Application of machine learning initiatives and intelligent perspectives for CO2 emissions reduction in construction

Abstract

The construction sector is one of the main contributors to carbon dioxide (CO2) emission and causes of global warming. CO2 mitigation solutions are vital. New technologies can facilitate and improve these efforts. Thus, the paper reviews how new technologies of artificial intelligence and machine learning have contributed to CO2 emissions reduction in construction and what techniques have been applied in the literature to provide significant information that will be beneficial for the construction sector design and management. The paper provides the results of a content review, including their contributions and gaps. A total of 78 papers were identified to develop the dataset. The method was a combination of systematic reviews, including co-occurrence analytical map development of the main keywords, co-authorship network analyses, publication source analyses, and content analysis, including theme identification and review of the selected papers, which were divided into five conceptual clusters based on their scopes: (1) sustainable materials and components design/production, (2) on-site vehicles and equipment, (3) energy and life cycle assessment, (4) optimization, decision-making and solution-based platforms, and (5) real-world monitoring. The content of each cluster of papers was also reviewed, and the potential gaps were identified and discussed. A set of directions for future research investigations were presented that can be a valuable source for researchers in their future research. This paper contributes to the current knowledge base by presenting insights into intelligent techniques in the construction industry to mitigate CO2 emissions.