Artificial intelligence improving safety and risk analysis: A comparative analysis for critical infrastructure

Author(s):  
A. Guzman ◽  
S. Ishida ◽  
E. Choi ◽  
A. Aoyama
2021 ◽  
Author(s):  
Vladimir Tregubov

The article describes applications of using voice recognition technology based on artificial intelligence to the educational process. The author presents a comparative analysis of existing examples artificial intelligence in the educational process. Artificial intelligence uses in specialized software it makes educational process more convenient for both the students and the teachers. There is a description of an application “Academic phrase bank" developed by author. The application consists of two specialising actions for Google assistant. The application allows to increase academic vocabulary, train of creating grammatically correct academic expressions, and memorize templates of academic phrases. In active mode, this application helps to create correct phrases of academic English and improve the abilities of understanding English speech.


2021 ◽  
Author(s):  
Roman Schotten ◽  
Daniel Bachmann

<p><span>In flood risk analysis it is a key principle to predetermine consequences of flooding to assets, people and infrastructures. Damages to critical infrastructures are not restricted to the flooded area. The effects of directly affected objects cascades to other infrastructures, which are not directly affected by a flood. Modelling critical infrastructure networks is one possible answer to the question ‘how to include indirect and direct impacts to critical infrastructures?’.</span></p><p>Critical infrastructures are connected in very complex networks. The modelling of those networks has been a basis for different purposes (Ouyang, 2014). Thus, it is a challenge to determine the right method to model a critical infrastructure network. For this example, a network-based and topology-based method will be applied (Pant et al., 2018). The basic model elements are points, connectors and polygons which are utilized to resemble the critical infrastructure network characteristics.</p><p>The objective of this model is to complement the state-of-the-art flood risk analysis with a quantitative analysis of critical infrastructure damages and disruptions for people and infrastructures. These results deliver an extended basis to differentiate the flood risk assessment and to derive measures for flood risk mitigation strategies. From a technical point of view, a critical infrastructure damage analysis will be integrated into the tool ProMaIDes (Bachmann, 2020), a free software for a risk-based evaluation of flood risk mitigation measures.</p><p>The data on critical infrastructure cascades and their potential linkages is scars but necessary for an actionable modelling. The CIrcle method from Deltares delivers a method for a workshop that has proven to deliver applicable datasets for identifying and connecting infrastructures on basis of cascading effects (de Bruijn et al., 2019). The data gained from CIrcle workshops will be one compound for the critical infrastructure network model.</p><p>Acknowledgment: This work is part of the BMBF-IKARIM funded project PARADes (Participatory assessment of flood related disaster prevention and development of an adapted coping system in Ghana).</p><p>Bachmann, D. (2020). ProMaIDeS - Knowledge Base. https://promaides.myjetbrains.com</p><p>de Bruijn, K. M., Maran, C., Zygnerski, M., Jurado, J., Burzel, A., Jeuken, C., & Obeysekera, J. (2019). Flood resilience of critical infrastructure: Approach and method applied to Fort Lauderdale, Florida. Water (Switzerland), 11(3). https://doi.org/10.3390/w11030517</p><p>Ouyang, M. (2014). Review on modeling and simulation of interdependent critical infrastructure systems. Reliability Engineering and System Safety, 121, 43–60. https://doi.org/10.1016/j.ress.2013.06.040</p><p>Pant, R., Thacker, S., Hall, J. W., Alderson, D., & Barr, S. (2018). Critical infrastructure impact assessment due to flood exposure. Journal of Flood Risk Management, 11(1), 22–33. https://doi.org/10.1111/jfr3.12288</p>


2019 ◽  
Vol 18 (1) ◽  
pp. 40-54
Author(s):  
Mohamed Seddik Hellas ◽  
Rachid Chaib ◽  
Ion Verzea

Purpose Nowadays, artificial intelligence computational methods, such as knowledge-based systems, neural networks, genetic algorithms and fuzzy logic, have been increasingly applied to several industrial research studies, the purpose of this paper is to study the contribution of fuzzy and possibilistic techniques to quantitative risk analysis (QRA) in the presence of imperfect knowledge about the occurrence and consequences of accidental phenomena. Design/methodology/approach To solve the problem of uncertainties related to the elements of the accident scenario such as the frequency and severity of the consequences, the authors used fuzzy logic. Using this type of analysis, it is possible to visualize the contours of the dead or fuzzy injury by fireball thermal effect (first- and second-degree burn, death) and lesions caused by vapor cloud explosion overpressure (lung damage, eardrum rupture, head impact, whole-body displacement). The frequency and severity of fuzzy results are calculated by extended multiplication using the alpha-cuts method. Findings This research project aims to reflect the real situation in the in Amenas industrial area (SONATRACH company), specifically the liquefied petroleum gas storage tank On-Spec 05-V-411A, to deal with this type of risk. Using this analysis allows us to estimate the fuzzy individual risk using the approach of fuzzy logic to treating this uncertainty in the parameter information of accident scenarios. This index individuel risk (IR) was evaluated against the criterion of acceptability and then used for decision-making in the field of industrial risk analysis and evaluation. Originality/value The originality of the work is to identify the weak points of the classical QRA to solve the problem of the uncertainties related to the elements of the accident scenario such as the frequency and severity of the consequences to visualize the fuzzy risk contours. On the one hand and the development of software to calculate the probability of death by the overpressure effect and classify the most sensitive organs on the other hand. Given the importance of this study, it can be generalized for similar sites in the region.


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