Safety risk assessment intelligent system for on-foot construction worker using fuzzy fault tree

2021 ◽  
pp. 1-26
Author(s):  
Nabeel Mahmood ◽  
Rongjun Qin ◽  
Tarunjit Butalia
Keyword(s):  
Risk Assessment ◽  
Intelligent System ◽  
Fault Tree ◽  
Logic Gates ◽  
Holistic Approach ◽  
Residual Risk ◽  
Assessment Model ◽  
Construction Worker ◽  
Safety Risk ◽  
Conditional Risk

A risk assessment model is developed to estimate the potential combined influence of concurrent safety risks facing on-foot construction worker at a certain point in space or instant of time. The model is based on a holistic approach that comprehensively systemizes principal types and subjective values of possible safety risk events. Fuzzy fault tree is built using a deductive approach to identify possible concurrent basic and conditional risk events, not risk symptoms, from the major subgroups of triggering, enabling and environment-related risks. The inclusive risk breakdown structure helps in combating assessment underestimation related to overlooking influential risks. Adequate logic gates are suggested at tree junctions to overcome assessment overestimation related to accumulating the effect of dependent, redundant, and non-concurrent risks, and ignoring the effectiveness of safety precautions and measures that may reduce or eliminate risks. Operational logic gates are applied to properly combine the residual risk of static (non-moving) events and dynamic (moving) events that can concurrently influence safety. The model is programmed into an interactive interfaced intelligent system to simulate cases of risk assessment input, computations, and output. The system shows the advantages of using the model as a prognostic or diagnostic tool to estimate top risk event. Subjective linguistic risk values can be induced for basic risk events at the bottom of the tree, and conditional risk events controlling residual risk values can be induced at different levels of the tree. Fuzzy logic plays a key role in hosting subjective risk evaluation into computational truth values to generate realistic and meaningful assessment values that are helpful for risk control.

scholarly journals Construction Safety Risk Assessment of Large-Sized Deep Drainage Tunnel Projects

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Kai Hu ◽  
Junwu Wang ◽  
Han Wu
Keyword(s):  
Risk Assessment ◽  
Fuzzy Comprehensive Evaluation ◽  
Construction Safety ◽  
Assessment Model ◽  
Economic Losses ◽  
Risk Level ◽  
Deep Drainage ◽  
Safety Risk ◽  
Drainage Tunnel ◽  
Weight Method

Frequent extreme climate events and rapid global urbanization have amplified the occurrence of accidents such as waterlogging or the overflow of pollution in big cities. This has increased the application scenarios of large-sized deep drainage tunnel projects (LSDDTPs). The scientific and accurate evaluation of the construction safety risks of LSDDTP can effectively reduce the corresponding economic losses and casualties. In this paper, we employed the hierarchical holographic model to construct the safety risk list of LSDDTPs in terms of the risk source and construction unit. Based on social network analysis, we then screened key indicators and calculated the weights of all secondary indicators from the correlation between risk factors. We subsequently developed a construction safety risk assessment model of LSDDTPs based on the matter-element extension method. The Donghu Deep Tunnel Project in Wuhan, China, was selected as a case study for the proposed method. The results of empirical research demonstrated that eight indicators (e.g., failure to effectively detect the change of the surrounding environment of the tunnel project) were key factors affecting the construction safety risk of IV, which is within the acceptable risk level. Our proposed model outperformed other methods (the fuzzy comprehensive evaluation, analytic hierarchy process, entropy weight method, and comprehensive weight method) in terms of scientific validity and research advancements.

Workplace Safety Risk Assessment Model Based on Fuzzy Regression

2018 ◽  
Vol 24 (3) ◽  
pp. 1656-1659 ◽  
Author(s):  
Nureize Arbaiy ◽  
Hamijah Ab Rahman ◽  
Mohd Zaki Mohd Salikon ◽  
Pei Chun Lin
Keyword(s):  
Risk Assessment ◽  
Workplace Safety ◽  
Assessment Model ◽  
Fuzzy Regression ◽  
Risk Assessment Model ◽  
Safety Risk ◽  
Model Based

scholarly journals Weather Hazard Risk Quantification for sUAS Safety Risk Management

2020 ◽  
Vol 37 (7) ◽  
pp. 1251-1268
Author(s):  
Christopher A. Roseman ◽  
Brian M. Argrow
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
High Resolution ◽  
Weather Forecast ◽  
Environmental Data ◽  
Assessment Model ◽  
Safety Risk ◽  
Research Areas ◽  
Hazard Risk ◽  
Weather Risk

AbstractAs the number of applications for small unmanned (i.e., remotely operated) aircraft systems (sUAS) continues to grow, comprehensive safety risk assessment studies are required to ensure their safe integration into the National Airspace System. One source of hazards for sUAS that has not been extensively addressed is adverse weather. A framework is presented for analyzing weather forecast data to provide sUAS operators with risk assessment information that they can use for making risk-aware decisions. The sUAS Weather Risk Model (sWRM) framework quantifies weather hazard risk for sUAS operations in rural to urban environments using weather forecast, population density, structure density, and sUAS data. sWRM is developed by following the safety risk management guidelines from the U.S. Federal Aviation Administration. Development of sWRM highlights a number of aerospace and meteorological research areas that must be addressed prior to weather risk models for sUAS becoming operational. Primary among these research areas is developing widely available finescale (<1 km) weather forecasts and conducting extensive sUAS flight-report studies to accurately estimate parameters of Bayesian belief network conditional probability tables used in the proposed framework. As a proof of concept, sWRM was applied over Boulder, Colorado, using the High-Resolution Rapid Refresh weather product. This initial demonstration of sWRM highlights the potential effectiveness of a detailed risk assessment model that takes into account high-resolution weather and environmental data.

Expert elicitation to estimate the feed safety impact of criteria included in the Canadian Food Inspection Agency risk assessment model for feed mills

2020 ◽  
Author(s):  
Virginie Lachapelle ◽  
Manon Racicot ◽  
Geneviève Comeau ◽  
Mohamed Rhouma ◽  
Alexandre Leroux ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Expert Elicitation ◽  
Assessment Model ◽  
Assessment Criteria ◽  
Risk Assessment Model ◽  
Safety Risk ◽  
Inspection Agency ◽  
Canadian Food Inspection Agency ◽  
Feed Safety ◽  
Feed Mill

The Canadian Food Inspection Agency (CFIA) is developing an Establishment-based Risk Assessment model for commercial and on-farm mills involved in the manufacture, storage, packaging, labelling or distribution of livestock feed (ERA-Feed Mill model). This model will help inform the allocation of inspection resources based on feed safety risk, including animal health and food safety risk. In a previous study, 34 risk factors, grouped into inherent, mitigation and compliance clusters, along with their assessment criteria were selected. The objective of this current study was to estimate the relative risk (RR) of the 203 assessment criteria based on their impact on feed safety to design an ERA-Feed Mill model algorithm. Furthermore, the intent of this study was to assess the maximum increase or decrease of risk obtained when multiple criteria belonging to a same cluster were identified in a specific feed mill. To do so, a two-round face-to-face expert elicitation was conducted with 28 Canadian feed experts. Results showed no significant association between respondent profiles (years of experience, work sector) and estimated RR. Uniformity of answers between experts improved between rounds. Criteria having the highest increase in risk (median RR≥4) included the presence of materials prohibited to be fed to ruminants in a facility that produces ruminant feed, the presence of multiple livestock species on site and historical non-compliances related to the inspection of the feed mill’s process control and end-product control programs. Risk mitigation criteria having the highest impact on decreasing the risk were the implementation of feed safety certifications, the use of dedicated manufacturing lines (prohibited materials, medications) and having a hazard sampling plan in place for finished feed. The median RR assigned to each criterion and cluster will be used to build an algorithm of the CFIA’s ERA-Feed Mill model.

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