Prediction of Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilised Soil Using Artificial Intelligence Techniques

Suman, Shakti; Mahamaya, Mahasakti; Das, Sarat Kumar

doi:10.1007/s40891-016-0051-9

Cited by 55 publications

(11 citation statements)

References 40 publications

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“…The authors evaluated the effectiveness of the two models and discovered that the ANN is less superior to the ANFIS. Machine learning models were developed by [10] to predict the UCS and MDD of soils stabilized by cement. The adopted machine learning algorithms were multivariate adaptive regression splines and functional networks, and their performance was compared to four models presented in [7], namely SVM and ANN (Bayesian regularization, differential evolution, and Levenberg-Marquardt).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compaction and Strength Properties of Amended Soil Using Machine Learning

Taffese

Abegaz

2022

Buildings

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In the current work, a systematic approach is exercised to monitor amended soil reliability for a housing development program to holistically understand the targeted material mixture and the building input derived, focusing on the three governing parameters: (i) optimum moisture content (OMC), (ii) maximum dry density (MDD), and (iii) unconfined compressive strength (UCS). It is in essence the selection of machine learning algorithms that could optimally show the true relation of these factors in the best possible way. Thus, among the machine learning approaches, the optimizable ensemble and artificial neural networks were focused on. The data sources were those compiled from wide-ranging literature sources distributed over the five continents and twelve countries of origin. After a rigorous manipulation, synthesis, and results analyses, it was found that the selected algorithms performed well to better approximate OMC and UCS, whereas that of the MDD result falls short of the established threshold of the set limits referring to the MSE statistical performance evaluation metrics.

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Section: Introductionmentioning

confidence: 99%

Prediction of Compaction and Strength Properties of Amended Soil Using Machine Learning

Taffese

Abegaz

2022

Buildings

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“…The results of the tests confirm that the proposed models are satisfactory. Suman et al [10] developed AI models to determine the MDD and the UCS of cement stabilized soil. The employed algorithms were functional networks (FN) and multivariate adaptive regression splines (MARS) and compared their performance with four models presented in [8], which are BRNN, LMNN, DENN, and SVM.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Prediction of Physical and Mechanical Properties of Stabilized Soil for Affordable Housing

Taffese

Abegaz

2021

Applied Sciences

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Soil stabilization is the alteration of physicomechanical properties of soils to meet specific engineering requirements of problematic soils. Laboratory examination of soils is well recognized as appropriate for examining the engineering properties of stabilized soils; however, they are labor-intensive, time-consuming, and expensive. In this work, four artificial intelligence based models (OMC-EM, MDD-EM, UCS-EM+, and UCS-EM−) to predict the optimum moisture content (OMC), maximum dry density (MDD), and unconfined compressive strength (UCS) are developed. Experimental data covering a wide range of stabilized soils were collected from previously published works. The OMC-EM, MDD-EM, and UCS-EM− models employed seven features that describe the proportion and types of stabilized soils, Atterberg limits, and classification groups of soils. The UCS-EM+ model, besides the seven features, employs two more features describing the compaction properties (OMC and MDD). An optimizable ensemble method is used to fit the data. The model evaluation confirms that the developed three models (OMC-EM, MDD-EM, and UCS-EM+) perform reasonably well. The weak performance of UCS-EM− model validates that the features OMC and MDD have substantial significance in predicting the UCS. The performance comparison of all the developed ensemble models with the artificial neural network ones confirmed the prediction superiority of the ensemble models.

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“…Molaabasi et al [59] applied the group method of data handling (GMDH)-type ANNs to predict the stress-strain behavior of zeolitecemented sand based on results of 216 UCS tests, showing GMDH can be a reliable tool for capturing the influence of various parameters on the behavior of stabilized sand mixtures. Suman et al [60] developed predictive models for the UCS of cement-stabilized soils using functional networks and multivariate adaptive regression splines based on a literature database, reporting these AI techniques' generalization ability to be better than ANNs. They found the most influential input parameters to be moisture content and gravel content, among others (Atterberg limits, sand and cement content).…”

Section: Introductionmentioning

confidence: 99%

Stiffness and Strength of Stabilized Organic Soils—Part II/II: Parametric Analysis and Modeling with Machine Learning

et al. 2021

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Predicting the range of achievable strength and stiffness from stabilized soil mixtures is critical for engineering design and construction, especially for organic soils, which are often considered “unsuitable” due to their high compressibility and the lack of knowledge about their mechanical behavior after stabilization. This study investigates the mechanical behavior of stabilized organic soils using machine learning (ML) methods. ML algorithms were developed and trained using a database from a comprehensive experimental study (see Part I), including more than one thousand unconfined compression tests on organic clay samples stabilized by wet soil mixing (WSM) technique. Three different ML methods were adopted and compared, including two artificial neural networks (ANN) and a linear regression method. ANN models proved reliable in the prediction of the stiffness and strength of stabilized organic soils, significantly outperforming linear regression models. Binder type, mixing ratio, soil organic and water content, sample size, aging, temperature, relative humidity, and carbonation were the control variables (input parameters) incorporated into the ML models. The impacts of these factors were evaluated through rigorous ANN-based parametric analyses. Additionally, the nonlinear relations of stiffness and strength with these parameters were developed, and their optimum ranges were identified through the ANN models. Overall, the robust ML approach presented in this paper can significantly improve the mixture design for organic soil stabilization and minimize the experimental cost for implementing WSM in engineering projects.

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Prediction of Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilised Soil Using Artificial Intelligence Techniques

Cited by 55 publications

References 40 publications

Prediction of Compaction and Strength Properties of Amended Soil Using Machine Learning

Prediction of Compaction and Strength Properties of Amended Soil Using Machine Learning

Artificial Intelligence for Prediction of Physical and Mechanical Properties of Stabilized Soil for Affordable Housing

Stiffness and Strength of Stabilized Organic Soils—Part II/II: Parametric Analysis and Modeling with Machine Learning

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