Prediction of the resilient modulus of compacted subgrade soils using ensemble machine learning methods

Kardani, Navid; Aminpour, Mohammad A.; Raja, Muhammad Nouman Amjad; Kumar, Gaurav; Bardhan, Abidhan; Nazem, Majidreza

doi:10.1016/j.trgeo.2022.100827

Cited by 47 publications

(10 citation statements)

References 43 publications

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“…It utilizes multiple decision trees in its framework and progressively reduces the residuals in each ensemble tree, thereby iteratively enhancing the model [17,18]. GBR operates on a boosting methodology using a collection of high-bias and low-variance models to simultaneously reduce bias and maintain low variance [19,20]. Each decision tree partitions the input space into separate regions and assigns constant values to each region.…”

Section: Gbrmentioning

confidence: 99%

Predicting the shear modulus and damping ratio of soils using machine learning models

Bayat,

Mousavi,

Feng

2024

IOP Conf. Ser.: Earth Environ. Sci.

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In geotechnical engineering, the accurate estimation of fundamental soil properties, such as the shear modulus ratio (G/G max) and damping ratio (D), is crucial to design and analyze various structures subjected to dynamic loads. This study presents a comprehensive investigation on harnessing the power of machine learning techniques to precisely predict G/G max and D of granular soils. Using an extensive dataset gathered from cyclic triaxial and resonant column tests on diverse mixtures of sand and gravel, combined with previous research findings, a series of advanced machine learning algorithms including shallow neural networks, support vector regression, gradient boosting regression, and deep feed forward neural network (DFFNN) were developed. The proposed models elucidate various influential parameters, including the grading characteristics, void ratio, confining pressure, consolidation stress ratio, and specimen preparation techniques. The superiority of the DFFNN model in terms of accuracy and predictive performance was demonstrated through rigorous evaluation and comparison. This study contributes to a better understanding of soil behavior under dynamic conditions. It also provides a robust framework to employ machine learning in predicting G/G max and D of granular soils, thereby enhancing the efficiency and reliability of geotechnical designs and construction practices.

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Section: Gbrmentioning

confidence: 99%

Predicting the shear modulus and damping ratio of soils using machine learning models

Bayat,

Mousavi,

Feng

2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…The R 2 , WI, and VAF values of the corresponding optimal model should be higher, and the RMSE value should be lower. The above indicators are defined as follows [68][69][70][71]:…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

Zhao,

Bing

2024

Buildings

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Unconfined compressive strength (UCS) is an important parameter of rock and soil mechanical behavior in foundation engineering design and construction. In this study, salinized frozen soil is selected as the research object, and soil GDS tests, ultrasonic tests, and scanning electron microscopy (SEM) tests are conducted. Based on the classification method of the model parameters, 2 macroscopic parameters, 38 mesoscopic parameters, and 19 microscopic parameters are selected. A machine learning model is used to predict the strength of soil considering the three-level characteristic parameters. Four accuracy evaluation indicators are used to evaluate six machine learning models. The results show that the radial basis function (RBF) has the best UCS predictive performance for both the training and testing stages. In terms of acceptable accuracy and stability loss, through the analysis of the gray correlation and rough set of the three-level parameters, the total amount and proportion of parameters are optimized so that there are 2, 16, and 16 macro, meso, and micro parameters in a sequence, respectively. In the simulation of the aforementioned six machine learning models with the optimized parameters, the RBF still performs optimally. In addition, after parameter optimization, the sensitivity proportion of the third-level parameters is more reasonable. The RBF model with optimized parameters proved to be a more effective method for predicting soil UCS. This study improves the prediction ability of the UCS by classifying and optimizing the model parameters and provides a useful reference for future research on salty soil strength parameters in seasonally frozen regions.

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“…A group of machine learning models referred to as artificial neural networks (ANNS) are modelled after the neural network of the human brain. They are made up of layered networks of interconnected nodes, or neurons [ 15 ]. Each neuron takes in information, uses weighted connections to process it, and then generates an output.…”

Section: Introductionmentioning

confidence: 99%

Predicting resilient modulus: A data driven approach integrating physical and numerical techniques

Riaz,

Ahmad

2024

Heliyon

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Prediction of the resilient modulus of compacted subgrade soils using ensemble machine learning methods

Cited by 47 publications

References 43 publications

Predicting the shear modulus and damping ratio of soils using machine learning models

Predicting the shear modulus and damping ratio of soils using machine learning models

Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

Predicting resilient modulus: A data driven approach integrating physical and numerical techniques

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