Szczegóły publikacji
Opis bibliograficzny
Assessing the risk of damage to underground utilities caused by spatial data quality with fuzzy logic / Marek Ślusarski, Anna PRZEWIĘŹLIKOWSKA // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417 . — 2025 — vol. 15 iss. 22 art. no. 11980, s. 1–21. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 20–21, Abstr. — Publikacja dostępna online od: 2025-11-11
Autorzy (2)
- Ślusarski Marek
- AGHPrzewięźlikowska Anna
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164474 |
|---|---|
| Data dodania do BaDAP | 2025-11-28 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app152211980 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
One of the sources of risk inherent to construction projects is the quality of spatial data. Damage to buried pipes and cables often causes accidents, delays, or stoppages of construction works. Fuzzy logic is a method for studying the risk. It is employed to describe complex or poorly defined phenomena that can hardly be characterised with probabilistic methods. The article proposes a method for assessing the risk of damaging underground utilities based on a fuzzy inference engine. The author first defined linguistic variables and assigned them values based on risk factors. The membership functions for the linguistic variables were modelled using expert judgement. Then, the author determined qualitative fuzzy sets with the rule base. Finally, the values were converted into crisp values. The defuzzification technique employed was the centre of gravity. The proposed method can assess the risk of damage to underground utilities for spatial data exhibiting diverse quality classes. It will be employed to generate large-scale risk maps. The proposed fuzzy logic solution is an effective and appropriate tool for assessing the risk of damage to underground utilities arising from the quality of subsurface data. It should not be regarded as a universal substitute for PRA (Probabilistic Risk Assessment) but as a complementary methodology that is particularly well-suited to risk assessment in data-poor environments characterised by epistemic uncertainty and reliance on qualitative expert judgement.
