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ABSTRACT As technological advances appear, it is desirable to integrate them into new engineering education teaching methods, aiming to enhance students' comprehension and engagement with complex subjects. Augmented reality (AR) emerges as a promising tool in this effort, offering students opportunities to visualize and conceptualize challenging topics that are otherwise too abstract or difficult to grasp. Within civil engineering curriculums, structural analysis, a junior‐level course forming the foundation of many other courses, poses challenges in visualization and understanding. This paper investigates the development of a mobile AR application intended to improve the conceptual understanding of structural analysis material. This application is designed to overlay schematic representations of structural components (i.e., beams, columns, frames, and trusses) onto images of iconic local campus buildings, allowing students to interactively explore exaggerated deflections and internal and external forces under various loading conditions. By contextualizing structural analysis calculations within familiar settings, the goal is to leverage a sense of relevance and place‐based attachments in students' learning. Furthermore, the paper examines the development process and usability of the AR application, providing insights into its implementation in educational settings. Experimental results, including comparisons with a control group, are analyzed to assess the efficacy of the AR application in improving students' understanding of structural analysis concepts. Furthermore, the paper examines the development process and usability of the AR application, providing insights into its implementation in educational settings. Perspectives from structural analysis faculty members are also discussed, shedding light on the potential benefits and challenges associated with integrating AR technology into engineering education. In addition, the study highlights the value of place‐based learning, wherein students engage with real‐world structures in their immediate environment, fostering deeper connections between theoretical concepts and practical applications. Overall, this research contributes to the growing body of literature on innovative teaching approaches in engineering education and highlights the potential of AR as a valuable tool for enhancing student learning experiences in structural analysis and related disciplines. 

ABSTRACT The presented methodology results in an optimal portfolio of resilience‐oriented resource allocation under weather‐related risks. The pre‐event mitigations improve the capacity of the transportation system to absorb shocks from future natural hazards, contributing to risk reduction. The post‐event recovery planning results in enhancing the system's ability to bounce back rapidly, promoting network resilience. Considering the complex nature of the problem due to uncertainty of hazards, and the impact of the pre‐event decisions on post‐event planning, this study formulates a nonlinear two‐stage stochastic programming (NTSSP) model, with the objective of minimizing the direct construction investment and indirect costs in both pre‐event mitigation and post‐event recovery stages. In the model, the first stage prioritizes a bridge group that will be retrofitted or repaired to improve the system's robustness and redundancy. The second stage elaborates the uncertain occurrence of a type of natural hazard with any potential intensity at any possible network location. The damaged state of the network is dependent on decisions made on first‐stage mitigation efforts. While there has been research addressing the optimization of pre‐event or post‐event efforts, the number of studies addressing two stages in the same framework is limited. Even such studies are limited in their application due to the consideration of small networks with a limited number of assets. The NTSSP model addresses this gap and builds a large‐scale data‐driven simulation environment. To effectively solve the NTSSP model, a hybrid heuristic method of evolution strategy with high‐performance parallel computing is applied, through which the evolutionary process is accelerated, and the computing time is reduced as a result. The NTSSP model is implemented in a test‐bed transportation network in Iowa under flood hazards. The results show that the NTSSP model balances the economy and efficiency on risk mitigation within the budgetary investment while constantly providing a resilient system during the full two‐stage course.