Digital Twin-Based Intelligent Safety Risks Prediction of Prefabricated Construction Hoisting

Zhan-Sheng Liu, Xin-Tong Meng, Ze-Zhong Xing, Cun-Fa Cao, Yue-Yue Jiao and An-Xiu Li
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Zhan-Sheng Liu: Department of Urban Construction, Beijing University of Technology, Beijing 100124, China
Xin-Tong Meng: Department of Urban Construction, Beijing University of Technology, Beijing 100124, China
Ze-Zhong Xing: Department of Urban Construction, Beijing University of Technology, Beijing 100124, China
Cun-Fa Cao: Department of Urban Construction, Beijing University of Technology, Beijing 100124, China
Yue-Yue Jiao: Department of Urban Construction, Beijing University of Technology, Beijing 100124, China
An-Xiu Li: Department of Urban Construction, Beijing University of Technology, Beijing 100124, China

Sustainability, 2022, vol. 14, issue 9, 1-22

Abstract: Prefabricated construction hoisting has one of the highest rates of fatalities and injuries compared to other construction processes, despite technological advancements and implementations of safety initiatives. Current safety risk management frameworks lack tools that are able to process in-situ data efficiently and predict risk in advance, which makes it difficult to guarantee the safety of hoisting. Thus, this article proposed an intelligent safety risk prediction framework of prefabricated construction hoisting. It can predict the hoisting risk in real-time and investigate the spatial-temporal evolution law of the risk. Firstly, the multi-dimensional and multi-scale Digital Twin model is built by collecting the hoisting information. Secondly, a Digital Twin-Support Vector Machine (DT-SVM) algorithm is proposed to process the data stored in the virtual model and collected on the site. A case study of a prefabricated construction project reveals its prediction function and deduces the spatial-temporal evolution law of hoisting risk. The proposed method has made advancements in improving the safety management level of prefabricated hoisting. Moreover, the proposed method is able to identify the deficiencies regarding digital-twin-level control methods, which can be improved towards automatic controls in future studies.

Keywords: Digital Twin; prefabricated construction hoisting; safety risks prediction; intelligent risk prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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