Prediction of flowability and strength in controlled low-strength material through regression and oversampling algorithm with deep neural network-Reference-Cited by-同舟云学术

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Prediction of flowability and strength in controlled low-strength material through regression and oversampling algorithm with deep neural network

Published:2024-07 Issue: Volume:20 Page:e03192
ISSN:2214-5095
Container-title:Case Studies in Construction Materials
language:en
Short-container-title:Case Studies in Construction Materials

Author:

Han WooJin,Lee Dongsoo,Lee Jong-Sub,Lim Dae Sung,Yoon Hyung-Koo

Funder

Daejeon University

National Research Foundation of Korea

Korea Agency for Infrastructure Technology Advancement

Ministry of Education

Ministry of Land, Infrastructure and Transport

Publisher

Elsevier BV

Reference56 articles.

1. Effect of fly-ash as fine aggregate on the workability and mechanical properties of cemented paste backfill;Yin;Case Stud. Constr. Mater.,2022

2. Suitability of Rice Husk Ash (RHA) with lime as a soil stabilizer in geotechnical applications;Pushpakumara;Int. J. Geo-Eng.,2022

3. Comparative study on engineering properties of cement-based backfill material for sustainable urban area;Han;Constr. Build. Mater.,2022

4. Volume, strength, and stiffness characteristics of expandable foam grout;Han;Constr. Build. Mater.,2021

5. Pore topology, volume expansion and pressure development in chemically-induced foam cements;Han;Sci. Rep.,2022

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. Enhancing Material Property Predictions through Optimized KNN Imputation and Deep Neural Network Modeling;IgMin Research;2024-06-13

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