Author

Jun Tang

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Open access Sep 2026

Residual-based ARIMA_CNN_LSTM modeling for borehole stress time-series forecasting

This research establishes are residual-based ARIMA–CNN–LSTM model for predicting borehole stress in non-stationary, nonlinear, and noise-affected monitoring situations. The ARIMA (2,1,5) initially models the foreseeable linear and autocorrelated part. Subsequently, a CNN–LSTM network captures short-term patterns and lo...

Jun Tang, Ting, Guo-Lin Guo et al. · Acta geodynamica et geomater... · 0 citations

Aug 2026

CNN-LSTM detection and short-term prediction of concrete crack severity

A leakage-controlled convolutional neural network long/short-term memory (LSTM) framework for static crack detection and short-term prediction of crack severity from time-ordered image sequences that takes this method as an interpretable short-horizon monitoring framework, rather than a fully calibrated crack metrology...

Jun Tang, Guo-Lin Guo, Ting Wang et al. · Proceedings of the Instituti... · 0 citations