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

Cuiying Zhou, Jinwu Ouyang, Zhen Liu and Lihai Zhang
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Cuiying Zhou: School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
Jinwu Ouyang: School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
Zhen Liu: School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
Lihai Zhang: Department of Infrastructure Engineering, The University of Melbourne, Melbourne 3010, Australia

Sustainability, 2022, vol. 14, issue 6, 1-28

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.

Keywords: highway soft rock slopes; slope instability; machine learning; FNN; risk identification; early risk warning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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