Integration of Intelligent Models and Sensing to Prevent Hardly Predictable Hazards in Construction Sites

This article reports on the current state of an ongoing research project which is aimed at implementing intelligent models for hardly predictable hazard scenarios identification in construction sites. As past evidences showed that no programmatic action can deal with the unpredictable nature of many risk dynamics, we tried to survey on how the current approach for safety management in the construction industry could be improved. In our previous research the use of Bayesian networks elicited from subjective knowledge were preliminarily tested. Those networks might be meant as a reliable knowledge map about accident dynamics and they showed that a relevant ratio of occurrences fall in “hardly predictable hazards” class, which cannot be warded off by programmatic safety measures. This paper reports the second outcome of our research project, which focused on the development of first elementary fragments, regarding the occurrence of a possible “hardly predictable scenario”. Instead of experts’ contributions (who, over their carrier, seldom incurred in accidents), we used “legal cases” as an accurate source of information. They suggested which categories of “hidden hazard scenarios” are more likely to happen. We found that the most frequent hidden hazard scenarios are linked to operator’s negligence and abnormal behavior, e.g. irregular removal of scaffolding’s components, unprotected openings, improper use of PPE, etc. Every pattern determined by legal cases has been formalized by a fragment (i.e. elementary network) of the overall Bayesian network. Finally, all the elementary networks were integrated into a comprehensive intelligent tool for realtime hardly predictable hazards prevention. The final setup, asked for interfacing these intelligent models to a low-level sensor network and used to feed them with inputs about the current state of the context, is discussed too.