Utilization of Artificial Intelligence System for Detecting and Classifying Rigid Concrete Pavement Distress: A Comprehensive Review
Year: 2026
Type: article
Abstract: This paper provides a literature survey of AI techniques used in rigid pavement monitoring, including the acquisition of image and sensor data, machine learning, and deep learning models, and mobile/IoT applications to resource-poor areas. Some of the most important AI applications are convolutional neural networks (CNNs) to identify crack segments, object detectors to identify distress in a specific location, and machine learning with accelerating sensors in smartphones to determine roughness. We address the model measures of evaluation (accuracy, F-score, IoU), existing datasets, and explore the issue of implementation. Lastly, we describe future directions which include multi-sensor fusion, edge devices and lightweight models and self-supervised learning to enhance generalization of a variety of pavement conditions.
Authors: Ali Nashat Subhi, Asma Thamir Ibraheem
Institution: Nahrain University
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7212160941