Soft computing for modeling pipeline risk index under uncertainty

Thikra Dawood, Emad Elwakil, Hector Mayol Novoa, José Fernando Gárate Delgado

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Article number104949
JournalEngineering Failure Analysis
Volume117
DOIs
StatePublished - Nov 2020

Bibliographical note

Funding Information:
This work is supported by the collaboration of the Universidad Nacional de San Agust?n (UNSA) in Arequipa, Peru, and Purdue University in Indiana, USA, through Discovery Park's Center for the Environment (C4E).

Funding Information:
This work is supported by the collaboration of the Universidad Nacional de San Agustín (UNSA) in Arequipa, Peru, and Purdue University in Indiana, USA , through Discovery Park's Center for the Environment (C4E).

Publisher Copyright:
© 2020 Elsevier Ltd

Keywords

  • Failure
  • Fuzzy inference system
  • Modeling
  • Risk index
  • Soft computing
  • Water pipelines

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