Soft computing for modeling pipeline risk index under uncertainty. (November 2020)

• Record Type: Journal Article
• Authors: Dawood, Thikra; Elwakil, Emad; Novoa, Hector Mayol; Delgado, José Fernando Gárate
• Is Part Of: Engineering failure analysis. Volume 117(2020)
• Publication Date: 2020-11
• Subjects: Risk index -- Failure -- Water pipelines -- Modeling -- Soft computing -- Fuzzy inference system; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2020.104949
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: A pipeline risk index under the uncertainty approach is developed. The hypothesis is pipe's failure risk upsurges as severity of factors increases. This research integrates simulation and FIS functions to develop risk index. The model is validated successfully using the Monte Carlo simulation. Abstract: The risk of water pipe failure is deemed one of the significant challenges of the 21st century. The risk analysis and modeling are intricate tasks due to the complexity of the buried piping system, which is nonlinear, dynamic, and includes vast arrays of indicators that cannot be measured meticulously in any conventional metrics. Deterioration indicators extracted from field inspections and/or experts' questionnaires have inherently certain degrees of uncertainty and subjective judgments. Soft computing techniques are capable of coping with the imprecision, uncertainty, and fuzziness of data. The objective of this research is to develop a soft computing approach grounded in a fuzzy inference system (FIS) to enable encoding the deterioration indicators into a risk index while dealing with the ambiguity and imprecision. The approach involves five phases that are based on data from Arequipa City in Peru, simulation and FIS, and supported by 3D schematic representations. Data are channeled to the FIS engine after defining the membership functions and 127 fuzzy rules. Subsequent to successive simulation iterations, aggregation, and defuzzification of the outputs, the indices for risk-of-failure are generated along with 3D visualization models. To ensure the coherency of the proposed model, Monte Carlo simulation is performed in conjunction with sensitivity analysis on the data. The validation results of a sample revealed the efficacy of the model with the Coefficient of Variation and the Mean Standard Error of 0.0296 and 0.03, respectively. The five phases are chained together to enable the model to fulfill its functions.