DEEP TRANSFER LEARNING FOR IMAGEBASED STRUCTURAL DAMAGE RECOGNITION USING AI

Structural damage recognition is a difficult task for the field of civil engineering, as it can help prevent catastrophic failures of infrastructure. The traditional approach to damage recognition involves visual inspection by human experts, which can be timeconsuming, expensive, and subjective. In recent years, artificial intelligence (AI) has been gaining attention in the field of structural damage recognition, with deep transfer learning being a promising technique. This research paper investigates the use of deep transfer learning for image-based structural damage recognition and proposes a methodology for its implementation. The process to performance of different deep transfer learning models and identifies the key factors that influence their effectiveness. The method use by AI model involves the use of pre-trained deep learning models, finetuning, and transfer learning to improve the accuracy of structural damage recognition. The deep transfer learning can significantly improve the accuracy and efficiency of image-based structural damage recognition and recommends the adoption of the proposed methodology in the field of civil engineering. Artificial intelligence technology has advanced quickly, particularly machine learning algorithms, which have achieved outstanding outcomes in a number of industries while also garnering attention. Artificial intelligence technology has advanced quickly. The study on damage detection and artificial intelligence approaches is detailed in this publication. Sensing data is the starting point for several Image structure processing AI techniques. The quality of the subsequent processing techniques directly depends on the strengths and weaknesses of the sensing data itself. Since there are many different forms of sensing data, this study divides damage detection investigations into a few groups based on those types: visual image, point cloud, infrared thermal imaging, vibration response, and other sorts of data.