Estimation of modified expansive soil CBR with multivariate adaptive regression splines, random forest and gradient boosting machine

Ikeagwuani, Chijioke Christopher

doi:10.1007/s41062-021-00568-z

Cited by 34 publications

(12 citation statements)

References 56 publications

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“…ear data, etc. [38][39][40]. In addition, XGBoost supports parallel processing to increase the speed of model training and provides feature significance to study the impact of features on model results.…”

Section: Extreme Gradient Boostingmentioning

confidence: 99%

“…The advantages of XGBoost include a more accurate approximation of the loss function, faster convergence of the model, more stable training of the model, more optimized splitting strategy, and a better handling of nonlinear data, etc. [38][39][40]. In addition, XGBoost supports parallel processing to increase the speed of model training and provides feature significance to study the impact of features on model results.…”

Section: Model Performance Evaluation Metricsmentioning

confidence: 99%

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Prediction of Soil Compaction Parameters Using Machine Learning Models

Li,

You,

et al. 2024

Applied Sciences

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Maximum Dry Density (MDD) and Optimum Moisture Content (OMC) are two important parameters of soil filling, which affect the soil stability and bearing capacity, and thus the reliability and durability of facilities such as highways and bridges. Therefore, it is important to make reasonable predictions of OMC and MDD. Four machine learning algorithms, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting Tree (XGBoost), are adopted in this paper to establish MDD and OMC prediction models. After training and testing, the best models of the four algorithms are compared. The results show that, as an ensemble learning algorithm, XGBoost is the best model for predicting MDD and OMC, with an R2 of 0.9234 for OMC, and an R2 of 0.9098 for MDD. Finally, the feature importance analysis concludes that the plastic limit (PL) and the liquid limit (LL) are the two features that affect OMC and MDD the most. The prediction of soil compaction parameters using machine learning models, especially ensemble learning, can significantly reduce the amount of laboratory work and improve the efficiency of optimizing design for soil resource utilization in engineering construction.

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Section: Extreme Gradient Boostingmentioning

confidence: 99%

Section: Model Performance Evaluation Metricsmentioning

confidence: 99%

Prediction of Soil Compaction Parameters Using Machine Learning Models

Li,

You,

et al. 2024

Applied Sciences

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“…Expansive clay subgrades frequently severely impact the design and execution of infrastructures, especially highways [1,2]. The seasonal changes in different seasons' moisture that cause the expansive clays to regularly expand and contract in volume and negatively impact the service life of roadways are well known [3,4].…”

Section: Introductionmentioning

confidence: 99%

Response of High Swelling Montmorillonite Clays with Aqueous Polymer

Panda

Bahrami

Nagaraju³

et al. 2023

Minerals

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Expansive clays containing mineral montmorillonite exhibit swelling and shrinkage due to variations in the moisture content, leading to significant distresses. There has been a growing interest in chemical and polymer additives treated for high swelling montmorillonite clays in recent years. However, limited attention has been paid to the effect of polyacrylamide on the soil’s swelling behavior. Moreover, nontraditional methods of the soil treatment are applied for the rapid stabilization of soil. In this article, polyacrylamide polymer is used as an additive to expansive clay to control the swelling phenomenon. Three different percentages—2.5%, 5%, and 7.5%—of polymer are blended with oven-dried soil to determine Atterberg limits, compaction features, and swelling characteristics. Additionally, electrical impedance measurement is conducted on treated soil samples with different moisture contents. The electrical resistance of soils and polymer-treated soils is measured based on the electrical resistivity correlation of soils. Tests results for soils stabilized with polyacrylamide show that swelling is significantly reduced with increasing the additive content. Moreover, the addition of polymer improves resistivity of soil. Aqueous polyacrylamide can be utilized as an effective stabilization additive to enhance properties of expansive clays.

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“…For this reason, numerical methods based on the finite element approach have recently become well-known for the evaluation of bearing capacity, yielding effective results [19,20]. Recently, the application of some new advanced techniques, namely "artificial intelligence (AI)" or "machine learning (ML)", has witnessed a spectrum of interest, and they provided exceptional results in solving several issues by learning from the available data [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, the use of machine-learning methods to predict pile-bearing capacity has witnessed considerable development since the early 1990s [21][22][23][24]. Several studies are now able to estimate the pile-bearing capacity with a higher degree of precision in comparison to traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques

et al. 2021

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Estimating the bearing capacity of piles is an essential point when seeking for safe and economic geotechnical structures. However, the traditional methods employed in this estimation are time-consuming and costly. The current study aims at elaborating a new alternative model for predicting the pile-bearing capacity based on eleven new advanced machine-learning methods in order to overcome these limitations. The modeling phase used a database of 100 samples collected from different countries. Additionally, eight relevant factors were selected in the input layer based on the literature recommendations. The optimal inputs were modeled using the machine-learning methods and their performance was assessed through six performance measures using a K-fold cross-validation approach. The comparative study proved the effectiveness of the DNN model, which displayed a higher performance in predicting the pile-bearing capacity. This elaborated model provided the optimal prediction, i.e., the closest to the experimental values, compared to the other models and formulae proposed by previous studies. Finally, a reliable and easy-to-use graphical interface was generated, namely “BeaCa2021”. This will be very helpful for researchers and civil engineers when estimating the pile-bearing capacity, with the advantage of saving time and money.

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Estimation of modified expansive soil CBR with multivariate adaptive regression splines, random forest and gradient boosting machine

Cited by 34 publications

References 56 publications

Prediction of Soil Compaction Parameters Using Machine Learning Models

Prediction of Soil Compaction Parameters Using Machine Learning Models

Response of High Swelling Montmorillonite Clays with Aqueous Polymer

Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques

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