, it records the specific strength grade of the cement used; for steel reinforcement components, it records the smelting and production batch information. This raw material-level data provides accurate input for carbon accounting, enabling carbon emission calculations to be traced back to the raw material production stage.

[0029] Through the event-driven engine built into the BIM platform, this implementation method will monitor multiple key change conditions in real time: when design parameters change, such as adjustments to the thickness of building walls, the engine will automatically trigger a recalculation of the relevant building material usage; when the construction plan is modified, such as replacing a tower crane with a truck crane, the engine will immediately update the carbon emission conversion factor of the corresponding construction machinery; when supply chain data is received, such as changes in the steel production location, the engine will automatically correct the carbon emission calculation factor in the material transportation process.

[0030] Based on the aforementioned multi-dimensional BIM model architecture and dynamic response mechanism, this implementation method enables precise and independent tracking of the carbon footprint of individual building components, such as independent steel beams. Through real-time data linkage, the model instantly feeds back changes in carbon emissions caused by design changes to the decision-making end, providing a quantitative basis for low-carbon design optimization. This component-level carbon footprint traceability and dynamic feedback characteristic significantly improves the precision and timeliness of carbon management throughout the building's entire lifecycle.

[0031] In step S2 of this embodiment, a digital twin is constructed based on the aforementioned multi-dimensional BIM model, dynamically linked to the physical construction site. This digital twin continuously acquires real-time operational data through an IoT sensor network deployed at the construction site, achieving accurate mapping and synchronous updates between the physical state and the digital model. The IoT sensor network covers key energy consumption and material data collection points, including core parameters such as the fuel consumption rate or electricity consumption intensity of construction machinery, the arrival time and transportation route of building materials, and the energy efficiency of temporary construction facilities.

[0032] At the data acquisition level, this implementation method preferably employs specialized sensing devices for multi-dimensional monitoring. For example, vibration acceleration sensors capture the operational characteristics of large equipment such as tower cranes or concrete pump trucks; fuel flow sensors record the fuel consumption of diesel-powered machinery in real time; positioning terminals that integrate laser ranging and RFID technology accurately track the location of materials entering the site; and power quality analysis instruments are deployed to monitor the load characteristics of temporary power supply systems. These heterogeneous sensor data collectively constitute a true feedback of the physical site's status.

[0033] As a preferred embodiment of this implementation, to improve the timeliness of data processing, edge devices with local computing capabilities, such as industrial smart gateways or edge computing servers, are configured near the data source. These devices perform multi-level preprocessing operations before the raw sensor data is transmitted to the cloud platform: performing a data cleaning process to remove outliers caused by sensor failures or environmental interference; implementing a missing data compensation mechanism, such as predicting the coordinate position of a transport vehicle based on its historical operating trajectory when the signal is briefly interrupted; performing feature value extraction and transformation to convert the raw current waveform data into feature parameters that can identify the operating power of the equipment; and completing the standardization conversion of different communication protocols to ensure the compatibility of heterogeneous data.

[0034] Through the collaborative operation of the aforementioned edge computing architecture and cloud-based digital twin, this implementation method enables real-time digital mapping of dynamic energy consumption and material flow at the construction site. The digital twin continuously integrates pre-processed on-site data streams, dynamically updating key parameters in the model such as mechanical operating status, energy consumption distribution, and material flow paths, establishing a highly reliable data foundation for subsequent dynamic carbon emission accounting. This source data processing and hierarchical computing model effectively ensures the synchronization accuracy between the physical site and the digital model, while significantly reducing the data transmission load on the cloud.

[0035] In step S3 of this embodiment, based on the aforementioned real-time mapping of physical site data by the digital twin, continuous monitoring and quantitative calculation of carbon emission intensity throughout the construction process can be achieved through the fusion analysis of a dynamic carbon emission factor library and real-time energy consumption and material data. The dynamic carbon emission factor library contains the following: grid carbon emission factors updated based on geographical location, concrete carbonization absorption coefficients that change over time, and nonlinear relationship curves between the load and emissions of construction machinery.

[0036] Among them, the power grid carbon emission factor is a power grid carbon emission conversion factor that is adjusted in real time based on geographical regional differences, which can reflect the differences in the cleanliness of electricity in different regions; the concrete carbonization absorption coefficient is a carbonization absorption correction coefficient that changes dynamically with the curing time of concrete, which can accurately characterize the carbon fixation effect of the material itself; and the nonlinear relationship curve between the load and emissions of construction machinery is a nonlinear correlation model between its load rate and carbon emissions, which can accurately describe the emission characteristics of construction machinery under partial load conditions.

[0037] As a preferred embodiment of this approach, the dynamic carbon emission factor database employs blockchain distributed ledger technology for version management and data traceability, ensuring the complete and immutable historical record of carbon emission parameters through decentralized storage. A smart contract mechanism automatically executes relevant factor update rules; for example, when a new version of the emission standard is released, a preset factor replacement process can be triggered, and the change operation is permanently recorded. This architecture achieves trusted management throughout the entire lifecycle, from factor entry and version iteration to audit traceability.

[0038] This implementation method continuously outputs carbon emission intensity indicators for each stage of the construction process by matching and calculating the real-time data stream preprocessed by the edge computing layer with a dynamic carbon emission factor library. Specifically, this includes core parameters such as instantaneous emissions from machinery operation, cumulative carbon footprint of material transportation, and emission intensity per unit time of temporary facilities. This calculation process provides quantitative basis for some key data for low-carbon optimization decisions in construction, identifying high-emission machinery operation periods or locating carbon hotspots along transportation routes. Furthermore, the collaborative analysis mechanism of dynamic factors and real-time data significantly improves the spatiotemporal accuracy and environmental adaptability of carbon emission monitoring.

[0039] As a preferred embodiment of this implementation, step S3, while monitoring and calculating real-time carbon emission intensity, also constructs dynamic scenario simulation capabilities through a discrete event simulation engine. This engine decomposes the construction process into discrete event sequences such as material arrival and machinery operation, and combines real-time monitoring data to simulate carbon emission changes under various working conditions. For example, it assesses the differences in equipment idle energy consumption caused by different tower crane scheduling schemes, quantifies the emission increment due to construction machinery waiting time caused by multi-trade cross-operations, and predicts the negative impact of extreme weather on construction efficiency and corresponding carbon emission fluctuations. The simulation process dynamically calculates the carbon emission intensity triggered by each event and analyzes the impact of risk events such as material supply delays on the carbon footprint through a Monte Carlo module. The Monte Carlo module is a functional unit built based on the Monte Carlo simulation method, used for analyzing uncertainty, risk assessment, and decision support. The Monte Carlo simulation method is a statistical simulation technique that can obtain corresponding numerical results through repeated random sampling, and can solve probability distribution problems that are difficult to solve directly due to complexity or inherent uncertainty.

[0040] To improve simulation accuracy, this implementation also established a carbon emission fingerprint database for various construction machinery. This database records the time-scale emission characteristics of different equipment models under typical operating conditions, such as the minute-level emission fluctuation curve of excavators in earthmoving operations, and the energy consumption difference coefficient of concrete pump trucks in continuous pouring and intermittent operation modes. These equipment-level emission fingerprints provide underlying data support for the simulation.

[0041] This implementation uses a UWB positioning system to capture the spatial movement trajectory of construction machinery in real time during simulation, and compares the actual path with the preset optimal path plan in the multi-dimensional BIM model in real time. When a significant deviation is detected, an early warning is triggered, such as the additional fuel consumption caused by the detour of transport vehicles or the idling emissions caused by the ineffective movement of the tower crane.

[0042] This implementation also simulates and constructs a full-chain carbon traceability mechanism for materials, which can be used based on actual data in practical applications. For example, for steel materials, it records the transportation methods and corresponding emissions from the steel mill to the processing plant; for prefabricated components, it calculates the steam energy consumption allocation value during the factory curing stage. This part of the function can be simulated and constructed as a spatiotemporal traceability module for material carbon footprint and applied in practice. Furthermore, for off-site prefabricated components, an adaptive learning algorithm is used to optimize the transportation scheme. By simulating the combined effects of different loading rates and departure frequencies, the optimal scheduling strategy that minimizes carbon emissions per unit of material transportation is found.

[0043] This implementation method identifies the root causes of high emissions and generates optimization decisions by comparing actual carbon emission data with simulated scenarios. For example, it adjusts equipment coordination timing based on tower crane scheduling simulation results to reduce idle operation, or corrects on-site work routes based on transportation path deviation warnings. Through this kind of dynamic optimization mechanism that combines virtual and real data, the predictability and accuracy of carbon emission control during construction can be significantly improved.

[0044] In step S3 of this embodiment, when calculating real-time carbon emission intensity, a carbon emission responsibility sharing model is also constructed. The responsible entities are divided according to hierarchical dimensions, and the carbon responsibility sharing ratio of each participant is preset, including: the decision weight of the general contractor in the selection of construction machinery, the influence coefficient of the subcontractor team on the implementation efficiency of the construction site, and the responsibility ratio of the design institute for high-carbon building materials. At the same time, a concrete curing carbon absorption calculation submodule is constructed to determine the carbon dioxide absorption kinetic equation of different grades of concrete, as well as the correction factor of environmental temperature and humidity on the carbonization rate, and update it to the dynamic carbon emission factor library.

[0045] In step S4 of this embodiment, the mixed-integer programming algorithm simultaneously processes continuous variables (e.g., concrete usage) and discrete variables (e.g., construction machinery selection) in the construction scheme optimization. Through mathematical modeling, it outputs the optimal low-carbon construction scheme that satisfies the preset multiple constraints represented by the optimization decision. These preset multiple constraints include: minimizing carbon emissions, ensuring the construction period deviation does not exceed 5% of the baseline plan, and controlling cost increases within 8% of the budget.

[0046] In step S4 of this embodiment, after outputting the low-carbon construction optimization scheme, a carbon emission knowledge graph corresponding to the construction project is also constructed. For example, the association rules of low-carbon construction methods for pile foundation engineering under similar geological conditions, the carbon efficiency ratio law of temporary facility heating schemes in different climate zones, etc. Based on these knowledge graphs, graph neural networks can be used to predict potential carbon emission hotspots of newly started projects of the same type.

[0047] When conducting actual on-site construction based on the low-carbon construction optimization scheme, a carbon emission early warning system based on digital twins is constructed. This system is used to implement multi-level early warning and emergency emission reduction. The multi-level early warning includes three alarm scenarios, and the warning level can be modified according to the actual values, as follows: (1) The daily carbon emission intensity exceeds the industry benchmark value by 20%; (2) The emission growth rate exceeds the preset threshold for three consecutive hours; (3) Key emission reduction measures were not implemented as planned.

[0048] When a carbon emission early warning system is triggered, it can automatically generate an emergency emission reduction plan package, including suggestions for suspending construction on non-critical routes, a list of backup low-energy equipment replacements, and a work shift schedule. Simultaneously, during actual on-site construction, a rolling time-domain control method is used to dynamically adjust the carbon budget allocation, optimizing and updating subsequent low-carbon construction optimization plans on a 24-hour cycle.

[0049] This implementation method, when outputting low-carbon construction optimization solutions, incorporates dedicated carbon emission optimization strategies for prefabricated buildings, including: precast component hoisting sequence and machinery coordination strategies, spatiotemporal distribution of carbon emissions during node grouting operations, and reuse strategies for modular temporary support systems. Specific application examples of these three strategies include: during precast component hoisting, rationally planning machinery movement paths and operation sequences to reduce equipment idling and repetitive work; during node grouting operations, combining real-time monitoring systems to control material usage and work rhythm to achieve precise spatiotemporal management of carbon emissions; and for modular temporary support systems, improving resource utilization efficiency and reducing material waste and waste emissions through standardized design and multiple reuses.

[0050] As a preferred embodiment of this method, the method further includes the following steps: S5. Establish a digital credit system for construction carbon emissions. Based on the cumulative carbon emission intensity throughout the construction process and the emission reduction performance after the implementation of low-carbon construction optimization schemes, determine the amount of tradable carbon credits to be obtained, and automatically execute carbon credit settlement through smart contracts preset in blockchain technology.

[0051] S6. Establish a verification mechanism for carbon emission scenario simulation. In the simulation scenario, a prediction model is used to predict future carbon emissions. The predicted data is then compared with the corresponding real data using KS verification. When the error exceeds a preset threshold, the parameters of the prediction model are calibrated.

[0052] In this implementation, based on the cumulative carbon emission intensity benchmark value throughout the construction process and combined with the actual emission reductions verified after the implementation of the low-carbon optimization plan, digital carbon credits can be generated for market trading. The digital credit system for construction carbon emissions achieves automated settlement through blockchain smart contracts. Credit allocation is automatically executed when preset trigger conditions are met, such as when emission reductions verified by a third-party institution reach a threshold, or when the project obtains a specific level of green construction certification. The contract automatically retrieves emission reduction performance data to calculate the amount of credits due and writes it into the distributed ledger. This mechanism ensures the reliable transfer and ownership traceability of carbon assets.

[0053] By permanently recording the generation and trading of carbon credits through a blockchain distributed ledger, a trusted and verifiable record is achieved across the entire chain, from emission reduction activities to carbon asset conversion. Credit settlement rules are clearly defined through programmable contract terms; for example, energy savings from optimized tower crane scheduling can be converted into standard credit units, or the carbon emission reduction rate of prefabricated component transportation can be matched with corresponding credit weights.

[0054] This digital credit system provides construction companies with a visualized path to accumulate carbon assets, significantly enhancing the market value conversion capability of low-carbon construction measures. It is also an important means to incentivize companies to fully value the significance of low-carbon practices and adopt low-carbon technologies.

[0055] This implementation method simultaneously constructs a dynamic verification system for the carbon emission prediction model. After running the future carbon emission prediction algorithm in the simulation environment, the statistical distribution consistency test (KS test) is performed between the predicted data stream and the actual emission data collected during the actual construction process. When the test results show that the deviation between the predicted value and the measured value exceeds the allowable error range (e.g., 15%), the model parameter calibration procedure is automatically initiated. This verification mechanism continuously optimizes the adaptability of the prediction model, such as correcting the impact coefficient of extreme weather or adjusting the efficiency parameters of mechanical collaborative operations, thereby improving the reliability of long-term carbon emission prediction.

Claims

1. A method for dynamic simulation and optimization decision-making of construction carbon emissions based on BIM and digital twins, characterized in that, Includes the following steps: S1. Establish a multi-dimensional BIM model that includes geometric information of building materials, a material carbon footprint database, and construction process parameters. This model provides static basic data support for simulation optimization. S2. Based on a multi-dimensional BIM model, construct a digital twin that is linked with the physical construction site in real time. This twin continuously maps and updates the real-time energy consumption and material data of the site. S3. Based on real-time energy consumption and material data updated by mapping, combined with a dynamic carbon emission factor library, monitor and calculate the real-time carbon emission intensity throughout the construction process, providing a quantitative basis for optimization decisions. S4. Based on the calculated real-time carbon emission intensity and optimization decision instructions, a mixed integer programming algorithm is used to solve multi-objective optimization problems, outputting a low-carbon construction optimization scheme to guide on-site implementation.

2. The construction carbon emission dynamic simulation and optimization decision-making method according to claim 1, characterized in that: In step S1, the components of the multi-dimensional BIM model adopt LOD 400 level component details, and each component is associated with a dynamic carbon footprint label, whose label data is automatically updated as the design changes. The event-driven engine of the multi-dimensional BIM model platform listens for the triggering conditions of design changes, including: when basic design parameters change, material usage recalculation is triggered; when construction plans are modified, mechanical carbon emission factors are updated; and when supply chain data is pushed and updated, transportation carbon emission factors are corrected. Independently track the carbon footprint of each component in the multi-dimensional BIM model, and use the multi-dimensional BIM model to provide feedback on optimization decisions corresponding to design changes.

3. The construction carbon emission dynamic simulation and optimization decision-making method according to claim 1, characterized in that: In step S2, the digital twin collects real-time energy consumption and material data of the physical construction site through an IoT sensor network. The real-time energy consumption and material data include: the fuel or electricity consumption rate of construction machinery, the material arrival time and transportation route, and the energy efficiency of temporary facilities on site. Edge devices with computing capabilities are deployed at the locations corresponding to the data sources on the physical construction site to perform localized preprocessing before real-time energy consumption and material data are uploaded to the cloud or central platform. Localized preprocessing includes data cleaning, missing data compensation, feature extraction, and protocol conversion.

4. The construction carbon emission dynamic simulation and optimization decision-making method according to claim 1, characterized in that, In step S3, the dynamic carbon emission factor library contains the following: grid carbon emission factors updated based on geographical location, concrete carbonation absorption coefficient that varies over time, and nonlinear relationship curves between load and emissions of construction machinery. The dynamic carbon emission factor library uses blockchain technology for version management and achieves trusted control over the relevant parameters of carbon emission data throughout the entire declaration lifecycle through distributed ledgers and smart contracts.

5. The construction carbon emission dynamic simulation and optimization decision-making method according to claim 1, characterized in that: In step S3, while monitoring and calculating real-time carbon emission intensity, a discrete event simulation engine is used to simulate carbon emission scenarios. Based on the comparison and evaluation between the simulated scenarios and actual carbon emission intensity, an optimization decision is made. The following simulations are performed in the simulated scenarios: The construction process is broken down into a discrete sequence of events, the carbon emission intensity triggered by each event is dynamically calculated, and the probability distribution of the impact of material supply delay risk on carbon emissions is assessed by integrating a Monte Carlo module. Establish a carbon emission fingerprint database for various types of construction machinery, including carbon emission curves for various types of construction machinery at different time scales and energy consumption difference coefficients under different modes; The UWB positioning system tracks the movement path of construction machinery in real time and compares the movement path with the optimal path plan simulated in the multi-dimensional BIM model to provide early warning of deviation. A spatiotemporal traceability module for material carbon footprint is constructed. Reinforcement learning algorithms are used to simulate and optimize transportation batches and vehicle loading rates for off-site prefabricated components in multi-dimensional BIM models, thereby reducing carbon emissions per unit weight of material during transportation.

6. The construction carbon emission dynamic simulation and optimization decision-making method according to claim 1, characterized in that: In step S3, when calculating real-time carbon emission intensity, a carbon emission responsibility sharing model is constructed, the responsible entities are divided according to hierarchical dimensions, and the carbon responsibility sharing ratio of each participant is preset, including: the decision weight of the general contractor in the selection of construction machinery, the influence coefficient of the subcontractor team on the implementation efficiency of the construction site, and the responsibility ratio of the design institute for high-carbon building materials; at the same time, a concrete curing carbon absorption calculation submodule is constructed to determine the carbon dioxide absorption kinetic equation of different grades of concrete, as well as the correction factor of environmental temperature and humidity on the carbonization rate, and update it to the dynamic carbon emission factor library.

7. The construction carbon emission dynamic simulation and optimization decision-making method according to claim 1, characterized in that: In step S4, the mixed integer programming algorithm simultaneously processes continuous and discrete variables in the construction scheme optimization, and outputs the optimal low-carbon construction scheme that satisfies the preset multiple constraints represented by the optimization decision through mathematical modeling. The preset multiple constraints include: minimizing carbon emissions, construction period deviation not exceeding the first preset percentage of the baseline plan, and cost increase controlled within the second preset percentage of the budget.

8. The construction carbon emission dynamic simulation and optimization decision-making method according to claim 1, characterized in that: In step S4, after outputting the low-carbon construction optimization scheme, a carbon emission knowledge graph corresponding to the construction project is also constructed, and a graph neural network is used to predict potential carbon emission hotspots for similar newly started projects. When carrying out actual on-site construction based on the low-carbon construction optimization scheme, a carbon emission early warning system based on digital twins is constructed, and multi-level early warning and emergency emission reduction are implemented through the system; During the actual construction process on site, the carbon budget allocation is dynamically adjusted using a rolling time-domain control method, and the low-carbon construction optimization scheme is optimized and updated according to a preset cycle. When outputting low-carbon construction optimization solutions, we match them with special carbon emission optimization strategies for prefabricated buildings, including: prefabricated component hoisting sequence and mechanical coordination strategies, the spatiotemporal distribution of carbon emissions from node grouting operations, and the reuse strategy of modular temporary support systems.

9. The construction carbon emission dynamic simulation and optimization decision-making method according to claim 5, characterized in that, The method also includes the following steps: S5. Establish a digital credit system for construction carbon emissions. Based on the cumulative carbon emission intensity throughout the construction process and the emission reduction performance after the implementation of low-carbon construction optimization schemes, determine the amount of tradable carbon credits to be obtained, and automatically execute carbon credit settlement through smart contracts preset in blockchain technology.

10. The construction carbon emission dynamic simulation and optimization decision-making method according to claim 9, characterized in that, The method also includes the following steps: S6. Establish a verification mechanism for carbon emission scenario simulation. In the simulation scenario, a prediction model is used to predict future carbon emissions. The predicted data is then compared with the corresponding real data using KS verification. When the error exceeds a preset threshold, the parameters of the prediction model are calibrated.

Images