Classroom study demonstrates improved damage assessment skills using VR training in civil engineering education, suggesting an effective learning alternative.
Identification of damage and key structural elements is vital to the monitoring and management of civil engineering projects, education, and training. However, practical inspection training is often constrained by cost, safety risk, and limited access to real structures, which reduces opportunities for repeated practice and feedback‐rich learning. To address these constraints, recent research has explored virtual reality (VR) in civil engineering to deliver immersive training for infrastructural inspections and reduce reliance on in‐person field trips and site visits. Despite the many advantages of VR as a learning tool, its adoption in civil engineering education remains limited. As a result, many engineers‐in‐training receive limited opportunities to practice realistic inspection workflows that combine defect recognition with structural health monitoring (SHM) interpretation. This paper presents a novel VR‐based educational tool designed to teach visual damage identification and structural condition assessment through immersive, scaffolded simulations. In this research, users explore a photorealistic 3D bridge reconstructed through drone‐based photogrammetry, annotate multiple damage types, and interact with embedded virtual sensors displaying multi‐year structural data collected from real‐world instrumentation. Unlike traditional approaches, the system integrates gamified scoring, real‐time feedback, and both qualitative and quantitative analysis tasks into a single, performance‐tracked learning experience. A classroom study with graduate students evaluated the tool's impact on learner motivation and confidence using a structured motivation model and a validated engineering self‐efficacy scale, demonstrating measurable improvements in damage assessment skills. This study advances the educational use of VR in civil engineering by combining interactive infrastructure scans, authentic sensor data, and experiential learning to offer a compelling, cost‐effective alternative to traditional field‐based inspection training.
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Carter et al. (2026) studied this question.
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