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Sustainability-driven structural design using artificial intelligence

 Sustainability-driven structural design using artificial intelligence
Author(s): , , ORCID
Presented at IABSE Congress: The Evolving Metropolis, New York, NY, USA, 4-6 September 2019, published in , pp. 1057-1064
DOI: 10.2749/newyork.2019.1057
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The construction industry is responsible for a large share of the global environmental impact. The need for addressing sustainability and increased competition calls for the development of innovati...
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Bibliographic Details

Author(s): (NCC AB; Chalmers University of Technology)
(NCC AB; Chalmers University of Technology)
ORCID (Chalmers University of Technology)
Medium: conference paper
Language(s): English
Conference: IABSE Congress: The Evolving Metropolis, New York, NY, USA, 4-6 September 2019
Published in:
Page(s): 1057-1064 Total no. of pages: 8
Page(s): 1057-1064
Total no. of pages: 8
DOI: 10.2749/newyork.2019.1057
Abstract:

The construction industry is responsible for a large share of the global environmental impact. The need for addressing sustainability and increased competition calls for the development of innovative design methods that include sustainability in a transparent way. The aim of this work is to propose a framework to use machine learning and artificial intelligence (AI) for structural design optimization based on sustainability and buildability criteria. AI opens up new possibilities to optimize and assess structures early in the planning and design stages. In that way, it is possible to decrease the negative and enhance the positive environmental, economic and social impacts and create a more time‐ and cost‐effective design process. The work is meant to serve as a first step toward the development of AI‐based methods in the construction industry, which can bring digitalization in the construction industry to a new level and create new services and business models.

Keywords:
sustainability construction structural design optimization artificial intelligence set‐based design life-cycle sustainability assessment multi‐criteria decision analysis