Authors
1
Research Scholar, School of Water Resources, Indian Institute of Technology, Kharagpur,India-721302
2
PhD Student, Graduate School of Natural and Applied Sciences, Department of Civil Engineering, Dokuz Eylul University, Izmir, Turkey - 35390
3
Associate Professor, Department of Civil Engineering, National Institute of Technology Patna, India – 800005
4
Associate Professor, Department of Civil Engineering, Erzincan Binali Yildirim University, Erzincan, Turkey - 24000.
5
Dr, Department of Civil Engineering, Erzincan Binali Yildirim University, Erzincan, Turkey - 24000.
Abstract
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.
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