Prediction of hydraulic conductivity of porous granular media by establishment of random forest algorithm-Reference-Cited by-同舟云学术

Prediction of hydraulic conductivity of porous granular media by establishment of random forest algorithm

Published:2023-02 Issue: Volume:366 Page:130065
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

Javad Azarhoosh Mohammad,Koohmishi Mehdi

Publisher

Elsevier BV

Subject

General Materials Science,Building and Construction,Civil and Structural Engineering

Reference50 articles.

1. Modeling and optimization of catalytic performance of SAPO-34 nanocatalysts synthesized sonochemically using a new hybrid of non-dominated sorting genetic algorithm-II based artificial neural networks (NSGA-II-ANNs);Askari;RSC Adv.,2015

2. Providing laboratory rutting models for modified asphalt mixes with different waste materials;Azarhoosh;Periodica Polytechnica Civil Engineering,2018

3. A study on the Wilkins and Forchheimer equations used in coarse granular media flow;Banerjee;Acta Geophys.,2018

4. The development of a performance specification for granular base and subbase material (No. FHWA-NJ-2005-003);Bennert,2005

5. Drainage equations and non-Darcian modelling in coarse porous media or geosynthetic materials;Bordier;J. Hydrol.,2000

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