A Novel Framework for Estimation of the Maintenance and Operation Cost in Construction Projects: A Step Toward Sustainable Buildings

Maher Abuhussain and Ahmad Baghdadi ()
Additional contact information
Maher Abuhussain: Department of Civil and Environmental Engineering, College of Engineering and Computing in Al-Qunfudhah, Umm al-Qura University, Mecca 24382, Saudi Arabia
Ahmad Baghdadi: Department of Civil and Environmental Engineering, College of Engineering and Computing in Al-Qunfudhah, Umm al-Qura University, Mecca 24382, Saudi Arabia

Sustainability, 2024, vol. 16, issue 23, 1-21

Abstract: Building maintenance and operation costs represent a significant portion of the life cycle costs (LCC) of construction projects. The accurate estimation of these costs is essential for ensuring the long-term sustainability and financial efficiency of buildings. This study aims to develop a novel framework for predicting maintenance and operation costs in construction projects by integrating an emotional artificial neural network (EANN). Unlike traditional models that rely on linear regression or static machine learning, the EANN dynamically adapts its learning through synthetic emotional feedback mechanisms and advanced optimization techniques. The research collected input data from 313 experts in the field of building management and construction in Ha’il, Saudi Arabia, through a comprehensive questionnaire. The integration of expert opinions with advanced machine learning techniques contributes to the innovative approach, providing more reliable and adaptive cost predictions. The proposed EANN model was then compared with a classic artificial neural network (ANN) model to evaluate its performance. The results indicate that the EANN model achieved an R 2 value of 0.85 in training and 0.81 in testing for buildings aged 0 to 10 years, significantly outperforming the ANN model, which achieved R 2 values of 0.78 and 0.72, respectively. Additionally, the Root Mean Squared Error (RMSE) for the EANN model was 1.57 in training and 1.60 in testing, lower than the ANN’s RMSE values of 1.82 and 1.90. These findings show that the superior capability of the EANN model in estimating maintenance and operation costs.. This led to more accurate long-term maintenance cost projections, reduced budgeting uncertainty, and enhanced decision-making reliability for building managers.

Keywords: building; life cycle cost; construction; maintenance and operation cost; questionnaire; emotional artificial neural network; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/23/10441/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/23/10441/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:23:p:10441-:d:1532042

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().