Probabilistic risk assessment of fire occurrence in residential buildings: Application to the Grenfell Tower

Fire occurrence is one of the most devastating events in residential buildings, among other civil engineered structures. The importance of providing mathematical tools that support fire risk assessments is imperative to improve fire containment measurements as well as accident prevention. In this paper, a novel probabilistic method based on credal networks is proposed to assess the impact on the expected risk of the variables involved in the cause and prevention of fire events. This approach can capture the epistemic uncertainty associated with data available in the form of the probability intervals. This helps to avoid hard assumptions based on the use of crisp probabilities that may lead to unrealistic results. A general model is proposed and then adapted to the Grenfell Tower fire by introducing as evidence the specific conditions of the case study. Different fire scenarios are created to study the effects of the components involved in the accident. The probabilistic outcomes of those scenarios are used to compute the expected risk of unwanted factors, e.g., fatalities and fire costs as part of the fire risk assessment. Different data sources and experts have been consulted to enhance the accuracy and quality of the report.The first author gratefully acknowledges the Consejo Nacional de Ciencia y Tecnología (CONACyT) for the funding that allowed this research project. Special thanks to the former Mechanical Engineering student BSc Mohanad Khalid Al-Shabibi for his contribution to the Bayesian network model presented in this article

Pseudo Credal Networks for Inference With Probability Intervals

Abstract The computation of the inference corresponds to an NP-hard problem even for a single connected credal network. The novel concept of pseudo networks is proposed as an alternative to reduce the computational cost of probabilistic inference in credal networks and overcome the computational cost of existing methods. The method allows identifying the combination of intervals that optimizes the probability values of each state of the queried variable from the credal network. In the case of no evidence, the exact probability bounds of the query variable are calculated. When new evidence is inserted into the network, the outer and inner approximations of the query variable are computed by means of the marginalization of the joint probability distributions of the pseudo networks. The applicability of the proposed methodology is shown by solving numerical case studies.</jats:p

Workplace accident analysis in the Algerian oil and gas industry

A total of 42,032 workplace accidents were reported in 2021 by the Algerian National Social Insurance Fund for Salaried Workers in Algeria, of which 38,225 were accidents within the workplace and 3807 were related to traffic accidents or other reasons. Most of these accidents were recorded in the building and construction field, followed by the oil and gas company exploration and drilling, which comes at the forefront of the Sonatrach company. This study aims to analyze accidents in the workplace using quantitative and qualitative methods to determine the corresponding causes. Our study was carried out in collaboration with Sonatrach in south Algeria where information and reports on work accidents from years 2017 to 2021 were collected. Data acquired were classified and analyzed according to the location and time of the accident. Based on the results obtained, we found that the human factor was the main cause of most accidents due to nonrespect of safety procedures and lack of concentration of workers. The results of the analysis suggest implementation of a break time during the afternoon, avoiding long overtime hours, suspending work outside stations during high temperatures periods in July and August as well as suspending work when sandstorms appear in winter

Robust data-driven human reliability analysis using credal networks

Despite increasing collection efforts of empirical human reliability data, the available databases are still insufficient for understanding the relationships between human errors and their influencing factors. Currently, probabilistic tools such as Bayesian network are used to model data uncertainty requiring the estimation of conditional probability tables from data that is often not available. The most common solution relies on the adoption of assumptions and expert elicitation to fill the gaps. This gives an unjustified sense of confidence on the analysis. This paper proposes a novel methodology for dealing with missing data using intervals comprising the lowest and highest possible probability values. Its implementation requires a shift from Bayesian to credal networks. This allows to keep track of the associated uncertainty on the available data. The methodology has been applied to the quantification of the risks associated to a storage tank depressurisation of offshore oil & gas installations known as FPSOs and FSOs. The critical task analysis is converted to a cause-consequence structure and used to build a credal network, which extracts human factors combinations from major accidents database defined with CREAM classification scheme. Prediction analysis shows results with interval probabilities rather than point values measuring the effect of missing-data variables. Novel decision-making strategies for diagnostic analysis are suggested to unveil the most relevant variables for risk reduction in presence of imprecision. Realistic uncertainty depiction implies less conservative human reliability analysis and improve risk communication between assessors and decision-makers

Video analysis for ergonomics assessment in the manufacturing industry: initial feedback on a case study

The manufacturing industry is being benefited from the new technologies developed in the field of artificial intelligence. However, as part of the European AI strategy, the role of workers in the industry must be protected by including human-centered ethical values. The TEAMING.AI project is developing a revolutionary human-AI teaming software platform comprised of interconnected utilities. This work reflects the preliminary results of some of the methodologies that are being developed within the project. An ergonomics assessment of manual activities performed by operators in a manufacturing workplace is carried out. The data for the assessment comes from video recordings obtained with cameras installed in strategic points of the shop floor. In this work, the assessment is done by manually selecting the images from the videos and scoring them based on the Rapid Upper-Limb Assessment and Rapid Entire Body Assessment methods. Once a scored is computed, an analysis of the activity is provided. The preliminary results show that distortion in the image from recording can affect the assessments. A method to enhance the video analysis in two major directions is proposed. The first direction focuses in the automatic operator detection. The second, on generating 3D information for ergonomic assessment with undistorted images. Some details related to the use case are omitted to preserve the anonymity of the operators in the company