DEVELOPING FACTOR-BASED MODELS FOR MEASURING AND PREDICTING HAZARD RECOGNITION CAPABILITY OF CONSTRUCTIONWORKERS
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 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.
1.1 Background to the Study
Construction has been severally referred to as the most dangerous industry globally due to its alarming rate of accidents and fatality, with specific reference to its small scale of accidents of high frequency and diverse hazards sources (Zhou, Goh, & Li, 2015). According to Sunindijo & Zou, (2012), the construction industry employs only about 7% of the global work force, but it accounts for 30–40% of fatalities. Lingard (2013) reported that more than 60,000 fatalities are reported from construction projects around the world. This equates to approximately one fatal construction accident in every 10 minutes. In the U.S., the construction sector employs 7% of the work force, but it was reported to be responsible for 20% of fatal work injuries; the highest in 2013. It was also found that the sector recorded 908 fatal injuries and more than 200,000 non-fatal injuries in 2014 (U S Bureau of Labour Statistics, 2013; Marks & Teizer, 2013). In the U.K, the construction sector employs 5% of the workforce, but accounts for 31% of fatal work injuries (HSE, 2014), while in Hong Kong, the construction industry was ranked as the most dangerous in 2012, accounting for 24% of the total fatalities in 2011 (Li, Chan, & Skitmore, 2013). These are examples of the developed countries, with more proactive safety measures and technological advancements.
The situation is uglier in developing countries, where there is poor enabling environment, inadequate resources and low usage of technology to address construction safety issues. In India, the construction sector has been is the second most hazardous industry, recording the average fatal accident frequency rate (FAFR; incidents/1,000 employees/year) of between 0.22 to 15.8 between 2008 to 2012 (Patel & Jha, 2014). Kheni, Dainty, & Gibb (2008); Kheni, Gibb, & Dainty (2006) reported that Ghana‘s construction industry employs 1.4% of the country‘s workforce but contributed up to 14% of occupational fatalities in the year 2000. In Nigeria, there is dearth of reliable statistics on the occupational accidents and injury of the construction sector (Okeola, 2009). Notwithstanding, hundreds of construction workers are being killed while several others are rendered permanently disabled on construction sites each year (Smallwood & Haupt, 2005; Kheni et al., 2008; Okeola, 2009). These alarming and disproportionate statistics indicate the dangers associated with working in the construction sector.
To address this problem, a plethora of research efforts in different perspectives have been made to improve safety performance of the construction industry. These efforts range from the safety management point of view, (with focus on accident cause analyses, safety climate, safety culture, workers‘ safety perception and competency, behaviour-based safety, hazard management, etc) to the technological applications (such as the application of Building Information Modelling (BIM), Data Mining, Geographic Information System (GIS), Radio Frequency Identification (RFID), robotics, sensing technology, wireless networks and virtual reality among others). These strides have been reported to bring about significant improvements in the safety performance of the construction industry in recent times (Chan et al., 2008; Hallowell, 2012; Huang & Hinze, 2006; Mitropoulos, Howell, & Abdelhamid, 2005; Shishlov, Schoenfisch, Myers, & Lipscomb, 2011). However, despite the reported improvement in the accident profile of the construction industry, unacceptable injury and fatality rates in construction continue to be a source of great concern globally (Goh & Binte Sa‘adon, 2015; Namian, Zuluaga, & Albert, 2016). This is more so, considering the amount of emotional and physical distress and the annual cost of the injuries, which was reported to exceed 48 billion dollars in the United States (Jeelani et al., 2017; Namian, Albert, & Feng, 2018b). It also adversely affects profit margins and overall success of construction projects. The efforts have therefore proved to be inadequate for addressing the safety incidence of the industry and reversing the ugly trend of construction accidents towards the globally acclaimed vision of zero accidents/injuries espoused by several construction stakeholders. This inadequacy has been attributed to poor hazard recognition of workers.
Hazard recognition has been identified as a fundamental requirement for addressing the health and safety challenges encountered on construction sites (Namian, Albert, Zuluaga, & Behm, 2016; Namian, Albert, Zuluaga, & Jaselskis, 2016). It is the ability of managers and workers to sense, analyse, and extract physical or mental stimuli that indicates the existence of a hazardous situation in a complex and dynamic scenario of construction environments (Albert, Hallowell, Kleiner, Chen, & Golparvar-Fard, 2014; Namian, Zuluaga, et al., 2016). Every safety management initiative rely on managing the identified hazards, which occur in multitude of ways (Namian,et al., 2016), because of the specific nature of the construction that often presents highly complex and dynamic scenarios of workers, equipment and materials interacting in close proximity to potentially hazardous conditions (Teizer & Cheng, 2015). These hazardous situations often when not recognized and managed lead to unsavory safety incidences and fatalities in construction. Furthermore, considering the nature of the construction workforce, the high proportion of which are unskilled and unlettered, coupled with the high labour turnover in the sector, hazard recognition must be given proper attention if the safety profile of the industry is to be improved.
Unfortunately, poor hazard recognition of workers have consistently been reported to lead to several accidents in the construction industry. (Carter & Smith, 2006; Perlman, Sacks,
Barak, 2014; Haslam et al., 2005; Bahn, 2013; Albert, Hallowell, & Kleiner, 2014). It therefore, becomes imperative to address the problem of hazard recognition performance of construction workers in order to improve safety management practices of the industry.
1.2 Statement of the Research Problem
The construction industry suffers from poor safety records that include a high accident rate and a large number of fatalities (Mohamed, 1999). To address this problem, workers‘ hazard recognition capabilities have become the core issue in safety management. This is because risks cannot be assessed, and control measures cannot be developed and implemented, if those involved (workers) are not aware of the hazards in the first place (Carter & Smith, 2006).
Despite the importance of hazard recognition to safety management, many studies have reported the poor ability of construction workers to identify hazards in dynamic and unpredictable environments as a result of the diversity and fragmentation of the industry (Carter & Smith, 2006; Perlman, Sacks, & Barak, 2014). Haslam et al., (2005) observed that 42% of accidents involve inadequate hazard identification and appraisal skills of workers. This notion was corroborated by Bahn, (2013) who reported that novice workers failed to recognise an average of 57% of hazards in occupational environments in Australia. More recently, in a study of several projects across the United States of America, Albert, Hallowell, Kleiner, et al., (2014), discovered that more than 40% of hazards were not recognized at the time of pre-job safety meetings. As a result of these shortcomings, workers that are unable to recognise the active, emerging or latent hazards, in the work environment may not behave safely, and may, then, be exposed to uncontrolled safety risks, leading to catastrophic accidents and injuries. This, therefore, shows the need to improve hazard recognition performance of workers on construction sites, which would give rise to improvement in safety management practices in the industry.
Generally, safety managers conduct hazard identification inspections on a construction site using a process often called job hazard analysis (Albert, Hallowell, & Kleiner, 2013; Patel Jha, 2017). Albeit these efforts, many studies (Albert, Hallowell,and Kleiner, 2014; Carter & Smith, 2006; Mitropoulos et al., 2005) have indicated that a number of hazards remain unidentified or not well assessed in a construction project. Consequently, a large portion of construction accidents occur as a result of the inability of construction workers to predict, identify, and respond to hazardous situations in dynamic construction environments. Regrettably, there has not been any effort made towards determining the hazard recognition competence of construction workers based on the different attributes of hazard recognition related to the workers and their environment, and 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 is important because different construction trades, present different safety risks, and thus different hazards (Patel & Jha, 2017). Such techniques only consider the hazards that have been incorporated in the risk assessment process (Albert & Hallowell, 2012). This may lead to underestimation of potential hazards, which can cause poor control measures, and workers developing false sense of safety in the face of great danger. Therefore, no effective hazard management strategy can be developed without improving the hazard recognition performance of workers.
1.3 Aim and Objectives
1.3.1 Aim of the study
The aim of this research is to develop model for measuring and predicting hazard recognition capability of construction workers with view to improving safety performance.
To identify the factors affecting hazard recognition capability of construction workersTo assess the extent to which the factors influence hazard recognition capability of construction workersTo establish the determinants of hazard recognition capability of workersTo determine the relationships among the determinants of hazard recognition capabilityTo develop factor-based models for predicting hazard recognition capability of workers based on their trades
Recognition of hazards by construction workers leads to better safety performance of projects. The evaluation of the hazard recognition capability of workers in advance will identify the areas of weaknesses which will be useful in the adoption of improvement measures prior to the occurrence of any accident. This will also lead to an effective management of safety issues and a continuous improvement of safety management practices at construction sites. Similarly, identifying hazard recognition capabilities of construction workers will enable construction managers and safety supervisors to channel safety training initiatives to specific areas of weaknesses in order to improve recognition capacities of workforce.
Most importantly, safety hazards in construction vary with different construction trades that workers engage in, therefore, linking hazard recognition to the nature of construction jobs performed by workers will provide an opportunity for the construction industry to be more effective in hazard identification and safety management in general. Hazard recognition improvement initiatives will be directed to areas of needs, in order to reduce accidents and fatality rates in construction.
This research has focused on factors influencing the ability of construction workers to recognize hazards in selected construction trades comprising excavation, roof and other works at height, and steel construction; that have already been identified among the most hazardous in construction. This formed the basis for developing the hazard recognition evaluation tool and the subsequent models.
Data for the research were collected on reasonably complex construction projects, involving a minimum of three storey buildings where all the construction trades interact in the same environment. Moreover, the models developed in this research are meant for evaluation of hazard recognition capability of construction workers in the specific trades considered for the data collection. These models yield the result of hazard recognition capability of the workers as a score approximated to either ‗yes‘ or ‗no‘. No specific grades are given. The result is therefore serving as an important guide to users in taking job assignment decisions to workers on the basis of their hazard recognition capability.
The fact that the research variables were tested by the use of questionnaire, the validity of the data collected might have been influenced by the possible difficulty in the respondents‘ understanding of those questions, especially in the case of the workers/tradesmen whose level of knowledge might impede clear understanding in some cases. The researcher had to interpret the questions to most of them, however, the accuracy is only limited to their level of understanding of the interpretations. Similarly, their willingness to respond to those questions honestly might also affect the quality of the conclusions reached.
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