ISSN 2096-4498

   CN 44-1745/U

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Tunnel Construction ›› 2026, Vol. 46 ›› Issue (S1): 317-335.DOI: 10.3973/j.issn.2096-4498.2026.S1.028

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Performance Boundary and Failure Mechanism of Visual Algorithm for Displacement Monitoring of Tunnel Structure

DENG Shiqiang1, LUO Gaofeng2, 3, LI Yong2, 3, LI Ke4 , *, HUANG Yichun2, 3, WANG Tong2, 3   

  1. (1. School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China; 2. Zhejiang Communications Investment Group Transportation Construction Management Co., Ltd., Hangzhou 310000, Zhejiang, China; 3. Zhejiang Taizhou Shenhai Expressway Co., Ltd., Taizhou 317000, Zhejiang, China; 4. China Merchants Communications Technology (Chongqing) Limited, Chongqing 400067, China)
  • Online:2026-06-30 Published:2026-06-20

Abstract: To address the issues of unclear performance boundaries, unclarified failure mechanisms, and lack of early warning criteria for visual displacement monitoring algorithms in tunnel environments, two representative algorithms—phase-only correlation (POC) and fast normalized cross-correlation (FNCC)—are taken as research objects to construct a collaborative quantitative analysis framework of “algorithm principle, marker feature, and environmental interference” based on four types of markers, namely pure circle, coded circle, checkerboard, and speckle, with peak correlation score (PCS) and pixel displacement error as core indicators. A total of 10 024 multi-dimensional controlled experiments are designed and completed, covering noise, illumination, occlusion, rotation, scaling, multi-factor coupling, temporal dynamic interference, and image resolution attenuation. A four-level performance evaluation standard, including stable operation, performance sensitivity, critical failure, and complete failure, is established, and the performance boundaries and failure critical points of different algorithm-marker combinations are quantitatively calibrated. The engineering applicability of the optimal combination is verified through a scale physical model test. The results show that: (1) POC and FNCC exhibit significant differences in intrinsic performance: POC has 1.5 to 2.0 times the computational efficiency of FNCC but lower accuracy with a long-tail error distribution, while FNCC achieves higher measurement accuracy, and complex texture markers effectively improve the matching reliability of both algorithms. (2) The inherent vulnerability of POC to scale changes and the high PCS but low accuracy hidden failure characteristic of FNCC under local distortion are revealed at the mechanism level, clarifying the coupling adaptation law between marker texture features and algorithm principles. (3) The integer-pixel matching algorithm and sub-pixel refinement method must follow the principle of morphological adaptation, as mismatch will lead to sharp deterioration of measurement accuracy. (4) The scale model test of an underwater tunnel verifies the engineering applicability of the POC plus coded circular marker combination in the whole process monitoring of local deformation and reset of the tunnel, demonstrating its ability to accurately capture a wide dynamic range from ±1.5 mm micro-amplitude fluctuations to 17 mm large deformations.

Key words: tunnel structure displacement, visual displacement measurement, phase-only correlation, fast normalized cross-correlation, performance boundary, failure mechanism