Prediction of Ultimate Bearing Capacity of Aggregate Pier Reinforced Clay Using Machine Learning

Dadhich, Sharad; Sharma, Jitendra Kumar; Madhira, Madhav

doi:10.1007/s40891-021-00282-x

Cited by 17 publications

(3 citation statements)

References 27 publications

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“…Statistical indicators R 2 , RMSE and MAE are generally used for the evaluation of accuracy of ML prediction or forecast models [18,19]. R2 measures the relationship between independent and dependent variables [20].…”

Section: Evaluation Methodsmentioning

confidence: 99%

Using GA-ANFIS machine learning model for forecasting the load bearing capacity of driven piles

Dam Duc Nguyen,

Hai Phu Nguyen,

Dung Quang Vu

et al. 2023

JSTT

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This paper is aimed to apply hybrid machine learning model namely GA-ANFIS, which is a combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA), for the prediction of total bearing capability of driven piles. A database of 95 Pile Driving Analyzer (PDA) tests carried out at the win power project in Hoa Binh province, Vietnam was used to develop hybrid model. The database was split into 70:30 ratio for training (70%) and validating (30%) model. Accuracy of the model was evaluated using statistical standard indicators: Coefficient of determination (R2), Mean Absolute Error (MAE), and Root mean squared error (RMSE). Results indicated that the GA-ANFIS model has a good performance in correct prediction of the total bearding capability of driven piles on both training (R2 = 0.976) and testing (R2 =0.925) datasets. Therefore, the GA-ANFIS hybrid model is a promising tool for quick and accurate prediction of the total bearing capability of driven piles for the consideration in design and construction of the structures.

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Section: Evaluation Methodsmentioning

confidence: 99%

Using GA-ANFIS machine learning model for forecasting the load bearing capacity of driven piles

Dam Duc Nguyen,

Hai Phu Nguyen,

Dung Quang Vu

et al. 2023

JSTT

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“…As a result, both models were able to signifcantly improve the prediction accuracy compared to the existing models, and the MLR and DNN models were found to have similar accuracies for the ultimate bearing capacity prediction. Using the load test data generated by Bong et al [1], Dadhich et al [34] also performed modeling to predict the ultimate bearing capacity of aggregate pier-reinforced clay using machine learning. Although artifcial intelligence models, such as ANN and DNN, have achieved success in improving prediction accuracy, these black-box models have limitations in identifying the relationship between independent and dependent variables.…”

Section: Prediction Of Ultimate Bearingmentioning

confidence: 99%

Influence of Load Test Scale on Prediction of Ultimate Bearing Capacity of Aggregate Pier Reinforced Clay

Baek

Bong

2022

Advances in Civil Engineering

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Predicting the bearing capacity of spread footings on aggregate pier-reinforced soil is particularly important for ground improvement. Various methods for predicting the bearing capacity of spread footings supported on aggregate pier-reinforced clay have been proposed. In addition, many fields or laboratory load tests have been conducted to identify the improvement effects of aggregate pier. However, no study has quantitatively compared the effect of the experimental scale on the prediction of bearing capacity. In this study, multiple linear regression analyses were performed for field and laboratory load tests, and the effects of the load test scale on bearing capacity prediction were identified. The sensitivity analysis showed that the prediction model for the laboratory load test (MLRL) exhibited a higher sensitivity for the undrained shear strength than that of the field load test model (MLRF). However, the sensitivity of the area replacement ratio of MLRL was less than half that of the MLRF. As the undrained shear strength increased, the predicted bearing capacity of the MLRL was larger than that of the MLRF owing to the influence of boundary conditions on the experimental equipment.

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“…A performance evaluation was carried out on each phase (training and testing phases), and the overall performance of the model was evaluated. For this purpose, different statistical parameters (Hassanvand et al, 2018;Saadat et al, 2018;Dadhich et al, 2021) where 'y predict is the predicted output; y actual is the actual value; y mean is the mean of the actual value, and n is the total number of the dataset.…”

Section: Performance Evaluationmentioning

confidence: 99%

Estimation of the Uplift Resistance for an Under-Reamed Pile in Dry Sand Using Machine Learning

Dadhich

Sharma

Madhira

2022

Slovak Journal of Civil Engineering

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Under-reamed piles are extensively used to resist uplift forces and settlements. The objective of the present study is to develop various machine learning models (linear and non-linear) and determine the best model to estimate the ultimate uplift resistance of under-reamed piles embedded in cohesionless soil. The machine learning models were developed considering the following input parameters: the density index, dry density, base diameter, angle of an enlarged base with a vertical axis, shaft diameter, and embedment ratio. A linear equation is proposed to estimate the ultimate uplift resistance based on Multivariate Linear Regression analysis with a mean absolute error equaling 0.25kN and 0.50kN for loose and dense sands respectively. The Decision Tree Regression model provides an excellent degree of accuracy with a mean absolute error of 0.02kN and 0.02kN in cases of loose and dense sands respectively.

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Prediction of Ultimate Bearing Capacity of Aggregate Pier Reinforced Clay Using Machine Learning

Cited by 17 publications

References 27 publications

Using GA-ANFIS machine learning model for forecasting the load bearing capacity of driven piles

Using GA-ANFIS machine learning model for forecasting the load bearing capacity of driven piles

Influence of Load Test Scale on Prediction of Ultimate Bearing Capacity of Aggregate Pier Reinforced Clay

Estimation of the Uplift Resistance for an Under-Reamed Pile in Dry Sand Using Machine Learning

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