Machine learning in life cycle assessment and low carbon material discovery: Challenges and pathways forward for the construction industry

Here we explore machine learning (ML) integration within life cycle assessment (LCA) frameworks across diverse domains, emphasizing its transformative potential for assessing and mitigating environmental impacts in the construction industry. The literature shows that implementing ML can significantly enhance life cycle inventory modeling, predict environmental impacts more accurately, and facilitate decision-making and interpretability in various life cycle stages. Additionally, subfields like deep learning (DL) are advancing material development and optimization, which could be paired with other metrics to systematically determine low-carbon material alternatives faster than humans can. Despite notable advances through the use of ML, challenges such as data integration, model generalization, and standardization persist. We highlight some key areas of future research that would potentially overcome these barriers and advance the ability to rapidly address pressing environmental concerns. Finally, to provide initial context for how ML algorithms can be used to advance materials sustainability in the construction industry, we present a summary of their implementation in some ventures and impacts tied to the utilization of this rapidly evolving technology.