Faqih Ma'arif, Harun Usman Ghifarsyam, Slamet Widodo, Zhengguo Gao, Bécaye Cissokho Ndiaye, Iskandar Yasin, Maris Setyo Nugroho, Pramudiyanto Pramudiyanto, Zainul Faizien Haza
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. © 2023 Author(s).
Department of Civil Engineering, Yogyakarta State University, Yogyakarta, 55281, Indonesia; School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China; School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China; Department of Civil Engineering, Sarjanawiyata Tamansiswa University, Yogyakarta, 55167, Indonesia
