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Evaluation and prediction of time overruns in Jordanian construction projects using coral reefs optimization and deep learning methods

Within the complex domain of construction project management, the accurate anticipation of time overruns is a significant obstacle, particularly within the specific context of the construction sector in Jordan. This study aimed to utilize deep learning, specifically the Multi-Layer Perceptron (MLP),...

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Main Authors: Shihadeh, Jumana, Al-Shaibie, Ghyda, Bisharah, Majdi, Alshami, Dania, Alkhadrawi, Sajeda, Al-Bdour, Hamza
Format: Article
Published: Springer 2024
Online Access:http://psasir.upm.edu.my/id/eprint/105751/
https://link.springer.com/article/10.1007/s42107-023-00936-3?error=cookies_not_supported&code=b1ea9bc5-56ed-4791-b9cb-13359e60b686
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spelling my.upm.eprints.1057512024-07-02T02:44:58Z http://psasir.upm.edu.my/id/eprint/105751/ Evaluation and prediction of time overruns in Jordanian construction projects using coral reefs optimization and deep learning methods Shihadeh, Jumana Al-Shaibie, Ghyda Bisharah, Majdi Alshami, Dania Alkhadrawi, Sajeda Al-Bdour, Hamza Within the complex domain of construction project management, the accurate anticipation of time overruns is a significant obstacle, particularly within the specific context of the construction sector in Jordan. This study aimed to utilize deep learning, specifically the Multi-Layer Perceptron (MLP), and enhance its overrun predictive ability by incorporating the Coral Reefs Optimization Algorithm (CROA). The approach employed in our study involved the utilization of a comprehensive dataset encompassing diverse aspects of building projects, ranging from financial indicators to project durations. The Multilayer Perceptron (MLP) was utilized as the underlying framework, and the model's parameters were refined utilizing the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with Rank-One Adaptation (CROA) to enhance the predictive capabilities. The results of our study indicate that the MLP-CROA continuously showed superior performance compared to the standalone MLP across many metrics. Notably, the MLP-CROA had particularly excellent values in metrics such as R-squared, which suggests its solid predictive capabilities. In conclusion, this study highlights the significant potential for transformation in the Jordanian construction industry by integrating deep learning and bio-inspired optimization methodologies. This process improves forecast accuracy and facilitates proactive project management, potentially directing projects toward timely and cost-effective completion. Springer 2024-01 Article PeerReviewed Shihadeh, Jumana and Al-Shaibie, Ghyda and Bisharah, Majdi and Alshami, Dania and Alkhadrawi, Sajeda and Al-Bdour, Hamza (2024) Evaluation and prediction of time overruns in Jordanian construction projects using coral reefs optimization and deep learning methods. Asian Journal of Civil Engineering, 25 (3). pp. 2665-2677. ISSN 1563-0854; ESSN: 2522-011X https://link.springer.com/article/10.1007/s42107-023-00936-3?error=cookies_not_supported&code=b1ea9bc5-56ed-4791-b9cb-13359e60b686 10.1007/s42107-023-00936-3
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Within the complex domain of construction project management, the accurate anticipation of time overruns is a significant obstacle, particularly within the specific context of the construction sector in Jordan. This study aimed to utilize deep learning, specifically the Multi-Layer Perceptron (MLP), and enhance its overrun predictive ability by incorporating the Coral Reefs Optimization Algorithm (CROA). The approach employed in our study involved the utilization of a comprehensive dataset encompassing diverse aspects of building projects, ranging from financial indicators to project durations. The Multilayer Perceptron (MLP) was utilized as the underlying framework, and the model's parameters were refined utilizing the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with Rank-One Adaptation (CROA) to enhance the predictive capabilities. The results of our study indicate that the MLP-CROA continuously showed superior performance compared to the standalone MLP across many metrics. Notably, the MLP-CROA had particularly excellent values in metrics such as R-squared, which suggests its solid predictive capabilities. In conclusion, this study highlights the significant potential for transformation in the Jordanian construction industry by integrating deep learning and bio-inspired optimization methodologies. This process improves forecast accuracy and facilitates proactive project management, potentially directing projects toward timely and cost-effective completion.
format Article
author Shihadeh, Jumana
Al-Shaibie, Ghyda
Bisharah, Majdi
Alshami, Dania
Alkhadrawi, Sajeda
Al-Bdour, Hamza
spellingShingle Shihadeh, Jumana
Al-Shaibie, Ghyda
Bisharah, Majdi
Alshami, Dania
Alkhadrawi, Sajeda
Al-Bdour, Hamza
Evaluation and prediction of time overruns in Jordanian construction projects using coral reefs optimization and deep learning methods
author_facet Shihadeh, Jumana
Al-Shaibie, Ghyda
Bisharah, Majdi
Alshami, Dania
Alkhadrawi, Sajeda
Al-Bdour, Hamza
author_sort Shihadeh, Jumana
title Evaluation and prediction of time overruns in Jordanian construction projects using coral reefs optimization and deep learning methods
title_short Evaluation and prediction of time overruns in Jordanian construction projects using coral reefs optimization and deep learning methods
title_full Evaluation and prediction of time overruns in Jordanian construction projects using coral reefs optimization and deep learning methods
title_fullStr Evaluation and prediction of time overruns in Jordanian construction projects using coral reefs optimization and deep learning methods
title_full_unstemmed Evaluation and prediction of time overruns in Jordanian construction projects using coral reefs optimization and deep learning methods
title_sort evaluation and prediction of time overruns in jordanian construction projects using coral reefs optimization and deep learning methods
publisher Springer
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/105751/
https://link.springer.com/article/10.1007/s42107-023-00936-3?error=cookies_not_supported&code=b1ea9bc5-56ed-4791-b9cb-13359e60b686
_version_ 1804067032818253824
score 13.211869

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