Detection and monitoring of bottom-up cracks in road pavement using a machine-learning approach
Authors:
- Filippo Giammaria Praticò,
- Rosario Fedele,
- Vitalii Naumov,
- Tomas Sauer
Abstract
The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (using acoustic-sensors, located at the roadside) and classifying the pavement’s SHS through ML models. Different ML classifiers (i.e., multilayer perceptron, MLP, convolutional neural network, CNN, random forest classifier, RFC, and support vector classifier, SVC) were used and compared. Results show the possibility of associating with great accuracy (i.e., MLP = 91.8%, CNN = 95.6%, RFC = 91.0%, and SVC = 99.1%) a specific vibro-acoustic signature to a differently cracked road pavement. These results are encouraging and represent the bases for the application of the proposed method in real contexts, such as monitoring roads and bridges using wireless sensor networks, which is the target of future studies.
- Record ID
- CUT87807babaa444c599eae714cdb21bc98
- Publication categories
- ;
- Author
- Journal series
- Algorithms, ISSN 1999-4893, Monthly
- Issue year
- 2020
- Vol
- 13
- No
- 4
- Pages
- [1-16]
- Article number
- 81
- Other elements of collation
- fot.; schem.; tab.; wykr.; Bibliografia (na s.) - 14-16; Bibliografia (liczba pozycji) - 52; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 13, Iss. 4, Spec. Iss.
- Substantive notes
- Special Issue: Models and Technologies for Intelligent Transportation Systems
- Keywords in English
- machine learning classifier, vibro-acoustic signature, road pavement, structural health monitoring, concealed cracks identification and monitoring
- DOI
- DOI:10.3390/a13040081 Opening in a new tab
- URL
- https://www.mdpi.com/1999-4893/13/4/81 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 40
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT87807babaa444c599eae714cdb21bc98/
- URN
urn:pkr-prod:CUT87807babaa444c599eae714cdb21bc98
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.