Concrete Subsurface Crack Detection Using Thermal Imaging in a Deep Neural Network

Abstract

Impact actions, such as a zone directly affected by conflict and warfare, can negatively impact the structural integrity of concrete structures. Even indirect impact actions can make structures unsafe, creating subsurface defects in concrete. However, the result of indirect impact actions is often undetected because of the time required and expert knowledge needed to assess the structure. Yet, there are no techniques currently available to assess the usability and the safety of a concrete structure rapidly and with no expert knowledge.. This paper presents a combination of thermal imaging and artificial intelligence (AI) to enable a novel, contactless, autonomous, and fast technique for detecting hidden defects in concrete structures. In this paper, a ResNet50 model was trained on simulated data of subsurface defected and defect-free concrete blocks to test if it is possible to classify between the two. The model developed achieved a validation accuracy of 99.93%. Because of the success of this model, a laboratory experiment was conducted by compressing concrete blocks and recording the process using a thermal camera to create a dataset of concrete blocks with and without subsurface cracks. This dataset was used to train a new model with the same architecture and hyper-parameters as the initial model and achieved a validation accuracy of 100%. This investigation proves it is possible for AI to detect subsurface cracks and hidden defects by classifying the thermal images of concrete surfaces.

Keywords

• deep learning
• thermal imaging
• concrete defects
• artificial intelligence