Extreme learning machine-based surrogate model for analyzing system reliability of soil slopes

Kang, Fei; Li, Jing-Shuang; Wang, Yuan; Li, Junjie

doi:10.1080/19648189.2016.1169225

Cited by 31 publications

(19 citation statements)

References 42 publications

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“…Pourkhosravani and Kalantari (2011) summarizes the current methods for slope stability evaluation, which were grouped into Limit Equilibrium (LE) methods, Numerical Analysis methods, Artificial Neural Networks and Limit Analysis methods. There are also approaches based on finite elements methods (Suchomel et al 2010), reliability analysis (Sivakumar Babu and Murthy 2005;Husein Malkawi et al 2000), as well as some methods making use of soft computing algorithms (Gavin and Xue 2009;Wang and Sassa 2005;Cheng and Hoang 2016;Ahangar-Asr et al 2010;Lu and Rosenbaum 2003;Sakellariou and Ferentinou 2005;Cheng et al 2012b;Yao et al 2008;Kang et al 2015;Kang et al 2016b; Kang and Li 2016;Kang et al 2016a;Kang et al 2017;Das et al 2011;Suman et al 2016). More recently, a new flexible statistical system was proposed by Pinheiro et al (2015), based on the assessment of different factors that affect the behavior of a given slope.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features

Tinoco

Correia

Cortez

et al. 2018

J. Comput. Civ. Eng.

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Keeping large-scale transportation infrastructure networks, such as railway networks, operational under all conditions is one of the major challenges today. The budgetary constraints for maintenance purposes and the network dimension are two of the main factors that make the management of a transportation network such a challenging task. Accordingly, aiming to assist the management of a transportation network, a data-driven model is proposed for stability condition prediction of embankment slopes. For such purpose, the highly flexible learning capabilities of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were used to fit data-driven models for Earthwork Hazard Category (EHC) prediction. Moreover, the data-driven models were created using visual information that is easy to collect during routine inspections. The proposed models were addressed following two different data modeling strategies: nominal classification and regression. Moreover, to overcome the problem of imbalanced data (since typically good conditions are much common than bad ones), three training sampling approaches were explored: no resampling, SMOTE and Oversampling. The achieved modeling results are presented and discussed, comparing the predictive performance of ANN and SVM algorithms, as well as the effect of the sampling approaches. A comparison between nominal classification and regression strategies was also carried out. Moreover, aiming a better understanding of the proposed data-driven 1 Tinoco et al., May 29, 2018 models, a detailed sensitivity analysis was applied, allowing to quantify the relative importance of each model input, as well as measuring their global effect on the prediction of embankments stability conditions.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features

Tinoco

Correia

Cortez

et al. 2018

J. Comput. Civ. Eng.

View full text Add to dashboard Cite

show abstract

“…Artificial Neural Networks and Limit Analysis methods. There are also approaches based on finite elements methods (Suchomel et al 2010), reliability analysis (Sivakumar Babu and Murthy 2005;Husein Malkawi et al 2000), as well as some methods making use of soft computing algorithms (Gavin and Xue 2009;Cheng and Hoang 2016;Ahangar-Asr et al 2010;Lu and Rosenbaum 2003;Sakellariou and Ferentinou 2005;Cheng et al 2012b;Yao et al 2008;Kang et al 2015;Kang et al 2016b; Kang and Li 2016;Kang et al 2016a;Kang et al 2017;Das et al 2011;Suman et al 2016). More recently, a new flexible statistical system was proposed by Pinheiro et al (2015), based on the assessment of different factors that affect the behaviour of a given slope.…”

Section: Introductionmentioning

confidence: 99%

Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing

Tinoco

Correia

Cortez

et al. 2018

J. Comput. Civ. Eng.

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For transportation infrastructure, one of the greatest challenges today is to keep large-scale transportation networks, such as railway networks, operational under all conditions. This task becomes even more difficult to accomplish if taken into account budget limitations for maintenance and repair works. In this paper, it is presented a tool aimed at helping in management tasks related to maintenance and repair works for a particular element of this infrastructure, the slopes. The highly flexible learning capabilities of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were applied in the development of a tool able to identify the stability condition of rock and soil cutting slopes, keeping in mind the use of information usually collected during routine inspection activities (visual information) to feed the models. This task was addressed following two different strategies: nominal classification and regression. Moreover, to overcome the problem 1 Tinoco et al.

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“…Then, a differential evolutionary was utilized to solve the problem. Kang and Li [73] employed an ELM based surrogate model for the analysis of soil slope reliability. Artificial bee colony optimization was used to obtain the best values for the hidden weights and biases in ELM.…”

Section: Surrogate Modelingmentioning

confidence: 99%

“…Then, the features with their labels were sent into ELM for training and classification. Lama and Gwak [78] proposed a PCA based method to extract features from brain MRIs and compared three [134], Hao and Liu [44], Kang and Li [73], Pavelski and Delgado [133], Ghiasi and Ghasemi [40] different classification algorithms in experiment: SVM, import vector machine (IVM) and regularized ELM. Simulation results suggested that regularized ELM performed best.…”

Section: Mrimentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

275

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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Extreme learning machine-based surrogate model for analyzing system reliability of soil slopes

Cited by 31 publications

References 42 publications

Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features

Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features

Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing

A review on extreme learning machine

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