A sustainable approach for estimating soft ground soil stiffness modulus using artificial intelligence-Reference-Cited by-同舟云学术

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A sustainable approach for estimating soft ground soil stiffness modulus using artificial intelligence

Published:2023-11-17 Issue:23 Volume:82 Page:
ISSN:1866-6280
Container-title:Environmental Earth Sciences
language:en
Short-container-title:Environ Earth Sci

Author:

Nawaz Muhammad Naqeeb,Nawaz Muhammad Muneeb,Awan Tariq Ahmed,Jaffar Syed Taseer Abbas,Jafri Turab H.,Oh Tae-Min,Hassan Waqas,Azab Marc

Funder

Brain Korea 21 FOUR Project in the Education & Research Center for Infrastructure of Smart Ocean City

Publisher

Springer Science and Business Media LLC

Subject

Earth-Surface Processes,Geology,Pollution,Soil Science,Water Science and Technology,Environmental Chemistry,Global and Planetary Change

Link

https://link.springer.com/content/pdf/10.1007/s12665-023-11193-7.pdf

Reference69 articles.

1. Abbey SJ, Ngambi S, Ganjian E (2017) Development of strength models for prediction of unconfined compressive strength of cement/byproduct material improved soils. Geotech Test J 40:928–935

2. Al Bodour W, Hanandeh S, Hajij M, Murad Y (2022) Development of evaluation framework for the unconfined compressive strength of soils based on the fundamental soil parameters using gene expression programming and deep learning methods. J Mater Civ Eng 34:4021452

3. Ardakani A, Kordnaeij A (2019) Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm. Eur J Environ Civ Eng 23:449–462

4. ASTM D (2017) Standard test method for particle-size analysis of soils. ASTM International, West Conshohocken, PA, USA. https://doi.org/10.1520/D0422-63R98

5. ASTM D (2018) D4318-test method for liquid limit. Plastic limit, and plasticity index of soils. ASTM International, West Conshohocken, PA, USA. https://doi.org/10.1520/D4318-17E01

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