Neuro-Symbolic Vision Framework for Construction: Knowledge Graph-Augmented Monitoring and Productivity Analysis of Precast Concrete Assembly

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ID: 321241
2026
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Abstract
Abstract Monitoring precast concrete (PC) assembly is hindered by logical inconsistencies, occlusions, and the inability of frame-by-frame analysis to capture temporal sequences. To address this, we propose a neuro-symbolic vision framework integrating data-driven deep learning with knowledge-based reasoning. The system combines YOLOv11, DeepSORT, and Video Masked Autoencoders (VideoMAE), enhanced by a knowledge-graph reasoning engine that applies spatial and temporal domain constraints. Methodologically, vision outputs are temporally aligned and then passed through a multi-stage graph inference process that performs sequence and prerequisite filtering, fuses four complementary confidence scores, and continuously adapts its temporal parameters from site-specific observations; the framework was trained and evaluated on a multimodal dataset of 12,000 annotated images and over 12,000 activity clips. Experimental validation on real-world videos demonstrates that this integrated approach achieves 96.8% precision, 96.2% recall, and 96.5% F1 score while sustaining real-time throughput (28.3 FPS), significantly outperforming standalone VideoMAE by 8.3 percentage points. Furthermore, productivity analysis of 142 components shows minimal error (Mean Absolute Percentage Error (MAPE): 1.3–3.4%) and high correlation (τ ≥ 0.90) with actual data. These results confirm that neuro-symbolic integration effectively overcomes pure computer vision limitations, enabling accurate, real-time monitoring to improve project efficiency and decision-making in PC construction.
Reference Key
openalex_W7168518220 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Junyoung Jang, Eunbeen Jeong, Jongwoo Cho, Tae Wan Kim
Journal journal of computational design and engineering
Year 2026
DOI
10.1093/jcde/qwag067
URL
https://doi.org/10.1093/jcde/qwag067
Keywords Keywords not found

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