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

Dadhich, S. and Sharma, J.K. and Madhira, M. (2021) Prediction of Ultimate Bearing Capacity of Aggregate Pier Reinforced Clay Using Machine Learning. International Journal of Geosynthetics and Ground Engineering, 7 (2). ISSN 21999260

Full text not available from this repository. (Request a copy)


Aggregate piers are extensively in use for increasing bearing pressure and diminish settlement under the footing. The ultimate bearing capacity of aggregate pier reinforced clay is majorly affected by soil strength (cu), area replacement ratio (ar) of piles, geometry, and slenderness ratio (λ) of piles. Various prediction models have been proposed to predict the ultimate bearing capacity of aggregate piers. However, existing models have shown a broad range of bias, variation, errors, and as such they are unsuitable for practical design. In this study, machine learning algorithms (linear and non-linear regression) and Artificial neural networks (ANNs) were performed using field loading test results to predict the ultimate bearing capacity of ground reinforced by aggregate piers. Sensitivity analysis was conducted to identify the influence of input variables. To fulfil this objective, 37 test results were used for training and testing of different models and compared with each other based on statistical parameters (mean absolute error, root mean squared error, and r2-score). Random Forest Regression model came out to be the best suitable for prediction of ultimate bearing capacity with minimum mean absolute error (MAE = 38.93 kPa) and r2-score equal to 0.98. Figure not available: see fulltext. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Madhav, R. MadhiraUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Aggregate pier, Artificial neural networks, Machine learning, Sensitivity analysis, Ultimate bearing capacity
Subjects: Civil Engineering
Divisions: Department of Civil Engineering
Depositing User: Mrs Haseena VKKM
Date Deposited: 20 Jun 2022 09:45
Last Modified: 20 Jun 2022 10:37
Publisher URL:
OA policy:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 9318 Statistics for this ePrint Item