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

2020 ◽  
Vol 12 (6) ◽  
pp. 2339 ◽  
Author(s):  
Binh Thai Pham ◽  
Trung Nguyen-Thoi ◽  
Hai-Bang Ly ◽  
Manh Duc Nguyen ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Shear Strength ◽  
Monte Carlo Simulations ◽  
Extreme Learning Machine ◽  
Mean Squared Error ◽  
Majority Vote ◽  
Absolute Error ◽  
Liquid Limit ◽  
Backward Elimination ◽  
Learning Machine

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.

scholarly journals Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

2021 ◽  
Vol 2021 ◽  
pp. 1-15 ◽  
Author(s):  
Quang Hung Nguyen ◽  
Hai-Bang Ly ◽  
Lanh Si Ho ◽  
Nadhir Al-Ansari ◽  
Hiep Van Le ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Shear Strength ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Engineering Properties ◽  
Stable Model ◽  
Predictive Capability ◽  
Effective Manner ◽  
Soil Shear Strength

The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.

Equilibrium and nonequilibrium models on solomon networks with two square lattices

2017 ◽  
Vol 28 (08) ◽  
pp. 1750099
Author(s):  
F. W. S. Lima
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Critical Exponent ◽  
Critical Points ◽  
Majority Vote ◽  
Universality Class ◽  
2D Systems ◽  
Two Dimensional ◽  
Critical Properties ◽  
Regular Lattices

We investigate the critical properties of the equilibrium and nonequilibrium two-dimensional (2D) systems on Solomon networks with both nearest and random neighbors. The equilibrium and nonequilibrium 2D systems studied here by Monte Carlo simulations are the Ising and Majority-vote 2D models, respectively. We calculate the critical points as well as the critical exponent ratios [Formula: see text], [Formula: see text], and [Formula: see text]. We find that numerically both systems present the same exponents on Solomon networks (2D) and are of different universality class than the regular 2D ferromagnetic model. Our results are in agreement with the Grinstein criterion for models with up and down symmetry on regular lattices.

scholarly journals Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model

2020 ◽  
Vol 14 (1) ◽  
pp. 41-50 ◽  
Author(s):  
Hai-Bang Ly ◽  
Binh Thai Pham
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Moisture Content ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Liquid Limit ◽  
Support Vector ◽  
Plastic Limit ◽  
Svm Model ◽  
Soil Shear Strength

Background: Shear strength of soil, the magnitude of shear stress that a soil can maintain, is an important factor in geotechnical engineering. Objective: The main objective of this study is dedicated to the development of a machine learning algorithm, namely Support Vector Machine (SVM) to predict the shear strength of soil based on 6 input variables such as clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit. Methods: An important number of experimental measurements, including more than 500 samples was gathered from the Long Phu 1 power plant project’s technical reports. The accuracy of the proposed SVM was evaluated using statistical indicators such as the coefficient of correlation (R), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) over a number of 200 simulations taking into account the random sampling effect. Finally, the most accurate SVM model was used to interpret the prediction results due to Partial Dependence Plots (PDP). Results: Validation results showed that SVM model performed well for prediction of soil shear strength (R = 0.9 to 0.95), and the moisture content, liquid limit and plastic limit were found as the three most affecting features to the prediction of soil shear strength. Conclusion: This study might help in quick and accurate prediction of soil shear strength for practical purposes in civil engineering.

scholarly journals Intelligent Calibration of a Heavy-Duty Mechanical Arm in Coal Mine

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1186
Author(s):  
Yunhong Jia ◽  
Xiaodong Zhang ◽  
Zhenchong Wang ◽  
Wei Wang
Keyword(s):  
Extreme Learning Machine ◽  
Coal Mine ◽  
Absolute Error ◽  
Calibration Method ◽  
Heavy Duty ◽  
Maximum Absolute Error ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Roof Bolter ◽  
Learning Machine

Accurate positioning of an airborne heavy-duty mechanical arm in coal mine, such as a roof bolter, is important for the efficiency and safety of coal mining. Its positioning accuracy is affected not only by geometric errors but also by nongeometric errors such as link and joint compliance. In this paper, a novel calibration method based on error limited genetic algorithm (ELGA) and regularized extreme learning machine (RELM) is proposed to improve the positioning accuracy of a roof bolter. To achieve the improvement, the ELGA is firstly implemented to identify the geometric parameters of the roof bolter’s kinematics model. Then, the residual positioning errors caused by nongeometric facts are compensated with the regularized extreme learning machine (RELM) network. Experiments were carried out to validate the proposed calibration method. The experimental results show that the root mean square error (RMSE) and the mean absolute error (MAE) between the actual mast end position and the nominal mast end position are reduced by more than 78.23%. It also shows the maximum absolute error (MAXE) between the actual mast end position and the nominal mast end position is reduced by more than 58.72% in the three directions of Cartesian coordinate system.

scholarly journals Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3415 ◽  
Author(s):  
Muzhou Hou ◽  
Tianle Zhang ◽  
Futian Weng ◽  
Mumtaz Ali ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Renewable Energy Sources ◽  
Information Criterion ◽  
Absolute Error ◽  
Global Solar Radiation ◽  
Machine Model ◽  
Learning Machine

Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized online sequential extreme learning machine, integrated with variable forgetting factor (FOS-ELM), to predict global solar radiation at Bur Dedougou, in the Burkina Faso region. Bayesian Information Criterion (BIC) is applied to build the seven input combinations based on speed (Wspeed), maximum and minimum temperature (Tmax and Tmin), maximum and minimum humidity (Hmax and Hmin), evaporation (Eo) and vapor pressure deficiency (VPD). For the difference input parameters magnitudes, seven models were developed and evaluated for the optimal input combination. Various statistical indicators were computed for the prediction accuracy examination. The experimental results of the applied FOS-ELM model demonstrated a reliable prediction accuracy against the classical extreme learning machine (ELM) model for daily global solar radiation simulation. In fact, compared to classical ELM, the FOS-ELM model reported an enhancement in the root mean square error (RMSE) and mean absolute error (MAE) by (68.8–79.8%). In summary, the results clearly confirm the effectiveness of the FOS-ELM model, owing to the fixed internal tuning parameters.

Brain MRI morphological patterns extraction tool based on Extreme Learning Machine and majority vote classification

2016 ◽  
Vol 174 ◽  
pp. 344-351 ◽  
Author(s):  
Maite Termenon ◽  
Manuel Graña ◽  
Alexandre Savio ◽  
Anton Akusok ◽  
Yoan Miche ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Majority Vote ◽  
Brain Mri ◽  
Vote Classification ◽  
Learning Machine ◽  
Morphological Patterns

scholarly journals Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qiao Shi-fan ◽  
Tan Jun-kun ◽  
Zhang Yong-gang ◽  
Wan Li-jun ◽  
Zhang Ming-fei ◽  
...  
Keyword(s):  
Time Series ◽  
Extreme Learning Machine ◽  
Absolute Error ◽  
Ground Subsidence ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Foundation Pit ◽  
Machine Model ◽  
Model Based ◽  
Learning Machine

This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of time series, influence of settlement factors, and after optimization were established to predict the ground settlement near the foundation pit. The predictive results are that their average relative error and average absolute error are ranked from large to small as GWO-ELM model based on time series, GWO-ELM model based on settlement factors, and optimized GWO-ELM model for the three kinds of GWO-ELM models at monitoring points JC55-2 and JC56-1. Accordingly, the optimized GWO-ELM model has the strongest predictive ability.

scholarly journals Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

2019 ◽  
Vol 9 (16) ◽  
pp. 3221 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Deepak Kumar ◽  
Pijush Samui ◽  
Alaa R. Gabr ◽  
Jong Wan Hu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Resilient Modulus ◽  
Particle Swarm ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Swarm Optimization ◽  
Structural Support ◽  
Rigid Pavements ◽  
Learning Machine

Stabilized base/subbase materials provide more structural support and durability to both flexible and rigid pavements than conventional base/subbase materials. For the design of stabilized base/subbase layers in flexible pavements, good performance in terms of resilient modulus (Mr) under wet-dry cycle conditions is required. This study focuses on the development of a Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) to predict the performance of stabilized aggregate bases subjected to wet-dry cycles. Furthermore, the performance of the developed PSO-ELM model was compared with the Particle Swarm Optimization-based Artificial Neural Network (PSO-ANN) and Kernel ELM (KELM). The results showed that the PSO-ELM model significantly yielded higher prediction accuracy in terms of the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (r2) compared with the other two investigated models, PSO-ANN and KELM. The PSO-ELM was unique in that the predicted Mr values generally yielded the same distribution and trend as the observed Mr data.

scholarly journals Hand motion strength forecasting using Extreme Learning Machine for post-stroke rehabilitation

2021 ◽  
Vol 9 (2) ◽  
pp. 70-76
Author(s):  
Khairul Anam ◽  
Ali Rizal Chaidir ◽  
Fahrul Isman
Keyword(s):  
Extreme Learning Machine ◽  
Mean Squared Error ◽  
Data Extraction ◽  
The Body ◽  
Stroke Therapy ◽  
Hand Rehabilitation ◽  
Hand Motion ◽  
Emg Signal ◽  
Learning Machine ◽  
Therapy Robots

Stroke or Cerebrovascular accident (CVA) can cause weakness in one side of the body, including the upper limbs such as the hand. Rehabilitation is needed to restore the function of the hand. Rehabilitation should also measure the strength of the movements carried out. This article aims to forecast the strength of movement based on Electromyography (EMG) signals using the Extreme Learning Machine (ELM). This study collected EMG signal data and movement strength, carried out data pre-processing and data extraction using various extraction features, applied ELM for forecasting strength based on EMG signals, and applied created models in stroke therapy robots. The forecasting model is evaluated by measuring the Mean Squared Error (MSE). The average value of the best MSE in offline testing is 1.77, while the real-time testing is 0.79. A small MSE value indicates that the model is good enough. The resulted value of strength can be applied to make the stroke therapy robots actuating properly.

Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau

2016 ◽  
Vol 48 (4) ◽  
pp. 1156-1168 ◽  
Author(s):  
Yu Feng ◽  
Daozhi Gong ◽  
Xurong Mei ◽  
Ningbo Cui
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Loess Plateau ◽  
Meteorological Data ◽  
Crop Coefficient ◽  
Absolute Error ◽  
Generalized Regression Neural Network ◽  
Area Index ◽  
Generalized Regression ◽  
Learning Machine

Accurately estimating crop evapotranspiration (ET) is essential for agricultural water management in arid and semiarid croplands. This study developed extreme learning machine (ELM) and generalized regression neural network (GRNN) models for maize ET estimation on the China Loess Plateau. Maize ET, meteorological variables, leaf area index (LAI), and plant height (hc) were continuously measured during maize growing seasons of 2011–2013. The meteorological data and crop data including LAI and hc from 2011 to 2012 were used to train the ELM and GRNN using two different input combinations. The performances of ELM and GRNN were compared with the modified dual crop coefficient (Kc) approach in 2013. Results indicated that ELM1 and GRNN1 using meteorological and crop data as inputs estimated maize ET accurately, with root mean square error (RMSE) of 0.221 mm/d, mean absolute error (MAE) of 0.203 mm/d, and NS of 0.981 for ELM1, RMSE of 0.225 mm/d, MAE of 0.211 mm/d, and NS of 0.981 for GRNN1, respectively, which confirmed better performances than the modified dual Kc model. Performances of ELM2 and GRNN2 using only meteorological data as input were poorer than those of ELM1, GRNN1, and modified dual Kc approach, but its estimation of maize ET was acceptable when only meteorological data were available.

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