# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
###### Abstract  
Currently, damage in aging bridges is assessed visually, leading to significant personnel, time, and cost expenditures. Moreover, the results depend on the subjective judgment of the inspector. Machine-learning-based approaches, such as deep learning, can solve these problems. In particular, instance-segmentation models have been used to identify different types of bridge damage. However, the value of deep-learning-based damage identification may be reduced by insufficient training data, class imbalance, and model-reliability issues. To overcome these limitations, this study utilized photographic data from real bridge-management systems for the inspection and assessment of bridges as the training dataset. Six types of damage were considered. Moreover, the performances of three representative deep learning models--Mask R-CNN, BlendMask, and SWIN--were compared in terms of loss-function values. SWIN showed the best performance, achieving a loss value of 0.000005 after 269,939 training iterations. This shows that bridge-damage-identification performance can be maximized by setting an appropriate learning rate and using a deep learning model with a minimal loss value. deep learning; aging-bridge management; instance segmentation; damage identification; loss-functions +
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For example, a system that can remotely monitor the condition of bridges using sensors and Internet-of-Things (IoT) technologies for the analysis of data and the detection of anomalies in real time can assist in providing accurate information and building early warning systems [1]. IoT refers to the technology and concept where everyday objects around us are connected to each other through the Internet and exchange information. Non-destructive examination technologies, such as ultrasound and X-rays, can quickly check for internal defects and damage within structures without causing further damage themselves; moreover, they enable precise analysis of data and prediction of defects [2]. Research is also underway that proposes a defect recognition model to recognize four typical defects of phased-array ultrasonic testing (PA-UT) images for electrofusion (EF) joints [3]. If big data and artificial intelligence (AI) technologies are used, it is possible to identify patterns and trends from large datasets and establish preventive maintenance plans [4; 5; 6]. In a study that analyzed and verified images acquired with ground-penetrating radar (GPR) to determine the location of underground utilities (UU) with an end-to-end deep learning model based on a core-regression model, it was possible to optimize parameters [7]. Drones and robot-based systems can inspect and evaluate the condition of structures, even in low or hard-to-access areas. Drones capture photos and image data from the air, which are then used to assess the overall condition of structures; robots can enter narrow or hazardous areas to perform detailed defect detection and take appropriate corrective measures [8]. Studies are presently underway to identify and quantify bridge damage by analyzing image data captured by drones or by on-site inspectors [9]. Similar studies are being conducted in various fields; the goal is to identify the damage automatically and calculate its extent through the analysis of inspection images. Image-based crack detection and classification is an especially important research topic. Many studies have focused on methods to learn and detect crack patterns using convolutional neural networks (CNNs), which are a type of deep learning algorithm [10; 11]. Research is also being conducted to quantify the size and area of defects using image processing and computer vision technology; this can not only enable accurate calculation of the extent of damage but also analyze trends. Further, efforts are being made to utilize images for three-dimensional modeling of facilities and the detection of deformations in them; changes in the shape of structures and the extent of deformations can be tracked and assessed in this way.