A Bayesian Model for real-time safety management in construction sites

This article reports on an ongoing research project, which is aimed at implementing advanced probabilistic models for real time identification of hazardous events in construction sites. The model will own intelligent capabilities for automated recognition in near real-time of hazardous events during the execution phase. To this aim, the features offered by Bayesian Networks have been exploited. Given that there are no long time series on past occurrences of hazardous events in all the potential contextual combinations presently available, the probabilistic models cannot be trained just through datasets. For that reason, the few available data have been integrated with expert opinions. In particular, the conditional probabilities of the Bayesian networks are estimated by an elicitation process of subjective knowledge from the opinions of experts. The complexity of the phenomena under analysis are modeled as a tree structure with several levels (corresponding to the Work Breakdown Structure hierarchy); it provides therefore a clear view of the global picture. The built hierarchical tree allows the expert to weigh more easily causal relationships involved and to define the qualitative structure of the net, too. Furthermore, the article describes and tests how conditional probabilities of the variables in the networks can be estimated, through gathering and interviewing groups of stakeholders and experts. The presented research has led to the definition of a probabilistic model using elicitation techniques for subjective knowledge. Furthermore, the development of such a model is part of a wider system relying on the implementation of a real-time monitoring network.