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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).
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).
- Type: Journal Article
- Keywords: alternative project delivery; progressive design build; water and wastewater infrastructure
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13824
- Abstract:
The United States has invested heavily in water and wastewater infrastructure projects to address growing demand and aging systems. To ensure the effective delivery of these projects, agencies are shifting toward alternative delivery methods such as progressive design build (PDB), which has demonstrated accelerated schedule and enhanced cost performance across the literature as well as multiple projects compared to traditional DB. This has raised a need for evaluating PDB's state of adoption and performance in the water and wastewater sector. To this end, the authors: (1) conducted descriptive and statistical analyses of the 21 PDB water and wastewater projects available on the Design-Build Institute of America database evaluating their characteristics and performance metrics; (2) investigated the frequency of materialized risks impacting schedule and cost in these projects; and finally (3) identified the key adoption drivers and challenges for PDB in the water and wastewater sector by triangulating findings from the studied narratives with a literature and practice review. Results revealed that 71% and 57% of the investigated projects were completed on or before the contracted schedules and costs, respectively. From the studied project narratives, owner-led changes and COVID-19 impacts were the most frequently encountered risks. Also, it was shown that project planning and risk management drivers were the most influential causes for PDB adoption, whereas legal and contractual restrictions as well as the owner's mindset and culture-related concerns were the most pressing challenges. This study contributes to the body of knowledge by delivering managerial insights through an aggregated snapshot of PDB implementation in the water and wastewater sector. Ultimately, the provided managerial insights can assist stakeholders in making better-informed decisions by weighing the advantages and challenges of PDB identified in this research against more traditional delivery approaches.
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).