Abstracts – Browse Results
Click on the titles below to expand the information about each abstract.
Viewing 10 results ...
Aroke, O M (2022) Measuring attention, working memory and visual perception to reduce the risk of injuries in the construction industry, Unpublished PhD Thesis, , George Mason University.
Ceran, N (2002) Private participation in infrastructure: A risk analysis of long-term contracts in power sector, Unpublished PhD Thesis, , George Mason University.
Checherita, C D (2009) A macroeconomic analysis of investment under public-private partnerships and its policy implications—the case of developing countries, Unpublished PhD Thesis, , George Mason University.
Gholizadeh, P (2022) Analyzing accidents among specialty contractors: A data mining approach, Unpublished PhD Thesis, , George Mason University.
Hassan, M E (2013) Assessing the impact of lean/integrated project delivery system on final project success, Unpublished PhD Thesis, , George Mason University.
John Samuel, I (2023) A human-centered infrastructure asset management framework using BIM and augmented reality, Unpublished PhD Thesis, , George Mason University.
Li, Y (2023) Integrated multi-stage decision-support for enhanced infrastructure restoration under uncertainty, Unpublished PhD Thesis, , George Mason University.
Momtaz, M (2023) Damage life cycle analysis for present and future condition assessments using statistical and machine learning techniques, Unpublished PhD Thesis, , George Mason University.
Solomon, T (2021) Change blindness in the construction industry, Unpublished PhD Thesis, , George Mason University.
- Type: Thesis
- Keywords: accuracy; gender; hazards; construction safety; injury; safety; training; experiment; simulation; construction worker; professional
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/2700530737
- Abstract:
Since the construction industry is dynamic and complex, construction workers are constantly experiencing changes in their work environment. These changes on the job site can sometimes endanger the safety of workers if they fail to detect them. Failing to detect changes to visual scenes is known as change blindness, and this phenomenon can potentially put construction workers in harm’s way if they fail to detect hazard changes that compromise their safety. Better understanding the change blindness (i.e., inability of workers to recognize changes in their visual scenes) is vital to enhance safety at construction jobsites. The effect of change blindness in the context of conduction safety has not been studied before. Therefore, the main goal of this dissertation is to provide empirical evidence and theoretical considerations of the effect of change blindness in the construction industry. To achieve this overarching goal, three specific objectives were defined: The first objective was to investigate the effect of type of change (safety-relevant/irrelevant), work experience, and hazard types (e.g., elevated platforms, ladders) on change detection performance (measured by response time taken to detect a change and accuracy rate to detect change). The second objective was to examine how individual factors (age, work experience, personality, mindfulness, gender, injury exposure, formal safety training) affect change detection performance in construction. Understanding the extent of personal characteristics on change detection performance can be very instrumental for safety managers to detect at-risk workers. The third objective was to examine the changes in worker attentional allocation and their change detection performance using eye-tracking technology. A change blindness experiment was designed to address this gap, several construction scenario images consisting of fall hazards that vary in severity were modified to include either a safety-relevant change (fall-arrest systems, elevated platforms, unprotected openings, ladders, unprotected edge) or an irrelevant safety change (e.g., logo on machinery, stickers on windows and wall). The first manuscript analyzed the results using parametric and non-parametric tests showed that change blindness can influence workers’ hazard identification ability that can negatively impact their safety performance. The second manuscript examined the predictive power of potential personal characteristics associated with change detection performance by implementing a hierarchical agglomerative clustering technique to classify response time (Fast and Slow) and accuracy rate (High and Low). The results from logistic regression revealed that age, personality traits (agreeableness and conscientiousness), mindfulness, and injury exposure are associated with change detection performance. Lastly, attentional allocation of participants was measured using four fixation-related metrics: dwell time percent, first fixation time, fixation count and run count. Using randomization simulation tests, it was found that attentional allocation of observers was significantly different between safety-relevant and safety-irrelevant changes. In addition, attentional allocation of observers was also significantly different between different fall hazards types. This research contributes to academia by 1) providing a better understanding of the effect of type of change (safety-relevant or safety-irrelevant) and fall hazard type on change detection performance and the extent of change blindness on hazard identification in the construction industry; 2) identifying individual factors that significantly affect change detection performance in construction; and 3) examine the impact of change blindness on allocating visual attention towards fall hazards in construction. Regarding practice, the demonstration of the application of the change blindness phenomenon to the realm of construction safety is expected to lessen the impact of inattentiveness towards hazard changes and aid safety managers and industry professionals to identify at-risk workers. In addition, the change blindness concept can be integrated in safety training practices to design personalized training for workers to improve hazard detection ability and enhance safety performance on the job site.
Zhou, W (2023) Condition state-based decision making in evolving systems: Applications in asset management and delivery, Unpublished PhD Thesis, , George Mason University.