پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شمارهها | 6,572 |
| تعداد مقالات | 71,003 |
| تعداد مشاهده مقاله | 125,492,830 |
| تعداد دریافت فایل اصل مقاله | 98,752,983 |
Determination of DBTT of Functionally Graded Steels Using Artificial Intelligence | ||
| Civil Engineering Infrastructures Journal | ||
| مقاله 11، دوره 57، شماره 1، شهریور 2024، صفحه 189-203 اصل مقاله (877.18 K) | ||
| نوع مقاله: Research Papers | ||
| شناسه دیجیتال (DOI): 10.22059/ceij.2023.349334.1902 | ||
| نویسندگان | ||
| Kumari Anjali* 1؛ Sefa Nur Yesilyurt2؛ Pijush Samui3؛ Huseyin Yildirim Dalkilic4؛ Okan Mert Katipoglu5 | ||
| 1Ph.D. Candidate, Research Scholar, School of Water Resources, Indian Institute of Technology, Kharagpur, India. | ||
| 2Ph.D. Candidate, Graduate School of Natural and Applied Sciences, Department of Civil Engineering, Dokuz Eylul University, Izmir, Turkey. | ||
| 3Associate Professor, Department of Civil Engineering, National Institute of Technology, Patna, India. | ||
| 4Associate Professor, Department of Civil Engineering, Erzincan Binali Yildirim University, Erzincan, Turkey. | ||
| 5Professor, Department of Civil Engineering, Erzincan Binali Yildirim University, Erzincan, Turkey. | ||
| چکیده | ||
| This study applied three Artificial Intelligence (AI) models to project 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 have been divided into two groups: one for training and another for testing. 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. The primary objective of this study was to decrease the parameter count while also facilitating model comparisons. In this way, in areas with complex studies such as civil engineering; it allows the work to be completed more practically. | ||
| کلیدواژهها | ||
| Artificial Intelligence؛ ENN؛ Genetic Programming؛ Minimax Probability Machine Regression؛ Taylor Diagram | ||
| مراجع | ||
|
Al Adwan, J., Alzubi, Y., Alkhdour, A. and Alqawasmeh, H. (2023). “Predicting compressive strength of concrete using histogram-based gradient boosting approach for rapid design of mixtures”, Civil Engineering Infrastructures Journal, 56(1), 159-172, https://doi.org/10.22059/CEIJ.2022.337777.1811.
Alyousef, R., Rehman, M.F., Khan, M., Fawad, M., Khan, A.U., Hassan, A.M. and Ghamry, N.A. (2023). “Machine learning-driven predictive models for compressive strength of steel fiber reinforced concrete subjected to high temperatures”, Case Studies in Construction Materials, 19, e02418, https://doi.org/10.1016/j.cscm.2023.e02418.
Astarabadi, S.S.M. and Ebadzadeh, M.M. (2019). “Genetic programming performance prediction and its application for symbolic regression problems”, Information Sciences, 502, 418-433, https://doi.org/10.1016/j.ins.2019.06.040.
Babaie, T., Karimizandi, R. and Lucas, C. (2008). “Learning based brain emotional intelligence as a new aspect for development of an alarm system”, Soft Computing, 12, 857-873, https://doi.org/10.1007/s00500-007-0258-8.
Bae, K.O., Nguyen, T.T., Park, J., Park, J.S. and Baek, U.B. (2023). “Temperature dependency of hydrogen-related impact energy degradation of type 304 austenitic stainless steel”, Journal of Mechanical Science and Technology, 37(6), 2891-2901, https://doi.org/10.1007/s12206-023-0515-5.
Biswas, R., Samui, P. and Rai, B. (2019). “Determination of compressive strength using relevance vector machine and ENN”, Asian Journal of Civil Engineering, 20(8), 1109-1118, https://doi.org/10.1007/s42107-019-00171-9.
Bonakdari, H., Ebtehaj, I., Samui, P. and Gharabaghi, B. (2019). “Lake water-level fluctuations forecasting using minimax probability machine regression, relevance vector machine, Gaussian process regression, and extreme learning machine”, Water Resources Management, 33(11), 3965-3984, https://doi.org/10.1007/s11269-019-02346-0.
Cevik, A. and Sonebi, M. (2008). “Modelling the performance of self-compacting SIFCON of cement slurries using genetic programming technique”, Computers Concrete, 5(5), 475-490, https://doi.org/10.12989/cac.2008.5.5.475.
Dastorani, M.T., Mahjoobi, J., Talebi, A. and Fakhar, F. (2018). “Application of machine learning approaches in rainfall-runoff modeling (Case Study: Zayandeh_Rood basin in Iran)”, Civil Engineering Infrastructures Journal, 51(2), 293-310, https://doi.org/10.7508/CEIJ.2018.02.004.
Deng, Z., Chen, J., Zhang, T., Cao, L. and Wang, S. (2018). “Generalized hidden-mapping minimax probability machine for the training and reliability learning of several classical intelligent models”, Information Sciences, 436, 302-319, https://doi.org/10.1016/j.ins.2018.01.034.
Dubey, Y., Sharma, P. and Singh, M.P. (2023). “Optimization using genetic algorithm of GMAW parameters for Charpy impact test of 080M40 steel”, International Journal on Interactive Design and Manufacturing (IJIDeM), 1-11, https://doi.org/10.1007/s12008-023-01371-z.
Hoang, N.D. and Tran, D.V. (2023). “Machine learning-based estimation of concrete compressive strength: a multi-model and multi-dataset study”, Civil Engineering Infrastructures Journal, https://doi.org/10.1007/s12008-023-01371-z (Online).
Kar, S.S., Athawale, A.A., Bhushan, M. and Roy, L.B. (2023). “Dynamic soil-structure interaction of multi-story buildings using the finite element method and minimax probability machine regression”, Engineering, Technology & Applied Science Research, 13(4), 11170-11176, https://doi.org/10.48084/etasr.5870.
Kecman, V. (2001). Learning and soft computing: support vector machines, neural networks, and fuzzy logic models, MIT press.
Khashman, A. (2008). “A modified backpropagation learning algorithm with added emotional coefficients”, IEEE Transactions on Neural Networks, 19(11), 1896-1909, https://doi.org/10.1109/TNN.2008.2002913.
Khashman, A. (2009). “Blood cell identification using emotional neural networks”, Journal of Information Science & Engineering, 25(6), 1737-1751.
Kohestani, V.R., Vosoghi, M., Hassanlourad, M. and Fallahnia, M. (2017). “Bearing capacity of shallow foundations on cohesionless soils: A random forest based approach”, Civil Engineering Infrastructures Journal, 50(1), 35-49, https://doi.org/10.7508/ceij.2017.01.003.
Koshiyama, A.S., Vellasco, M.M. and Tanscheit, R. (2015). “GPFIS-CLASS: A genetic fuzzy system based on Genetic Programming for classification problems”, Applied Soft Computing, 37, 561-571, https://doi.org/10.1016/j.asoc.2015.08.055.
Kovalev, M. (2021). “Ontology-based representation of an Artificial Neural Networks”, International Conference on Open Semantic Technologies for Intelligent Systems, (pp. 132-151), Springer International Publishing, Cham, https://doi.org/10.1007/978-3-031-15882-7_8.
Koza, J.R. (1994). “Genetic programming as a means for programming computers by natural selection”, Statistics and C mputing, 4, 87-112, https://doi.org/10.1007/BF00175355.
Kumar, C., Vardhan, H. and Murthy, C.S. (2022). “Artificial neural network for prediction of rock properties using acoustic frequencies recorded during rock drilling operations”, Modeling Earth Systems and Environment, 8(1), 141-161, https://doi.org/10.1007/s40808-021-01103-w.
Kumar, M., Bardhan, A., Samui, P., Hu, J.W. and Kaloop, M.R. (2021). “Reliability analysis of pile foundation using soft computing techniques: A comparative study”, Processes, 9(3), 486, https://doi.org/10.3390/pr9030486.
Lanckriet, G., Ghaoui, L., Bhattacharyya, C. and Jordan, M. (2001). “Minimax probability machine”, Advances in Neural Information Processing Systems, 14.
Lin, C.C., He, R.X. and Liu, W.Y. (2018). “Considering multiple factors to forecast CO2 emissions: A hybrid multivariable grey forecasting and genetic programming approach”, Energies, 11(12), 3432, https://doi.org/10.3390/en11123432.
Nazari, A., Milani, A.A. and Zakeri, M. (2011). “Modeling ductile to brittle transition temperature of functionally graded steels by artificial neural networks”, Computational Materials Science, 50(7), 2028-2037.
Papa, J.P., Rosa, G.H. and Papa, L.P. (2017). “A binary-constrained geometric semantic GP for feature selection purposes”, Pattern Recognition Letters, 100, 59-66, https://doi.org/10.1016/j.patrec.2017.10.002.
Park, D. and Rilett, L.R. (1999). “Forecasting freeway link ravel times with a multi-layer feed forward neural network”, Computer Aided Civil and Infrastructure Engineering, 14, 358-367, https://doi.org/10.1111/0885-9507.00154.
Switzner, N.T., Anderson, J., Ahmed, L.A., Rosenfeld, M. and Veloo, P. (2023). “Algorithms to estimate the ductile to brittle transition temperature, upper shelf energy, and their uncertainties for steel using Charpy V-Notch shear area and absorbed energy data”, Metals, 13(5), 877, https://doi.org/10.3390/met13050877.
Taylor, K.E. (2001). “Summarizing multiple aspects of model performance in a single diagram”, Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192, https://doi.org/10.1029/2000JD900719.
Vouros, G.A. (2022). “Explainable deep reinforcement learning: state of the art and challenges”, ACM Computing Surveys, 55(5), 1-39, https://doi.org/10.1145/3527448.
Zhang, H., Zhou, J., Jahed Armaghani, D., Tahir, M.M., Pham, B.T. and Huynh, V.V. (2020). “A combination of feature selection and random forest techniques to solve a problem related to blast-induced ground vibration”, Applied Sciences, 10(3), 869, https://doi.org/10.3390/app10030869.
Ziggah, Y.Y., Issaka, Y. and Laari, P.B. (2022). “Evaluation of different artificial intelligent methods for predicting dam piezometric water level”, Modeling Earth Systems and Environment, 8(2), 2715-2731, https://doi.org/10.1007/s40808-021-01263-9. | ||
|
آمار تعداد مشاهده مقاله: 313 تعداد دریافت فایل اصل مقاله: 410 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب