DEVELOPING FACTOR-BASED MODELS FOR MEASURING AND PREDICTING HAZARD RECOGNITION CAPABILITY OF CONSTRUCTIONWORKERS

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Department of Environmental Design

ABSTRACT
The construction industry has been characterised with alarming rate of accidents and fatalities. This problem has been largely attributed to the inability of construction workers to recognise and manage hazards in complex and dynamic construction environments. Several studies have been conducted on improving hazard recognition of construction workers. However, existing studies have not made efforts in determining the hazard recognition competence of construction workers based on the different attributes of hazard recognition related to the workers and their environment. In addition, the existing hazard recognition techniques do not take into account the factors that influence workers‘ hazard recognition capabilities based on the nature of jobs and trades they engage in, and on which the hazards are being managed. This study has developed factor-based models for measuring and evaluating hazard recognition capability of construction workers. The factors were identified from literature review and experts interview. Through the use ofa structured questionnaire,distributed purposively to construction professionals and workers in excavation, roof works and steel construction works, the extent of influence of the factors on hazard recognition capability of construction workers was determined. Five Hundred and Sixty-One questionnaires were analysed using descriptive and inferential statistical tools. Factor analysis and reliability analysis were used to establish a structure of the key determinants of hazard recognition capability of workers, forming a self-assessment tool. Logistic regression analysis was then used to determine the relationship among the categories of factors and develop a model for predicting hazard recognition capability of construction workers. A total of 53 factors were identified in four categories as personal, organisational, social and project factors. The four categories of factors and ix their sub factors have been found to have significant influence on hazard recognition capability of construction worker. However, the organizational factors have the highest influence with a group mean value of 3.57, followed by project factors with a mean value of 3.56, then social factors, recording an overall mean of 3.50 and lastly, personal factors with an overall mean score of 3.49. Generally, there is no significant difference between the responses of the different groups of respondents on the extent of influence of the factors on hazard recognition of workers. For the determinants of hazard recognition among the groups of factors, factor analysis revealed a structure of four categories of determinants of hazard recognition capability of construction workers as organizational factors, inherent human factors, conditional human factors and project factors. Moreover, logistic regression analysis revealed that all the four of factors have direct positive relationship to hazard recognition of workers and have proved to be significant predictors of the recognition capability of construction workers. The results implied that any increase in the categories of determinants would yield an increase in workers‘ hazard recognition capability. Based on the coefficients of logistic regression (B), the organisational category has the highest level of predictive power for hazard recognition capability (B=4.024), followed by the inherent human factors category (B=3.088), then the conditional human factors (B=2.967) and lastly the project factors (B=2.195). The predictive model developed has been able to correctly classify up to 93.2% of the HRC cases with a significant chi-square value, (538.864, df=4, p<.000). The model validation revealed its ability to correctly predict up to 76% of the cases, which is good enough for prediction purposes.