G
Genserik Reniers
Researcher at Delft University of Technology
Publications - 439
Citations - 8655
Genserik Reniers is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Domino effect & Domino. The author has an hindex of 41, co-authored 407 publications receiving 5879 citations. Previous affiliations of Genserik Reniers include St. John's University & Katholieke Universiteit Leuven.
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Bibliometric analysis of safety culture research
TL;DR: In this paper, bibliometric analysis has been applied to the field of safety culture to identify fundamental influences and to obtain a structured overview of the characteristics and the developments in this research domain.
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A review of optimisation models for pedestrian evacuation and design problems
TL;DR: In this article, a review of the use of optimisation models for pedestrian evacuation and design problems is presented, which is classified according to the problem type that is studied, the level of model realism, and the modelling or solution technique.
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Learning about risk: Machine learning for risk assessment
TL;DR: A deep neural network model is developed and tested for a drive-off scenario involving an Oil & Gas drilling rig and shows reasonable accuracy for DNN predictions and general suitability to (partially) overcome risk assessment challenges.
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Safety analysis of process systems using Fuzzy Bayesian Network (FBN)
TL;DR: A comparison between the results of FBN and BN, especially in critically analysis of root events, shows the outperformance ofFBN in providing more detailed, transparent and realistic results.
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Dynamic risk management: a contemporary approach to process safety management
Faisal Khan,Seyed Javad Hashemi,Nicola Paltrinieri,Paul Amyotte,Valerio Cozzani,Genserik Reniers +5 more
TL;DR: In this paper, the main contributions in the area of dynamic risk assessment are investigated and an overall framework for dynamic risk management of process facilities is proposed, which is the basis for the next generation of risk management approaches that help to enable safer complex process systems operating in extreme environments.