A super-learner machine learning model for a global prediction of compression index in clays

Díaz, Esteban; Spagnoli, Giovanni

doi:10.1016/j.clay.2023.107239

Cited by 7 publications

(2 citation statements)

References 45 publications

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“…Liquid limit and plastic limit had relatively low importance, as in the linear regression analysis results. Compared to the results of a previous study that used data samples from multiple countries to predict the compression index of soft ground based on machine learning (R 2 = 0.93), the model proposed in this study shows relatively low results [39]. This was likely due to the number of data samples used in the analysis and the regional characteristics.…”

Section: Resultscontrasting

confidence: 75%

A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical Analysis and Machine Learning Techniques

Lee,

Kang,

Kim

et al. 2024

Applied Sciences

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As large cities are continually being developed around coastal areas, structural damage due to the consolidation settlement of soft ground is becoming more of a problem. Estimating consolidation settlement requires calculating an accurate compressive index through consolidation tests. However, these tests are time-consuming, and there is a risk of the test results becoming compromised while preparing and testing the specimens. Therefore, predicting the compression index based on the results of relatively simple physical property tests enables more reliable and accurate predictions of consolidation settlement by calculating the compression index at multiple points. In this context, this study collected geotechnical data from the soft ground of Korea’s south coast. The collected data were used to construct a dataset for developing a compression index prediction model, and significant influencing factors were identified through Pearson correlation analysis. Simple and multiple linear regression analysis was performed using these factors to derive regression equations, and compression index prediction models were developed by applying machine learning algorithms. The results of deriving the significance of the influencing factors from the developed compression index prediction model showed that natural water content was the most significant factor in predicting the compression index. By collecting a significant amount of high-quality data and using the compression index prediction model and the model construction process proposed in this study, more accurate predictions of the compressive index will be possible in the future.

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Section: Resultscontrasting

confidence: 75%

A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical Analysis and Machine Learning Techniques

Lee,

Kang,

Kim

et al. 2024

Applied Sciences

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“…RandomForestRegressor is robust to overfitting and can handle missing data. RandomForestRegressor has been successfully applied in various domains, including finance, healthcare, and marketing 41 , 42 .…”

Section: Random Forest Regressormentioning

confidence: 99%

Revolutionizing core muscle analysis in female sexual dysfunction based on machine learning

Abdel Hady,

Abd El-Hafeez

2024

Sci Rep

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The purpose of this study is to investigate the role of core muscles in female sexual dysfunction (FSD) and develop comprehensive rehabilitation programs to address this issue. We aim to answer the following research questions: what are the roles of core muscles in FSD, and how can machine and deep learning models accurately predict changes in core muscles during FSD? FSD is a common condition that affects women of all ages, characterized by symptoms such as decreased libido, difficulty achieving orgasm, and pain during intercourse. We conducted a comprehensive analysis of changes in core muscles during FSD using machine and deep learning. We evaluated the performance of multiple models, including multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), recurrent neural network (RNN), ElasticNetCV, random forest regressor, SVR, and Bagging regressor. The models were evaluated based on mean squared error (MSE), mean absolute error (MAE), and R-squared (R2) score. Our results show that CNN and random forest regressor are the most accurate models for predicting changes in core muscles during FSD. CNN achieved the lowest MSE (0.002) and the highest R2 score (0.988), while random forest regressor also performed well with an MSE of 0.0021 and an R2 score of 0.9905. Our study demonstrates that machine and deep learning models can accurately predict changes in core muscles during FSD. The neglected core muscles play a significant role in FSD, highlighting the need for comprehensive rehabilitation programs that address these muscles. By developing these programs, we can improve the quality of life for women with FSD and help them achieve optimal sexual health.

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Refining Compression Index Estimation for Fine Soils: Insights from Large Data and Sensitivity Analysis

Ali,

Mohammed

2024

Geotech Geol Eng

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A super-learner machine learning model for a global prediction of compression index in clays

Cited by 7 publications

References 45 publications

A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical Analysis and Machine Learning Techniques

A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical Analysis and Machine Learning Techniques

Revolutionizing core muscle analysis in female sexual dysfunction based on machine learning

Refining Compression Index Estimation for Fine Soils: Insights from Large Data and Sensitivity Analysis

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