Machine learning-based approach for optimizing mixture proportion of recycled plastic aggregate concrete considering compressive strength, dry density, and production cost

Seong Ho Han; Kamal H. Khayat; Sungwoo Park; Jinyoung Yoon

doi:10.1016/j.jobe.2023.108393

Machine learning-based approach for optimizing mixture proportion of recycled plastic aggregate concrete considering compressive strength, dry density, and production cost

Seong Ho Han
, Kamal H. Khayat
, Sungwoo Park
, Jinyoung Yoon

Civil, Architectural and Environmental Engineering

Chung-Ang University
Korean Institute of Civil Engineering and Building Technology

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	108393
Journal	Journal of Building Engineering
Volume	83
DOIs	https://doi.org/10.1016/j.jobe.2023.108393
State	Published - Apr 15 2024

ASJC Scopus Subject Areas

Civil and Structural Engineering
Architecture
Building and Construction
Safety, Risk, Reliability and Quality
Mechanics of Materials

Keywords

Machine learning
Optimal mixture proportions
Plastic aggregate concrete
Plastic waste
Random forest

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10.1016/j.jobe.2023.108393

Cite this

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title = "Machine learning-based approach for optimizing mixture proportion of recycled plastic aggregate concrete considering compressive strength, dry density, and production cost",

keywords = "Machine learning, Optimal mixture proportions, Plastic aggregate concrete, Plastic waste, Random forest",

author = "Han, \{Seong Ho\} and Khayat, \{Kamal H.\} and Sungwoo Park and Jinyoung Yoon",

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year = "2024",

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doi = "10.1016/j.jobe.2023.108393",

language = "English",

volume = "83",

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T1 - Machine learning-based approach for optimizing mixture proportion of recycled plastic aggregate concrete considering compressive strength, dry density, and production cost

AU - Han, Seong Ho

AU - Khayat, Kamal H.

AU - Park, Sungwoo

AU - Yoon, Jinyoung

PY - 2024/4/15

Y1 - 2024/4/15

KW - Machine learning

KW - Optimal mixture proportions

KW - Plastic aggregate concrete

KW - Plastic waste

KW - Random forest

UR - https://www.scopus.com/pages/publications/85182502096

UR - https://www.scopus.com/pages/publications/85182502096#tab=citedBy

U2 - 10.1016/j.jobe.2023.108393

DO - 10.1016/j.jobe.2023.108393

M3 - Article

AN - SCOPUS:85182502096

SN - 2352-7102

VL - 83

JO - Journal of Building Engineering

JF - Journal of Building Engineering

M1 - 108393

ER -

Machine learning-based approach for optimizing mixture proportion of recycled plastic aggregate concrete considering compressive strength, dry density, and production cost

ASJC Scopus Subject Areas

Keywords

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