Print Email Facebook Twitter Learning about risk Title Learning about risk: Machine learning for risk assessment Author Paltrinieri, Nicola (Norwegian University of Science and Technology (NTNU)) Comfort, Louise (University of Pittsburgh) Reniers, G.L.L.M.E. (TU Delft Safety and Security Science; Universiteit Antwerpen; Katholieke Universiteit Leuven) Date 2019 Abstract Risk assessment has a primary role in safety-critical industries. However, it faces a series of overall challenges, partially related to technology advancements and increasing needs. There is currently a call for continuous risk assessment, improvement in learning past lessons and definition of techniques to process relevant data, which are to be coupled with adequate capability to deal with unexpected events and provide the right support to enable risk management. Through this work, we suggest a risk assessment approach based on machine learning. In particular, a deep neural network (DNN) model is developed and tested for a drive-off scenario involving an Oil & Gas drilling rig. Results show reasonable accuracy for DNN predictions and general suitability to (partially) overcome risk assessment challenges. Nevertheless, intrinsic model limitations should be taken into account and appropriate model selection and customization should be carefully carried out to deliver appropriate support for safety-related decision-making. Subject Deep learningDynamic risk analysisMachine learningRisk assessment To reference this document use: http://resolver.tudelft.nl/uuid:1e7795d3-0462-499b-b152-021ea27bc43f DOI https://doi.org/10.1016/j.ssci.2019.06.001 ISSN 0925-7535 Source Safety Science, 118, 475-486 Part of collection Institutional Repository Document type journal article Rights © 2019 Nicola Paltrinieri, Louise Comfort, G.L.L.M.E. Reniers Files PDF 1_s2.0_S0925753518311184_main.pdf 2.02 MB Close viewer /islandora/object/uuid:1e7795d3-0462-499b-b152-021ea27bc43f/datastream/OBJ/view