Probabilistic Model For Predicting Construction Worker Accident Based On Bayesian Belief Networks - Diah Sarasanty; Tri Joko Wahyu Adi; I Putu Artama Wiguna (IPTEK Journal of Proceedings Series, 2017)
The construction industry has a very important role to the growth of a country. The unique characteristics and dynamic nature of the construction industry lead to a dangerous condition and prone to accidents. The death rate due to accidents in the construction industry in 2015 increased by 4% compared to 2014. The number of occupational accidents in Indonesia from year to year experienced a trend of an increase of 5%. Unsafe behavior of workers was the main cause of 88% of accidents in the construction site, 10% due to unsafe conditions, and 2% due to the unavoidable things. In addition, the complexity of construction equipment and unsafe environment significantly determined the type of accident and severity of injuries. This study aims to propose the probability model to predict the construction worker accidents in construction projects. To improve the accuracy of the assessment of workplace accidents, Bayesian Belief Networks used as a study analysis to represent the relationship among unsafe factors such as unsafe behavior factors, unsafe environment and unsafe equipment that lead to accidents. The data was collected through project site survey, questionnaire, and interview to OSH Managers in ten construction projects. The validation is done by applying the model on four case of a high rise building. This finding shows both the probability and accurate prediction of work accidents with APE mean is 4,564 that is useful to assist the practitioners and all stakeholders especially those directly involved in construction industry and to get some recommendations steps and preventive actions in order to minimize the occurrence of fatal work accidents and improve occupational safety and health (OSH) as well as to contribute knowledge about the factors that influence the occurrence of accidents.