Title: Towards identification of long-term building defects using transfer learning

Authors: Aravinda Boovaraghavan; Christy Jackson Joshua; Abdul Quadir Md; Kong Fah Tee; V. Sivakumar

Addresses: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India ' Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia ' Department of Information and Communication Technology, Manipal Institute of Science and Technology, Manipal Academy of Higher Education, 576104, Manipal, India

Abstract: Detecting long-term issues on various types of building wall surfaces, such as cracks, flakes, and roofs, is vital for timely maintenance and repairs before they become too risky and expensive. Currently, building managers manually assess the building conditions to survey and communicate the state of their buildings. However, this manual process is subjective, often leads to inaccuracies, and is time-consuming; thus, it needs to be more efficient. These flaws can severely influence a building's structural stability if they go undiscovered and ignored. In this context, this study proposes an approach named towards identification of long-term building defects using transfer learning (TILT) to identify unnoticed defects such as cracks, flakes, and roofs robustly and accurately in buildings. The proposed model has been tested using images taken from real-world onsite deployments, and the types of construction issues have been determined with 98.33% accuracy predicted by the VGG16 model and 79.13% accuracy predicted by the ResNet50 model. Overall, the VGG16 model gives better results compared to ResNet50.

Keywords: structural health monitoring; damage detection; building defects; classification; transfer learning; VGG16; ResNet50.

DOI: 10.1504/IJSTRUCTE.2025.146919

International Journal of Structural Engineering, 2025 Vol.15 No.2, pp.147 - 170

Received: 18 May 2024
Accepted: 23 Jan 2025
Published online: 26 Jun 2025 *

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