Data-driven AI algorithms for construction machinery

Based on the transition to Industry 4.0, construction operations are gradually moving towards large-scale and high-efficiency development. However, excessive manual labor is becoming a problem, affecting construction industry progress, and causing significant safety hazards. As the continuous development of artificial intelligence and big date technologies, intelligent construction machinery with data-driven methods is considered the best solution for enhancing construction safety and efficiency, which are mainly reflected in prognostic and health management, environment perception and automation control. Therefore, this paper reviews the widespread research on semi-automatic or even fully automatic construction methods reported in the literature. Firstly, it introduces several widely-used artificial intelligence algorithms and their variations. Secondly, three main topics were covered: prognostic and health management applications in experimental and real-world settings, environmental perception systems, and automation control methods for construction machinery. Finally, several research prospects and challenges were presented. [Display omitted] •An overview of common machine learning techniques is provided.•Enhancements in prognostics and health management are examined.•The application of machine learning in environmental perception and automation control is explored.•Recommendations for improving the use of AI technologies in construction machinery are suggested.