Random Small Sample Prediction Model on Displacement of Extensive Deep Soil Excavation

Zhou Shengquan,Zhao Xiaolong,Yao Zhaoming

doi:10.2174/1874149501509010107

Abstract

In order to forecast the displacement of deep foundation pit support, this document proposes a new method which combines the cross validation method and supports vector machine (SVM) based on random small samples. Because the random small monitoring data are difficult to fit and forecast, the cross validation method and different kernel function of support vector machine algorithm arerepeatedly used to establish and optimize the displacement prediction model of underground continuous wall, and then uses validation samples to test the accuracy of the models. The results show that this method can meet the requirements of precision relatively well, and Cauchy kernel function is better than the other. In the aspect of accuracy of model fitting and prediction, this method has great advantages, which can be applied to practical engineering.

Highlights

In order to support the process of foundation pit engineering, an important information feedback is the displacement of supporting structure for the deep foundation pits
To verify the supports vector machine (SVM) algorithm and the feasibility of the "cross validation" and its use in the field of geotechnical engineering, the choice of digital Huainan Golden Square deep excavation monitoring data is utilized.The pit is located in Huainan city center, the foundation pit excavation depth of 16.15 m, the north side of foundation pit houses distance is only 7.4 m, the bored piles + 5 rotary jet mixing stiffening pile supporting; Widely distributed with the venue fractured,over consolidated soil, the water or the soil through wet and dry cycles drastically reduces the soil strength and shear strength is very unstable
After 78 days ofmonitoring the displacement of supporting structure at the top of the measured data,thefoundation pit’s sensitive area is selected.The selection of the northern pit’s measured data in a sensitive area,measurespoints to days, as a learning sample,and to 78 days of eight measures data as the validation sample, to verify using SVM method, to establishes the forecast model’s applicability and superiority of displacement of pile top

Summary

INTRODUCTION

In order to support the process of foundation pit engineering, an important information feedback is the displacement of supporting structure for the deep foundation pits. This process is donethrough real-time monitoring of deep foundation pit displacement and with numerical calculation that can timely understand the stability of deep foundation pit and better guide the construction. Support vector machine algorithm based on statistical learning theory and using structural risk minimization principle is a convex quadratic optimization algorithm, which can ensure the extreme solution is a globally optimal solution [2,3,4], and there are solid mathematics theoretical foundation and the strict theoretical analysis as well as other algorithms incomparable superiority.

SMALL RANDOM SAMPLE CALCULATION MODEL

Random Small Sample Build

Implementation and Results Analysis of the Algorithm

The Influence of Various Parameters of the SVM Model Accuracy Analysis

CONFLICT OF INTEREST

CONCLUSIONS