Quantitative Assessment of Horizontal Directional Drilling Project Risk Using Fuzzy Fault Tree Analysis

Abdelgawad, Mohamed; Fayek, Aminah Robinson; Martínez, F.

doi:10.1061/41109(373)128

Cited by 4 publications

(1 citation statement)

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“…However, in many industries, including the construction industry, having sufficient data to derive probability distributions is often difficult . Hence, fuzzy‐based FTA is proposed by many researchers, to overcome the lack of historical data about the behavior of events over time. Based on the availability of historical data for BEs, two different types of uncertainties can be assumed: objective uncertainty and subjective uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Fast Fault Tree Analysis for Hybrid Uncertainties Using Stochastic Logic Implemented on Field‐Programmable Gate Arrays: An Application in Quantitative Assessment and mitigation of Welding Defects Risk

Shoar

Zarandi

Nasirzadeh

et al. 2016

Quality & Reliability Eng

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This paper presents a stochastic logic-based method for quantitative risk assessment using fault tree analysis (FTA) that can take into account both types of uncertainties including objective and subjective uncertainties. In the proposed method, each fault tree gate is translated to its corresponding stochastic logic template and then is implemented on a field programmable gate array (FPGA). Because the analysis does not utilize any transformation methods, the results of analysis are more accurate than those methods which are based on transformation from possibility to probability distributions or vice versa. Experimental results for a benchmark fault tree show that this method accelerates analysis time compared to conventional hybrid uncertainty analysis method and transformation methods. The efficiency of the proposed method is demonstrated by implementation in a real steel structure project. The quantitative risk assessment is performed for the incomplete penetration as one of the defects encountered in arc welding process, and its results are compared with transformation methods. The comparison results show the proposed hybrid uncertainty analysis method is also more accurate in comparison to the transformation-based approaches.

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Section: Introductionmentioning

confidence: 99%