Risk assessment for clearing and grading process of a natural gas pipeline project: An extended TOPSIS model with Pythagorean fuzzy sets for prioritizing hazards


Oz N. E., Mete S., Serin F., Gul M.

HUMAN AND ECOLOGICAL RISK ASSESSMENT, cilt.25, sa.6, ss.1615-1632, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 6
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1080/10807039.2018.1495057
  • Dergi Adı: HUMAN AND ECOLOGICAL RISK ASSESSMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1615-1632
  • Anahtar Kelimeler: risk assessment, natural gas pipeline, 2D risk matrix, Pythagorean fuzzy TOPSIS, OCCUPATIONAL-HEALTH, DECISION-MAKING, HYBRID METHOD, SAFETY, MANAGEMENT, CONSTRUCTION, EXTENSION, AHP
  • İstanbul Üniversitesi Adresli: Hayır

Özet

A natural gas pipeline project is a broad construction system consisting of both offshore and onshore section, which aims to supply the natural gas to one country to another. In such these projects, main focus of the works should cover workplace, environment, health, and safety of workers and employers. To achieve this, a new occupational health and safety (OHS) risk assessment is necessary for reducing the negative effects of emerged risks. In this article, a fuzzy-based risk model is developed for prioritizing primary and residual risk. A two-dimensional (2D) risk matrix method is initially performed and the Pythagorean fuzzy technique for order preference by similarity to ideal solution (PFTOPSIS) method is then used to prioritize previously identified hazards. For the prioritization of primary and residual risks, hazards are assessed against two parameters (probability and severity) of 2D risk matrix. Unlike previous risk assessment studies, PFTOPSIS is applied for the first time. The fuzzy model is applied to a case study for clearing and grading process of a natural gas pipeline project. A sensitivity analysis is also performed on parameter weights in order to validate the model.