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现代隧道技术 2017, Vol. 54 Issue (3) :50-57    DOI:
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隧道洞室地基稳定性双阶段多策略粒子群BP网络模型研究
(辽宁科技大学土木工程学院, 鞍山 114051)
Dual-Stage Multi-Strategy PSO-BP Model for Tunnel Cavern Foundation Stability
(School of Civil Engineering, Liaoning University of Science and Technology,Anshan 114051)
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摘要  影响隧道洞室地基稳定性的因素众多, 这些因素与隧道洞室地基稳定性之间存在着复杂的非线性关系, 并且常规的方法很难描述这种复杂的关系。文章提出了一种双阶段多策略粒子群算法(DMPSO)优化的BP神经网络隧道洞室地基稳定性评价模型。粒子群算法具有全局优化能力强、 搜索效率高等特点, 算法改进后使这些特点更加突出。BP算法有很强的非线性映射能力、 泛化能力, 但也有收敛速度慢, 容易陷入局部最优等缺陷。采用双阶段多策略粒子群算法 (DMPSO) 搜索BP模型的权值和阈值, 弥补了BP模型的缺陷, 提高了其预测的准确度。文章以重庆小什字车站洞室地基为例, 证明了双阶段多策略粒子群算法优化的 BP神经网络模型(DMPSO-BP)的可行性, 并且该模型比模糊神经网络和粒子群优化的BP神经网络 (PSO-BP) 模型有更好的预测精度。
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Abstract: Many factors affect the stability of a tunnel cavern foundation; there is a complex nonlinear relationship between these factors and the tunnel cavern foundation stability, and conventional methods have difficulty describing this complex relationship. Therefore, a dual-stage multi-strategy particle swarm optimization (DMPSO) BP neural network model is proposed for evaluating the stability of tunnel cavern foundations. The particle swarm algorithm is characterized by a strong capability for global optimization and a high search efficiency, with these characteristics being more prominent after improvement of the algorithm. The BP algorithm has a strong capacity for nonlinear mapping and generalization, with the disadvantages of slow convergence and local optimization. By using the dual-stage multi-strategy particle swarm optimization (DMPSO) to determine the weights and thresholds of the BP neural network, the shortcomings of the BP neural network are solved and the prediction accuracy is improved. Using the Chongqing Xiaoshizi station cavern foundation as an example, the feasibility of the dual-stage multi-strategy particle swarm optimization BP neural network model (DMPSO-BP) is verified, with the prediction accuracy of the proposed model being better than that of the fuzzy neural network model and particle swarm optimization BP neural network (PSO-BP) model.
KeywordsPSO,   Interaction,   BP neural network,   Tunnel cavern,   Foundation stability     
基金资助:基金项目: 国家自然科学基金项目 (50979014); 辽宁省教育厅基金项目 (L2011040) .
作者简介: 作者简介: 胡 军 (1977-), 男, 博士, 教授, 主要从事岩土工程安全监测方面的研究工作, E-mail: kdhj1977@126.com.
引用本文:   
.隧道洞室地基稳定性双阶段多策略粒子群BP网络模型研究[J]  现代隧道技术, 2017,V54(3): 50-57
.Dual-Stage Multi-Strategy PSO-BP Model for Tunnel Cavern Foundation Stability[J]  , 2017,V54(3): 50-57
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