Determining the feasibility of using the automated market basket analysis method to investigate a cause-and-effect pattern of construction accidents and its safety associations

Abstract

Construction sites are complex and dangerous. Over the past decade, construction fatality rates have been high in most countries. Construction accidents cause harm to construction workers and financial loss to construction firms. Smart wearable jackets are among the most effective personal protection equipment (PPE) for India's most recent and future generations. Prevention is better than Cure. To prevent the occurrences of construction accidents and to provide better safety and health to construction workers, the sensor data has to be collected from the IoT environment and has to make it subjected to cloud-based big data analytics to provide better decision support to project managers and doctors. Further, the decision support system can be enhanced by adding semantic capabilities using Ontology, Semantic Web Services, and data mining and artificial intelligence techniques. This study highlights the feasibility of using the automated market basket analysis method to investigate the cause-and-effect pattern of construction accidents, especially when using the Apriori algorithm to extract frequent item set associations. Data File preparation is one of the most essential and significant modules of incorporating automation. Therefore the value of this research effort lies in preparing a sample database and how such a sample database can become helpful in construction safety and health management to prevent accidents, as well as the computations of measures of the Apriori algorithm that support decisions regarding construction safety and health provision to construction supervisors and managers are explained