A Review on Machine Learning for Sustainable Construction Equipment

Abstract The construction sector is producing enormous amount of emission due to usage of heavy machinery. To address global climate concerns, mitigating these greenhouse gas (GHG) emissions is important. The latest technological advancement offers an opportunity to improve the sustainability of equipment operation, often deployed in large scale construction projects. Thus, this research evaluates machine learning algorithms to decrease equipment emission and encourages construction practitioners to adopt innovative tools to replace existing practices. A systematic review was conducted upon a collection of 15 publications related to the subject. Future research direction was given for improvement meeting real-world cases.