TCN-LSTM-MHSA model optimized by improved slime mould algorithm for stress prediction of roadway anchor bolts (cables)

TCN-LSTM-MHSA model optimized by improved slime mould algorithm for stress prediction of roadway anchor bolts (cables)

The variation process of anchor bolt (cable) stress exhibits distinct short-term fluctuations and long-term temporal dependencies. However, traditional single prediction models have limited capability in modeling long-term trends and insufficient sensitivity to local fluctuations, often making it di...

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Bibliographic Details
Main Authors: QI Junyan, CHE Yuhao, WANG Lei, YUAN Ruifu
Format: Article
Language:zho
Published: Editorial Department of Industry and Mine Automation 2025-05-01
Series:Gong-kuang zidonghua
Subjects:
anchor bolt (cable) support
anchor bolt (cable) stress prediction
slime mould algorithm
temporal convolutional network
long short-term memory network
multi-head self-attention mechanism
Online Access:http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025010025
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http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025010025

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