Empirical approach for strength prediction of geopolymer stabilized clayey soil using support vector machines

Mozumder, Ruhul Amin; Laskar, Aminul Islam; Hussain, Monowar

doi:10.1016/j.conbuildmat.2016.12.012

Cited by 46 publications

(11 citation statements)

References 60 publications

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“…On the other hand, Mozumder et al (2017) and Mozumder and Laskar (2015), use parameters such as sodium to aluminum ratio among other parameters which are specific to alkali activation studies. So far, the only literature that highlighted this issue was .…”

Section: Machine Learning In Geotechnical Engineeringmentioning

confidence: 99%

“…Several studies have been done to develop machine learning models that predicts the UCS value as function of various soil properties (Alavi et al 2009;Mozumder et al 2017;Mozumder and Laskar 2015;.…”

Section: Introduction and Research Problemmentioning

confidence: 99%

“…Furthermore,Mozumder and Laskar (2015) reported the efficacy of ANN over MLR in predicting the UCS value of geopolymer stabilized clayey soil based on the predictors such as LL, plasticity index (PI), % stabilizers, molar concentration of alkali activator, ratio of alkali to binder, ratio of Na/Al, and ratio of Si/Al. In another study,Mozumder et al (2017) demonstrated the performance of SVM in predicting UCS value using the same set of parameters and data asMozumder and Laskar (2015) that reported RMSE of 0.75 MPa (108.77 psi ) and MAPE of 4.5. In similar studies by, the applicability of various machine learning models in prediction of UCS value for jet grouting material was presented.…”

mentioning

confidence: 97%

“…This predicted amount can then be verified quickly in the laboratory thereby minimizing the amount of time and effort required for establishing optimal stabilizer amounts. Also, these models can be used to generate spatial maps indicating stabilizer effectiveness which can help to strategize sampling locations for laboratory testing.Machine learning methods have been widely used in recent years in geotechnical engineering and have shown potential for solving real engineering problemsDas 2012;Goh 1994;Goh et al 2005;Lai et al 2006;Marjanović et al 2011;Mozumder et al 2017;Mozumder and Laskar 2015;. Someof the widely used supervised learning methods in geotechnical engineering are support vector machines (SVM), classification and regression trees (CART), and artificial neural networks (ANN).…”

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confidence: 99%

See 3 more Smart Citations

Machine learning methods to map stabilizer effectiveness based on common soil properties

Gajurel

Chittoori

Mukherjee

et al. 2021

Transportation Geotechnics

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Section: Machine Learning In Geotechnical Engineeringmentioning

confidence: 99%

Section: Introduction and Research Problemmentioning

confidence: 99%

mentioning

confidence: 97%

mentioning

confidence: 99%

See 2 more Smart Citations

Machine learning methods to map stabilizer effectiveness based on common soil properties

Gajurel

Chittoori

Mukherjee

et al. 2021

Transportation Geotechnics

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“…In recent times, artificial intelligence paradigms relying on several machine learning (ML) techniques have begun to gain traction as alternatives for the determination of the UCS of soils [32][33][34]. That notwithstanding, the adoption of ML methods for the predictive modelling of improved ground properties has only been reported in a few studies [32,[35][36][37][38][39][40][41][42]. Moreover, an application that involves predictive modelling of compressive strength of soils stabilised by eco-friendly pozzolans enriched by cementitious additives has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans—A Machine Learning Approach

2022

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The unconfined compressive strength (UCS) of a stabilised soil is a major mechanical parameter in understanding and developing geomechanical models, and it can be estimated directly by either lab testing of retrieved core samples or remoulded samples. However, due to the effort, high cost and time associated with these methods, there is a need to develop a new technique for predicting UCS values in real time. An artificial intelligence paradigm of machine learning (ML) using the gradient boosting (GB) technique is applied in this study to model the unconfined compressive strength of soils stabilised by cementitious additive-enriched agro-based pozzolans. Both ML regression and multinomial classification of the UCS of the stabilised mix are investigated. Rigorous sensitivity-driven diagnostic testing is also performed to validate and provide an understanding of the intricacies of the decisions made by the algorithm. Results indicate that the well-tuned and optimised GB algorithm has a very high capacity to distinguish between positive and negative UCS categories (‘firm’, ‘very stiff’ and ‘hard’). An overall accuracy of 0.920, weighted recall rates and precision scores of 0.920 and 0.938, respectively, were produced by the GB model. Multiclass prediction in this regard shows that only 12.5% of misclassified instances was achieved. When applied to a regression problem, a coefficient of determination of approximately 0.900 and a mean error of about 0.335 were obtained, thus lending further credence to the high performance of the GB algorithm used. Finally, among the eight input features utilised as independent variables, the additives seemed to exhibit the strongest influence on the ML predictive modelling.

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Assessment of Unconfined Compressive Strength of Stabilized Soil Using Artificial Intelligence Tools: A Scientometrics Review

Sari-Ahmed,

Ghrici,

Benzaamia

et al. 2024

Studies in Systems, Decision and Control

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Empirical approach for strength prediction of geopolymer stabilized clayey soil using support vector machines

Cited by 46 publications

References 60 publications

Machine learning methods to map stabilizer effectiveness based on common soil properties

Machine learning methods to map stabilizer effectiveness based on common soil properties

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans—A Machine Learning Approach

Assessment of Unconfined Compressive Strength of Stabilized Soil Using Artificial Intelligence Tools: A Scientometrics Review

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