Fracture and Structural Integrity e-ISSN 1971-8993 Open access
Analytical, Numerical and Physical Models Vol. 14, No. 53 (2020)

Forecasting bearing capacity of the mixed soil using artificial neural networking

Author and affiliation details
  • Abdoullah Namdar Corresponding author Faculty of Architecture and Civil Engineering, Huaiyin Institute of Technology, Huai’an, China; Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam ab_namdar@yahoo.com
DOI
10.3221/IGF-ESIS.53.22
Pages
285-294
Published
Licence
CC BY 4.0
Published inVol. 14, No. 53 (2020)Go to the issue

Abstract

Abstract. The bearing capacity of soil changes owing to the mechanical properties of the soil and influences on structural stability. In most of the geotechnical engineering projects, there are several soil mechanic experiments, they need interpretation before application. The mechanical properties of soil interaction make complex predict of soil bearing capacity. However, to enhancement safety of construction project need to the interpretation of soil experiments and design results for proper application in a geotechnical engineering project. In this study, artificial neural networking is proposed for the evaluation of the mixed soil characteristics to forecast the safe bearing capacity of soil because of the mechanical properties of the soil interaction phenomenon. The results reveal for prediction of the safe bearing capacity, the R2 and RMSE for all mechanical properties effects on safe bearing capacity are 0.98 and 0.02, these values can provide a suitable accuracy for prediction safe bearing capacity of the mixed soil. The higher inaccuracy obtained when only the influence of single mechanical property on the mixed soil considered in prediction of the safe bearing capacity. This study supports the enhancement of geotechnical engineering design quality through prediction safe bearing capacity from characterized mechanical properties of the soil.

Keywords

Article history

  1. ReceivedUntil the next stage: 11 days
  2. Revision requestedUntil the next stage: 2 days
  3. RevisedUntil the next stage: 0 days
  4. AcceptedUntil the next stage: 71 days
  5. Published

How to cite

APA

Namdar, A. (2020). Forecasting bearing capacity of the mixed soil using artificial neural networking. Fracture and Structural Integrity, 14(53), 285–294. https://doi.org/10.3221/IGF-ESIS.53.22

IEEE

A. Namdar, “Forecasting bearing capacity of the mixed soil using artificial neural networking,” Fracture and Structural Integrity, vol. 14, no. 53, pp. 285–294, 2020, doi: 10.3221/IGF-ESIS.53.22.

MLA

Namdar, Abdoullah. “Forecasting bearing capacity of the mixed soil using artificial neural networking.” Fracture and Structural Integrity, vol. 14, no. 53, 2020, pp. 285–294. https://doi.org/10.3221/IGF-ESIS.53.22.

Chicago

Namdar, Abdoullah. 2020. “Forecasting bearing capacity of the mixed soil using artificial neural networking.” Fracture and Structural Integrity 14 (53): 285–294. https://doi.org/10.3221/IGF-ESIS.53.22.

Harvard

Namdar, A. (2020) ‘Forecasting bearing capacity of the mixed soil using artificial neural networking’, Fracture and Structural Integrity, 14(53), pp. 285–294. doi: 10.3221/IGF-ESIS.53.22.

Vancouver

Namdar A. Forecasting bearing capacity of the mixed soil using artificial neural networking. Fracture and Structural Integrity. 2020;14(53):285-294. doi:10.3221/IGF-ESIS.53.22

BibTeX

@article{namdar2020, author = {Namdar, Abdoullah}, title = {Forecasting bearing capacity of the mixed soil using artificial neural networking}, journal = {Fracture and Structural Integrity}, year = {2020}, volume = {14}, number = {53}, pages = {285--294}, doi = {10.3221/IGF-ESIS.53.22}, url = {https://doi.org/10.3221/IGF-ESIS.53.22}, issn = {1971-8993} }

RIS

TY - JOUR AU - Namdar, Abdoullah TI - Forecasting bearing capacity of the mixed soil using artificial neural networking JO - Fracture and Structural Integrity T2 - Fracture and Structural Integrity PY - 2020 DA - 2020/07/19 VL - 14 IS - 53 SP - 285 EP - 294 DO - 10.3221/IGF-ESIS.53.22 UR - https://doi.org/10.3221/IGF-ESIS.53.22 SN - 1971-8993 ER -

Licence

Creative Commons Attribution 4.0 International

May be shared and adapted, provided appropriate credit is given. Copyright © 2020 Abdoullah Namdar.

Copyright 
Authors are allowed to retain both the copyright and the publishing rights of their articles without restrictions.  

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