Evaluating unconfined compressive strength of cohesive soils stabilized with geopolymer: a computational intelligence approach

Javdanian, Hamed; Lee, Saro

doi:10.1007/s00366-018-0592-8

Cited by 27 publications

(16 citation statements)

References 38 publications

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“…The model exhibited strong performance, as evidenced by an R 2 value of 0.9420 and MAE of 1.071 MPa when evaluated on the testing dataset. Javdanian and Lee 45 developed a hybrid ML model that combines NF, GMDH and PSO, known as NF-GMDH-PSO was devised to predict the UCS of stabilized cohesive soils using geopolymers. The model's performance was evaluated using the following metrics: R 2 = 0.971, mean absolute error (MAE) = 0.231 MPa, and root mean square error (RMSE) = 0.401 MPa.…”

Section: Related Literaturementioning

confidence: 99%

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

Abdullah,

Ahmad,

Babur

et al. 2024

Sci Rep

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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.

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Section: Related Literaturementioning

confidence: 99%

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

Abdullah,

Ahmad,

Babur

et al. 2024

Sci Rep

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“…The ML methodologies utilized the accumulated UCS data for the soil-lime mixture as input data. After instructing the models, they were used to determine if the addition of calcium to the soil increased UCS values [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

“…Contaminated site remediation: Applying geotechnical techniques to remediate sites contaminated with pollutants, such as heavy metals, hydrocarbons, or hazardous chemicals, by using methods like soil stabilization, containment, and in-situ treatment. Geotechnical aspects of sustainable infrastructure: Incorporating geotechnical considerations into the design and construction of sustainable infrastructure, such as green roofs, permeable pavements, and engineered natural systems for stormwater management [ 42 ]. Both transportation geotechnics and environmental geotechnics require a thorough understanding of soil behavior, groundwater conditions, and the interaction between soil and man-made structures [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications

Guo,

Garcia,

Andrade Valle

et al. 2024

PLoS ONE

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In the field of soil mechanics, especially in transportation and environmental geotechnics, the use of machine learning (ML) techniques has emerged as a powerful tool for predicting and understanding the compressive strength behavior of soils especially graded ones. This is to overcome the sophisticated equipment, laboratory space and cost needs utilized in multiple experiments on the treatment of soils for environmental geotechnics systems. This present study explores the application of machine learning (ML) techniques, namely Genetic Programming (GP), Artificial Neural Networks (ANN), Evolutionary Polynomial Regression (EPR), and the Response Surface Methodology in predicting the unconfined compressive strength (UCS) of soil-lime mixtures. This was for purposes of subgrade and landfill liner design and construction. By utilizing input variables such as Gravel, Sand, Silt, Clay, and Lime contents (G, S, M, C, L), the models forecasted the strength values after 7 and 28 days of curing. The accuracy of the developed models was compared, revealing that both ANN and EPR achieved a similar level of accuracy for UCS after 7 days, while the GP model performed slightly lower. The complexity of the formula required for predicting UCS after 28 days resulted in decreased accuracy. The ANN and EPR models achieved accuracies of 85% and 82%, with R2 of 0.947 and 0.923, and average error of 0.15 and 0.18, respectively, while the GP model exhibited a lower accuracy of 66.0%. Conversely, the RSM produced models for the UCS with predicted R2 of more than 98% and 99%, for the 7- and 28- day curing regimes, respectively. The RSM also produced adequate precision in modelling UCS of more than 14% against the standard 7%. All input factors were found to have almost equal importance, except for the lime content (L), which had an average influence. This shows the importance of soil gradation in the design and construction of subgrade and landfill liners. This research further demonstrates the potential of ML techniques for predicting the strength of lime reconstituted G-S-M-C graded soils and provides valuable insights for engineering applications in exact and sustainable subgrade and liner designs, construction and performance monitoring and rehabilitation of the constructed civil engineering infrastructure.

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“…Basing this new database, Mozumder et al [ 13 ] predicted UCS of geopolymer stabilized soil using the Support Vector Machine (SVM) model with performance metric R 2 = 0.9801 for testing dataset. Javdanian and Lee [ 14 ] developed a hybrid Machine Learning model included neuro-fuzzy (NF)-group method of data handling (GMDH) and particle swarm optimization (PSO) abbreviated (NF-GMDH-PSO) in predicting UCS of stabilized cohesive soils using geopolymers with the performance metric such as R 2 = 0.971, Mean Absolute Error MAE = 0.231 MPa and Root Mean Square Error RMSE = 0.401 MPa. The efficiency of the particle swarm optimization (PSO) algorithm in predicting UCS of geopolymer stabilized expansive blended clays was also investigated by Nagaraju and Prasad [ 15 ].…”

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

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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.

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Evaluating unconfined compressive strength of cohesive soils stabilized with geopolymer: a computational intelligence approach

Cited by 27 publications

References 38 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

Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications

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

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