Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods
Source abstract
Abstract
The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Asphalt Pavement Performance Evaluation · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Civil Engineering, Shahrood University of Technology, Shahrood P.O. Box 3619995161, Iran
Department of Elite Relations with Industries, Khorasan Construction Engineering Organization, Mashhad P.O. Box 9185816744, Iran
Department of Civil Engineering, Faculty of Montazeri, Khorasan Razavi Branch, Technical and Vocational University (TVU), Mashhad P.O. Box 9176994594, Iran
Department of Civil Engineering, Mashhad Branch, Islamic Azad University, Mashhad P.O. Box 9187147578, Iran
Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad P.O. Box 9177948974, Iran
Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan
Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, Germany
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