Soft computing for nonlinear risk assessment of complex socio-technical systems
Authors: 
Ivenio Teixeira de Souza, Ana Carolina Rosa, Riccardo Patriarca, Assed HaddadAuthors Info & Claims
References
[1]
H. Alawad, S. Kaewunruen, M. An, A deep learning approach towards railway safety risk assessment, IEEE Access 8 (2020) 102811–102832,.
[2]
A. Alboghobeish, G.A. Shirali, Integration of functional resonance analysis with multicriteria analysis for sociotechnical systems risk management, Risk Analysis (2021),.
[3]
F. Anvarifar, M.Z. Voorendt, C. Zevenbergen, W. Thissen, An application of the Functional Resonance Analysis Method (FRAM) to risk analysis of multifunctional flood defences in the Netherlands, Reliability Engineering and System Safety 158 (2017) 130–141,.
[4]
E. Bellini, P. Ceravolo, P. Nesi, Quantify resilience enhancement of UTS through exploiting Connected Community and Internet of Everything emerging technologies, ArXiv 18 (1) (2017) 1–34.
[5]
E. Bellini, L. Coconea, P. Nesi, A functional resonance analysis method driven resilience quantification for socio-technical systems, IEEE Systems Journal 14 (1) (2020) 1234–1244,.
[6]
T. Bjerga, T. Aven, E. Zio, Uncertainty treatment in risk analysis of complex systems: The cases of STAMP and FRAM, Reliability Engineering and System Safety 156 (2016) 203–209,.
[7]
Cheraghi, M., Baladeh, A. E., & Khakzad, N. (2021). Optimal selection of safety recommendations: A hybrid fuzzy multi-criteria decision-making approach to HAZOP. Journal of Loss Prevention in the Process Industries, 74(September 2021), 104654.
[8]
Combs, W. E., & Andrews, J. E. (1998). Combinatorial rule explosion eliminated by a fuzzy rule configuration. IEEE Transactions on Fuzzy Systems, 6(1), 1–11.
[9]
E. Cox, Fuzzy Systems Handbook: A Practitioner’s Guide to Building, Using, and Maintaining Fuzzy Systems, McGraw-Hill Inc., 1994.
[10]
E.A. de Carvalho, J.O. Gomes, A. Jatobá, M.F. da Silva, P.V.R. de Carvalho, Employing resilience engineering in eliciting software requirements for complex systems: Experiments with the functional resonance analysis method (FRAM), Cognition, Technology and Work. (2020),.
[11]
Pardo-Ferreira, M. del C., Rubio-Romero, J. C., Gibb, A., & Calero-Castro, S. (2020). Using functional resonance analysis method to understand construction activities for concrete structures. Safety Science, 128(October 2019).
[12]
K. Demertzis, L.S. Iliadis, V.D. Anezakis, An innovative soft computing system for smart energy grids cybersecurity, Advances in Building Energy Research 12 (1) (2018) 3–24,.
[13]
Duan, G., Tian, J., & Wu, J. (2015). Extended FRAM by Integrating with Model Checking to Effectively Explore Hazard Evolution. Mathematical Problems in Engineering, 2015(Article ID 196107), 11.
[14]
Eghbal Ahmadi, M. H., Royaee, S. J., Tayyebi, S., & Bozorgmehry Boozarjomehry, R. (2020). A new insight into implementing Mamdani fuzzy inference system for dynamic process modeling: Application on flash separator fuzzy dynamic modeling. Engineering Applications of Artificial Intelligence, 90(November 2019), 103485.
[15]
J.E.M. França, E. Hollnagel, I.J.A.L. dos Santos, A.N. Haddad, FRAM AHP approach to analyse offshore oil well drilling and construction focused on human factors, Cognition, Technology and Work. (2019),.
[16]
J.E.M. França, E. Hollnagel, I.J.A. Luquetti, A.N. Haddad, Analysing human factors and non - technical skills in offshore drilling operations using FRAM (functional resonance analysis method), Cognition, Technology & Work. (2020),.
[17]
A. Geramian, A. Abraham, Customer classification: A Mamdani fuzzy inference system standpoint for modifying the failure mode and effect analysis based three dimensional approach, Expert Systems with Applications 186 (August) (2021),.
[18]
M. Gul, M.F. Ak, A comparative outline for quantifying risk ratings in occupational health and safety risk assessment, Journal of Cleaner Production 196 (2018) 653–664,.
[19]
Hill, R. (2019). FMV PRO 2.0.2. 13th FRAMily Meeting. http://www.puroresu.com/wrestlers/londos/death.html.
[20]
Hirose, T., & Sawaragi, T. (2020). Extended FRAM model based on cellular automaton to clarify complexity of socio-technical systems and improve their safety. Safety Science, 123(June 2019), 104556.
[21]
T. Hirose, T. Sawaragi, Y. Horiguchi, H. Nakanishi, Safety analysis for resilient complex socio-technical systems with an extended functional resonance analysis method, International Journal of Astronautics and Aeronautical Engineering 2 (2) (2017) 1–18,.
[22]
E. Hollnagel, FRAM: The Functional Resonance Analysis Method - Modelling the Complex. Socio-technical Systems, Ashgate, 2012.
[23]
E. Hollnagel, D.D. Woods, N. Leveson, Resilience Engineering: Concepts and precepts, Ashgate (2006),.
[24]
H. Hsu, C. Chen, Aggregation of fuzzy opinions under group decision making, Fuzzy Sets and Systems 79 (1996) 279–285.
[25]
W. Huang, Y. Zhang, Railway Dangerous Goods Transportation System Risk Assessment: An Approach Combining FMEA with Pessimistic-Optimistic Fuzzy Information Axiom Considering Acceptable Risk Coefficient, IEEE Transactions on Reliability 70 (1) (2021) 371–388,.
[26]
A. Jatobá, H.C. Bellas, I. Koster, R. Arcuri, M.C.R. Vidal, P.V.R. de Carvalho, Patient visits in poorly developed territories: A case study with community health workers, Cognition, Technology and Work 20 (1) (2018) 125–152,.
[27]
Kaya, G. K., & Hocaoglu, M. F. (2020). Semi-quantitative application to the Functional Resonance Analysis Method for supporting safety management in a complex health-care process. Reliability Engineering and System Safety, 202(November 2019), 106970.
[28]
Kaya, G. K., Ovali, H. F., & Ozturk, F. (2019). Using the functional resonance analysis method on the drug administration process to assess performance variability. Safety Science, 118(December 2018), 835–840.
[29]
Kaya, G. K., Ozturk, F., & Sariguzel, E. E. (2021). System-based risk analysis in a tram operating system: Integrating Monte Carlo simulation with the functional resonance analysis method. Reliability Engineering and System Safety, 215(August 2020), 107835.
[30]
Kim, Y. C., & Yoon, W. C. (2021). Quantitative representation of the functional resonance analysis method for risk assessment. Reliability Engineering & System Safety, 214(July 2020), 107745.
[31]
G.J. Klir, B. Yuan, Fuzzy sets and fuzzy logic: Theory and applications, Prentice Hall, 1995.
[32]
K. Kokkinos, E. Lakioti, E. Papageorgiou, K. Moustakas, V. Karayannis, Fuzzy cognitive map-based modeling of social acceptance to overcome uncertainties in establishing waste biorefinery facilities, Frontiers in Energy Research 6 (OCT) (2018) 1–17,.
[33]
Y. Liu, C.M. Eckert, C. Earl, A review of fuzzy AHP methods for decision-making with subjective judgements, Expert Systems with Applications 161 (2020),.
[34]
J. Liu, Y. Zhang, J. Han, J. He, J. Sun, T. Zhou, Intelligent Hazard-Risk Prediction Model for Train Control Systems, IEEE Transactions on Intelligent Transportation Systems 21 (11) (2020) 4693–4704,.
[35]
E. Mamdani, S. Assilian, An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, International Journal of Man-Machine Studies 1 (7) (1975) 1–13.
[36]
J.M. Mendel, Uncertain Rule-Based Fuzzy Systems. Introduction and new directions, Springer, 2017.
[37]
O. Obajemu, M. Mahfouf, J.W.F. Catto, A New Fuzzy Modeling Framework for Integrated Risk Prognosis and Therapy of Bladder Cancer Patients, IEEE Transactions on Fuzzy Systems 26 (3) (2018) 1565–1577,.
[38]
R. Patriarca, J. Bergström, Modelling complexity in everyday operations: Functional resonance in maritime mooring at quay, Cognition, Technology and Work 19 (4) (2017) 711–729,.
[39]
R. Patriarca, G. Di Gravio, R. Cioponea, A. Licu, Safety intelligence: Incremental proactive risk management for holistic aviation safety performance, Safety Science 118 (June) (2019) 551–567,.
[40]
R. Patriarca, G. Di Gravio, R. Woltjer, F. Costantino, G. Praetorius, P. Ferreira, E. Hollnagel, Framing the FRAM: A literature review on the functional resonance analysis method, Safety Science 129 (April) (2020),.
[41]
Patriarca, R, Falegnami, A., Costantino, F., & Bilotta, F. (2018). Resilience engineering for socio-technical risk analysis: Application in neuro-surgery. Reliability Engineering and System Safety, 180(November 2017), 321–335.
[42]
D.C. Raben, S.B. Bogh, B. Viskum, K.L. Mikkelsen, E. Hollnagel, Proposing leading indicators for blood sampling: Application of a method based on the principles of resilient healthcare, Cognition, Technology and Work 19 (4) (2017) 809–817,.
[43]
H. Raeihagh, A. Behbahaninia, M. Macki Aleagha, Risk assessment of sour gas inter-phase onshore pipeline using ANN and fuzzy inference system – Case study: The south pars gas field, Journal of Loss Prevention in the Process Industries 68 (July) (2020),.
[44]
L.V. Rosa, A.N. Haddad, P.V.R. de Carvalho, Assessing risk in sustainable construction using the Functional Resonance Analysis Method (FRAM), Cognition, Technology and Work 17 (4) (2015) 559–573,.
[45]
T.J. Ross, Fuzzy Logic With Engineering Application, 3rd ed., John Wiley & Sons, 2010.
[46]
Salehi, V., Veitch, B., & Smith, D. (2020). Modeling complex socio ‐ technical systems using the FRAM: A literature review. May, 1–25.
[47]
H. Slim, S. Nadeau, A Proposal for a Predictive Performance Assessment Model in Complex Sociotechnical Systems Combining Fuzzy Logic and the Functional Resonance Analysis Method (FRAM), American Journal of Industrial and Business Management 09 (06) (2019) 1345–1375,.
[48]
H. Slim, S. Nadeau, A Mixed Rough Sets/Fuzzy Logic Approach for Modelling Systemic Performance Variability with FRAM, Sustainability 12 (1918) (2020) 1–21,.
[49]
I.T. De Souza, A.C. Rosa, M.C.R. Vidal, M.K. Najjar, A.W.A. Hammad, A.N. Haddad, Information Technologies in Complex Socio-Technical Systems Based on Functional Variability : A Case Study on HVAC Maintenance Work Orders, Applied Sciences 11 (2021).
[50]
Steen, R., & Ferreira, P. (2020). Resilient flood-risk management at the municipal level through the lens of the Functional Resonance Analysis Model. Reliability Engineering and System Safety, 204(September 2019), 107150.
[51]
L. Tan, B. Liu, X. Li, S. Yang, X. Liu, Insights into the complexity: A method to manage the complex system by controlling the couplings based on the systemic modeling, in: 2017 2nd International Conference on Reliability Systems Engineering, 2017,.
[52]
Vries, L. De, & Bligård, L. (2019). Visualising safety : The potential for using sociotechnical systems models in prospective safety assessment and design. Safety Science, 111(May 2018), 80–93.
[53]
Wahl, A., Kongsvik, T., & Antonsen, S. (2020). Balancing Safety I and Safety II: Learning to manage performance variability at sea using simulator-based training. Reliability Engineering and System Safety, 195(September 2019), 106698.
[54]
J. Wang, X. Ma, J. Dai, J. Zhan, A novel three-way decision approach under hesitant fuzzy information, Information Sciences 578 (2021) 482–506,.
[55]
W. Wang, J. Zhan, J. Mi, A three-way decision approach with probabilistic dominance relations under intuitionistic fuzzy information, Information Sciences 582 (2022) 114–145,.
[56]
J.J. Weinschenk, W.E. Combs, R.J. Marks, Avoidance of rule explosion by mapping fuzzy systems to a union rule configuration, IEEE International Conference on Fuzzy Systems 1 (2003) 43–48,.
[57]
R.R. Yager, D. Filev, On the issue of defuzzification and selection based on a fuzzy set, Fuzzy Sets and Systems 55 (3) (1993) 255–271,.
[58]
P. Yariyan, H. Zabihi, I.D. Wolf, M. Karami, S. Amiriyan, Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran, International Journal of Disaster Risk Reduction 50 (June) (2020),.
[59]
Y. Yener, G.F. Can, A FMEA based novel intuitionistic fuzzy approach proposal : Intuitionistic fuzzy advance MCDM and mathematical modeling integration, Expert Systems With Applications 183 (June) (2021),.
[60]
M. Yu, M. Erraguntla, N. Quddus, C. Kravaris, A data-driven approach of quantifying function couplings and identifying paths towards emerging hazards in complex systems, Process Safety and Environmental Protection 150 (2021) 464–477,.
[61]
L.A. Zadeh, Fuzzy sets, Information and Control 8 (1965) 338–353,.
[62]
Zarei, E., Ramavandi, B., Darabi, A. H., & Omidvar, M. (2021). A framework for resilience assessment in process systems using a fuzzy hybrid MCDM model. Journal of Loss Prevention in the Process Industries, 69(August 2020), 104375.
[63]
Zhan, J., Jiang, H., & Yao, Y. (2020). Three-way multi-attribute decision-making based on outranking relations. IEEE Transactions on Fuzzy Systems, 6706(c), 1–1.
[64]
J. Zhan, J. Ye, W. Ding, P. Liu, A novel three-way decision model based on utility theory in incomplete fuzzy decision systems, IEEE Transactions on Fuzzy Systems 6706 (c) (2021) 1–15,.
[65]
K. Zhang, J. Zhan, W.Z. Wu, On Multicriteria Decision-Making Method Based on a Fuzzy Rough Set Model with Fuzzy α-Neighborhoods, IEEE Transactions on Fuzzy Systems 29 (9) (2021) 2491–2505,.
[66]
Z. Zheng, J. Tian, T. Zhao, Refining operation guidelines with model-checking-aided FRAM to improve manufacturing processes: A case study for aeroengine blade forging, Cognition, Technology and Work 18 (4) (2016) 777–791,.
Index Terms
- Soft computing for nonlinear risk assessment of complex socio-technical systems
Index terms have been assigned to the content through auto-classification.
Recommendations
Study on the Risk Assessment Method for Complex Information Systems
MACE '12: Proceedings of the 2012 Third International Conference on Mechanic Automation and Control EngineeringThis paper analyzes the necessity of risk assessment for complex information systems in the context of increasingly complexion. According to the characteristics of complex information systems and the existed risk assessment methods, the method to assess ...
Risk Management and Risk Assessment at ENISA: Issues and Challenges
ARES '06: Proceedings of the First International Conference on Availability, Reliability and SecurityIn this talk, the main directions followed in current and future work in the area of Risk Management and Risk Assessment at ENISA will be presented. The efforts in this area range from an initial inventory of Risk Management /Risk Assessment methods and ...
Risk assessment and mitigation for electric power sectors: A developing country's perspective
Highlights- A novel MCDM based risk identification and mitigation framework has been proposed.
AbstractThe electric power sector is the driving force behind a country's economy and disruptions in its services have dire consequences. The purpose of this study is to identify the risk mitigation measures that should be incorporated by the ...
Graphical abstractDisplay Omitted
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Elsevier Ltd.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 15 November 2022
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 04 Jan 2025