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ANJALI, KUMARI, Yesilyurt, Sefa, Samui, Pijush, Dalkilic, Huseyin, Katipoglu, Okan. (1402). Determination of DBTT of functionally graded steels using Artificial Intelligence. , (), -. doi: 10.22059/ceij.2023.349334.1902
KUMARI ANJALI; Sefa Nur Yesilyurt; Pijush Samui; Huseyin Yildirim Dalkilic; Okan Mert Katipoglu. "Determination of DBTT of functionally graded steels using Artificial Intelligence". , , , 1402, -. doi: 10.22059/ceij.2023.349334.1902
ANJALI, KUMARI, Yesilyurt, Sefa, Samui, Pijush, Dalkilic, Huseyin, Katipoglu, Okan. (1402). 'Determination of DBTT of functionally graded steels using Artificial Intelligence', , (), pp. -. doi: 10.22059/ceij.2023.349334.1902
ANJALI, KUMARI, Yesilyurt, Sefa, Samui, Pijush, Dalkilic, Huseyin, Katipoglu, Okan. Determination of DBTT of functionally graded steels using Artificial Intelligence. , 1402; (): -. doi: 10.22059/ceij.2023.349334.1902

Determination of DBTT of functionally graded steels using Artificial Intelligence

Civil Engineering Infrastructures Journal
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 19 مهر 1402    اصل مقاله (1.13 M)
شناسه دیجیتال (DOI): 10.22059/ceij.2023.349334.1902
نویسندگان
KUMARI ANJALI* 1؛ Sefa Nur Yesilyurt2؛ Pijush Samui3؛ Huseyin Yildirim Dalkilic4؛ Okan Mert Katipoglu5
1Research Scholar, School of Water Resources, Indian Institute of Technology, Kharagpur,India-721302
2PhD Student, Graduate School of Natural and Applied Sciences, Department of Civil Engineering, Dokuz Eylul University, Izmir, Turkey - 35390
3Associate Professor, Department of Civil Engineering, National Institute of Technology Patna, India – 800005
4Associate Professor, Department of Civil Engineering, Erzincan Binali Yildirim University, Erzincan, Turkey - 24000.
5Dr, Department of Civil Engineering, Erzincan Binali Yildirim University, Erzincan, Turkey - 24000.
چکیده
This study applied three Artificial Intelligence (AI) models to predict the ductile to the brittle transition temperature (DBTT) of functionally graded steels (FGS). These prediction models are Minimax Probability Machine Regression (MPMR) model, Genetic Programming (GP), and Emotional Neural Network (ENN) algorithms with strong prediction performance. The data of FGS type, crack tip configuration, the thickness of the graded ferritic zone, the thickness of the graded austenitic region, the distance of the notch from the Bainite or Martensite intermediate layer, and temperature were used as inputs in the establishment of the AI models. Charpy impact test (CVN) values obtained from experiments used as output. The datasets are classified into two sets of training and testing datasets. The performance of the established AI models was evaluated through 16 statistical indicators and graphically used regression error characteristics, an area over the curve, Taylor diagrams, and scatter plots. As a result, the GP model showed superior prediction performance to other models. This study aimed to reduce the number of parameters while providing a comparison of models. In this way, in areas with complex studies such as civil engineering, It allows the work to be completed more practically.
کلیدواژه‌ها
Artificial Intelligence؛ Minimax Probability Machine Regression؛ ENN؛ Genetic Programming؛ Taylor diagram
آمار
تعداد مشاهده مقاله: 104
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