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Arabi, S, Eshtehardian, E and Shafiei, I (2022) Using Bayesian Networks for Selecting Risk-Response Strategies in Construction Projects. Journal of Construction Engineering and Management, 148(08).

Chang, T, Chi, S and Im, S (2022) Understanding User Experience and Satisfaction with Urban Infrastructure through Text Mining of Civil Complaint Data. Journal of Construction Engineering and Management, 148(08).

Dias Barkokebas, R, Al-Hussein, M and Li, X (2022) VR–MOCAP-Enabled Ergonomic Risk Assessment of Workstation Prototypes in Offsite Construction. Journal of Construction Engineering and Management, 148(08).

Ezzeddine, A, Shehab, L, Lucko, G and Hamzeh, F (2022) Forecasting Construction Project Performance with Momentum Using Singularity Functions in LPS. Journal of Construction Engineering and Management, 148(08).

  • Type: Journal Article
  • Keywords:
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0002320
  • Abstract:
    Construction planning and control are crucial for project success. The last planner system (LPS) presents a proactive approach to plan and control production, designed to increase reliability and enhance the make-ready process. Several metrics guide the planning process at the macro level (master and phase scheduling) and the micro level (lookahead and weekly work planning). However, LPS still lacks a mathematical model that can systematically and continuously analyze such metrics, especially to forecast project performance. Moreover, there are no studies on the effect of the fluctuations of lookahead-planning LPS metrics on the metrics at the weekly work plan level. This research, therefore, proposed a new mathematical model using singularity functions, which are types of range-based expressions that track the different paths that each task can follow, from lookahead planning to weekly work planning, and evaluate LPS metrics. To assess project performance, the concept of momentum was introduced as the rate of change in metrics from week to week. Momentum was applied to the Tasks Made Ready (TMR) metric to predict the Percent Plan Complete (PPC). Through machine learning models, results show that momentum can predict PPC with over 93% correlation between actual and predicted PPC values. Data from actual construction execution in the United States were used to validate the proposed model. The contribution of this research lies in (1) conceiving a mathematical model method for project control; and (2) introducing the concept of momentum, which takes the rate of change of any metric into account, incorporated into the LPS for more reliable planning. The methodology proposed in this study can help industry better plan its projects and leverage the concept of momentum to better predict PPC, which is essential for every planning and control process in construction projects.

Feng, K, Wang, S, Lu, W, Liu, C and Wang, Y (2022) Planning Construction Projects in Deep Uncertainty: A Data-Driven Uncertainty Analysis Approach. Journal of Construction Engineering and Management, 148(08).

Gomes Araújo, L and Lucko, G (2022) Best Practices for Case Studies in Construction Engineering and Management Research. Journal of Construction Engineering and Management, 148(08).

Heaton, R, Martin, H, Chadee, A, Milling, A, Dunne, S and Borthwick, F (2022) The Construction Materials Conundrum: Practical Solutions to Address Integrated Supply Chain Complexities. Journal of Construction Engineering and Management, 148(08).

Islam, M S, Mohandes, S R, Mahdiyar, A, Fallahpour, A and Olanipekun, A O (2022) A Coupled Genetic Programming Monte Carlo Simulation–Based Model for Cost Overrun Prediction of Thermal Power Plant Projects. Journal of Construction Engineering and Management, 148(08).

Koo, H J and O’Connor, J T (2022) A Strategy for Building Design Quality Improvement through BIM Capability Analysis. Journal of Construction Engineering and Management, 148(08).

Le, C, Jeong, H D, Damnjanovic, I and Bukkapatnam, S (2022) Pareto Principle in Scoping-Phase Cost Estimating: A Multiobjective Optimization Approach for Selecting and Applying Optimal Major Work Items. Journal of Construction Engineering and Management, 148(08).

Lee, C, Chong, H, Tanko, B L and Klufallah, M (2022) Effect between Trust in Communication Technology and Interorganizational Trust in BIM-Enabled Projects. Journal of Construction Engineering and Management, 148(08).

Lee, G H, Kim, J I, Koo, C and Kim, T W (2022) Automated Generation of Precast Concrete Slab Stacks for Transportation in Offsite Construction Projects. Journal of Construction Engineering and Management, 148(08).

Liu, Y, Yao, F, Ji, Y, Tong, W, Liu, G, Li, H X and Hu, X (2022) Quality Control for Offsite Construction: Review and Future Directions. Journal of Construction Engineering and Management, 148(08).

Mmereki, D and Brouwer, D (2022) Application of Innovative Materials and Methods in Green Buildings and Associated Occupational Exposure and Health of Construction Workers: A Systematic Literature Review. Journal of Construction Engineering and Management, 148(08).

Simmons, D R, Polmear, M, Bae, H and McCall, C (2022) Applying a New Lens: Using Photo Elicitation in Construction Engineering Management Research. Journal of Construction Engineering and Management, 148(08).

Son, J, O’Brien, W J and Thomas, S R (2022) Recommended Practices for Effective Management of Academia–Industry Collaborative Research Teams in Construction Management. Journal of Construction Engineering and Management, 148(08).

Wang, Y, Thangasamy, V K, Tiong, R L K and Zhang, L (2022) Improved Workflow for Precast Element Design Based on BIM and Lean Construction. Journal of Construction Engineering and Management, 148(08).

Xia, P, Xu, F, Zhou, T and Du, J (2022) Benchmarking Human versus Robot Performance in Emergency Structural Inspection. Journal of Construction Engineering and Management, 148(08).

Zarghami, S A (2022) Prioritizing Construction Activities: Addressing the Flaws of Schedule-Based Indexes. Journal of Construction Engineering and Management, 148(08).