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Al-Bayati, A J; Chellappa, V (2025) Identifying desirable safety actions of upper management to foster higher levels of construction safety culture. Journal of Construction Engineering and Management, 151(7).
Al-Khiami, M I; Lindhard, S M; Wandahl, S (2025) Paradox in practice: Work-related musculoskeletal disorder prevalence and reporting among construction workers in Kuwait and Denmark. Journal of Construction Engineering and Management, 151(7).
Alshboul, O; Shehadeh, A; Tamimi, M (2025) Sustainability-focused pavement management under climate variability. Journal of Construction Engineering and Management, 151(7).
Bayona, A; Hallowell, M R; Bhandari, S; Moyen, N; Lien, A (2025) Impact of energy-based safety training on quality of prejob safety meetings and control of hazardous energy in construction: Multiple baseline experiment. Journal of Construction Engineering and Management, 151(7).
Cao, Q; Zou, X; Zhang, L (2025) A flexible scheduling framework for repetitive construction projects based on constraint programming. Journal of Construction Engineering and Management, 151(7).
Chan, I Y S; Ma, P; Ho, T Y K (2025) Impacts of relational and formal governance on information sharing and project management performance in collaborative contracts: A mixed-method approach. Journal of Construction Engineering and Management, 151(7).
Chen, H; Dong, Z; Chan, I Y S (2025) Biometric evaluation and immersive construction environments: A research overview of the current landscape, challenges, and future prospects. Journal of Construction Engineering and Management, 151(7).
Ghalenoei, N; Babaeian Jelodar, M; Paes, D; Sutrisna, M; Rahmani, D (2025) Offsite construction and BIM integration framework across project life cycle. Journal of Construction Engineering and Management, 151(7).
Hua, X; Zhang, S; Shi, X; Zhang, Y (2025) Differences in risk analysis between workers and managers: Study from the perspective of neuroscience. Journal of Construction Engineering and Management, 151(7).
Jezzini, Y; Assaad, R H; El-Adaway, I H (2025) Modeling framework to quantify and gauge project cost risks due to construction material price volatilities using predictive probabilistic deep-learning algorithms and stochastic risk modeling. Journal of Construction Engineering and Management, 151(7).
- Type: Journal Article
- Keywords: construction materials; deep learning; price escalation clauses; price fluctuations; probabilistic forecasting; stochastic risk modeling
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/JCEMD4.COENG-16055
- Abstract:
Material price fluctuations pose significant challenges for executing construction projects and adhering to budgetary estimates. Existing research studies focused on forecasting construction material prices rather than quantifying and gauging overall project cost risks related to price volatilities, and they relied on traditional time-series forecasting methods that are incapable of offering full probabilistic distributions of price fluctuations and of providing a comprehensive assessment of risk uncertainties associated with material price fluctuations. This paper addresses these gaps by developing an integrated framework to quantify and gauge project risks due to construction material price volatilities. The framework's validity and practicality were demonstrated using real-world projects with various characteristics and different market conditions, including an 11-month bridge replacement project and a 25-month major roadway project. Historical Producer Price Index (PPI) values were collected for four construction materials: steel reinforcement, asphalt, aggregate, and concrete. Three probabilistic deep-learning models - deep autoregressive models, probabilistic feed-forward neural networks, and transformers - were developed to forecast PPI probabilistic distributions. The performance of the developed models was evaluated using probabilistic metrics, and the top-performing models were identified for each material and were compared with a baseline artificial neural network model and a Bayesian prophet model. Finally, stochastic risk models were developed to integrate the predicted distributions into the price escalation clauses of standard construction contracts (i.e., FIDIC) to model risk uncertainties and plot stochastic risk profiles. The findings provided valuable insights about patterns and fluctuations in prices across various construction materials, market volatilities, extreme events, and different types of clauses, including "any-increase escalation clauses"and "threshold escalation clauses."This study contributes to the growing body of knowledge on construction material price escalation by offering a comprehensive approach that provides project parties with data-driven insights that inform strategies to mitigate financial setbacks resulting from price fluctuations and volatilities in their projects.
Li, C Z; Gao, T; Chen, Z; Wu, H; Deng, Y; Tam, V W Y; Le, K N (2025) Exploring the power of laser scanning technology toward smart construction: Status quo, challenges, and future directions. Journal of Construction Engineering and Management, 151(7).
Li, L; Cheng, M; Tu, K; Ding, R; Zhang, J; Xu, B (2025) Human-machine interface based on constraint velocity polytope for safe and efficient operation of large-size hydraulic manipulator. Journal of Construction Engineering and Management, 151(7).
Ma, Q; Cheung, S O (2025) Augmenting the incentivizing power of target cost contracting in integrated project delivery. Journal of Construction Engineering and Management, 151(7).
Moussa, A; Ezzeldin, M; El-Dakhakhni, W (2025) Data-driven assessment of complexity-induced risks in infrastructure projects. Journal of Construction Engineering and Management, 151(7).
Peng, L; Man, S S; Chung, H T; Chan, A H S; Zhang, Z (2025) Prospective workers' perceptions of crane operation risks: Using a pairwise comparison. Journal of Construction Engineering and Management, 151(7).
Poudel, O; Assaad, R H (2025) A real-time intelligent acoustic IoT-enabled embedded construction site monitoring and alert system: Integrating deep learning-based machine-listening algorithms, edge computing, and cloud computing. Journal of Construction Engineering and Management, 151(7).
Rasheed, U; Ordaz, C; Xu, X; Hu, Y; Li, S; Sutton, T; Cai, J (2025) Understanding the impact of teleoperation technology on the construction industry: Adoption dynamics, workforce perception, and the role of broader workforce participation. Journal of Construction Engineering and Management, 151(7).
Xue, H; Li Teh, K K; Ling, F Y Y (2025) Effects of crisis management leadership, perceived self-efficacy, and job performance on facility management professionals' job satisfaction in a crisis. Journal of Construction Engineering and Management, 151(7).
Yan, L; Wang, Y; Ning, Y (2025) Configuring governance mechanisms to improve the engineering consulting project performance. Journal of Construction Engineering and Management, 151(7).
Young, T; Hunter, J A; Bentley, S V; Millear, P; Alexander Haslam, S; Haslam, C (2025) A social identity intervention to improve mental health in construction workers. Journal of Construction Engineering and Management, 151(7).
Zhang, P; Sing, M C P; Guo, S; Chan, I Y S; Fung, I W H (2025) Causal factors of near misses and accidents in urban railway construction: A complex network approach. Journal of Construction Engineering and Management, 151(7).
Zu, F; Zhang, X (2025) Integrating a DfMA guideline matrix to facilitate value engineering workshops for construction projects. Journal of Construction Engineering and Management, 151(7).