Abu Dabous, S; Ibrahim, F and Alzghoul, A (2025) Modelling bridge deterioration using long short-term memory neural networks: A deep learning-based approach. Smart and Sustainable Built Environment, 14(5), pp. 1632-1655. ISSN 2046-6099
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; bridge management system; bridges; condition monitoring; deep learning; deterioration; long short-term memory; neural networks |
| Index terms: | artificial intelligence, neural network, deep learning, monitoring, modelling, management system, deterioration |
| Subjects: | analytical methods, artificial intelligence, control systems, management, material degradation and durability |
| Topics: | Construction Materials, Site Management, Organizational Design, Engineering Principles, Digital Applications |
| Descriptive scope: | 2 PC |
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