An adaptive evacuation system for the Gran Sasso underground laboratory

Abstract

The Gran Sasso National Laboratory (LNGS) is, at present, the largest deep underground laboratory in operation for astroparticle physics and rare event research. The LNGS was created to carry out this research exploiting an overburden of 1,400 m of rock to reduce the flux of muons from cosmic rays. Operating an underground laboratory and its facilities implies a high level of risk. To mitigate risks at the LNGS, a crucial aspect is represented by the evacuation of people from an underground environment during emergencies. The connection between the underground facilities and the outside infrastructure is limited, and the intervention by rescue teams is complicated. This paper reports the study of an adaptive evacuation system to improve the evacuation performance in underground laboratories. The system proposed is composed of a combination of passive, dynamic, and adaptive signage that is able to adapt itself to lead the laboratory occupants to the safe location for evacuation (assembly point). The system collects information from all safety plants, and the data are processed using a customized path-finding algorithm. In the computational algorithm, the underground laboratory is represented as a grid, and the customized path-finding algorithm discovers all available paths to reach the identified evacuation assembly point.