Improving the Classification Performance of Asphalt Cracks After Earthquake With a New Feature Selection Algorithm

Abstract

Large-scale earthquakes can cause huge loss of life and material losses. After an earthquake, highways are the most commonly used type of transportation for the delivery of the necessary aid teams and materials to the scene of the event. If the highways are not well maintained, it may cause serious disruption of transportation after the earthquake or aftershocks. In this study, field studies were conducted in the provinces where the earthquake was felt severely after the earthquakes in Turkey on February 6, 2023. In these studies, images were collected according to the condition of asphalt cracks on the highways. These images were labeled as in need of urgent maintenance (Major) and not in need of urgent maintenance (Minor) and a new dataset was created. The classification performance of popular pre-trained CNN models is evaluated on this dataset. First, classification algorithms other than softmax were used to improve the classification performance. The Combined Metaheuristic Optimization-Relieff (CMO-R) algorithm was designed to improve the classification performance by one more level. Extensive experiments were conducted on the dataset, and the VGG16 model demonstrated superior performance, reaching an accuracy of 80.32% without encountering overfitting.

Keywords

• Classification
• deep learning
• new asphalt cracks dataset
• new feature selection algorithm