TitleMachine Learning for The Deterioration Modeling of Water Transmission Pipes |
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AuthorsDr. Thikra Dawood, Purdue University, United States of AmericaAssoc. Prof. Dr. Emad Elwakil, Purdue University, United States of America Lecturer Hector Mayol Novoa, University of St Augustin of Arequipa, Peru Lecturer José Fernando Gárate Delgado, University of St Augustin of Arequipa, Peru |
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AbstractMachine learning (ML) algorithms and techniques are in the main trend of water distribution networks modeling in the last two decades. This is due to the high capabilities of ML in simulating and analyzing complex nonlinear relationships between input variables and outputs, as in the failure processes of water systems. A multitude of researches was dedicated to assess the condition of water networks and develop failure prediction models using different ML techniques; however, a comprehensive critical review pertaining to this field is missing in the literature. This paper aims to address this major gap by presenting the state-of-the-art in ML-based deterioration modeling of urban water infrastructure. The mainstream of the current practice, highlighted in this paper covers a proliferation of ML and soft computing applications to forecast and model the pipeline risk of failure. First, the state-of-knowledge of ML-based deterioration modeling for urban water systems is revealed along with models' methodologies, contributions, drawbacks, comparisons, and critiques. Second, future research directions and challenges are recommended to assist the construction automation research community in setting a vibrant agenda for the upcoming years. |
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Keywordsmachine learning, artificial intelligence, pipe failure, condition assessment, water main deterioration, infrastructure, state-of-the-art review. |
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CitationDawood, T., Elwakil, E., Novoa, H.M. & Delgado, J.F.G. (2020). Machine Learning for The Deterioration Modeling of Water Transmission Pipes . In M. Shelley & I. Sahin (Eds.), Proceedings of IConEST 2020--International Conference on Engineering, Science and Technology (pp. 9-18). Monument, CO, USA: ISTES Organization. Retrieved 19 April 2024 from www.2020.iconest.net/proceedings/11/. |
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