ORIGINAL RESEARCH article
Front. Built Environ.
Sec. Geotechnical Engineering
Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1594924
This article is part of the Research TopicEmerging Artificial Intelligence tools in Geotechnical Engineering AdvancementsView all 4 articles
A Comparative Study between LSSVM, LSTM, and ANN in Predicting the Unconfined Compressive Strength of Virgin Fine-Grained Soil
Provisionally accepted
- 1Jodhpur Institute of Engineering & Technology (JIET), Jodhpur, Rajasthan, India
- 2Rajasthan Technical University, Kota, Rajasthan, India
- 3National Institute of Technology Patna, Patna, Bihar, India
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The present investigation introduces a robust soft computing model by comparing twelve least square support vector machine (LSSVM), six long short-term memory (LSTM), and thirty-six artificial neural network (ANN) models to predict the unconfined compressive strength (UCS) of fine-grained soil. For that purpose, a database of fine content, dry unit weight, porosity, void ratio, degree of saturation, and specific gravity results of 85 soil specimens has been compiled from the literature. 75 and 10 soil specimens were trained and tested for each model. Six training databases have been prepared to analyze the effect of quality and quantity of training database by selecting 50%, 60%, 70%, 80%, 90%, and 100% of 75 soil specimens. The performance comparison demonstrated that the LSTM model (MD 113) requires fewer training datasets (50% of 75) than the LSSVM (MD 102 & MD 108) and ANN (MD 120, MD 127, MD 136, MD139, MD 148, & MD 150) models. Also, it was observed that the nonlinear LSSVM model (MD 108) is unaffected by multicollinearity in training datasets and predicted UCS better than the linear LSSVM model (MD 102). Furthermore, the Levenberg-Marquardt neural network model (MD 120) has outperformed the other ANN models with the root mean square error (RMSE) of 5.1214 N/cm 2 , the mean absolute error (MAE) of 4.1379 N/cm 2 , and correlation (R) of 0.9836. The overall performance comparison revealed that the LSTM model is more potent than the LSSVM and ANN models. The LSTM model predicted the UCS of fine-grained soil with the RMSE of 4.7539 N/cm 2 , the MAE of 4.2461 N/cm 2 , and R of 0.9880. Conversely, cosine amplitude sensitivity analysis demonstrated that the fine content and dry unit weight influence the prediction of virgin UCS of fine-grained soils.
Keywords: artificial neural networks, Long-short term memory, Least-square support vector machine, multicollinearity, Quality and Quantity of Training Datasets, Unconfined compressive strength
Received: 17 Mar 2025; Accepted: 08 May 2025.
Copyright: © 2025 Khatti, Grover and Samui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jitendra Khatti, Jodhpur Institute of Engineering & Technology (JIET), Jodhpur, 342802, Rajasthan, India
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