Dynamic damage identification of tunnel portal and verification via shaking table test

Wang, Qi; Geng, Peng; Li, Peisong; Chen, Junbo; He, Chuan

doi:10.1016/j.tust.2022.104923

Cited by 7 publications

(1 citation statement)

References 36 publications

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“…In the theoretical study of the stability of tunnel portal rock, Aygar et al [11] analyzed the relationship between highway tunnel stability and portal slope in Turkey using numerical simulation. Wang et al [15,16] performed a large-scale shaking table test on a tunnel portal and developed a method for detecting dynamic damage based on the Hilbert-Huang transform. This method accurately identifies macroscopic damage in tunnel linings and portal slopes.…”

Section: Introductionmentioning

confidence: 99%

Multifractal Characteristics and Displacement Prediction of Deformation on Tunnel Portal Slope of Shallow Buried Tunnel Adjacent to Important Structures

Zhou,

He,

Zhang

et al. 2024

Buildings

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The tunnel portal section is often in extremely weak and fragmented strata, and the deformation of the portal side and slope will affect the stability of the surrounding rock and the tunnel-supporting structure. However, the deformation characteristics and displacement development patterns of slopes in the tunnel portal section are not clear. In this paper, the multifractal characteristics and displacement prediction of the deformation sequence of the tunnel portal slope at of a weak and water-rich shallow buried tunnel adjacent to an important structure are studied in depth. Combined with the deformation characteristics of the tunnel portal slope, a suitable slope monitoring and measurement scheme is designed to analyze the deformation pattern of the tunnel portal slope. Based on the multifractal detrended fluctuation analysis (MF-DFA) method, the multifractal characteristics of the deformation monitoring sequences at each monitoring point of the tunnel portal slope are analyzed. The multifractal characteristics of displacement sequences at different monitoring points of the tunnel portal slope are consistent with the actual monitoring results. Furthermore, the Long Short-Term Memory (LSTM) model is optimized using the Particle Swarm Optimization (PSO) algorithm to predict the deformation of the tunnel portal slope. The results show that the maximum mean square error (MSE) of the horizontal displacement test set prediction results is 0.142, and the coefficient of determination (R2) is higher than 91%. The maximum value of MSE for vertical displacement test set prediction is 0.069, and the R2 are higher than 91%. The study shows that the performance of the PSO-LSTM prediction model can meet the requirements for predicting the displacement of the tunnel portal slope. Based on the MF-DFA method and PSO-LSTM prediction model, the fluctuation characteristics of the displacement value of the tunnel portal section can be accurately identified and the displacement development pattern can be effectively predicted. The conclusions of the study are of great practical significance for the safe construction of the tunnel portal section.

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Section: Introductionmentioning

confidence: 99%