基于BP神经网络方法的边坡地表沉降预测研究
Research on Surface Settlement Prediction of Rock Mass Slope Based on BP Artificial Neural Network
露天开采地表变形受岩体力学性质、地质构造、岩体结构、地应力场、地下水，露天矿采剥、工程荷载条件、地震作用、气象条件及植被等不确定因素共同影响，应用BP神经网络方法对抚顺发电有限责任公司厂区地表沉陷变形进行了预测。由预测结果可知，神经网络预测具有很高的精确性，可以作为一种预测手段对厂区以后的变形量进行预测。
Surface settlement of rock mass slope is influenced by rock mechanical property, geological structure, rock mass structure, ground stress field, groundwater, excavation, engineering load fluctuation, earthguake, meteorologic condition and vegetation etc. The surface settlement is predictied based on BP artificial neural network in Fushun power Co., Ltd. The results indicated: prediction’accuracy of artificial neural network is very well, deformation prediction used artificial neural network is feasible in the future.
孙维吉、郭嗣琮、梁冰
矿业工程理论与方法论矿山开采计算技术、计算机技术
神经网络，边坡，地表沉降，预测
artificial neural network slope surface settlement prediction
孙维吉,郭嗣琮,梁冰.基于BP神经网络方法的边坡地表沉降预测研究[EB/OL].(2008-01-04)[2025-05-04].http://www.paper.edu.cn/releasepaper/content/200801-119.点此复制
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