Prediction of the Soil Compaction Parameters Using Deep Neural Networks

Othman, Kareem; Abdelwahab, Hassan T.

doi:10.1007/s40515-021-00213-3

Cited by 14 publications

(2 citation statements)

References 25 publications

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“…Similarly, Othman [31] utilized ANNs to develop prediction models that can be used for predicting the characteristics of the hot asphalt mixes. Additionally, ANNs have been used in a number of studies for the prediction of the soil properties such as the study by Ardakani and Kordnaeij (2017) [25], the study by Sinha and Wang (2006) [16], the study by Özbeyaz, and Soylemez (2020) [28], and the study by Othman and Abdelwhab [32]. In general, ANN is a system that tries to mimic the human brain system or the neural system.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Deep Neural Network Models for the Prediction of the Aggregate Base Course Compaction Parameters

Othman

2021

Designs

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Laboratory tests for the estimation of the compaction parameters, namely the maximum dry density (MDD) and optimum moisture content (OMC) are time-consuming and costly. Thus, this paper employs the artificial neural network technique for the prediction of the OMC and MDD for the aggregate base course from relatively easier index properties tests. The grain size distribution, plastic limit, and liquid limits are used as the inputs for the development of the ANNs. In this study, multiple ANNs (240 ANNs) are tested to choose the optimum ANN that produces the best predictions. This paper focuses on studying the impact of three different activation functions: number of hidden layers, number of neurons per hidden layer on the predictions, and heatmaps are generated to compare the performance of every ANN with different settings. Results show that the optimum ANN hyperparameters change depending on the predicted parameter. Additionally, the hyperbolic tangent activation is the most efficient activation function as it outperforms the other two activation functions. Additionally, the simplest ANN architectures results in the best predictions, as the performance of the ANNs deteriorates with the increase in the number of hidden layers or the number of neurons per hidden layers.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Deep Neural Network Models for the Prediction of the Aggregate Base Course Compaction Parameters

Othman

2021

Designs

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“…Extending this approach to different soil types, another study harnessed the potential of deep neural networks to predict soil compaction parameters specific to Egyptian soil. The study achieved remarkable accuracy, with R 2 values of 0.864 for ρ dmax and 0.924 for w opt , thereby surpassing the performance of a simple artificial neural network with just one hidden layer [27]. In the context of highway projects, a multi-layer perceptron neural network model was developed to predict modified compaction parameters for both coarse and fine-grained soil samples.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Multivariate Adaptive Regression Splines (MARS) for Precise Estimation of Soil Compaction Parameters

Abed,

Kadhim,

Almusawi

et al. 2023

Applied Sciences

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Traditional laboratory methods for estimating soil compaction parameters, such as the Proctor test, have been recognized as time-consuming and labor-intensive. Given the increasing need for the rapid and accurate estimation of soil compaction parameters for a range of geotechnical applications, the application of machine learning models offers a promising alternative. This study focuses on employing the multivariate adaptive regression splines (MARS) model algorithm, a machine learning method that presents a significant advantage over other models through generating human-understandable piecewise linear equations. The MARS model was trained and tested on a comprehensive dataset to predict essential soil compaction parameters, including optimum water content (wopt) and maximum dry density (ρdmax). The performance of the model was evaluated using coefficient of determination (R2) and root mean square error (RMSE) values. Remarkably, the MARS models showed excellent predictive ability with high R2 and low RMSE, MAE, and relative error values, indicating its robustness and reliability in predicting soil compaction parameters. Through rigorous five-fold cross-validation, the model’s predictions for wopt returned an RMSE of 1.948%, an R2 of 0.893, and an MAE of 1.498%. For ρdmax, the results showcased an RMSE of 0.064 Mg/m3, an R2 of 0.899, and an MAE of 0.050 Mg/m3. When evaluated on unseen data, the model’s performance for wopt prediction was marked with an MAE of 1.276%, RMSE of 1.577%, and R2 of 0.948. Similarly, for ρdmax, the predictions were characterized by an MAE of 0.047 Mg/m3, RMSE of 0.062 Mg/m3, and R2 of 0.919. The results also indicated that the MARS model outperformed previously developed machine learning models, suggesting its potential to replace conventional testing methods. The successful application of the MARS model could revolutionize the geotechnical field through providing quick and reliable predictions of soil compaction parameters, improving efficiency for construction projects. Lastly, a variable importance analysis was performed on the model to assess how input variables affect its outcomes. It was found that fine content (Cf) and plastic limit (PL) have the greatest impact on compaction parameters.

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Prediction of Compaction Parameters Based on the Atterberg Limit by Using a Machine Learning Approach

Kassa,

Wubineh,

Geremew

et al. 2024

EAI/Springer Innovations in Communication and Computing

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Prediction of the Soil Compaction Parameters Using Deep Neural Networks

Cited by 14 publications

References 25 publications

Deep Neural Network Models for the Prediction of the Aggregate Base Course Compaction Parameters

Deep Neural Network Models for the Prediction of the Aggregate Base Course Compaction Parameters

Utilizing Multivariate Adaptive Regression Splines (MARS) for Precise Estimation of Soil Compaction Parameters

Prediction of Compaction Parameters Based on the Atterberg Limit by Using a Machine Learning Approach

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