Abstracts – Browse Results
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Fathi, M (2020) Public-private partnership contract framework development and project performance analysis compared to design-build in the US highway projects, Unpublished PhD Thesis, , University of Nevada, Las Vegas.
Maharjan, R (2017) Effects of contract procurement factors on performance of transportation projects, Unpublished PhD Thesis, , University of Nevada, Las Vegas.
Motaharikarein, S (2019) Development of natural perlite based concrete for sustainable construction, Unpublished PhD Thesis, , University of Nevada, Las Vegas.
Nikkhah Manesh, S (2020) Temporal and spatial analysis of the wage gap for women and underrepresented minorities in the architecture, engineering, and construction (AEC) workforce, Unpublished PhD Thesis, , University of Nevada, Las Vegas.
Sakhakarmi, S (2022) Automated approach for the enhancement of scaffolding structure monitoring with strain sensor data, Unpublished PhD Thesis, , University of Nevada, Las Vegas.
- Type: Thesis
- Keywords: construction activities; failure; learning; monitoring; safety; machine learning; construction site
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/2825127410
- Abstract:
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.
Shrestha, B K (2021) Relationship between standardization critical success factors (CSFs) and project performance, Unpublished PhD Thesis, , University of Nevada, Las Vegas.
Shrestha, K (2016) Framework of performance-based contracting for chip seal and striping maintenance activities, Unpublished PhD Thesis, , University of Nevada, Las Vegas.
Shrestha, K K (2016) Causes of change orders and its impact on road maintenance contracts, Unpublished PhD Thesis, , University of Nevada, Las Vegas.
Tafazzoli, M (2017) Dynamic risk analysis of construction delays using fuzzy-failure mode effects analysis, Unpublished PhD Thesis, , University of Nevada, Las Vegas.