Chi-Ho Jeon

The growing adoption of off-site construction methods has increased the critical role of mobile cranes within the construction sector. This study develops a Construction Digital Twin (CDT) framework to optimize crane lifting positions and capacities for the installation of prefabricated bridge decks. By integrating 3D site modeling, Building Information Modeling (BIM), and crane simulations within the Unity game engine, the CDT overcomes the limitations of conventional 2D-based planning by providing a three-dimensional representation of site conditions. An exhaustive search method identifies optimal crane configurations, enhancing precision and efficiency. Simulation calibration using video analysis of real bridge deck installations aligns crane speed and cycle times with actual operations, improving reliability. Case studies demonstrate the CDT’s ability to reduce crane operation costs by 27% when employing a smaller capacity crane while maintaining operational efficiency. Additional DFA-focused simulations with varying deck dimensions revealed a potential 10% cost reduction by optimizing crane operations and deck design strategies. The CDT framework supports early-stage planning, reduces operational risks, and contributes to cost-effective and safer construction practices, offering a scalable solution adaptable to various construction scenarios.

Structural health monitoring using various sensors has been widely employed to assess the structural conditions of bridges. In addition, the concept of a digital twin was introduced, which encompasses the life cycle information of a bridge and its real-time data acquisition and utilization. However, the obtained real-time data from sensors primarily reflect the global behavior of the system, making it challenging to identify the root causes of structural changes. For a highly reliable assessment of the global behavior of a bridge, previous history information, that is, a prerequisite model, is required. This study defines a baseline digital twin model (B-DTM) as the stage preceding real-time data utilization in digital twins. The B-DTM is structured into a pre-update phase, which involves the collection of members and system historical data, and a post-update phase, which focuses on model updating. For the case of model updating, due to the inherent complexity of bridge systems, identifying the global optimum for updating remains challenging. In the pre-update phase, a probabilistic approach to historical data such as member stiffness restricts the search domain for model updating, whereas, in the post-update phase, deflection, mode shapes, and natural frequencies derived from load test results representing the real bridge’s behavior are utilized to explore the global optimum solution. The proposed B-DTM was validated using collected data and load test results from a PSC-I girder bridge decommissioned after 45 years of service.

Bridge maintenance faces challenges regarding data management and decision-making efficiency, primarily owing to the manual processing of extensive inspection data and the absence of integrated digital solutions. This study addresses these challenges by proposing a health index (HI)-based bridge evaluation framework for prestressed concrete bridges, based on building information modeling (BIM) for inspection data integration and long short-term memory (LSTM) models for accurate deterioration prediction. The proposed framework categorizes and analyzes bridge deterioration data from inspection reports and develops a predictive LSTM model that allows quantitative bridge evaluation based on accumulated historical data. The results demonstrate that this approach enhances the efficiency and accuracy of bridge condition evaluation while enabling long-term prediction of deterioration trends. In a case study of a bridge, the bridge-level HI value decreased by 17% over 16 years, while the condition grade remained unchanged. The findings suggest that integrating BIM with HI-based bridge evaluation can support sustainable bridge maintenance strategies.

Concrete-filled steel tubes (CFTs) offer significant structural advantages in terms of stiffness, strength, and ductility. The concrete core enhances the stiffness and compressive strength of columns, whereas the steel tube serves as a reinforcement to resist tension and bending by confining the concrete. Moreover, CFT columns offer exceptional resistance, such as high strength, ductility, and energy absorption capacity. This study presents experiments focused on prefabricated bridge piers featuring multiple CFT columns. Commercial circular steel tubes were utilized to streamline fabrication efforts, with bracings employed to enhance structural performance by connecting the CFT columns. Component tests were conducted for different connection details to prevent premature failure owing to cyclic loading. A full-scale modular pier was designed to explore its cyclic behavior using the bracing connection details derived from the component test. The plastification location in a modular pier can be designed using the connection details, as validated experimentally. The results of this study indicate that CFT columns, as the main component of the bridge pier, can be protected by designing connection details to induce stress concentration in the braces, thereby achieving ductile behavior.

The Health Index (HI) serves as an essential tool for assessing the structural and functional condition of bridges, calculated based on the condition of structural components and the serviceability of the bridge. Its primary purpose is to identify the most deteriorated structures in an asset inventory and prioritize those in most urgent need of repair. However, a frequently cited issue is the lack of accurate and objective data, with the determination of the HI often being heavily reliant on expert opinions and engineering judgment. Furthermore, the HI systems used in most countries are dependent on the current state of bridge components, making it challenging to use as a proactive indicator for factors such as the rate of bridge aging. To address this issue, this study introduces a novel HI as a quantitative evaluation metric for reinforced concrete slab bridges and details the process of deriving the HI based on deterioration models. The deterioration models are derived by preprocessing the deterioration data of reinforced concrete (RC) slab bridges, wherein the relationship between time and deterioration is directly employed for training a long short-term memory model. The HI was validated through a case study involving six RC slab bridges, wherein accuracies of >93% were achieved, confirming that the proposed quantitative evaluation methodology can significantly contribute to maintenance decisions for bridges.