Review the Application of ML in Construction Equipment Safety Management

Abstract

Accidents that occur frequently due to the use of construction equipment (CE) on sites are a significant concern for authorities and the Academy by the consequences for workers and society. The current method of managing and forecasting such equipment's safety relies heavily on manual inspections and checklists, which are time-consuming and may not be reliable in several cases. However, in recent years, artificial intelligence (AI) techniques, especially Machine Learning (ML) have been widely adopted in analyzing, predicting, and alerting issues related to equipment in construction management. This paper presents a preliminary review of studies that have employed machine learning to manage equipment at construction sites, revealing that these techniques have substantial potential in enhancing equipment management, predicting device lifespan, and optimizing maintenance schedules. The findings indicate that previous studies developed ML models that can also provide highly accurate monitoring and judgment in CE safety management on construction sites through assistance from additional data collection devices. In summary, this paper highlights the considerations and the flow of ML applications in enhancing equipment management and ensuring safety in construction sites. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.