边坡勘察钻孔信息价值评价及优化布置方法

Value of Information Assessment and Optimization of Slope Boreholes

为了解决现有边坡勘察试验钻孔布置的优化方法概念复杂、计算量大,需要预先定量估计边坡失稳损失,在实际应用中不方便的问题,利用边坡响应面机器学习模型提出了一种边坡勘察方案的信息价值量化指标,进而给出了边坡钻孔布置方案优化方法.利用边坡部分特征响应面模型建立了安全系数与勘察数据之间的关系.利用随机模拟样本即可实现对边坡勘察钻孔方案的信息价值量化指标计算,分析不同勘察方案时不需要额外重复计算安全系数,大幅提高了分析效率.基于提出的方法,对不排水边坡案例进行了分析,分析结果与文献中相似,算法复杂度和计算量大幅降低.本方法可以快速评价和对比边坡勘察方案的信息价值,进而实现钻孔布置方案优化,具有概念清晰、算法简单、计算方便的特点,计算量也相比传统方法大幅降低,易于工程勘察设计人员接受和采用.

The conventional method to optimize the slope investigation program is usually assigned with complicated concept and arduous computational efforts.Also,the quantitative evaluation of slope failure loss is required,which is not convenient in practice.In this paper it aims to solve the above problem with a suggested method based on training of response surfacebased machine learning model with incomplete features.The relationship between the factor of safety and the site investigation data is established.Then a prediction function is imported and calibrated with simulated samples.This method adopts the root mean square error of factor of safety as the indicator to assess the effectiveness of slope borehole program.The algorithm is provided and applied in an illustrative example of an undrained slope.The results accord well with those reported in literatures.The suggested method provides an efficient way to assess the effectiveness of site investigation program for slope.It has the characteristics of clear concept,simple algorithm and convenient calculation.Also the computational efforts are greatly reduced.This method will be more acceptable for practitioners.