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The construction industries are growing green to create a more sustainable society with eco-friendly materials. The building materials are expected to possess not only the attributes of reliability, consistency, and durability but also green characteristics. The materials are labeled as green based on their environmental impacts and lifecycle assessments and the eco components are considered to be an integral component of these materials. The choice making of these building materials as green materials depends both on their material properties and environmental performances. However, the decision-making on green material selection is an intricate process and this research work employs deep learning networks in formulating a choice-making decision model. The deep learning model is trained with different sets of structured data encompassing different input features. The resultants of the decision model assist the decision-makers in making optimal choices of materials possessing low carbon impacts, minimal waste generation, and building sustainability. This deep learning-based model is highly potent in contributing to the goal of attaining a greener and more sustainable society.

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Data-Driven Approaches to Green Building Material Selection Using Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Sakshi Taaresh Khanna,
  • N. Anitha,
  • Utpal Saikia,
  • S. Indrakumar,
  • M. Clement Joe Anand,
  • S. Sujitha Priyadharshini

摘要

The construction industries are growing green to create a more sustainable society with eco-friendly materials. The building materials are expected to possess not only the attributes of reliability, consistency, and durability but also green characteristics. The materials are labeled as green based on their environmental impacts and lifecycle assessments and the eco components are considered to be an integral component of these materials. The choice making of these building materials as green materials depends both on their material properties and environmental performances. However, the decision-making on green material selection is an intricate process and this research work employs deep learning networks in formulating a choice-making decision model. The deep learning model is trained with different sets of structured data encompassing different input features. The resultants of the decision model assist the decision-makers in making optimal choices of materials possessing low carbon impacts, minimal waste generation, and building sustainability. This deep learning-based model is highly potent in contributing to the goal of attaining a greener and more sustainable society.