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Afzali Borujeni, S H, Zare, M and Adelzade Saadabadi, L (2024) Comparison of factors affecting the acceptance of the trenchless technology and open-trench method using ANP and AHP: Case study in Iran. Journal of Construction Engineering and Management, 150(01).
Aghililotf, M, Ramezanianpour, A M, Arbabi, H and Maghrebi, M (2024) Identifying construction managers' challenges: A novel approach based on social network analysis. Journal of Construction Engineering and Management, 150(01).
Alikhani, H and Latifi, M (2024) Evaluation of the healing performance of hot-mix asphalt containing waste steel shavings under different microwave induction healing cycles. Journal of Construction Engineering and Management, 150(01).
Anwer, S, Li, H, Antwi-Afari, M F, Mirza, A M, Rahman, M A, Mehmood, I, Guo, R and Wong, A Y L (2024) Evaluation of data processing and artifact removal approaches used for physiological signals captured using wearable sensing devices during construction tasks. Journal of Construction Engineering and Management, 150(01).
Arowoiya, V A, Oke, A E, Ojo, L D and Adelusi, A O (2024) Driving factors for the adoption of digital twin technology implementation for construction project performance in Nigeria. Journal of Construction Engineering and Management, 150(01).
- Type: Journal Article
- Keywords: construction performance; digital twin; professionals; pull factors; technology
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13659
- Abstract:
The adoption of digital twin technology (DTT) for enhancing construction project performance, sustainability, and safety of construction workers, among others, is common practice in developed countries. Meanwhile, construction projects in developing nations are suffering from poor outcomes, which could benefit from DTT. Therefore, this research investigates the drivers of the adoption of DTT implementation in the Nigerian construction industry. Close-ended questionnaires were administered to digitally inclined professionals using purposive and snowballing techniques to elicit necessary information on the drivers of DTT implementation. These respondents include architects, engineers, quantity surveyors, and builders in the study area. It was found that technological advancement/trend, reliable data storage, safety, availability of software, customer satisfaction, and accessibility were the top-ranked drivers for implementing DTT. A Shapiro-Wilk test was conducted to know whether the data are normally distributed or not, which led to the use of the Kruskal-Wallis H-test. The Kruskal-Wallis test revealed that government policies have diverging views among the professionals, whereas other factors have converging opinions among the respondents. Factor analysis was conducted to group the key drivers for the implementation of DTT into innovation, operation, quality, performance, and policy drivers. The findings of this study will provide a reference point for researchers and construction organizations on the driving force that brings about the implementation of DTT in a business context and the construction industry. This study realizes that one of the key drivers of the adoption of this technology is a desire for innovation and technological advancement that can be achieved when the technology performs to the expectation of clients, organizations, and the architecture, engineering, and construction (AEC) industry. The study also suggests ways of implementing DTT in the construction industry as well.
D'Orazio, M, Bernardini, G and Di Giuseppe, E (2024) Improving sustainable management of university buildings based on occupancy data. Journal of Construction Engineering and Management, 150(01).
Da Silva, W O P, Farias, B A, Monteiro, I B, Pegorini, V, Casanova, D and Bisconsini, D R (2024) Development of global quality index of unpaved roads. Journal of Construction Engineering and Management, 150(01).
Ding, S, Hu, H, Chai, Z and Wang, W (2024) Secure and formalized blockchain-IPFS information sharing in precast construction from the whole supply chain perspective. Journal of Construction Engineering and Management, 150(01).
Garcia-Lopez, N P and Fischer, M (2024) Managing on-site production using an activity and flow-based construction model. Journal of Construction Engineering and Management, 150(01).
Jalloul, H, Choi, J, Manheim, D, Yesiller, N and Derrible, S (2024) Incorporating disaster debris into sustainable construction research and practice. Journal of Construction Engineering and Management, 150(01).
Koc, K, Budayan, C, Ekmekcioǧlu, Ö and Tokdemir, O B (2024) Predicting cost impacts of nonconformances in construction projects using interpretable machine learning. Journal of Construction Engineering and Management, 150(01).
Leung, M Y, Wei, X and Ojo, L D (2024) Developing a value-risk management model for construction projects. Journal of Construction Engineering and Management, 150(01).
Liu, Y, Wang, X, Guo, S, Shi, X and Wang, D (2024) Analyzing the optimization of subsidies for PPP urban rail transit projects: A choice between passenger demand, vehicle kilometer, or an improved efficiency-oriented framework. Journal of Construction Engineering and Management, 150(01).
Miao, K, Lou, W, Schonfeld, P and Xiao, Z (2024) Optimal earthmoving-equipment combination considering carbon emissions with an indicator-based multiobjective optimizer. Journal of Construction Engineering and Management, 150(01).
Ning, X, Zhai, F, Xia, N and Hu, X (2024) Protecting the ego: Anticipated image risk as a psychological deterrent to construction workers' safety citizenship behavior. Journal of Construction Engineering and Management, 150(01).
Olayiwola, J, Yusuf, A, Akanmu, A, Gonsalves, N and Abraham, Y (2024) Efficacy of annotated video-based learning environment for drawing students' attention to construction practice concepts. Journal of Construction Engineering and Management, 150(01).
Salih, F, Eissa, R and El-Adaway, I H (2024) Data-driven analysis of progressive design build in water and wastewater infrastructure projects. Journal of Construction Engineering and Management, 150(01).
Zhong, B, Shen, L, Pan, X, Zhong, X and He, W (2024) Dispute classification and analysis: Deep learning-based text mining for construction contract management. Journal of Construction Engineering and Management, 150(01).