摘要
地下工程依托地质体的岩石类型条件(围岩)直接关系到工程的精确口部选址、工程主体建设轮廓、工程施工手段和工程跨度,并且影响对工程的防护性能评估。为了精确评估围岩的稳定性,在BP神经网络的模型的基础上对其进行优化,建立了作业工程掌子面前方围岩稳定性评估模型。主要通过加入动量因子、采用自适应调节率这两种方法对传递函数进行优化,提高神经网络收敛速度;对于一些非线性、多模型、多目标的函数优化问题具有内在的隐并行性和更好的全局寻优能力;采用概率化的寻优方法,能自动获取和指导优化的搜索空间,自适应地调整搜索方向,不需要确定的规则。通过对BP神经网络的的初始权值和阈值进行优化,跟围岩的受力特性进行比较,能精准快速评估围岩的稳定性,节约大量人力资源,提高工作效率。
Underground works relying on the geological conditions of the rock types （wall rock） is directly related to project precise mouth site, project main building outline, means of construction and engineering span, and affect the protective performance evaluation on the project. In order to accurately assess the stability of surrounding rock, based on the BP neural network model, optimize the operations engineering in front of face surrounding rock stability assessment model. By adding the momentum factor, use these two methods of adaptive adjustment of the transfer function to be optimized to improve the neural network convergence speed. For some non-linear, multi-model, multi-objective function, optimization problem is inherently implicit parallelism and better ability of global optimization. Use a probability-based optimization method that can automatically access and guidance to optimize the search space and adaptively adjust the search direction do not need to determine the rules. Optimize the initial weights and thresholds of BP neural network, compare them with the characteristics of the surrounding rock by the force, it can make accurate rapid assessment of the stability of surrounding rock, save human resources, and improve work efficiency.
出处
《兵工自动化》
2012年第7期55-58,共4页
Ordnance Industry Automation
基金
国家自然科学基金资助项目(70971137)
关键词
BP神经网络
遗传算法
围岩自稳能力评估
BP neural network
genetic algorithm
self-stability assessment of rock
