4.6 Article

ANALYSIS OF STOCHASTIC PROCESS TO MODEL SAFETY RISK IN CONSTRUCTION INDUSTRY

Journal

JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
Volume 27, Issue 2, Pages 87-99

Publisher

VILNIUS GEDIMINAS TECH UNIV
DOI: 10.3846/jcem.2021.14108

Keywords

civil engineering construction; safety accidents; probability prediction; Markov process; Normal process; Poisson process

Funding

  1. [19B020]
  2. [51678067]
  3. [51808054]
  4. [2016ZDK014]

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Construction safety accidents are influenced by various factors and can be considered as a statistical random rule. By applying stochastic process theory, probability prediction methods can be used to study and improve the management and prediction of construction accidents.
There are many factors leading to construction safety accident. The rule presented under the influence of these factors should be a statistical random rule. To reveal those random rules and study the probability prediction method of construction safety accident, according to stochastic process theory, general stochastic process, Markov process and normal process are respectively used to simulate the risk-accident process in this paper. First, in the general-random-process-based analysis the probability of accidents in a period of time is calculated. Then, the Markov property of the construction safety risk evolution process is illustrated, and the analytical expression of probability density function of first-passage time of Markov-based risk-accident process is derived to calculate the construction safety probability. In the normal-process-based analysis, the construction safety probability formulas in cases of stationary normal risk process and non-stationary normal risk process with zero mean value are derived respectively. Finally, the number of accidents that may occur on construction site in a period is studied macroscopically based on Poisson process, and the probability distribution of time interval between adjacent accidents and the time of the nth accident are calculated respectively. The results provide useful reference for the prediction and management of construction accidents.

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