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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Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

Cement and Concrete Research, Journal Year: 2021, Volume and Issue: 145, P. 106449 - 106449

Published: April 17, 2021

Language: Английский

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High-performance concrete strength prediction based on ensemble learning

Construction and Building Materials, Journal Year: 2022, Volume and Issue: 324, P. 126694 - 126694

Published: Feb. 4, 2022

Language: Английский

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Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

Construction and Building Materials, Journal Year: 2022, Volume and Issue: 322, P. 126500 - 126500

Published: Jan. 22, 2022

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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Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques

Engineering With Computers, Journal Year: 2021, Volume and Issue: 38(S4), P. 3283 - 3316

Published: July 4, 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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Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures

Construction and Building Materials, Journal Year: 2022, Volume and Issue: 349, P. 128737 - 128737

Published: Aug. 18, 2022

Language: Английский

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Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 206, P. 117754 - 117754

Published: June 11, 2022

Rotating equipment is considered as a key component in several industrial sectors. In fact, the continuous operation of many machines such sub-sea pumps and gas turbines relies on correct performance their rotating equipment. order to reduce probability malfunctions this equipment, condition monitoring, fault diagnosis systems are essential. work, novel approach proposed perform based permutation entropy, signal processing, artificial intelligence. To that aim, vibration signals employed for an indication bearing performance. facilitate diagnosis, detection isolation performed two separate steps. As first, once received, faulty state determined by entropy. case detected, type using processing Wavelet packet transform envelope analysis utilized extract frequency components fault. The allows automatic selection band includes characteristic resonance fault, which subject change different operational conditions. method works extracting proper features used decide about bearing's multi-output adaptive neuro-fuzzy inference system classifier. effectiveness assessed Case Western Reserve University dataset: demonstrates method's capabilities accurately diagnosing faults compared existing approaches.

Language: Английский

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Data-Driven Compressive Strength Prediction of Fly Ash Concrete Using Ensemble Learner Algorithms

Buildings, Journal Year: 2022, Volume and Issue: 12(2), P. 132 - 132

Published: Jan. 27, 2022

Concrete is one of the most popular materials for building all types structures, and it has a wide range applications in construction industry. Cement production use have significant environmental impact due to emission different gases. The fly ash concrete (FAC) crucial eliminating this defect. However, varied features cementitious composites exist, understanding their mechanical characteristics critical safety. On other hand, forecasting concrete, machine learning approaches are extensively employed algorithms. goal work compare ensemble deep neural network models, i.e., super learner algorithm, simple averaging, weighted integrated stacking, as well separate stacking order develop an accurate approach estimating compressive strength FAC reducing high variance predictive models. Separate with random forest meta-learner received predictions (97.6%) highest coefficient determination lowest mean square error variance.

Language: Английский

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