# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 2 Literature Review  
### Structural Health Monitoring  
Over the past few decades, driven by advancements in sensing technology, data processing, and artificial intelligence (AI), bridge structural health monitoring (SHM) technology has experienced significant advancements. In the past, SHM mainly relied on manual inspections and non-destructive testing (NDT) techniques, such as acoustic emission, X-ray diffraction, and interferometry [7]. However, these methods have certain limitations, particularly in addressing complex bridge structures, as well as in real-time monitoring and accuracy [8]. Recent developments have underscored the potential of big data and AI for transforming SHM, particularly in terms of interpreting vast amounts of data to facilitate effective condition assessment [9]. The advent of digital technologies has facilitated a transition to more sophisticated, automated SHM systems. Long-span bridges worldwide, such as the Tsing Ma Bridge in Hong Kong and the Akashi Kaikyo Bridge in Japan, have been equipped with long-term monitoring systems, enabling continuous data collection and damage detection [10]. These systems utilize networks of sensors, including strain gauges, fiber optics, and wireless sensors, to monitor key parameters such as vibration, strain, and temperature [11]. The integration of robotics, including ground, wall-climbing, and flying robots, has further enhanced these monitoring capabilities by enabling access to hard-to-reach areas and increasing the efficiency of data collection [12]. The Internet of Things (IoT) has also contributed to the transition from traditional wire-based systems to wireless, real-time SHM, enhancing data accessibility and system efficiency [13]. For instance, [PERSON] et al. (2021) demonstrated the importance of integrating diverse sensing techniques, such as seismic and electromagnetic methods, to monitor the Gravina Bridge in Matera, Italy [14]. Their study showcased how multi-source data fusion enables a holistic analysis of structural behavior, improving the reliability of SHM systems while addressing challenges associated with merging heterogeneous data types. While significant progress has been made, several challenges persist in SHM, particularly regarding data interpretation, model accuracy, and system reliability. Traditional data-driven methods, although widely used, often struggle to accurately capture the complexity of bridge behavior under varying environmental and operational conditions [15]. Heterogeneous data from diverse sensor types further complicate SHM processes, requiring advanced data fusion techniques to improve model accuracy and system reliability [9]. In this context, the evaluation of safety formats for non-linear finite element analysis offers valuable insights. For instance, [PERSON], [PERSON] et al. (2020) critically analyzed widely used methods such as the partial factor method, global resistance methods, and probabilistic approaches, highlighting significant uncertainties and statistically vague formulations in current structural codes [16]. These findings emphasize the need for advanced methodologies to address variability in structural responses and improve the reliability of safety evaluations, particularly for aging bridge infrastructure. Numerical modeling approaches, including non-linear analysis methods, further contribute to the field by addressing uncertainties in structural performance assessment. As noted by [PERSON] et al. (2023), non-linear analysis frameworks enable accurate simulations of bridge behavior, providing robust methodologies for assessing safety under complex conditions [17]. The integration of big data and AI technologies into SHM offers significant potential to address these gaps. Machine learning algorithms, particularly those used for vibration- and image-based SHM, have exhibited considerable potential in damage detection, pattern recognition, and predictive analysis [18].