Optimizing pervious concrete with machine learning: Predicting permeability and compressive strength using artificial neural networks-Reference-Cited by-同舟云学术

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Optimizing pervious concrete with machine learning: Predicting permeability and compressive strength using artificial neural networks

Published:2024-09 Issue: Volume:443 Page:137619
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

Wu Yinglong^ORCID,Pieralisi R.,B. Sandoval F. Gersson,López-Carreño R.D.,Pujadas P.

Funder

Agència Catalana de l'Aigua

AGAUR

Publisher

Elsevier BV

Reference78 articles.

1. A comprehensive review on pervious concrete;Adresi;Constr. Build. Mater.,2023

2. Engineering properties and lifecycle impacts of pervious all-road all-weather Multilayered pavement;Singh;Resour. Conserv Recycl,2022

3. Research and application of pervious concrete as a sustainable pavement material: A state-of-the-art and state-of-the-practice review;Zhong;Constr. Build. Mater.,2018

4. Pervious concrete as an alternative pavement strategy: a state-of-the-art review;Debnath;Int. J. Pavement Eng.,2020

5. A review of sustainable pervious concrete systems: Emphasis on clogging, material characterization, and environmental aspects;Singh;Constr. Build. Mater.,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting the compressive strength of self-compacting concrete by developed African vulture optimization algorithm-Elman neural networks;Scientific Reports;2024-08-29

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