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Abdalla, A, Li, X and Yang, F (2024) Expatriate construction professionals' performance in international construction projects: The role of cross-cultural adjustment and job burnout. Journal of Construction Engineering and Management, 150(03).

Chen, S, Chen, D, Li, L, Miramini, S and Zhang, L (2024) Optimized bridge maintenance strategies: A system reliability-based approach to enhancing road network performance. Journal of Construction Engineering and Management, 150(03).

Do, Q, Le, T and Le, C (2024) Uncovering critical causes of highway work zone accidents using unsupervised machine learning and social network analysis. Journal of Construction Engineering and Management, 150(03).

Gu, J and Guo, F (2024) Promoting digital sustainability through project digital responsibility implementation: An empirical analysis. Journal of Construction Engineering and Management, 150(03).

Guo, J and Kato, H (2024) Role of government equity investment in capital structure of project finance: Global evidence from PPP projects in developing countries. Journal of Construction Engineering and Management, 150(03).

Halder, A and Batra, S (2024) Navigating the ethical discourse in construction: A state-of-the-art review of relevant literature. Journal of Construction Engineering and Management, 150(03).

Harode, A, Thabet, W and Leite, F (2024) Formulation of feature and label space using modified Delphi in support of developing a machine-learning algorithm to automate clash resolution. Journal of Construction Engineering and Management, 150(03).

Heydari, M, Heravi, G, Raeisinafchi, R and Karimi, H (2024) A dynamic model to assess the role of site supervision systems in the safety performance of construction projects. Journal of Construction Engineering and Management, 150(03).

Ibrahim, A, Nnaji, C, Namian, M and Shakouri, M (2024) Evaluating the impact of hazard information on fieldworkers' safety risk perception. Journal of Construction Engineering and Management, 150(03).

Ko, T, Lee, J and David Jeong, H (2024) Project requirements prioritization through NLP-driven classification and adjusted work items analysis. Journal of Construction Engineering and Management, 150(03).

Lee, D, Nie, G Y and Han, K (2024) Automatic and real-time joint tracking and three-dimensional scanning for a construction welding robot. Journal of Construction Engineering and Management, 150(03).

Li, Q, Yang, Y, Yao, G, Wei, F, Xue, G and Qin, H (2024) Multiobject real-time automatic detection method for production quality control of prefabricated laminated slabs. Journal of Construction Engineering and Management, 150(03).

Oguz Erkal, E D, Hallowell, M R, Ghriss, A and Bhandari, S (2024) Predicting serious injury and fatality exposure using machine learning in construction projects. Journal of Construction Engineering and Management, 150(03).

Qureshi, A H, Alaloul, W S, Murtiyoso, A, Hussain, S J, Saad, S and Musarat, M A (2024) Automated scaling of point cloud rebar model via aruco-supported controlled markers. Journal of Construction Engineering and Management, 150(03).

Rajabi Asadabadi, M and Zwikael, O (2024) Unrealistic project goals: Detection and modification. Journal of Construction Engineering and Management, 150(03).

Sadeghi, N, Dehghani, M S and Ingolfsson, A (2024) Choice of probability distributions for activity durations in project networks with limited sample size. Journal of Construction Engineering and Management, 150(03).

  • Type: Journal Article
  • Keywords:
  • ISBN/ISSN: 0733-9364
  • URL: http://doi.org/10.1061/JCEMD4.COENG-13753
  • Abstract:
    Accurate estimation of activity durations is a critical project scheduling step. If abundant data are available for activity durations, then distribution fitting is straightforward. However, if sample sizes are small, then choosing an appropriate theoretical distribution for activity duration is challenging. In this study, we propose a methodology for experimenting with the impact of sample size on the accuracy of the estimated duration of a stochastic project network. We synthesized a total of 3,264 stochastic project networks and estimated the true project duration distribution for each network using large-sample Monte Carlo simulation. We varied the sample size per activity and investigated how the choice of theoretical distribution (normal, beta, triangular, and lognormal) for estimated activity durations impacted the accuracy of the estimated project duration distribution. We find that the best choice for a theoretical activity duration distribution highly depends on the sample size. For sample sizes below 50, we find that not much is gained from the common practice of putting extensive effort into selecting best-fit theoretical distributions compared to simply using normal distributions.

Seo, W, Kim, B, Bang, S and Kang, Y (2024) Identifying key financial variables predicting the financial performance of construction companies. Journal of Construction Engineering and Management, 150(03).

Tarekegn Gurmu, A and Mahmood, M N (2024) Critical factors affecting quality in building construction projects: Systematic review and meta-analysis. Journal of Construction Engineering and Management, 150(03).

Withrow, J, Dadi, G, Nassereddine, H and Sturgill, R (2024) Asphalt material e-ticketing workflow: Qualitative and quantitative analysis. Journal of Construction Engineering and Management, 150(03).

Zhang, Y, Ren, X, Zhang, J and Ma, Z (2024) A method for deformation detection and reconstruction of shield tunnel based on point cloud. Journal of Construction Engineering and Management, 150(03).