## 1 Introduction
developed a crack feature measurement algorithm using skeleton images and the CBAM-Unet algorithm, enabling accurate measurements of crack width and length [6]. These studies demonstrate the variety of available methods for bridge crack identification, including drones, convolutional neural networks, image processing, nonlinear dynamic features, and active learning frameworks. However, there remains a lack of integration with multi-modal bridge data, underscoring the need for accurate and efficient crack identification methods that incorporate 3D point cloud data. Specifically, image processing-based crack detection methods remain traditional and widely used. Early research mainly relied on image preprocessing and feature extraction, including edge detection, thresholding, and morphological processing. Algorithms such as Canny edge detection, Otsu thresholding, and the Hough transform have been extensively employed. For instance, [PERSON] et al. (2018) used a combination of Canny edge detection and the Hough transform to detect cracks in bridge images, yielding promising results [7]. Recently, deep learning techniques have achieved significant breakthroughs in image processing and pattern recognition, garnering attention for their application in bridge crack detection. Convolutional neural network (CNN)-based crack detection methods learn features from vast amounts of crack images, facilitating automated and efficient identification. [PERSON] and [PERSON] (2015) proposed a deep learning-based method, using CNNs to extract crack features from bridge images, achieving high-precision detection [8]. Despite the progress, image-based methods remain sensitive to lighting variations and noise, limiting their robustness. To improve accuracy and robustness, researchers have increasingly focused on integrating multiple techniques. Previous studies have employed contact sensors and imagery data (i.e., 2D images and 3D point clouds) for structural health monitoring (SHM) and finite element model updating, yet these methods often face limitations in their applicability to aging bridges and require substantial resources for data collection and processing. To address these challenges, this study proposes a non-contact approach utilizing 3D laser scanning techniques. This method not only captures spatiotemporal details but also updates the finite element model based on the analysis of spatiotemporal changes. The proposed method's contributions include developing a process model for handling large volumes of 3D point cloud data, identifying structure deterioration patterns through non-contact sensing, and establishing an effective model updating process. In comparison to the existing methods, such as those of [PERSON] et al. (2021) and [PERSON] et al. (2024), who have each made significant contributions to crack detection using different approaches ([PERSON]'s study integrating image processing with point cloud data, and [PERSON]'s focusing on deep learning with 3D point cloud data), the proposed method holds distinct advantages in methodological integration, particularly in its focus on specific structural applications such as bridge health monitoring. The contribution of the proposed method includes (1) developing a process model for processing huge amounts of 3D point cloud data, (2) identifying structure deterioration patterns through non-contact sensing techniques, and (3) establishing an effective model updating process for updating finite element model parameters. Additionally, the novel algorithms or technologies introduced in this study may address limitations in the existing methods, potentially improving detection speed, reducing false positives, and enhancing adaptability to complex environments. For example, [PERSON] et al. (2021) proposed a crack identification method that fuses image processing with point cloud data, significantly enhancing detection accuracy and stability through multi-source data fusion [9]. Additionally, [PERSON] et al.