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Article

Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction

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Construction and Project Management Research Institute, Housing and Building National Research Centre, Giza 12311, Egypt
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Structural Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt
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Department of Buildings, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
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Department of Architecture & Environmental Planning, College of Engineering & Petroleum, Hadhramout University, Mukalla 50512, Yemen
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Department of Architecture and Building Science, College of Architecture and Planning, King Saud University, Riyadh 11421, Saudi Arabia
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Author to whom correspondence should be addressed.
Academic Editors: Bruno Brunone and Inmaculada Pulido-Calvo
Water 2021, 13(24), 3546; https://doi.org/10.3390/w13243546
Received: 14 September 2021 / Revised: 28 November 2021 / Accepted: 8 December 2021 / Published: 11 December 2021
(This article belongs to the Section Urban Water Management)
The bulk of water pipes experience major degradation and deterioration problems. This research aims at estimating the condition of water pipes in Shattora and Shaker Al-Bahery’s water distribution networks, in Egypt. The developed models involve training the Elman neural network (ENN) and feed-forward neural network (FFNN) coupled with particle swarm optimization (PSO), genetic algorithms (GA), the sine cosine algorithm (SCA), and the teaching-learning-based optimization (TLBO) algorithm. For the Shattora network, the inputs to these models are pipe characteristics such as length, wall thickness, diameter, material, lining and coating, surface type, traffic distribution, cathodic protection, flow velocity, and c-factor. For the Shaker Al-Bahery network, the data gathered include length, material, age, diameter, depth, and wall thickness. Three assessment criteria are used to evaluate the suggested machine learning models, namely index of agreement (IOA), correlation coefficient (R), and root mean squared error (RMSE). The results reveal that coupling FFNN with the TLBO algorithm outperforms other prediction models. Therefore, the FFNN-TLBO model can be a valuable tool for simulating the water network pipe condition. This study could help the water municipality allocate the available budget effectively and plan the required maintenance and rehabilitation actions. View Full-Text
Keywords: teaching-learning-based optimization; optimized neural network; machine learning; optimization algorithm; condition prediction teaching-learning-based optimization; optimized neural network; machine learning; optimization algorithm; condition prediction
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MDPI and ACS Style

Elshaboury, N.; Abdelkader, E.M.; Al-Sakkaf, A.; Alfalah, G. Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction. Water 2021, 13, 3546. https://doi.org/10.3390/w13243546

AMA Style

Elshaboury N, Abdelkader EM, Al-Sakkaf A, Alfalah G. Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction. Water. 2021; 13(24):3546. https://doi.org/10.3390/w13243546

Chicago/Turabian Style

Elshaboury, Nehal, Eslam M. Abdelkader, Abobakr Al-Sakkaf, and Ghasan Alfalah. 2021. "Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction" Water 13, no. 24: 3546. https://doi.org/10.3390/w13243546

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