# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 1 Introduction  
Bridge bearings play a crucial role in maintaining structural integrity by transmitting loads, including tensile, shear, and bending stresses, between the superstructure and the substructure [1, 2, 3]. These bearings are designed to accommodate displacement and rotation, thereby absorbing expansions and contractions caused by traffic loads and temperature changes [4, 5]. Abnormal bearing behavior can signal significant structural issues, such as problems with the girder or foundation, necessitating prompt inspection to prevent further damage [6, 7]. Therefore, monitoring bridge bearings is essential for the early detection of such issues. For example, the 2007 I-35W bridge collapse in Minneapolis involved significant movement and the tilting of piers and bearings during the collapse, underscoring the critical role of bearing monitoring in structural integrity [8]. Similarly, the 2022 Ferm Hollow Bridge collapse in Pittsburgh was caused by extensive corrosion on the bridge legs, leading to structural failure [9]. Currently, bearing defects are primarily identified through manual visual inspections, where on-site inspectors use rulers to verify if bearing movements remain within design limits [10, 11]. This method is subjective and heavily reliant on the inspector's experience,making it less reliable. Moreover, physical inspections can be challenging and costly, especially in hard-to-reach areas. Therefore, there is a critical need for a system that can continuously monitor and measure bearing behavior to prevent potential bridge failures. To address these inspection challenges, various studies have explored bearing monitoring techniques. In the short term, robotic approaches have been introduced to facilitate inspections in areas that are difficult to access manually [12; 13; 14]. Sensor-based methods have also been implemented to allow for more efficient and accurate measurements. LVDTs have been used to measure horizontal displacements caused by thermal expansion of the superstructure, assessing bridge performance [15; 16]. Fiber Bragg Grating (FBG)-based sensor systems have measured the longitudinal rotation of bearings and identified the modal shapes and natural frequencies of bridges [17]. While short-term inspections like visual examinations and structural property estimations through experiments are crucial, long-term monitoring technologies using the IoT are actively being researched to respond to risks continuously. Methods for measuring bearing displacement and rotation using Micro-Electro-Mechanical Systems (MEMSs) and LVDT-based IoT systems have been proposed [18; 19]. However, these methods typically require contact sensor installations, which involve complex and precise calibration processes, such as auxiliary equipment setups and extensive wiring, leading to increased costs and maintenance challenges [20]. Moreover, current methods have primarily focused on 1D longitudinal measurements, which are essential but insufficient for curved or asymmetrical bridges, where multi-directional displacement monitoring is crucial. In these cases, various directions of displacement can occur, necessitating comprehensive monitoring strategies. In contrast, computer vision (CV) techniques which use non-contact measurement and measure movement by image processing techniques [21; 22; 23; 24] show promise in measuring multi-dimensional displacements. Several efforts have been made to utilize CV for monitoring bridge bearing behavior. For instance, [PERSON] et al. addressed errors caused by the ego motion of stationary cameras by using two 3D targets [25], and [PERSON] et al. developed a remote CV-based system for bridge bearing monitoring that achieved high precision over an 8-month field experiment [26]. These two methods for measuring bridge bearing's motion is summarized in Table 1.  
However, despite these advancements, challenges remain in achieving stable, long-term monitoring of bearing behavior.