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Using ANN in emergency reconstruction projects post disaster
The purpose of this study is to avoid delays and cost changes that occur in emergency reconstruction projects especially in post disaster circumstances. This study is aimed to identify the factors that affect the real construction period and the real cost of a project against the estimated period of construction and the estimated cost of the project. The case study is related to the construction projects in Iraq. Thirty projects in different areas of construction in Iraq were selected as a sample for this study. Project participants from the projects authorities provided data about the projects through a data collection distributed survey made by the authors. Mathematical data analysis was used to construct a model to predict change in time and cost of the projects before the start of the construction. The artificial neural networks analysis was selected as a mathematical approach. The most important factors identified leading to schedule delays and cost increase were contractor failure, redesigning of designs/plans and change orders, security issues, selection of low-price bids, weather factors, and owner failures. The use of the ANN model for such a problem is expected to be an effective method for modeling this complicated phenomenon ; publishedVersion ; © 2020 The Author(s) DOI:10.1177/1847979020967835 This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/ open-access-at-sage).
Using ANN in emergency reconstruction projects post disaster
The purpose of this study is to avoid delays and cost changes that occur in emergency reconstruction projects especially in post disaster circumstances. This study is aimed to identify the factors that affect the real construction period and the real cost of a project against the estimated period of construction and the estimated cost of the project. The case study is related to the construction projects in Iraq. Thirty projects in different areas of construction in Iraq were selected as a sample for this study. Project participants from the projects authorities provided data about the projects through a data collection distributed survey made by the authors. Mathematical data analysis was used to construct a model to predict change in time and cost of the projects before the start of the construction. The artificial neural networks analysis was selected as a mathematical approach. The most important factors identified leading to schedule delays and cost increase were contractor failure, redesigning of designs/plans and change orders, security issues, selection of low-price bids, weather factors, and owner failures. The use of the ANN model for such a problem is expected to be an effective method for modeling this complicated phenomenon ; publishedVersion ; © 2020 The Author(s) DOI:10.1177/1847979020967835 This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/ open-access-at-sage).
Using ANN in emergency reconstruction projects post disaster
Waheeb, Rasha A (Autor:in) / Andersen, Bjørn S (Autor:in) / Suhili, Rafea AL (Autor:in)
01.01.2020
cristin:1854771
1-21 ; 12 ; International Journal of Engineering Business Management (IJEBM)
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
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