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The integration of Artificial Intelligence (AI) methodologies into Building Life Cycle Assessment (BLCA) represents a significant advancement in the field of sustainable construction. This paper proposes an advanced methodology by integrating Artificial Intelligence (AI) with Building Information Modelling (BIM) for improved Building Life Cycle Assessment (BLCA) in sustainable construction to reduce energy consumption and carbon emissions. Investigating AI's potential for predictive environmental impact assessment, the study utilizes machine learning (ML) techniques to efficiently analyse several datasets, predicting impacts across building lifecycle stages. Emphasizing the essential role of AI in addressing sustainability challenges, the methodology supports informed decision-making for environmental mitigation. AI-BIM for BLCA also optimizes designs and operational strategies, promoting resource efficiency and reducing environmental footprints. Through a comprehensive review of literature and case studies, the paper underscores integrated AI-BIM benefits, including enhanced predictive capabilities, improved decision-making, and identification of novel sustainable opportunities. This approach promotes innovation by revealing insights into emerging technologies and materials, enhancing overall environmental performance. Despite challenges like data availability and algorithm complexity, ongoing research efforts are addressing these issues, paving the way for widespread implementation of AI-BIM for BLCA, marking a significant leap forward in environmental impact assessment within the construction industry.

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A Methodology Proposing AI-BIM Integration for Enhanced Environmental Impact Assessment in Building Life Cycle Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Sadaf Gachkar,
  • Darya Gachkar,
  • Antonio García Martínez,
  • Cecilio Angulo Bahón

摘要

The integration of Artificial Intelligence (AI) methodologies into Building Life Cycle Assessment (BLCA) represents a significant advancement in the field of sustainable construction. This paper proposes an advanced methodology by integrating Artificial Intelligence (AI) with Building Information Modelling (BIM) for improved Building Life Cycle Assessment (BLCA) in sustainable construction to reduce energy consumption and carbon emissions. Investigating AI's potential for predictive environmental impact assessment, the study utilizes machine learning (ML) techniques to efficiently analyse several datasets, predicting impacts across building lifecycle stages. Emphasizing the essential role of AI in addressing sustainability challenges, the methodology supports informed decision-making for environmental mitigation. AI-BIM for BLCA also optimizes designs and operational strategies, promoting resource efficiency and reducing environmental footprints. Through a comprehensive review of literature and case studies, the paper underscores integrated AI-BIM benefits, including enhanced predictive capabilities, improved decision-making, and identification of novel sustainable opportunities. This approach promotes innovation by revealing insights into emerging technologies and materials, enhancing overall environmental performance. Despite challenges like data availability and algorithm complexity, ongoing research efforts are addressing these issues, paving the way for widespread implementation of AI-BIM for BLCA, marking a significant leap forward in environmental impact assessment within the construction industry.