AI-Based 3D Modeling Strategies for Civil Infrastructure: Quantitative Assessment of NeRF and Photogrammetry

Three-dimensional (3D) modeling technologies are increasingly vital in civil engineering, providing precise digital representations of infrastructure for analysis, supervision, and planning. This study presents a comparative assessment of Neural Radiance Fields (NeRFs) and digital photogrammetry using a real-world case study involving a terrace at the Civil Engineering School of the Pontificia Universidad Católica de Valparaíso. The comparison is motivated by the operational complexity of image acquisition campaigns, where large image datasets increase flight time, fieldwork effort, and survey costs. Both techniques were evaluated across varying levels of data availability to analyze reconstruction behavior under progressively constrained image acquisition conditions, rather than to propose new algorithms. NeRF and photogrammetry were compared based on visual quality, point cloud density, geometric accuracy, and processing time. Results indicate that NeRF delivers fast, photorealistic outputs even with reduced image input, enabling efficient coverage with fewer images, while photogrammetry remains superior in metric accuracy and structural completeness. The study concludes by proposing an application-oriented evaluation framework and potential hybrid workflows to guide the selection of 3D modeling technologies based on specific engineering objectives, survey design constraints, and resource availability while also highlighting how AI-based reconstruction methods can support emerging digital workflows in infrastructure monitoring under variable or limited data conditions.