Spatial Analysis and Failure Management in Water Distribution Networks Using Fuzzy Inference System

Pouri, Zahra; Heidarimozaffar, Morteza

doi:10.1007/s11269-022-03104-5

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Taking computer graphics theory course as an example, its teaching evaluation index set is shown in Table 11. e contents can also be adjusted according to the actual application effect feedback [20,21]. e following Table 12 shows a teaching quality evaluation information base transferred out of the system, which is the scores of supervisors, peers, and students for teachers in terms of teaching attitude, teaching content, teaching art, classroom structure, classroom management, and teaching effect.…”

Section: Determine the Evaluation Index System And Commentmentioning

confidence: 99%

Evaluation of Students’ Educational Management Quality Based on Intuitionistic Fuzzy Information

Cao

2022

Advances in Multimedia

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In order to further improve the scientificity and rationality of students’ education management quality evaluation, this paper proposes a method of students’ education management quality evaluation based on intuitive fuzzy information. This method makes a scientific judgment on the teaching management process and teaching results in combination with the objectives of students’ education and teaching management. On the basis of teaching evaluation, with the help of intuitionistic fuzzy information integration algorithm method, this paper focuses on using intuitionistic fuzzy-weighted average operator and normal distribution weighting method to construct the quality evaluation model of students’ education and teaching management, and develops the teaching quality evaluation system based on intuitionistic fuzzy theory. Through the two-level fuzzy comprehensive evaluation, the teacher evaluation result score is 81.45, which is an algebraic value from 0 to 100, and the score is between 80 and 90, which verifies that the classroom teaching evaluation result is good.

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Section: Determine the Evaluation Index System And Commentmentioning

confidence: 99%

Evaluation of Students’ Educational Management Quality Based on Intuitionistic Fuzzy Information

Cao

2022

Advances in Multimedia

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Investigating the Impact of Cumulative Pressure-Induced Stress on Machine Learning Models for Pipe Breaks

Konstantinou,

Jara-Arriagada,

Stoianov

2023

Water Resour Manage

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Significant financial resources are needed for the maintenance and rehabilitation of water supply networks (WSNs) to prevent pipe breaks. The causes and mechanisms for pipe breaks vary between different WSNs. However, it is commonly acknowledged that the operational management and water pressure influence significantly the frequency of pipe breaks. Pipe breaks occur when the water pressure exceeds the tensile strength of a pipe, or due to repetitive pressure cycles that result in fatigue-related failures. Considering these pipe failure modes, a new metric known as cumulative pressure-induced stress has been introduced. This metric takes into account both static and dynamic pressure components that contribute to pipe breaks, including mean pressure and the magnitude and frequency of pressure fluctuations, respectively. The impact of CPIS on pipe breaks has not been extensively investigated. Consequently, this study investigates and evaluates the impact of this metric when incorporated as an explanatory variable in Random Forest (RF) models that analyse the key causes of pipe breaks in two WSNs. Different RF models were developed both with and without incorporating pressure components. Subsequently, the performance of these models and the significance of each input variable were assessed. The results of this study suggest that CPIS is an important variable, especially in cases where pressure-related factors play a significant role in pipe breaks. Consequently, incorporating CPIS has shown a notable improvement in the accuracy of pipe break models.

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Development of sustainable water infrastructure: A proper understanding of water pipe failure

Taiwo

Shaban

Zayed

2023

Journal of Cleaner Production

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Spatial Analysis and Failure Management in Water Distribution Networks Using Fuzzy Inference System

Cited by 4 publications

References 23 publications

Evaluation of Students’ Educational Management Quality Based on Intuitionistic Fuzzy Information

Evaluation of Students’ Educational Management Quality Based on Intuitionistic Fuzzy Information

Investigating the Impact of Cumulative Pressure-Induced Stress on Machine Learning Models for Pipe Breaks

Development of sustainable water infrastructure: A proper understanding of water pipe failure

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