Please use this identifier to cite or link to this item:
Title: Stochastic state sequence model to predict construction site safety states through real-time location systems
Authors: Li, H 
Yang, X
Wang, F
Rose, T
Chan, G
Dong, S
Keywords: Construction
Discrete-time markov chain (DTMC)
Real-time location system (RTLS)
Stochastic sequences
Issue Date: 2016
Publisher: Elsevier
Source: Safety science, 2016, v. 84, p. 78-87 How to cite?
Journal: Safety science 
Abstract: 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.
ISSN: 0925-7535
DOI: 10.1016/j.ssci.2015.11.025
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Oct 8, 2018


Last Week
Last month
Citations as of Oct 19, 2018

Page view(s)

Last Week
Last month
Citations as of Oct 15, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.