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Identifying geomechanical parameters of high cut rock slopes by an improved multi-output support vector machine method

机译:改进的多输出支持向量机方法识别高边坡岩土力学参数

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摘要

Mechanical parameters of geomaterials are very important to slope stability assessment, design, and safety construction. Back analysis is a practical engineering tool which is commonly used to identify geomechanical parameters based on monitored displacement during slope excavation. The key problem is to establish the nonlinear relationship between geomechanical parameters and the monitored displacements at different excavation steps. This paper presents an improved multi-output support vector machine (MSVM) method which integrates the artificial bee colony algorithm (ABC), numerical analysis, and updated displacement monitoring. MSVM was used to map the complex relationship between geomechanical parameters and slope displacement increments. Training samples were chosen following a Latin hypercube model and their data were applied to numerical model. ABC formed the back analysis, searching for optimal geomechanical parameters based on the MSVM model and field-monitored displacement. The proposed method was applied to a high abutment steep rock slope of the Longtan hydropower station in China and successfully represented the relationship between geomechanical parameters and displacement increments. Updating the displacements at different excavation steps improved the precision of recognition. Reasonable mechanical parameters were identified and adopted in the slope stability analysis.
机译:岩土材料的力学参数对于边坡稳定性评估,设计和安全施工非常重要。反分析是一种实用的工程工具,通常用于根据边坡开挖过程中监测到的位移来识别岩土力学参数。关键问题是在不同开挖步骤中建立岩土力学参数与监测位移之间的非线性关系。本文提出了一种改进的多输出支持向量机(MSVM)方法,该方法集成了人工蜂群算法(ABC),数值分析和更新的位移监测。 MSVM被用来绘制地质力学参数和边坡位移增量之间的复杂关系。根据拉丁超立方体模型选择训练样本,并将其数据应用于数值模型。 ABC进行了反向分析,根据MSVM模型和现场监测的位移来寻找最佳的地质力学参数。将该方法应用于中国龙滩水电站高基台陡峭岩质边坡,成功地表征了岩土力学参数与位移增量之间的关系。更新不同开挖步骤的位移可提高识别精度。确定了合理的力学参数,并将其用于边坡稳定性分析。

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