Predictive modeling of Atterberg's limits of soil passing through sieve #40 and #200 using artificial neural networks and multivariate regression: advancing sustainable construction practices-Reference-Cited by-同舟云学术

Predictive modeling of Atterberg's limits of soil passing through sieve #40 and #200 using artificial neural networks and multivariate regression: advancing sustainable construction practices

Published:2024-08-03 Issue: Volume: Page:
ISSN:2520-8160
Container-title:Multiscale and Multidisciplinary Modeling, Experiments and Design
language:en
Short-container-title:Multiscale and Multidiscip. Model. Exp. and Des.

Author:

Qamar Sana Ullah^ORCID,Alshameri Badee^ORCID,Hassan Waqas^ORCID,Maqsood Zain,Haider Abbas

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s41939-024-00560-x.pdf

Reference68 articles.

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2. Alshameri B (2020) Maximum dry density of sand–kaolin mixtures predicted by using fine content and specific gravity. SN Appl Sci 2:1693. https://doi.org/10.1007/s42452-020-03481-9

3. Alshameri B (2022) Prediction the shear strength and shear modulus of sand-clay mixture using bender element. J Appl Eng Sci 20:168–176. https://doi.org/10.5937/jaes0-30619

4. Alshameri B, Madun A (2019) Comprehensive correlations between the geotechnical and seismic data conducted via bender element. Geotech Geol Eng. https://doi.org/10.1007/s10706-019-00963-5

5. Alshameri B, Madun A, Bakar I (2017a) Assessment on the effect of fine content and moisture content towards shear strength. Geotech Eng 48:76–86

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