Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis

Nguyen, Manh Duc; Pham, Binh Thai; Tuyen, Tran Thi; Yen, Hoang Phan Hải; Prakash, Indra; Vu, Thanh; Chapi, Kamran; Shirzadi, Ataollah; Shahabi, Himan; Dou, Jie; Quoc, Nguyen Kim; Bui, Dieu Tien

doi:10.2174/1874836801913010178

Cited by 35 publications

(15 citation statements)

References 56 publications

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“…The study also suggested to carry out the sensitivity analysis to select the best suitable factors for developing and applying the ML models. The same observation has been pointed out in other studies of Nguyen et al [10] and Pham et al [11]. However, these studies used a trial-manual process for sensitivity analysis, which might not cover all the cases of variation of input parameters.…”

supporting

confidence: 60%

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

Pham

Nguyen-Thoi

et al. 2020

Sustainability

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Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model. Sustainability 2020, 12, 2339 2 of 29 the laboratory, including direct shear test, triaxial compression tests, or unconfined compression tests which might increase the cost and prolong the time of completing the projects. Moreover, the test accuracy depends significantly on the instruments, the meticulous procedures, and the expertise of the experimenters [1]. Therefore, the development of new advanced techniques for quick and accurate prediction of shear strength of soil is essential and practical.Traditionally, the shear strength of soil is often predicted by using traditional formula-based methods. Garven and Vanapalli [2] summarized and evaluated nineteen empirical techniques that are available for the prediction of the shear strength of unsaturated soils. Out of these, six techniques used tool of the soil-water retention curve (SWRC) and the remainder thirteen procedures are based on mathematical formulations. In these empirical techniques, various parameters of soil were used to correlate with the shear strength in unsaturated soils such as the texture of soil surface, pore size distribution, residual suction. In another study, Sheng et al. [3] proposed different empirical equations for the prediction of shear strength of unsaturated soils using different approaches, which are based on the independent stress, Bishop's stress, and constitutive models. Vanapalli and Fredlund [4] compared different empirical approaches for the prediction of shear strength of unsaturated soils. Various parameters used for forming the correlation equations such as particle gain distribution, liquid limit, plasticity indices, water con...

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confidence: 60%

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

Pham

Nguyen-Thoi

et al. 2020

Sustainability

Self Cite

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“…ACC is the ratio of the rate number of correct predictions and the total number of predictions [88]. RMSE represents the difference between data observations and data estimates [89][90][91][92][93][94][95][96][97][98][99][100][101][102][103]. Equations for the different measures are given below:…”

Section: Validation Methodsmentioning

confidence: 99%

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Pham

Phong

Nguyen

et al. 2020

Water

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Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

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“…(ii) Data pre-processing: in this step, PCA and the Savitzky-Golay filter were used to reduce the dimensions data and reduce extreme values in the distribution of data. (iii) Data preparation: in this study, the holdout validation method was used for training and validating the models as it is a popular and effective method for generating the datasets for training and testing the models [24,[44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61], and thus the collected data were divided into two parts. The first part included 70% data which was used to train the models, whereas the second part contained 30%, the remaining data and this was used to validate the models as the ratio 70/30 for dividing the training and testing dataset was a common ratio used in applying the ML models [29,[62][63][64][65][66][67][68][69][70][71].…”

Section: Methodology Frameworkmentioning

confidence: 99%

Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

et al. 2019

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The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.

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Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis

Cited by 35 publications

References 56 publications

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

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