- Title
- Prediction of secant shear modulus and damping ratio for an extremely dilative silica sand based on machine learning techniques
- Creator
- Baghbani, Abolfazl; Choudhury, Tanveer; Samui, Pijush; Costa, Susanga
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190445
- Identifier
- vital:17616
- Identifier
-
https://doi.org/10.1016/j.soildyn.2022.107708
- Identifier
- ISSN:0267-7261 (ISSN)
- Abstract
- A better understanding of soil response to dynamic loads, including earthquakes, can result in safer designs that can reduce casualties. The damping ratio and shear modulus are critical parameters in soil dynamics, and several factors affect these parameters, including density and moisture content. The non-linear and multiple influences on these two parameters make their estimation difficult. Machine learning techniques are very powerful mapping tools with a remarkable capacity to perform nonlinear multivariate function approximations. In this study, to predict sand secant shear modulus and damping ratio from input variables, artificial neural networks (ANN) and classification and regression random forests (CRRF) were used as alternative estimators. The database was created using a series of simple shear tests that accurately assessed damping ratios and secant shear modulus to predict these two dynamic parameters. The input variables of the proposed predictive models included vertical stress, relative density and cyclic stress ratio, and its outputs included secant shear modulus and damping ratio. The Bayesian Regularization (BR) back-propagation ANN model produced correlation coefficient (R) and mean absolute error (MAE) values of 0.998 and 0.006, respectively, while CRRF models gave R and MAE values of 0.995 and 66.051, respectively. Additionally, sensitivity analysis of artificial intelligence (AI) models demonstrated that vertical stress and relative density played a vital role in predicting damping ratio, while all three parameters were important in predicting secant shear modulus. In this study, two developed artificial intelligence models were compared with existing literature models. According to the results, for test database, the existing models were able to predict the shear modulus and damping ratio with R of 0.911 and 0.918, respectively. However, the proposed ANN and CRRF models were able to predict shear modulus with R of 0.993 and 0.996, and damping ratios with R of 0.992 and 0.990, respectively. The results showed that ANNs and CRRFs were more robust than existing models for predicting damping ratio and shear modulus, as well as identifying the influence of input variables on sand dynamic properties. © 2022 Elsevier Ltd
- Publisher
- Elsevier Ltd
- Relation
- Soil Dynamics and Earthquake Engineering Vol. 165, no. (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 Elsevier Ltd
- Subject
- 4005 Civil engineering; Artificial neural network; Classification and regression random forests; Damping ratio; Data-driven modelling; Parameter sensitivity; Shear modulus; Soil dynamics
- Reviewed
- Funder
- This research is supported by an Australian Government Research Training Program (RTP) Scholarship.
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