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      Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests

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          Abstract

          Assessment of the mechanical properties of structural steels characterizing their strength and deformation parameters is an essential problem in the monitoring of structures that have been in operation for quite a long time. The properties of steel can change under the influence of loads, deformations, or temperatures. There is a problem of express determination of the steel grade used in structures—often met in the practice of civil engineering or machinery manufacturing. The article proposes the use of artificial neural networks for the classification and clustering of steel according to strength characteristics. The experimental studies of the mechanical characteristics of various steel grades were carried out, and a special device was developed for conducting tests by shock indentation of a conical indenter. A technique based on a neural network was built. The developed algorithm allows with average accuracy—over 95%—to attribute the results to the corresponding steel grade.

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          Most cited references48

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          Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections

          The main aim of this study is to develop different hybrid artificial intelligence (AI) approaches, such as an adaptive neuro-fuzzy inference system (ANFIS) and two ANFISs optimized by metaheuristic techniques, namely simulated annealing (SA) and biogeography-based optimization (BBO) for predicting the critical buckling load of structural members under compression, taking into account the influence of initial geometric imperfections. With this aim, the existing results of compression tests on steel columns were collected and used as a dataset. Eleven input parameters, representing the slenderness ratios and initial geometric imperfections, were considered. The predicted target was the critical buckling load of columns. Statistical criteria, namely the correlation coefficient (R), the root mean squared error (RMSE), and the mean absolute error (MAE) were used to evaluate and validate the three proposed AI models. The results showed that SA and BBO were able to improve the prediction performance of the original ANFIS. Excellent results using the BBO optimization technique were achieved (i.e., an increase in R by 7.15%, RMSE by 40.48%, and MAE by 38.45%), and those using the SA technique were not much different (i.e., an increase in R by 5.03%, RMSE by 26.68%, and MAE by 20.40%). Finally, sensitivity analysis was performed, and the most important imperfections affecting column buckling capacity was found to be the initial in-plane loading eccentricity at the top and bottom ends of the columns. The methodology and the developed AI models herein could pave the way to establishing an advanced approach to forecasting damages of columns under compression.
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            Analytical models of scratch hardness

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              Correlation of spherical nanoindentation stress-strain curves to simple compression stress-strain curves for elastic-plastic isotropic materials using finite element models

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                Author and article information

                Journal
                Materials (Basel)
                Materials (Basel)
                materials
                Materials
                MDPI
                1996-1944
                27 May 2020
                June 2020
                : 13
                : 11
                : 2445
                Affiliations
                [1 ]Department of Transport Systems, Faculty of Roads and Transport Systems, Don State Technical University, Gagarin, 1, 344000 Rostov-on-Don, Russia
                [2 ]Department of Information Systems in Construction, Faculty of IT-systems and Technologies, Don State Technical University, Gagarin, 1, 344000 Rostov-on-Don, Russia; lyapin.rnd@ 123456yandex.ru
                [3 ]Faculty of Civil Engineering, Warsaw University of Technology, Al. Armii Ludowej 16, 00-637 Warsaw, Poland; h.anysz@ 123456il.pw.edu.pl
                [4 ]Department of Life Safety and Environmental Protection, Faculty of Life Safety and Environmental Engineering, Don State Technical University, Gagarin, 1, 344000 Rostov-on-Don, Russia; reception@ 123456donstu.ru (B.M.); mozgovoy.dstu@ 123456mail.ru (A.M.)
                [5 ]Department of Motor Roads, Faculty of Roads and Transport Systems, Don State Technical University, Gagarin, 1, 344000 Rostov-on-Don, Russia; veremeenko78@ 123456mail.ru
                Author notes
                [* ]Correspondence: besk-an@ 123456yandex.ru ; Tel.: +7-8632-738454
                Author information
                https://orcid.org/0000-0002-6173-9365
                https://orcid.org/0000-0001-5809-8504
                https://orcid.org/0000-0002-3804-5859
                Article
                materials-13-02445
                10.3390/ma13112445
                7321333
                32471095
                b5ca2c38-12e3-4dee-a4c8-de4d77e3a943
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 13 April 2020
                : 25 May 2020
                Categories
                Article

                non-destructive test,machine learning,clustering,steel,cone indentation,impact,artificial neural networks

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