Automated Precamber Estimation Framework for PC Components from 2D Images Using a Vision Transformer

Abstract

On-site quality assessment of precast concrete (PC) components is essential to ensure structural integrity, but precamber measurement relies on labor-intensive manual procedures due to time constraints and site congestion. This study proposes an automated precamber estimation framework for PC components using two-dimensional (2D) images. The proposed method integrates a reference-object-free geometric estimation, which utilizes predefined design specifications as ground truth, with vision transformer (ViT)-embedded architecture trained on hybrid synthetic data to robustly capture high-level features. Field validation demonstrated that the framework achieves an average measurement error of 1.91 mm, confirming a reliable performance comparable to manual methods. Notably, the automated process reduced inspection time by 79.82% and labor costs by 91.70% compared to traditional manual procedures. These results demonstrate the practical feasibility of the proposed framework for supporting rapid and efficient on-site quality assessment. Furthermore, the framework holds significant potential to extend into a comprehensive dimensional inspection system for managing various geometric deformations beyond precamber.