Short description:

This book aims to apply data mining and analytic techniques to past workplace accident data to discover patterns that facilitate the development of innovative models and strategies.

Long description:

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.

Table of Contents:

Preface i

Acknowledgements ii

Figures iii

Tables v

Abbreviations vi

1. Introduction











2. Curtailing construction fatalities using analytics











3. Reducing uncertainties in compensation for occupational diseases in construction using analytics











4. Curbing psychological injuries in construction using analytics











5. Predicting and preventing secondary psychological injuries in construction using analytics











6. Conclusion