Poisson Model of Construction Incident Occurrence

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.