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

Mathematical Problems in Engineering, Journal Year: 2021, Volume and Issue: 2021, P. 1 - 15

Published: Feb. 5, 2021

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

Language: Английский

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Prediction of cement-based mortars compressive strength using machine learning techniques

Neural Computing and Applications, Journal Year: 2021, Volume and Issue: 33(19), P. 13089 - 13121

Published: April 23, 2021

Language: Английский

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Soft computing-based models for the prediction of masonry compressive strength

Engineering Structures, Journal Year: 2021, Volume and Issue: 248, P. 113276 - 113276

Published: Oct. 5, 2021

Language: Английский

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Introducing stacking machine learning approaches for the prediction of rock deformation

Transportation Geotechnics, Journal Year: 2022, Volume and Issue: 34, P. 100756 - 100756

Published: March 22, 2022

Language: Английский

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Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data

Ultrasonics, Journal Year: 2024, Volume and Issue: 141, P. 107347 - 107347

Published: May 20, 2024

Language: Английский

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A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

Structural Concrete, Journal Year: 2023, Volume and Issue: 24(4), P. 5538 - 5555

Published: Feb. 11, 2023

Abstract Concrete which is the most commercialized construction material and thus it plays a key role in this era of development hence its evolution utmost importance therefore quality concrete to that highly evolved type namely, ultra‐high performance (UHPC) undeniably boon sector. Though, correlations between technical characteristics UHPC composition mixture are complicated, nonlinear, complex characterize using standard statistical techniques. This paper intended use both deep neural network ensemble machine learning algorithms namely gradient boosting, extreme random forest regressor, extra tree voting regressor trained with an 810 collections 15 input variables predict compressive strength. After adjusting regression model, prediction efficiency generalization ability developed models validated number parameters. It was established all employed performed better at forecasting result, accurate followed by boosting.

Language: Английский

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A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs

Materials, Journal Year: 2020, Volume and Issue: 13(17), P. 3902 - 3902

Published: Sept. 3, 2020

When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use machine learning seems be a great way improve the accuracy empirical equations currently used in this field. Accordingly, study utilized tree predictive models (i.e., random forest (RF), (RT), and classification regression trees (CART)) as well novel feature selection (FS) technique introduce new model capable estimating SFRC slabs. Furthermore, automatically create structure models, current employed sequential algorithm FS model. In order perform training stage for proposed dataset consisting 140 samples with six influential components depth slab, effective length column, compressive strength concrete, reinforcement ratio, fiber volume) were collected from relevant literature. Afterward, trained verified using above-mentioned database. To evaluate both testing datasets, various statistical indices, including coefficient determination (R2) root mean square error (RMSE), utilized. results obtained experiments indicated that FS-RT outperformed FS-RF FS-CART terms prediction accuracy. range R2 RMSE values 0.9476–0.9831 14.4965–24.9310, respectively; regard, hybrid demonstrated best performance. It was concluded three techniques paper, i.e., FS-RT, FS-RF, FS-CART, could applied predicting

Language: Английский

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A novel approach for classification of soils based on laboratory tests using Adaboost, Tree and ANN modeling

Transportation Geotechnics, Journal Year: 2020, Volume and Issue: 27, P. 100508 - 100508

Published: Dec. 31, 2020

Language: Английский

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Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance

Engineering With Computers, Journal Year: 2021, Volume and Issue: 38(S5), P. 3811 - 3827

Published: Jan. 5, 2021

Language: Английский

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An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity

Artificial Intelligence Review, Journal Year: 2021, Volume and Issue: 55(3), P. 2313 - 2350

Published: Sept. 3, 2021

Language: Английский

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