Prediction of quality material using machine learning algorithm performance

Faqih Ma'arif; Harun Usman Ghifarsyam; Slamet Widodo; Zhengguo Gao; Bécaye Cissokho Ndiaye; Iskandar Yasin; Maris Setyo Nugroho; Pramudiyanto Pramudiyanto; Zainul Faizien Haza

doi:10.1063/5.0129263

Prediction of quality material using machine learning algorithm performance

Faqih Ma'arif^*
, Harun Usman Ghifarsyam
, Slamet Widodo
, Zhengguo Gao
, Bécaye Cissokho Ndiaye
, Iskandar Yasin
, Maris Setyo Nugroho
, Pramudiyanto Pramudiyanto
, Zainul Faizien Haza

^*Corresponding author for this work

School of Transportation Science and Engineering

Yogyakarta State University
Beijing Jiaotong University
Beihang University
Sarjanawiyata Tamansiswa University

Research output: Contribution to journal › Conference article › peer-review

Abstract

This study aims to develop a machine learning approach based on a material quality evaluation system (diagnosis). The proposed method is a custom implementation of the Naïve Bayes algorithm. The specimens consisted of four mortar variants with categories M, S, N, and O, tested at different ages of 3, 7, 14, 21, and 28 days, respectively. The pulse velocity results were analyzed and validated with a machine learning approach. The introduction of machine learning into this system facilitates the detection of experimental test results through ultrasonic wave propagation speed (UPV). Applying code to models proves that machine learning can test, evaluate, and interpret results.

Original language	English
Article number	040016
Journal	AIP Conference Proceedings
Volume	2629
Issue number	1
DOIs	https://doi.org/10.1063/5.0129263
State	Published - 2 Aug 2023
Event	4th International Conference on Sustainable Infrastructure: Research and Innovation in Sustainable Infrastructures During the Covid-19 Pandemic, ICSI 2021 - Virtual, Online, Indonesia Duration: 5 Oct 2021 → …

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AU - Yasin, Iskandar

AU - Nugroho, Maris Setyo

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AU - Haza, Zainul Faizien

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AB - This study aims to develop a machine learning approach based on a material quality evaluation system (diagnosis). The proposed method is a custom implementation of the Naïve Bayes algorithm. The specimens consisted of four mortar variants with categories M, S, N, and O, tested at different ages of 3, 7, 14, 21, and 28 days, respectively. The pulse velocity results were analyzed and validated with a machine learning approach. The introduction of machine learning into this system facilitates the detection of experimental test results through ultrasonic wave propagation speed (UPV). Applying code to models proves that machine learning can test, evaluate, and interpret results.

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Prediction of quality material using machine learning algorithm performance

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