Momtaz, M (2023) Damage life cycle analysis for present and future condition assessments using statistical and machine learning techniques. PhD thesis, George Mason University, USA.
Abstract
In-service structures experience many changes and damages during construction and operation, affecting their serviceability and remaining life. Infrastructure assessment protocols require regular evaluation of a given structure for a variety of defects and aging phenomena. While there has been extensive research on improving data collection using Non-Destructive Evaluation (NDE) methods , the state of art is limited with regards to NDE data analysis with regards to damage quantification and multi-modal data integration. The purpose of this study is to provide an integrated framework for NDE data assessment including, damage detection and quantification, data correlation, and data fusion. Such analysis initially detects and quantify damages and then the damages are correlated to understand the relation between various measurement techniques. Finally, multi-modal data fusion combines the results of separate NDE methods to improve the assessment of condition ratings. This approach to NDE data analysis provides new and more reliable damage analysis capabilities and a more comprehensive understanding of a damaged structure’s condition, thereby improving decision-making for asset management. The individual aspects of this analytical framework were evaluated through a combination of laboratory and field experiments, yielding promising results.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Lattanzi, D |
| Uncontrolled Keywords: | measurement; asset management; decision making; integration; learning; life cycle; life cycle analysis; quantification; machine learning; experiment |
| Index terms: | damages, experiment, asset management, life cycle analysis, data analysis, laboratory, machine learning, analytical framework, infrastructure assessment, decision-making, integration, life cycle, quantification, data fusion |
| Subjects: | asset management, research management, infrastructure engineering, environmental impact, organizational analysis, decision analysis, measurement and scaling, data analysis and analytics, data science, artificial intelligence, data collection methods, dispute resolution, value management |
| Topics: | Business Strategy, Research Practice, Digital Applications, Organizational Design, Engineering Principles, Project Management, Risk Management, Sustainability, Legal Issues |
| 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