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Bernold, L E (2005) Automatic As-Built Generation with Utility Trenchers. Journal of Construction Engineering and Management, 131(06), 740–7.

Chan, W T, Chen, C, Messner, J I and Chua, D K (2005) Interface Management for China’s Build–Operate–Transfer Projects. Journal of Construction Engineering and Management, 131(06), 645–55.

Chen, H, O’Brien, W J and Herbsman, Z J (2005) Assessing the Accuracy of Cash Flow Models: The Significance of Payment Conditions. Journal of Construction Engineering and Management, 131(06), 669–76.

Chua, D K and Goh, Y M (2005) Poisson Model of Construction Incident Occurrence. Journal of Construction Engineering and Management, 131(06), 715–22.

  • Type: Journal Article
  • Keywords: Statistical models; Construction site accidents; Occupational safety; Poisson density functions;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2005)131:6(715)
  • Abstract:
    Construction incidents are essentially random events because they have a probabilistic component that causes their occurrence to be indeterministic. Thus, as with most random events, one of the best ways to understand and analyze construction incidents is to apply statistical methods and tools. Consequently, this paper presents a statistical framework based on the modified loss causation model (MLCM). Even though the MLCM has been used for the framework, the approach can be readily adapted for other incident causation models. The MLCM is separated into two basic components: random and systematic. The random component is represented by a probability density function (PDF), which has parameters influenced by the systematic component of the MLCM, while the systematic component is represented by the situational variables and quality of the safety management system. In particular, this paper proposes that the PDF can be represented by the Poisson distribution. Besides being a convenient and simple distribution that can be easily used in applications, the Poisson distribution had been used in various industries to model random failures or incidents. The differences in contexts and the undesirable effects of adopting an unrepresentative distribution will require formal analysis to determine the suitability of the Poisson distribution in modeling the random component of construction incident occurrence. Incident records for 14 major projects were used in the analysis. Hypothesis testing using the chi-square goodness-of-fit and dispersion tests shows that the incident occurrences can be modeled as a Poisson process characterized by some mean arrival rate. The paper also presents some applications of the proposed Poisson model to improve construction safety management, focusing on two specific concepts: the Bayesian approach and the partitioned Poisson.

Hanna, A S, Taylor, C S and Sullivan, K T (2005) Impact of Extended Overtime on Construction Labor Productivity. Journal of Construction Engineering and Management, 131(06), 734–9.

Song, J, Fagerlund, W R, Haas, C T, Tatum, C B and Vanegas, J A (2005) Considering Prework on Industrial Projects. Journal of Construction Engineering and Management, 131(06), 723–33.

Sturts, C S and (Bud) Griffis, F H (2005) Addressing Pricing: Value Bidding for Engineers and Consultants. Journal of Construction Engineering and Management, 131(06), 621–30.

Tamate, S, Suemasa, N and Katada, T (2005) Analyses of Instability in Mobile Cranes due to Ground Penetration by Outriggers. Journal of Construction Engineering and Management, 131(06), 689–704.

Zayed, T M (2005) Productivity and Cost Assessment for Continuous Flight Auger Piles. Journal of Construction Engineering and Management, 131(06), 677–88.

Zayed, T M and Halpin, D W (2005) Pile Construction Productivity Assessment. Journal of Construction Engineering and Management, 131(06), 705–14.

Zhang, X (2005) Criteria for Selecting the Private-Sector Partner in Public–Private Partnerships. Journal of Construction Engineering and Management, 131(06), 631–44.

Zhang, X (2005) Financial Viability Analysis and Capital Structure Optimization in Privatized Public Infrastructure Projects. Journal of Construction Engineering and Management, 131(06), 656–68.