STATISTICAL AND AI MODELING OF UK AND FRANCE ELEVATOR ACCIDENTS AND THEIR VIOLATING SAFETY RULES - Zarikas, Vasilios; Zholdasbayeva, Moldir; Mitra, Ayan (Journal of Applied Engineering Science, 2023)
This study presents a statistical analysis applying different statistical techniques, including trained Bayesian Networks an artificial intelligence (AI) method, to explore datasets of lift accidents involving safety rules for two countries: UK and France. The study concerns six years data for both countries and covers almost all elevator accidents taken place during private and professional uses; 218 cases for UK and 205 cases for France. The relevant time interval for U.K. is 6th January 2006 to 29th December 2012, while for France data concern the period of 18th February 2003 to 17th December 2009. The major aim of the study is to exhibit and demonstrate that for accident datasets, at least for similar datasets, multiple statistical methods have to be applied in order to extract reliable information, i.e. investigate interactions among factors and therefore help to develop prevention measures. Three statistical models were built to derive associations between factors concerning violation of rules related to the installation and maintenance of elevators, passengers’ safety rules, risks and unforeseen circumstances. Associations between severity of injury and categories of gender or age of injured people have been found. Furthermore, specific influences between severity of injury and categories of type of rules or of type of accident have been identified. The obtained results will contribute to the design of efficient methods to avoid future accidents in both countries.