## 1 Introduction  
Traditional methods of damage detection in bridges are often time-consuming and require significant manual effort, despite the value they bring in terms of lifetime safety, performance and intervention measures for these structures [1]. Automated computer vision or image processing methods can often provide a more objective inspection while reducing uncertainties. Several methods exist in this regard, often with robotics involved. These methods are effective and can also positively influence the health and safety of workers but protect them from having to reach in far or into a potentially hazardous location. Machine learning techniques have become rapidly popular to improve robustness, efficiency, accuracy and Precision. However, such improvement is contingent upon real-life variabilities like variation illumination [2], environmental conditions [3], occlusion [2], motion blur [4] and turbidity for underwater conditions [5]. The availability of large and varied datasets and high processing power lead to high accuracy in image classification through deep learning (DL). Here, convolutional neural networks (CNNs) have the ability to extract features of defects from images to create meaningful representations for object recognition [6]. Computer vision algorithms for defect detection can be broadly categorized into four families: image classification, object detection, semantic segmentation and anomaly detection [7]. The present study focuses on deep learning-based classification due to its simplicity and suitability for scenarios where image-level annotation is available. Image processing of recent examples like San Francisco-Oakland Bay Bridge [8] and Morandi Bridge in Genoa, Italy [9], indicates cracks in components and failure of connections as critical causes of disasters where the classification of damages was very important and vital. Image classification is a fundamental task in visual recognition, encompassing object classification, detection and segmentation, with far reaching consequences in the safety and performance of bridge structures. Traditional machine learning methods rely on manual feature design for image classification but often suffer from poor generalization and portability. CNNs address this limitation to a certain extent by extracting hierarchical features, pooling and dense layers, and architectures like AlexNet, VGGNet, GoogLeNet, ResNet, DenseNet, Xception, MobileNet, and EfficientNet have significantly advanced image classification accuracy and efficiency [10]. Recent developments in machine learning have extended beyond bridge inspection to include subsurface and tunnel-related structural assessments. For instance, [PERSON] et al. [11] employed a probabilistic CNN-RF hybrid model to predict rock mass classification in TBM operations, while [PERSON] et al. [12] proposed a feature fusion framework combining TBM operation data and cutter wear indicators. These studies reinforce the broader relevance of tuning strategies, data fusion and model interpretability across civil engineering domains. Table 1 presents an overview of some of the popular datasets and modern sources of images. Table 2 compares various CNN-based models, datasets and reported accuracies for both binary and multiple defects classification tasks in concrete structures. This paper presents a framework to detect five types of bridge damage, including cracks, spalling, efflorescence, exposed bars and corrosion stains based on the ResNet-18/50/101, ResNeXt-50/101 and EfficientNet-B3 models. To boost the detection accuracy, a transfer learning method with fully pre-trained weights from the ImageNet dataset was used to improve the original models. The model performance in concrete defect detection is highly dependent on fine-tuning strategies, especially in transfer learning scenarios. Full fine-tuning, partial tuning and parameter freezing are commonly used approaches.