# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 1 Introduction  
Since the 1970s, the number of bridges in China has expanded rapidly. According to the 2023 Statistical Bulletin of Transportation Industry Development, there were 1,079,300 highway bridges nationwide by the end of 2023. The rapid increase in the number of bridges, combined with higher vehicle density and load demands, has created significant challenges in ensuring their safe and efficient operation and maintenance (O&M) [1]. Aging bridges face structural issues such as cracking, water seepage, and damage to expansion joints, requiring advanced solutions for long-term monitoring and maintenance. In the United States alone, approximately USD 20.5 billion is needed annually to address theescalating deterioration of bridges [2]. Therefore, a smart bridge management system is necessary for ensuring safe and efficient O&M of aging bridges. Traditional visual inspections of bridges are labor-intensive and rely heavily on subjective judgments, often leading to inconsistent and error-prone results. This highlights the need for automated solutions that can improve both the accuracy and the efficiency of bridge health monitoring. Recent developments in computer vision techniques and image processing algorithms show potential in bridge inspection and structural health monitoring (SHM) of bridges [3]. Despite their widespread applications, computer vision techniques and image processing algorithms still face challenges, including noise interference, limited sensitivity, the complexity of setting appropriate alarm thresholds, and sensor reliability. Especially when dealing with complex bridge structures, these factors may result in missed detections, false positives, or system malfunctions, thereby affecting the overall effectiveness and reliability of the bridge health monitoring system [4]. It is therefore necessary to establish a smart O&M system that can monitor structural dynamics and capture spatiotemporal details for analyzing structure deterioration patterns and assessing structure failure risks. A trustworthy BDT enables seamless integration between physical and virtual environments by providing an accurate digital representation of physical structures, utilizing real-time data, and facilitating closed-loop interactions between the virtual and physical bridges [5]. The integration of virtual and physical systems overcomes limitations in time, space, cost, and safety, enabling comprehensive real-time assessments and predictive maintenance for bridge structures. Therefore, DT technology can be applied in the field of bridge SHM, significantly improving its operational efficiency. In fact, DT technology has been extensively applied in various fields, including manufacturing, aerospace, transportation, healthcare, and energy. DT technology has gained substantial attention and recognition from academic institutions, government agencies, and industry experts both domestically and internationally [6]. The current digital twin modeling (DTM) is typically constructed using collected data to construct simulation models. However, relying solely on data-driven DT models makes it difficult to comprehensively monitor and provide early warnings for the health status of complex bridge structures. This study proposed a DK-DTM framework for supporting smart structural health assessments of bridges. The proposed framework contains (1) data collection and processing of bridge health condition data from multiple sources and (2) spatiotemporal modeling of BDTs. The proposed DK-DTM framework uniquely integrates spatiotemporal modeling with expert knowledge reasoning to provide dynamic early warnings for potential structural issues. This comprehensive approach ensures accurate monitoring, predictive analysis, and enhanced decision-making for bridge O&M. As a result, the proposed framework significantly enhances the efficiency and safety of bridge O&M. The organization of the paper is as follows. Section 2 provides a systematic review for synthesizing previous studies on SHM and DT and identifying knowledge gaps.