Stochastic state sequence model to predict construction site safety states through Real-Time Location Systems

Li, Heng, Yang, Xincong, Wang, Fenglai, Rose, Timothy M., Chan, Greg, & Dong, Shuang (2016) Stochastic state sequence model to predict construction site safety states through Real-Time Location Systems. Safety Science, 84, pp. 78-87.

View at publisher


This paper addresses the challenge to design an effective method for managers to efficiently process hazardous states via recorded historical data by developing a stochastic state sequence model to predict discrete safety states – represent the hazardous level of a project or individual person over a period of time through a Real-Time Location System (RTLS) on construction sites. This involves a mathematical model for state prediction that is suitable for the big-data environment of modern complex construction projects. Firstly, an algorithm is constructed for extracting incidents from pre-analysis of the walk-paths of site workers based on RTLS. The algorithm builds three categories of hazardous region distribution – certain static, uncertain static and uncertain dynamic – and employs a frequency and duration filter to remove noise and misreads. Key regions are identified as either ‘hazardous’, ‘risky’, ‘admonitory’ or ‘safe’ depending on the extent of the hazard zone from the object’s boundary, and state recognition is established by measuring incidents occurring per day and classifies personal and project states into ‘normal’, ‘incident’, ‘near-miss’ and ‘accident’. A Discrete-Time Markov Chain (DTMC) mathematical model, focusing on the interrelationship between states, is developed to predict states on construction sites. Finally, a case study is provided to demonstrate how the system can assist in monitoring discrete states and which indicates it is feasible for the construction industry.

Impact and interest:

1 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 99912
Item Type: Journal Article
Refereed: Yes
Keywords: Real-Time Location System (RTLS), Construction, Stochastic sequences, Discrete-Time Markov Chain (DTMC)
DOI: 10.1016/j.ssci.2015.11.025
ISSN: 0925-7535
Subjects: Australian and New Zealand Standard Research Classification > BUILT ENVIRONMENT AND DESIGN (120000) > BUILDING (120200) > Building Construction Management and Project Planning (120201)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 Elsevier
Deposited On: 12 Oct 2016 00:45
Last Modified: 28 Jun 2017 20:02

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page