Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System
AbstractAs populations grow, facilities such as roads, bridges, railways lines, commercial and residential buildings, etc., must be expanded and maintained. There are extensive networks of underground facilities to fulfil the demand, such as water supply pipelines, sewage pipelines, metro structures, etc. Hence, a method to regularly assesses the risk of the underground facility failures is needed to decrease the chance of accidental loss of service or accidents that endanger people and facilities. In the proposed work, a cohesive hierarchical fuzzy inference system (CHFIS) was developed. A novel method is proposed for membership function (MF) determination called the heuristic based membership functions determination (HBMFD) method to determine an appropriate MF set for each fuzzy logic method in CHFIS. The proposed model was developed to decrease the number of rules for the full structure fuzzy inference system with all rule implementation. Four very crucial parameters were considered in the proposed work that are inputs to the proposed CHFIS model in order to calculate the risk of water supply pipelines. In order to fully implement the proposed CHFIS just 85 rules are needed while using the traditional Mamdani fuzzy inference system, 900 rules are required. The novel method greatly reduces implementation time and rule design sets that are extremely time consuming to develop and difficult to maintain. View Full-Text
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Fayaz, M.; Ahmad, S.; Hang, L.; Kim, D. Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System. Processes 2019, 7, 182.
Fayaz M, Ahmad S, Hang L, Kim D. Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System. Processes. 2019; 7(4):182.Chicago/Turabian Style
Fayaz, Muhammad; Ahmad, Shabir; Hang, Lei; Kim, DoHyeun. 2019. "Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System." Processes 7, no. 4: 182.