Enterprise AI Analysis
BIM- and AI-Driven Cost Optimization for Green-Energy Construction Projects: An Integrated Framework for Cost Prediction and Energy Performance Evaluation
This study proposes an intelligent cost prediction and energy performance collaborative analysis framework for prefabricated construction projects, integrating BIM modeling technology with AI prediction models to achieve structured extraction of engineering costs, error calibration of cost predictions, and visual evaluation of energy performance. Based on actual construction project data, the study establishes a cost prediction model and validates its accuracy through scatter point fitting and error analysis, demonstrating an average error below 3% with stable and repeatable performance. By applying an energy performance evaluation system and EPI indicators to classify building energy efficiency, the study establishes a quantitative correlation between costs and energy efficiency. A regression model coupling cost and energy benefits is further proposed to reveal causal trends between structural optimization, material cost variations, and energy performance differences. Sensitivity analysis identifies key drivers of cost and energy efficiency changes, including floor area, photovoltaic structure area, and energy conversion efficiency. The research innovations include integrated engineering data processing, collaborative cost-energy modeling, and data-driven decision support, providing technical foundations for green building cost control, energy system optimization, and intelligent engineering management. The findings hold significant engineering application value and offer scalable research directions for future green energy building development.
Executive Impact at a Glance
Key metrics demonstrating the immediate value and efficiency gains for your enterprise.
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Average Prediction Error
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Energy Efficiency
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EPI Rise per 1% PV Area Increase
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Introduction & Problem
The global construction industry faces immense pressure for energy structure transformation and green development due to 'dual carbon' strategic goals. Traditional cost management struggles with complex, dynamic cost components of green technologies (solar, energy storage, heat pumps), leading to budget variances and lack of precise metrics. BIM and AI offer advantages but are often localized, lacking an integrated framework for cost prediction and energy performance evaluation, leading to suboptimal energy allocation and cost deviations. This research aims to address these by integrating BIM data with AI algorithms for lifecycle cost optimization in green buildings, developing a full-cost optimization model, and establishing a quantifiable energy performance evaluation system.
Methodology & Framework
This study establishes a systematic design logic, data structure, model training, and energy performance evaluation methodology. It uses BIM data to build input feature sets, deep regression networks for total project costs, and integrates energy performance metrics for cost-benefit analysis. The framework consists of five phases: BIM data integration, cost forecasting modeling, energy performance evaluation, cost optimization control, and decision-making output. It defines structural factors (floor area, building envelope), energy system factors (PV capacity, annual electricity generation), cost factors (material, labor, maintenance), and energy evaluation indicators (efficiency, operating costs) to link cost and energy analysis. The BIM model is formalized as a multidimensional parameter set, undergoing preprocessing and work calculation for automated quantity extraction.
Results & Discussion
The BIM-AI system demonstrates high accuracy and stability in cost predictions, with an average error below 3%. Energy performance evaluation using EPI reveals Project A as optimal due to efficient PV systems and superior thermal insulation, highlighting the role of energy structure optimization. Cost-energy coupling analysis shows a diminishing return on EPI with increased total project costs, but significant enhancement with increased energy generation capacity. Sensitivity analysis identifies building area height and photovoltaic area as key drivers, validating the model's robustness and interpretability. Limitations include not incorporating dynamic environmental factors or adaptive adjustment mechanisms, and needing micro-level refinement for energy consumption breakdowns.
Below 3%
Average Prediction Error in Cost Forecasting
Enterprise Process Flow
BIM Data Integration
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Feature Modeling & Variable Definition
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AI Cost Prediction Model Training
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Energy Performance Evaluation
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Cost-Benefit Output & Sensitivity Analysis
| Feature | Traditional Approach | AI-Driven BIM Framework |
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| Cost Prediction Accuracy |
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| Energy Performance Evaluation |
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| Data Management & Interoperability |
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Project A: Optimal Green Energy Performance
Achieving highest EPI through integrated design
Problem: Optimizing green energy building performance requires balancing initial investment costs with long-term energy efficiency.
Solution: Project A leveraged the BIM-AI framework to analyze and optimize its energy system configuration, including highly efficient photovoltaic modules and superior thermal insulation for the building envelope. This enabled precise cost-benefit analysis and informed design decisions.
Result: Project A achieved the highest Energy Performance Index (EPI) among test samples, demonstrating optimal energy efficiency and a high-quality energy efficiency-to-cost ratio. This validates the framework's ability to drive successful green building investments.
0.92%
EPI Increase per 1% Photovoltaic Area Growth
Calculate Your Potential ROI
Estimate the financial and operational benefits of adopting an AI-driven approach for green building projects within your organization.
Estimated Annual Savings
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
A structured approach to integrating BIM and AI for cost optimization and energy performance in your projects.
Phase 1: Data Integration & Model Setup (2-4 Weeks)
Establish BIM data pipelines, define structural and energy system factors, and configure AI prediction models.
Phase 2: Model Training & Validation (4-6 Weeks)
Train deep regression networks with historical project data, validate cost prediction accuracy, and refine energy performance evaluation metrics.
Phase 3: Collaborative Analysis & Optimization (3-5 Weeks)
Conduct cost-energy coupling analysis, perform sensitivity testing, and generate actionable insights for project optimization.
Phase 4: Decision Support & Reporting (1-2 Weeks)
Integrate findings into a comprehensive decision-making framework, provide scenario analysis, and prepare detailed performance reports.
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