Surface Classification of Damaged Concrete Using Deep Convolutional Neural Network

Abstract

Concrete is known for its a strength and durability as a building material. It is heavily utilized in almost all infrastructures, from pipes, building structures to roads and dams. However, due to external factors or internal compositions, concrete can be damaged and hence affects the quality of the constructions. The type of damage that appeared on concrete is often the first a clue as to how it occurred. Therefore proper diagnosing of the problem can help engineers determine how quickly and how best to fix it. The application of information technology, especially artificial intelligence, to automatically classify the damage types can help tremendously in this aspect. There have been some studies in using computer vision to examine the surfaces of concrete for damages. This study attempts a more challenging task of classifying the five common types of concrete damage. A new dataset is built and the Convolutional Neural Network (CNN) architecture is used for classification. The results obtained have an accuracy of 95 and 93% on the training set and the test set respectively.