Sakhakarmi, S (2022) Automated approach for the enhancement of scaffolding structure monitoring with strain sensor data. PhD thesis, University of Nevada, Las Vegas, USA.
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.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Park, J W |
| Uncontrolled Keywords: | construction activities; failure; learning; monitoring; safety; machine learning; construction site |
| Index terms: | construction site, strategy, safety issue, scaffolding, data analysis, sensor data, laboratory, machine learning, field trial, exploration, construction activity, failure mode, modelling, testing, monitoring |
| Subjects: | financial risk, research management, reliability engineering, research products and data, operations management, environmental resource management, professional practice, construction operations, analytical methods, management, data collection methods, control systems, work location, artificial intelligence, data analysis and analytics |
| Topics: | Digital Applications, Site Management, Research Practice, Cost Management, Business Strategy, Engineering Principles, Sustainability |
| Descriptive scope: | 4 PCEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here