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Optimizing Sanitation Network Upgrading Projects in Slum Areas

Infrastructure upgrading projects are a key element in enhancing the livelihood of residents in slum areas. These projects face significant constructability challenges common to dense-urban construction coupled with the unique socioeconomic challenges of operating in slums. This research focuses on...

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Autores principales: Marzouk, Mohamed, Bahi, Mahmoud, El-Anwar, Omar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10447308/
https://www.ncbi.nlm.nih.gov/pubmed/37535302
http://dx.doi.org/10.1007/s11524-023-00751-w
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author Marzouk, Mohamed
Bahi, Mahmoud
El-Anwar, Omar
author_facet Marzouk, Mohamed
Bahi, Mahmoud
El-Anwar, Omar
author_sort Marzouk, Mohamed
collection PubMed
description Infrastructure upgrading projects are a key element in enhancing the livelihood of residents in slum areas. These projects face significant constructability challenges common to dense-urban construction coupled with the unique socioeconomic challenges of operating in slums. This research focuses on sanitation network upgrading projects in slum areas and proposes a novel methodology capable of (1) accounting for the unique constructability challenges for these projects, (2) accelerating the provision of sanitation services, and (3) optimizing construction decisions. The key contribution of this research to the body of knowledge is in developing a comprehensive construction planning framework capable of achieving these three objectives. The proposed framework focuses specifically on sewer lines upgrading within the larger sanitation networks upgrading projects. This framework consists of five main models that can guide planners in selecting the appropriate equipment sizes, trench system configuration, and optimal equipment routing, in addition to identifying all possible execution sequences along with the corresponding construction cost and duration of each sequence. Most notably, this framework proposes an approach to assess the serviceability of different construction plans measured by how fast sanitary services can be provided to slum dwellers. A multi-objective, genetic algorithms optimization model is developed to identify the optimal construction plans that accelerate the sanitary service provision to residents while minimizing construction costs. A real-world example is presented to demonstrate the model capabilities in optimizing construction plans.
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spelling pubmed-104473082023-08-25 Optimizing Sanitation Network Upgrading Projects in Slum Areas Marzouk, Mohamed Bahi, Mahmoud El-Anwar, Omar J Urban Health Original Article Infrastructure upgrading projects are a key element in enhancing the livelihood of residents in slum areas. These projects face significant constructability challenges common to dense-urban construction coupled with the unique socioeconomic challenges of operating in slums. This research focuses on sanitation network upgrading projects in slum areas and proposes a novel methodology capable of (1) accounting for the unique constructability challenges for these projects, (2) accelerating the provision of sanitation services, and (3) optimizing construction decisions. The key contribution of this research to the body of knowledge is in developing a comprehensive construction planning framework capable of achieving these three objectives. The proposed framework focuses specifically on sewer lines upgrading within the larger sanitation networks upgrading projects. This framework consists of five main models that can guide planners in selecting the appropriate equipment sizes, trench system configuration, and optimal equipment routing, in addition to identifying all possible execution sequences along with the corresponding construction cost and duration of each sequence. Most notably, this framework proposes an approach to assess the serviceability of different construction plans measured by how fast sanitary services can be provided to slum dwellers. A multi-objective, genetic algorithms optimization model is developed to identify the optimal construction plans that accelerate the sanitary service provision to residents while minimizing construction costs. A real-world example is presented to demonstrate the model capabilities in optimizing construction plans. Springer US 2023-08-03 2023-08 /pmc/articles/PMC10447308/ /pubmed/37535302 http://dx.doi.org/10.1007/s11524-023-00751-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Marzouk, Mohamed
Bahi, Mahmoud
El-Anwar, Omar
Optimizing Sanitation Network Upgrading Projects in Slum Areas
title Optimizing Sanitation Network Upgrading Projects in Slum Areas
title_full Optimizing Sanitation Network Upgrading Projects in Slum Areas
title_fullStr Optimizing Sanitation Network Upgrading Projects in Slum Areas
title_full_unstemmed Optimizing Sanitation Network Upgrading Projects in Slum Areas
title_short Optimizing Sanitation Network Upgrading Projects in Slum Areas
title_sort optimizing sanitation network upgrading projects in slum areas
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10447308/
https://www.ncbi.nlm.nih.gov/pubmed/37535302
http://dx.doi.org/10.1007/s11524-023-00751-w
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