Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning

Cuiying Zhou,Jinwu Ouyang,Zhen Liu,Lihai Zhang

doi:10.3390/su14063367

Abstract

Maintaining the stability of highway soft rock slopes is of critical importance for ensuring the safety of road networks. Although much research has been carried out to assess the stability of individual soft rock slope, the goal of efficient and effective risk management focusing on multiple highway soft rock slopes has not been fully achieved due to the many complex factors involved and the interactions among these factors. In the present study, a machine learning algorithm based on a fuzzy neural network (FNN) and a comprehensive evaluation method based on the FNN is developed, in order to identify and issue early warnings regarding the risks induced by soft rock slopes along highways, in an efficient and effective way. Using a large amount of collected soft rock slope information as training and validation data, an FNN-based risk identification model is first developed to identify the risk level of individual soft rock slope based on the meteorological conditions, topographical and geomorphological factors, geotechnical properties, and the measured horizontal displacement. An FNN-based comprehensive evaluation method is then developed, in order to quantify the risk level of a soft rock slope group according to the slope, road and external factors. The results show that the risk level identification accuracy obtained based on validation of the FNN model was higher than 90%, and the model showed a good training effect. On this basis, we further made early warnings of the risks of soft rock slope groups. The proposed early-warning model can quickly and accurately evaluate the risk posed by multiple soft rock slopes to a highway, thereby ensuring the safety of the highway.

Highlights

We focused on the risk level of the soft rock slope, and predicted that the safety factor was within the range required by the risk level
The results indicate that the model showed a good training effect, and can meet the accuracy requirement for prediction
Risk Identification Results and Discussion for Risk Identification of an Individual model, the model was used to predict the risk of each soft rock slope in the object soft rock slope

Summary

Introduction

The safety of soft rock slopes plays an important role in the construction of large-scale transport infrastructure, such as highways. As mitigation of the risks posed by soft rock slopes to highways is one of the most important factors in appraisal projects [2], the management and early warning of soft rock slope risk have become increasingly important for ensuring the safe operation of highways [3–11] To this end, various ground improvement methods for soft rock slopes have been proposed, including the use of piles [12] and geofoam [13–15]. The current risk-control mode used to address soft rock slopes is relatively singular, and case-by-case management and research methods are usually adopted. This management and control mode is associated with high costs and low efficiency. It is especially difficult to use this mode to effectively manage a large number of soft rock slopes composing a slope group

Methods

Results

Conclusion