Strategies for construction hazard recognition

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OBSERVED PROBLEM

Construction workers are exposed to diverse, dynamic, and rapidly changing environments and as a result incur disproportionate injury rates (Findley et al. 2004; Ho et al. 2000). In fact, the worldwide construction sector, annually, is estimated to incur more than 60000 fatal injuries International Labor Organization 2005). In 2012, construction activities in the United States were responsible for 755 work-related deaths, representing the highest among any industry sector, and a 5% increase from 2011 (Bureau of Labor Statistics 2013). Both fatal and nonfatal injuries in construction results in over $11.5 billion in lost revenue each year in the United States
(Waehrer et al. 2007). Such poor safety performance is partly due to the inability of construction workers to predict, detect, and respond to hazards in dynamic environments. For example,
Haslam et al’s (2005) review of injury records, illustrate that 42% of incidents are associated with workers inability to identify and respond to hazards. In another UK. based study, Carter and Smith (2006) evaluated typical activities in three construction projects and concluded that between 10 to 33.5% of construction hazards were not adequately recognized or assessed. More shockingly, Bahn’s (2012) recent study revealed that novice workers, on average, failed to identify 57% of hazards in work-representative environments. When hazardous construction work situations are not appropriately identified, workers are unable to alter behavior in order to avoid hazard exposure.
Several formal and informal hazard recognition methods are used in construction. These methods can broadly be classified as either being predictive or reactive in nature. Predictive hazard recognition methods, like the Job Safety Analysis (JSA), involve scenario-building where workers mentally visualize construction tasks to identify relevant hazards (Rozenfeld et al.
2010). Examples of other predictive methods include task-demand assessments and task-

3 planning safety sessions (Mitropoulos and Namboodiri 2011). Although useful, such methods (i) often fail to include hazards associated with adjacent work and changes in scope, methods, or conditions (ii) assume that workers can correctly predict the sequence in workflow and associated hazards in dynamic and often unpredictable environments (iii) and assume that workers already possess the required skill-sets to accurately detect hazardous stimuli (Findley et al. 2004). Alternatively, retrospective methods rely on past experiences and injuries in similar work-settings to identify relevant hazards. Methods like lessons learned and safety checklists fall under this category. Like predictive methods, reactive hazard recognition methods have several weaknesses, namely (i) near misses and past incidents are often not reported insufficient detail for future learning and improvement (ii) injury records and incident reports only represent a small subset of potential scenarios that unfortunately resulted in injuries and (iii) it is often invalid to generalize accidents across different settings in dynamic environments (Dong et al.
2011; Williamsen 2013). Therefore, there is an imminent need to research and propose more robust hazard recognition methods to improve construction safety performance.

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