Prediction of shear strength parameters of hydrocarbon contaminated sand based on machine learning methods-Reference-Cited by-同舟云学术

Prediction of shear strength parameters of hydrocarbon contaminated sand based on machine learning methods

Published:2020-12-22 Issue:4 Volume:15 Page:317-335
ISSN:1749-9518
Container-title:Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards
language:en
Short-container-title:Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards

Author:

Rezaee Milad¹,Mojtahedi Seyed Farid Fazel²,Taherabadi Ehsan³,Soleymani Kamyar⁴,Pejman Mehdi⁵

Affiliation:

1. Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada

2. Department of Civil Engineering, Sharif University of Technology, Tehran, Iran

3. Department of Civil Engineering, Imam Khomeini International University, Qazvin, Iran

4. School of Environment, College of Engineering, University of Tehran, Tehran, Iran

5. Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, Italy

Publisher

Informa UK Limited

Subject

Geology,Geotechnical Engineering and Engineering Geology,Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/17499518.2020.1861633

Reference67 articles.

1. Oil Contaminated Sand: An Emerging and Sustainable Construction Material

2. Effects of Light Crude Oil Contamination on the Physical and Mechanical Properties of Fine Sand

3. Effect of diesel and gasoline on the properties of sands — a comparative study

4. Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm

5. Using complex permittivity and artificial neural networks to identify and classify copper, zinc, and lead contamination in soil

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