Random Forest Algorithm for the Strength Prediction of Geopolymer Stabilized Clayey Soil

Zeini, Husein Ali; Al-Jeznawi, Duaa; Imran, Hamza; Bernardo, Luís; Al-Khafaji, Zainab S.; Ostrowski, Krzysztof

doi:10.3390/su15021408

Cited by 33 publications

(13 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nagaraju and Prasad 46 investigated the efficacy of the PSO technique in predicting the UCS of geopolymer-stabilized expansive blended clays. Zeini et al 9 utilized random forest (RF) algorithm to predict the UCS of geopolymer stabilized clayey soil. The researchers employed the primary database and assessed the efficacy of the machine learning model by analyzing the testing dataset.…”

Section: Related Literaturementioning

confidence: 99%

“…Geopolymers can be synthesized with a solid aluminosilicate material obtained from diverse sources of industrial waste, such as silicate and/or alumina components. The acronyms for these materials include ground-granulated blast-furnace slag (S), metakaolin, and fly ash (FA) 8 , 9 . In geotechnical engineering projects, FA or S has been utilized for soil improvement 10 , 11 .…”

Section: Introductionmentioning

confidence: 99%

“…In general, determining the geotechnical parameters of soft soil is a laborious, time-consuming, costly, and energy-intensive procedure that requires a great deal of effort, equipment, and time. In order to acquire precise data pertaining to the compaction characteristics and UCS of soils, a minimum of six tests and four tests, respectively, must be conducted 9 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

Abdullah,

Ahmad,

Babur

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.

show abstract

Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

Abdullah,

Ahmad,

Babur

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This approach involves systematically exploring a predefined grid of hyperparameter values, coupled with cross-validation for each combination, to identify the most effective set that enhances model performance. Considering the more compact and manageable hyperparameter spaces of these models, a comprehensive exploration proves both feasible and advantageous [99][100][101]. While acknowledging the larger and intricate hyperparameter spaces of the GBR and ANN models, requiring additional optimization time, the study opts for GridSearchCV for consistency.…”

Section: Hyperparameters Optimizationmentioning

confidence: 99%

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

Alnmr,

Hosamo,

Lyu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field.

show abstract

“…Soleimani et al [ 16 ] used the original database consisting of 282 samples and 8 input variables for building multi-gen genetic programming (MGGP) model for estimating UCS of geopolymer stabilized soil, the performance of this model is high with R 2 = 0.942 and MAE = 1.071 MPa for testing dataset. Recently, Zeini et al [ 17 ] used a popular Machine Learning algorithm named Random Forest (RF) in strength prediction of geopolymer stabilized clayey soil, the authors used the original database, the performance of the ML model is evaluated by R 2 = 0.9757 and RMSE = 0.9815 MPa for testing dataset.…”

Section: Introductionmentioning

confidence: 99%

Developing interpretable machine learning-Shapley additive explanations model for unconfined compressive strength of cohesive soils stabilized with geopolymer

Ngo¹,

Nguyen²,

Tran³

2023

PLoS ONE

View full text Add to dashboard Cite

This paper seeks to develop an interpretable Machine Learning (ML) model for predicting the unconfined compressive strength (UCS) of cohesive soils stabilized with geopolymer at 28 days. Four models including Random Forest (RF), Artificial Neuron Network (ANN), Extreme Gradient Boosting (XGB), and Gradient Boosting (GB) are built. The database consists of 282 samples collected from the literature with three different types of cohesive soil stabilized with three geopolymer categories including Slag-based geopolymer cement, alkali-activated fly ash geopolymer and slag/fly ash-based geopolymer cement. The optimal model is selected by comparing their performances with each other. The values of hyperparameters are tuned by Particle Swarm Optimization (PSO) algorithm and K-Fold Cross Validation. Statistical indicators show the superior performance of the ANN model with three metrics performance such as coefficient of determination R2 = 0.9808, Root Mean Square Error RMSE = 0.8808 MPa and Mean Absolute Error MAE = 0.6344 MPa. In addition, a sensitivity analysis was performed to determine the influence of different input parameters on the UCS of cohesive soils stabilized with geopolymer. The order of feature effect can be ordered in descending order using the Shapley additive explanations (SHAP) value as follows: Ground granulated blast slag content (GGBFS) > Liquid limit (LL) > Alkali/Binder ratio (A/B) > Molarity (M) > Fly ash content (FA) > Na/Al > Si/Al. The ANN model can obtain the best accuracy using these seven inputs. LL has a negative correlation with the growth of unconfined compressive strength, whereas GGBFS has a positive correlation.

show abstract

Random Forest Algorithm for the Strength Prediction of Geopolymer Stabilized Clayey Soil

Cited by 33 publications

References 68 publications

Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

Developing interpretable machine learning-Shapley additive explanations model for unconfined compressive strength of cohesive soils stabilized with geopolymer

Contact Info

Product

Resources

About