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Barriers to Big Data Techniques Application in Construction Safety, Health and Wellbeing

Umeokafor, N and Umar, T (2023) Barriers to Big Data Techniques Application in Construction Safety, Health and Wellbeing. In: Handbook of Construction Safety, Health and Well-being in the Industry 4.0 Era. Taylor & Francis. ISBN 9781032079929

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The adoption of digital technologies such as big data analytics (BDA) for health, safety, and wellbeing (HSW) improvement in construction has increased but continues to experience challenges. Reviewing extant literature, this chapter identifies and discusses the barriers to HSW improvement using BDA. The barriers include technical issues such as the inability of existing machine learning techniques such as the fuzzy-neural method to predict HSW risks by analysing incident data, and the large size, heterogeneous and dynamic nature of construction accident data. While the socio-technical barriers include BDA skills shortage, the financial ones cover the high cost associated with BDA. Data dispute among companies, organisational culture, and ignorance of the potential of BDA in improving HSW which results in its limited acceptance and implementation in HSW are identified. There are also operational barriers in terms of digital poverty in construction, and supply chain issues where the fragmented supply chain of the industry and the uniqueness of projects do not facilitate a collaborative environment, a prerequisite for digital solutions. The implications of the findings include the need for an adequate legal framework international standard to settle the dispute between countries arising from data issues. Empirical studies to assess the barriers are recommended.

Item Type: Book Section
Subjects: H Social Sciences > HD Industries. Land use. Labor
H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TH Building construction
Divisions: Civil Engineering & Built Environment
Publisher: Taylor & Francis
Date Deposited: 01 Apr 2022 12:55
Last Modified: 15 May 2023 10:05
URI: https://researchonline.ljmu.ac.uk/id/eprint/16578
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