Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq-Reference-Cited by-同舟云学术

Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq

Published:2021-03 Issue:6 Volume:14 Page:
ISSN:1866-7511
Container-title:Arabian Journal of Geosciences
language:en
Short-container-title:Arab J Geosci

Author:

Mawlood Yousif^ORCID,Salih Ahmed,Hummadi Rizgar,Hasan Ahmed,Ibrahim Hawkar

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

http://link.springer.com/content/pdf/10.1007/s12517-021-06712-4.pdf

Reference51 articles.

1. Ahmed C, Mohammed A, Saboonchi A (2020a) ArcGIS mapping, characterisations and modelling the physical and mechanical properties of the Sulaimani City soils, Kurdistan Region, Iraq. Geomech Geoeng:1–14

2. Ahmed C, Mohammed A, Tahir A (2020b) Geostatistics of strength, modeling and GIS mapping of soil properties for residential purpose for Sulaimani City soils, Kurdistan Region, Iraq. Modeling Earth Systems Environment:1–15

3. ASTM (D 7263 –2009) n.d.. Standard test methods for laboratory determination of density (unit weight) of soil specimens. West Conshohocken, PA, USA.: American Society for Testing of Materials.

4. ASTM (D2216−16) (n.d.). standard test method for unconfined compressive strength of cohesive soil. West Conshohocken, PA, USA.: American Society for Testing of Materials.

5. ASTM (D2435−2011) (n.d.). Standard test methods for one-dimensional consolidation properties of soils using incremental loading. West Conshohocken, PA, USA.: American Society for Testing of Materials.

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