Doctor of Philosophy (PhD)
Civil and Environmental Engineering and Construction
First Committee Member
Jee Woong Park
Second Committee Member
Third Committee Member
Jin Ouk Choi
Fourth Committee Member
Fifth Committee Member
Construction researchers have made a significant effort to improve the safety of scaffolding structures, as a large proportion of workers are involved in construction activities requiring scaffolds. However, most past studies focused on design and planning aspects of scaffolds. While limited studies investigated scaffolding safety during construction, they are limited to simple cases only with limited failure modes and simple scaffolds. In response to this limitation, this study aims to develop an automated scaffold monitoring approach capable of monitoring large scaffolds. Accordingly, this study developed an automated scaffold safety monitoring framework that leverages sensor data collected from a scaffold, scaffold modeling techniques, and a machine-learning approach. The proposed framework is based on the capability of the machine-learning approach to identify patterns, which in this study are the patterns of the scaffold structural response based on different loads acting on it. Due to the cost and safety issues related to testing an actual scaffold with varying load applications, the scaffold monitoring framework was experimentally tested under a controlled laboratory setting with a single-bay two-story scaffold with four safety cases. After the field trial, this approach was applied on a four-bay and three-story scaffold involving 1,411 safety cases through computational exploration. During this process, this study integrated a divide-and-conquer strategy with machine-learning models to improve the performance of large-scale classification. The results show that the proposed scaffold monitoring approach is capable of large-scale classification of scaffold safety status. Therefore, this approach can be reliably applied to monitor similar scaffolds on construction sites. Further, this approach is replicable to solve other classification problems. In addition, this study is expected to encourage the use of sensing technologies and data analysis techniques to develop automated monitoring approaches.
construction safety; divide-and-conquer; large-scale classification; machine learning; real-time monitoring; temporary structures
Civil Engineering | Occupational Health and Industrial Hygiene
University of Nevada, Las Vegas
Sakhakarmi, Sayan, "Automated Approach for the Enhancement of Scaffolding Structure Monitoring with Strain Sensor Data" (2022). UNLV Theses, Dissertations, Professional Papers, and Capstones. 4616.
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