Semantic Scholar Open Access 2020 241 sitasi

The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis

Yue Hou Qiuhan Li Chen Zhang Guoyang Lu Zhoujing Ye +3 lainnya

Lihat Sumber DOI

Abstrak

Abstract In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users. Therefore, monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance, which in turn ensures public transportation safety. Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions. Advanced technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods. This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Topik & Kata Kunci

Computer Science

Penulis (8)

Yue Hou

Qiuhan Li

Chen Zhang

Guoyang Lu

Zhoujing Ye

Yihan Chen

Linbing Wang

Dandan Cao

Format Sitasi

APA MLA BibTeX

Hou, Y., Li, Q., Zhang, C., Lu, G., Ye, Z., Chen, Y. et al. (2020). The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis. https://doi.org/10.1016/j.eng.2020.07.030

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.eng.2020.07.030

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 241×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.eng.2020.07.030
Akses: Open Access ✓