Work

Performance of Fine-Tuning Techniques for Multilabel Classification of Surface Defects in Reinforced Concrete Bridges

Year: 2025

Type: article

Abstract: Machine learning models often face challenges in bridge inspections, especially in handling complex surface features and overlapping defects that make accurate classification difficult. These challenges are common for image-based monitoring, which has become increasingly popular for inspecting and assessing the structural condition of reinforced concrete bridges with automated possibilities. Despite advances in defect detection using convolutional neural networks (CNNs), although challenges such as overlapping defects, complex surface textures, and data imbalance remain difficult, full fine-tuning of deep learning models helps them better adapt to these conditions by updating all the layers for domain-specific learning. The aim of this study is to demonstrate how effective the fine-tuning of several deep learning architectures for bridge damage classification allows for robust performance and the best utilization value of the methods. Six CNN architectures, ResNet-18, ResNet-50, ResNet-101, ResNeXt-50, ResNeXt-101 and EfficientNet-B3, were fine-tuned using the CODEBRIM dataset. Their performance was evaluated using Precision, Recall, F1 Score, Balanced Accuracy and AUC-ROC metrics to ensure a robust evaluation framework. This indicates that the EfficientNet-B3 and ResNeXt-101 models outperformed the other models and achieved the highest classification accuracy in all the error categories. EfficientNet-B3 showed the best-balanced Precision (0.935) and perfect Recall (1.000) in background classification, indicating its ability to distinguish defect-free areas from structural damage. These results highlight the potential of these models to improve automated bridge inspection systems and thus increase accuracy and efficiency in real-world applications, as well as provide guidance for the selection of methods based on whether accuracy or overall consistency is more important for a specific application.

Source: Applied Sciences

DOI: 10.3390/app15094725

Authors: Benyamin Pooraskarparast, Son N. Dang, Vikram Pakrashi, José C. Matos

Institutions: University of Minho, University College Dublin

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896