Deformation prediction is an important basis for roadway information construction, especially for soft rock roadway at high altitude and large buried depth, whether the deformation of roadway surrounding rock can be effectively and accurately predicted is an important basis for judging the stability of roadway surrounding rock. However, at present, the research on the informatization construction of the roadway is not in-depth, and the intelligent prediction technology for the deformation of the surrounding rock of the roadway is still in its infancy, and the accuracy of deformation prediction is also low. Therefore, based on the research of domestic and foreign researchers, in order to solve the breakthrough of related technology, this paper puts forward the deformation prediction and control technology of high altitude and deep buried soft rock roadway based on a neural network model. This method is based on the traditional prediction model and is replaced by the neural network, so as to improve the problems of low accuracy and large prediction deviation in the related deformation prediction of the traditional prediction model. At the same time, aiming at the problem of poor local weight and network search ability, an improved method using particle swarm optimization algorithm is proposed, which effectively considers the influence of local and global factors on the combined weight. Finally, the improved deformation prediction model of high altitude and deep buried soft rock roadway based on particle swarm optimization LSTM model is applied to an engineering example and compared with the traditional model to explore its feasibility and effectiveness. The results show that the prediction model has higher prediction accuracy than the traditional prediction model, and the relative deviation of the prediction results is controlled within 2%. At the same time, compared with other models (BP neural network model), it has relatively higher accuracy and stability. The research results can provide a new idea for the deformation prediction of soft rock roadway with high altitude and deep burial.

中文翻译：

---

基于粒子群优化LSTM模型的高海拔大埋深软岩巷道变形预测与分析

变形预测是巷道信息化建设的重要依据，特别是对于高海拔、大埋深的软岩巷道，能否有效、准确地预测巷道围岩变形是判断巷道围岩稳定性的重要依据。但目前对巷道信息化建设的研究还不深入，巷道围岩变形智能预测技术尚处于起步阶段，变形预测精度也较低。 . 因此，基于国内外研究人员的研究，为解决相关技术的突破，提出了基于神经网络模型的高海拔深埋软岩巷道变形预测与控制技术。该方法以传统预测模型为基础，以神经网络代替，以改善传统预测模型在相关变形预测中精度低、预测偏差大的问题。同时，针对局部权重和网络搜索能力较差的问题，提出了一种使用粒子群优化算法的改进方法，有效地考虑了局部和全局因素对组合权重的影响。最后，将基于粒子群优化LSTM模型的高海拔深埋软岩巷道变形预测改进模型应用于工程实例，并与传统模型进行对比，探讨其可行性和有效性。结果表明，该预测模型比传统预测模型具有更高的预测精度，预测结果的相对偏差控制在2%以内。同时，与其他模型（BP神经网络模型）相比，具有相对较高的准确性和稳定性。研究成果可为高海拔深埋软岩巷道变形预测提供新思路。结果表明，该预测模型比传统预测模型具有更高的预测精度，预测结果的相对偏差控制在2%以内。同时，与其他模型（BP神经网络模型）相比，具有相对较高的准确性和稳定性。研究成果可为高海拔深埋软岩巷道变形预测提供新思路。结果表明，该预测模型比传统预测模型具有更高的预测精度，预测结果的相对偏差控制在2%以内。同时，与其他模型（BP神经网络模型）相比，具有相对较高的准确性和稳定性。研究成果可为高海拔深埋软岩巷道变形预测提供新思路。