A novel hybrid fuzzy analytical hierarchy process–game theory model for prioritizing factors affecting the deterioration of water pipelines
Kaydedildi:
| Yayımlandı: | Applied Water Science vol. 14, no. 12 (Dec 2024), p. 265 |
|---|---|
| Baskı/Yayın Bilgisi: |
Springer Nature B.V.
|
| Konular: | |
| Online Erişim: | Citation/Abstract Full Text - PDF |
| Etiketler: |
Etiket eklenmemiş, İlk siz ekleyin!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3133068487 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2190-5487 | ||
| 022 | |a 2190-5495 | ||
| 024 | 7 | |a 10.1007/s13201-024-02274-4 |2 doi | |
| 035 | |a 3133068487 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 084 | |a 242435 |2 nlm | ||
| 245 | 1 | |a A novel hybrid fuzzy analytical hierarchy process–game theory model for prioritizing factors affecting the deterioration of water pipelines | |
| 260 | |b Springer Nature B.V. |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Water pipes face significant aging and degradation problems due to several pipe-related, soil-related, operational, and environmental factors. Hence, the paramount objective of this research paper is to prioritize the criticality of the factors affecting the deterioration of water pipes in Hong Kong. The framework of the developed model is envisioned based on two main modules, namely weight computation and weight aggregation. The first module incorporates identifying and categorizing water deterioration factors. Then, the relative importance priorities of water deterioration factors are scrutinized using seven weight computation methods. These methods encompass analytical hierarchy process (AHP), Monte Carlo AHP, fuzzy AHP, magnitude-based fuzzy AHP, total difference-based fuzzy AHP, spherical fuzzy AHP and Pythagorean fuzzy AHP. In this regard, fuzzy-based and Monte Carlo-based methods are leveraged to circumvent the critical shortcomings of classical AHP. The performances of weight computation methods are analyzed using statistical evaluation indicators of satisfactory index (SAT) and distance between weights (WD). The second module is a hybrid meta-heuristic-based game theory model designated for compiling the importance weights of deterioration factors obtained from the first module. In this context, a set of widely acclaimed meta-heuristics are exploited and examined for optimizing the significance of deterioration factors. Analytical results exemplified that soil-related factors implicate the deterioration process more than pipe-related, operational-related, and environmental-related factors. It was also inferred that water pressure (6.64%) is the most significant factor influencing water pipe deterioration followed by internal corrosion and protection method (6.11%), and then soil corrosivity (6.05%). On the other hand, length (1.93%), rain deficit (1.97%), and street block length (2.33%) constitute the least influencers on water pipe deterioration. Results also demonstrated that spherical FAHP outperformed other variants of AHP accomplishing SAT and WD of 0.065 and 0.057, respectively. Comparative analysis revealed that particle swarm optimization-based game theory is a better mechanism than the remainder of meta-heuristic-based game theory models in obtaining a more accurate compromised-based weighting vector to the experts’ judgments. It is envisaged that this research can assist the water supplies department in identifying, assessing, and prioritizing the impairment causes of water pipelines in Hong Kong. It can also aid in establishing more accurate deterioration models and more cost-effective maintenance intervention programs. | |
| 651 | 4 | |a Hong Kong China | |
| 651 | 4 | |a China | |
| 653 | |a Aggregation | ||
| 653 | |a Pipes | ||
| 653 | |a Particle swarm optimization | ||
| 653 | |a Water pressure | ||
| 653 | |a Computation | ||
| 653 | |a Game theory | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Analytic hierarchy process | ||
| 653 | |a Deterioration | ||
| 653 | |a Corrosion prevention | ||
| 653 | |a Weight | ||
| 653 | |a Water supply | ||
| 653 | |a Modules | ||
| 653 | |a Water pipelines | ||
| 653 | |a Soil degradation | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Soil | ||
| 653 | |a Water pipes | ||
| 653 | |a Environmental factors | ||
| 653 | |a Theories | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Problem solving | ||
| 653 | |a Statistical methods | ||
| 653 | |a Hierarchies | ||
| 653 | |a Soil analysis | ||
| 653 | |a Hydrostatic pressure | ||
| 653 | |a Corrosion | ||
| 653 | |a Economic | ||
| 773 | 0 | |t Applied Water Science |g vol. 14, no. 12 (Dec 2024), p. 265 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3133068487/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3133068487/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |