Artificial Neural Networks for Rock and Soil Cutting Slopes Stability Condition Prediction

This study aims to develop a tool able to help decision makers to find the best strategy for slopes management tasks. It is known that one of the main challenges nowadays for every developed or countries undergoing development is to keep operational under all conditions their transportations infrastructure. However, due to the network extension and increased budget constraints such challenge is even more difficult to accomplish. Keeping in mind the strong impact of a slope failure in the transportation infrastructure it is important to develop tools able to help minimizing this situation. Accordingly, and in order to achieve this goal, the high flexible learning capabilities of Artificial Neural Networks (ANNs) were applied in the development of a classification tool aiming to identify the stability condition of a rock and soil cutting slopes, keeping in mind the use of information usually collected during routine inspections activities (visual information) to feed them. For that, it was followed a nominal classification strategy and, in order to overcome the problem of imbalanced data, three training sampling approaches were explored: no resampling, SMOTE (Synthetic Minority Over-sampling Technique) and Oversampling. The achieved results are presented and discussed, comparing the achieved performance for both slope types (rock and soil cuttings) as well as the effect of the sampling approaches. An input-sensitivity analysis was applied, allowing to measure the relative influence of each model attribute.

[1]  Abdallah I. Husein Malkawi,et al.  Uncertainty and reliability analysis applied to slope stability , 2000 .

[2]  Paulo Cortez,et al.  Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool , 2010, ICDM.

[3]  Min-Yuan Cheng,et al.  Slope Collapse Prediction Using Bayesian Framework with K-Nearest Neighbor Density Estimation: Case Study in Taiwan , 2016, J. Comput. Civ. Eng..

[4]  António Gomes Correia,et al.  A new empirical system for rock slope stability analysis in exploitation stage , 2015 .

[5]  A. Gomes Correia,et al.  Artificial Intelligence Applications in Transportation Geotechnics , 2013, Geotechnical and Geological Engineering.

[6]  Paulo Cortez,et al.  Using sensitivity analysis and visualization techniques to open black box data mining models , 2013, Inf. Sci..

[7]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[8]  Min-Yuan Cheng,et al.  Evolutionary risk preference inference model using fuzzy support vector machine for road slope collapse prediction , 2012, Expert Syst. Appl..

[9]  R. Suchomel,et al.  Comparison of different probabilistic methods for predicting stability of a slope in spatially variable c–φ soil , 2010 .

[10]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[11]  Arie Ben-David,et al.  Control of properties in injection molding by neural networks , 2001 .

[12]  H. Wang,et al.  Slope stability evaluation using Back Propagation Neural Networks , 2005 .

[13]  L. Tham,et al.  Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China , 2008 .

[14]  Sarat Kumar Das,et al.  Slope stability analysis using artificial intelligence techniques , 2016, Natural Hazards.

[15]  Paulo Cortez,et al.  Jet grouting column diameter prediction based on a data-driven approach , 2018 .

[16]  Joaquim Agostinho Barbosa Tinoco,et al.  Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns , 2014 .

[17]  Akbar A. Javadi,et al.  A new approach for prediction of the stability of soil and rock slopes , 2010 .

[18]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[19]  Charles X. Ling,et al.  Data Mining for Direct Marketing: Problems and Solutions , 1998, KDD.

[20]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[21]  Christopher Power,et al.  Development of an Evidence-based Geotechnical Asset Management Policy for Network Rail, Great Britain , 2016 .

[22]  Yuan Wang,et al.  Extreme learning machine-based surrogate model for analyzing system reliability of soil slopes , 2017 .

[23]  G. L. Sivakumar Babu,et al.  Reliability Analysis of Unsaturated Soil Slopes , 2005 .

[24]  Jui-Sheng Chou,et al.  Peak Shear Strength of Discrete Fiber-Reinforced Soils Computed by Machine Learning and Metaensemble Methods , 2016, J. Comput. Civ. Eng..

[25]  Pedro M. Domingos A few useful things to know about machine learning , 2012, Commun. ACM.

[26]  Junjie Li,et al.  Artificial Bee Colony Algorithm Optimized Support Vector Regression for System Reliability Analysis of Slopes , 2016, J. Comput. Civ. Eng..