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Prof. Mario P. Brito
Prof. Mario P. Brito
Department of Decision Analytics and Risk, University of Southampton
Verified email at soton.ac.uk - Homepage
Title
Cited by
Cited by
Year
Risk analysis for autonomous underwater vehicle operations in extreme environments
MP Brito, G Griffiths, P Challenor
Risk Analysis: An International Journal 30 (12), 1771-1788, 2010
762010
Capturing the impact of COVID-19 on construction projects in developing countries: A case study of Iraq
MKS Al-Mhdawi, MP Brito, M Abdul Nabi, IH El-Adaway, BS Onggo
Journal of management in engineering 38 (1), 05021015, 2022
662022
Clean access, measurement, and sampling of Ellsworth Subglacial Lake: A method for exploring deep Antarctic subglacial lake environments
MJ Siegert, RJ Clarke, M Mowlem, N Ross, CS Hill, A Tait, D Hodgson, ...
Reviews of Geophysics 50 (1), 2012
662012
A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions
M Brito, G Griffiths
Reliability Engineering & System Safety 146, 55-67, 2016
642016
A Markov chain state transition approach to establishing critical phases for AUV reliability
MP Brito, G Griffiths
IEEE Journal of Oceanic Engineering 36 (1), 139-149, 2011
572011
Underwater glider reliability and implications for survey design
M Brito, D Smeed, G Griffiths
Journal of Atmospheric and Oceanic Technology 31 (12), 2858-2870, 2014
522014
A critique of the use of domain analysis for spatial collision risk assessment
A Rawson, M Brito
Ocean Engineering 219, 108259, 2021
512021
A behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments
M Brito, G Griffiths, J Ferguson, D Hopkin, R Mills, R Pederson, E MacNeil
Journal of Atmospheric and Oceanic Technology 29 (11), 1689-1703, 2012
432012
A review of risk analysis research for the operations of autonomous underwater vehicles
X Chen, N Bose, M Brito, F Khan, B Thanyamanta, T Zou
Reliability Engineering & System Safety 216, 108011, 2021
402021
A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis
A Rawson, M Brito
Transport Reviews 43 (1), 108-130, 2023
382023
A machine learning approach for monitoring ship safety in extreme weather events
A Rawson, M Brito, Z Sabeur, L Tran-Thanh
Safety science 141, 105336, 2021
372021
Predicting risk in missions under sea ice with autonomous underwater vehicles
G Griffiths, M Brito
2008 IEEE/OES Autonomous Underwater Vehicles, 1-7, 2008
362008
Adaptive autonomous underwater vehicles: an assessment of their effectiveness for oceanographic applications
MP Brito, RS Lewis, N Bose, G Griffiths
IEEE Transactions on Engineering Management 66 (1), 98-111, 2018
262018
The role of adaptive mission planning and control in persistent autonomous underwater vehicles presence
MP Brito, R Lewis, N Bose, P Alexander, G Griffiths, J Ferguson
2012 IEEE/OES Autonomous Underwater Vehicles (AUV), 1-9, 2012
252012
Intelligent geospatial maritime risk analytics using the Discrete Global Grid System
A Rawson, Z Sabeur, M Brito
Big Earth Data 6 (3), 294-322, 2022
242022
Analysis of causation of loss of communication with marine autonomous systems: A probability tree approach
MP Brito, DA Smeed, G Griffiths
Methods in Oceanography 10, 122-137, 2014
242014
Reliability of two REMUS-100 AUVs based on fault log analysis and elicited expert judgment
G Griffiths, M Brito, I Robbins, M Moline
Autonomous Undersea Systems Institute (AUSI), 2009
242009
Fuzzy system dynamics risk analysis (FuSDRA) of autonomous underwater vehicle operations in the Antarctic
TY Loh, MP Brito, N Bose, J Xu, K Tenekedjiev
Risk Analysis 40 (4), 818-841, 2020
212020
Eliciting expert judgment on the probability of Loss of an AUV operating in four environments
MP Brito, G Griffiths, A Trembranis
National Oceanography Centre Southampton, 2008
212008
A fuzzy‐based risk assessment framework for autonomous underwater vehicle under‐ice missions
TY Loh, MP Brito, N Bose, J Xu, K Tenekedjiev
Risk Analysis 39 (12), 2744-2765, 2019
182019
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