Modelling stress–strain and volume change behaviour of unsaturated soils using an evolutionary based data mining technique, an incremental approach

Ahangar‐Asr, Alireza; Johari, A.; Faramarzi, Asaad; Toll, D. G.

doi:10.1016/j.engappai.2012.03.006

Cited by 34 publications

(25 citation statements)

References 23 publications

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“…To model EHC prediction of soil embankments two of the most popular DM algorithms, namely ANNs and SVMs were applied. Both algorithms had already been successful applied in different knowledge domains (Liao et al 2012;Garg et al 2014;Javadi et al 2012) including in civil engineering (Tinoco et al 2014a;Tinoco et al 2014b;Gomes Correia et al 2013;Miranda et al 2011). There are also some examples of ANN and SVM applications in slope stability analysis Yao et al 2008;Cheng et al 2012a).…”

Section: Modellingmentioning

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: Modellingmentioning

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

“…To model EHC prediction of rock and soil cutting slopes two of the most flexible DM algorithms, namely ANNs and SVMs were applied. Both algorithms had already been successful applied in different knowledge domains (Liao et al 2012;Javadi et al 2012) including in civil engineering (Tinoco et al 2014a;Tinoco et al 2014b;Chou et al 2016;Gomes Correia et al 2013). There are also some examples of ANN and SVM applications in slope stability analysis (Wang et al 2005;Yao et al 2008;Cheng et al 2012a).…”

Section: Modelling Approaches and Learning Modelsmentioning

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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“…[35] In this study, the multi-objective EPR is used to develop the EPR-based models. Further details of the EPR technique can be found in [34][35][36][37][38][39].…”

Section: Epr Methodsmentioning

confidence: 99%

Prediction of sulphide build-up in filled sewer pipes

Alani¹,

Faramarzi²,

Mahmoodian³

et al. 2014

Environmental Technology

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Millions of dollars are being spent worldwide on the repair and maintenance of sewer networks and wastewater treatment plants. The production and emission of hydrogen sulphide has been identified as a major cause of corrosion and odour problems in sewer networks. Accurate prediction of sulphide build-up in a sewer system helps engineers and asset managers to appropriately formulate strategies for optimal sewer management and reliability analysis. This paper presents a novel methodology to model and predict the sulphide build-up for steady state condition in filled sewer pipes. The proposed model is developed using a novel data-driven technique called evolutionary polynomial regression (EPR) and it involves the most effective parameters in the sulphide build-up problem. EPR is a hybrid technique, combining genetic algorithm and least square. It is shown that the proposed model can provide a better prediction for the sulphide build-up as compared with conventional models.

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Modelling stress–strain and volume change behaviour of unsaturated soils using an evolutionary based data mining technique, an incremental approach

Cited by 34 publications

References 23 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

Prediction of sulphide build-up in filled sewer pipes

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