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An unsupervised deep learning surrounding rock perception method for TBM operational parameter multiobjective optimization
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DOI：10.1016/j.rineng.2025.106925.png)
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Abstract
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• Developed an integrated framework for TBM rock perception and parameter optimization. • Designed APCA-based reduction and ensemble outlier detection for TBM data preprocessing. • Introduced SSA-FC deep clustering for unsupervised surrounding rock classification. • NSGA-III optimization improved penetration by 50 %, cut energy use by 88 %, and wear by 79 %.
Keywords:
TBM operational parameters
Rock condition perception
Time series segmentation
Deep clustering
Multiobjective optimization
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