Artificial intelligence in building life cycle assessment

Abstract

As the world’s population becomes increasingly urbanized, the building sector is expected to consume even more energy and resources, exacerbating environmental damage. Life cycle Inventory Assessment is one of the main stages of Life Cycle Assessment (LCA) and it is a methodology for quantifying the environmental impacts of buildings and other assets on the environment. We propose a literature review for examining the potential for Artificial Intelligence (AI) to improve the LCA of buildings. In particular, by incorporating AI into the process, it is checked that more accurate predictive modelling is enabled and the time needed for data gathering can be reduced. However, it is also noted that many challenges remain and must be still addressed, such as the need for standardized data and the risk of bias in AI algorithms. Despite these latter open challenges, it can be concluded that AI can significantly enhance sustainability in the building sector.

Highlights

• Potential for AI to improve the life cycle assessment of buildings and reduce the time needed for data gathering.

• AI can significantly enhance sustainability in the building sector, but there are still open challenges that must be addressed.

• The use of AI techniques, such as machine learning and neural networks, for data collection can help gather more accurate and relevant data for LCI.

KEYWORDS:

• Life cycle assessment
• artificial intelligence (AI)
• buildings
• sustainability
• environmental impact
• renewable energy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.