Estimation of the compressive strength of green concretes containing rice husk ash: a comparison of different machine learning approaches-Reference-Cited by-同舟云学术

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Estimation of the compressive strength of green concretes containing rice husk ash: a comparison of different machine learning approaches

Published:2022-05-05 Issue:2 Volume:27 Page:961-983
ISSN:1964-8189
Container-title:European Journal of Environmental and Civil Engineering
language:en
Short-container-title:European Journal of Environmental and Civil Engineering

Author:

Tavana Amlashi Amir¹,Mohammadi Golafshani Emadaldin²^ORCID,Ebrahimi Seyed Abolfazl³,Behnood Ali⁴^ORCID

Affiliation:

1. Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran

2. Department of Civil Engineering, Monash University, Melbourne, Australia

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

4. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA

Publisher

Informa UK Limited

Subject

Civil and Structural Engineering,Environmental Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/19648189.2022.2068657

Reference105 articles.

1. Strength and Some Durability Properties of Concrete Containing Rice Husk Ash Produced in a Charcoal Incinerator at Low Specific Surface

2. Estimation of physical, mechanical and hydrological properties of permeable concrete using computational intelligence approach

3. Soft computing based formulations for slump, compressive strength, and elastic modulus of bentonite plastic concrete

4. Application of computational intelligence and statistical approaches for auto-estimating the compressive strength of plastic concrete

5. Using rice husk ash as a cement replacement material in concrete

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1. Assessing the compressive strength of eco-friendly concrete made with rice husk ash: A hybrid artificial intelligence-aided technique;Structures;2024-10

2. Investigation of mechanical properties of high-performance concrete via multi-method of regression tree approach;Materials Today Communications;2024-08

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4. Application of hybridized ensemble learning and equilibrium optimization in estimating damping ratios of municipal solid waste;Scientific Reports;2024-07-30

5. Novel models based on support vector regression to predict the compressive strength of concrete with recycled aggregate;Multiscale and Multidisciplinary Modeling, Experiments and Design;2024-07-29

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