Preventing Workplace Incidents in Construction: Data Mining and Analytics Applications, 1st Edition eBook
$30.00
The construction industry is vital to any national economy; it is also one of the industries most susceptible to workplace incidents.
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About this eTextbook
The construction industry is vital to any national economy; it is also one of the industries most susceptible to workplace incidents. The unacceptably high rates of incidents in construction have huge socio-economic consequences for the victims, their families and friends, co-workers, employers and society at large. Construction safety researchers have introduced numerous strategies, models and tools through scientific inquiries involving primary data collection and analyses.
While these efforts are commendable, there is a huge potential to create new knowledge and predictive models to improve construction safety by utilising already existing data about workplace incidents. In this new book, Imriyas Kamardeen argues that more sophisticated approaches need to be deployed to enable improved analyses of incident data sets and the extraction of more valuable insights, patterns and knowledge to prevent work injuries and illnesses. The book aims to apply data mining and analytic techniques to past workplace incident data to discover patterns that facilitate the development of innovative models and strategies, thereby improving work health, safety and well-being in construction, and curtailing the high rate of incidents.
It is essential reading for researchers and professionals in construction, health and safety and anyone interested in data analytics. ISBNs for Preventing Workplace Incidents in Construction are 9781351616133, 1351616137 and the print ISBNs are 9781138087453, 1138087459. Additional ISBNs include 9781315110462, 9781032177915, 9781351616140, 9781351616126.
Publication details
- Author: Imriyas Kamardeen
- Publisher: Routledge
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About this eTextbook
The construction industry is vital to any national economy; it is also one of the industries most susceptible to workplace incidents. The unacceptably high rates of incidents in construction have huge socio-economic consequences for the victims, their families and friends, co-workers, employers and society at large. Construction safety researchers have introduced numerous strategies, models and tools through scientific inquiries involving primary data collection and analyses.
While these efforts are commendable, there is a huge potential to create new knowledge and predictive models to improve construction safety by utilising already existing data about workplace incidents. In this new book, Imriyas Kamardeen argues that more sophisticated approaches need to be deployed to enable improved analyses of incident data sets and the extraction of more valuable insights, patterns and knowledge to prevent work injuries and illnesses. The book aims to apply data mining and analytic techniques to past workplace incident data to discover patterns that facilitate the development of innovative models and strategies, thereby improving work health, safety and well-being in construction, and curtailing the high rate of incidents.
It is essential reading for researchers and professionals in construction, health and safety and anyone interested in data analytics. ISBNs for Preventing Workplace Incidents in Construction are 9781351616133, 1351616137 and the print ISBNs are 9781138087453, 1138087459. Additional ISBNs include 9781315110462, 9781032177915, 9781351616140, 9781351616126.
Publication details
- Author: Imriyas Kamardeen
- Publisher: Routledge
What you receive
- A licensed digital edition of this title, supplied as PDF or EPUB. The format is set by the publisher, so email us the ISBN before ordering and we will confirm which one it is.
- Delivered to the email address on your order as a secure download link, usually within minutes and always within 24 hours of payment.
- Download links do not expire and there is no cap on re-downloads. Readable on any computer, tablet or phone, online or offline.
- This is a digital edition. Nothing is printed, packed or posted, and there is no delivery charge.
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