Aug 2026· International Conference on Information Security and Cryptology· pp. 1092-1098· 0 citations· 16 references
Abstract
The Prediction of accurate soil shear strength plays an important role in geotechnical engineering since it directly affects stability analysis and foundation designs. Traditional empirical methods face difficulties in dealing with the heterogeneity of soil data. This leads to the consideration of machine learning models to address this problem. This paper introduces EAS-DeepSoil, a novel solution combining evolutionary algorithm-based data split approach, attention mechanism, and a multilayer perceptron (MLP). Indeed, EAs are of great importance because it can help balance the distribution of the soil data used for both training and testing processes, as enable generation of unbiased training/testing datasets irrespective of any imbalances that may be inherent in the soil data distribution. Attention mechanisms are equally essential because it can assist in enhancing feature extraction, which highlights the important properties of soils. In addition, the use of the MLP considers the nonlinearity that exists in the mapping process of input features to output shear strength. EAS-DeepSoil achieves an accuracy of 97%, demonstrating superiority compared to other methods.
In geotechnical engineering, excess pore water pressure is one of the main reasons that cause structural instability. It greatly reduces the effective stress of soil and may lead to problems such as piping. At present, geocomposite drainage layers (GDL) are commonly used as effective drainage materials. However, it r...
Peng Tang, Zhi-Lin Wu, Si-Yu Gu et al.· Frontiers in Earth Science· 0 citations
The depletion of shallow coal resources necessitates the advancement of deep mining operations, where accurate prediction of coal–rock composite mechanical behavior is critical for disaster prevention. This study systematically develops and evaluates a machine learning (ML) framework for predicting key mechanical prope...
Qinghua Ou, G. Lacidogna, Luwang Chen et al.· Acta Geophysica· 0 citations
Accurate prediction of bearing capacity in geosynthetic-reinforced working platforms over soft subgrade remains a challenge in geotechnical engineering practice. Existing methods provide practical solutions to estimate the bearing capacity in two-layered systems with soft subgrades but produce inconsistent results unde...
Syed Shadman Sakib, S. Demirdogen, Jie Han· E3S Web of Conferences· 0 citations
Abstract
Reliable foundation design requires simultaneous control of ultimate bearing capacity and serviceability settlement, yet conventional prediction methods commonly treat soil parameters as static and omit rainfall, moisture, matric suction and groundwater fluctuations. This article develops a machine-learning-dr...
Afolabi I. Awodeyi, E. R. Iwemah, Omokaro Idama et al.· International Journal of Sci...· 0 citations
Investigation of machine learning models for predicting the compressive strength of concrete using a publicly available experimental dataset reveals that curing age and cement content are the most influential parameters affecting compressive strength, followed by water content, which is consistent with established conc...
S. Rouabah· ITEGAM- Journal of Engineeri...· 0 citations
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