Chapter 9 Optimization and Artificial Neural Network Models for Reinforced Concrete Members Melda Yücel, Sinan Melih Nigdeli, Aylin Ece Kayabekir, and Gebrail Bekda¸ s 9.1 Introduction In recent years, especially nowadays, artificial intelligence (AI) and machine learning technology that AI’s applications are reflected to it are benefited very often in many different fields of study. As the leading cause of this, it can be demonstrated to be able to provide any unknown parameter with higher accuracy, quick as namely effective in terms of consuming time, besides of these, with the usage of less effort and manpower because of the mentioned technologies. In the medical field, diagnosing of disease speedily by be defined of foreign cells; in chemistry or pharmacology, prediction of measures of ingredients within a medicine; or in civil engineering and structural area, detection what properties of materials such as concrete, steel, fly ash, lean concrete that they can ensure of sufficient resistance and safety in any structural member, can be given as examples to this case. Not only mentioned samples, it is possible to find many examples like these in lots of other study fields such as electronics and telecommunication, space researches, language science, and genetics. This technology has an “intelligence,” which is artificial, as understood from its name, and this intelligence model was actually put forward by inspiring from humans. On the other side, machine learning is concerned with the usage and progress of M. Yücel (B ) · S. M. Nigdeli · A. E. Kayabekir · G. Bekda¸ s Department of Civil Engineering, Istanbul University-Cerrahpa¸ sa, 34320 Avcılar, Istanbul, Turkey e-mail: melda.yucel@yahoo.com.tr S. M. Nigdeli e-mail: melihnig@istanbul.edu.tr A. E. Kayabekir e-mail: ecekayabekir@gmail.com G. Bekda¸ s e-mail: bekdas@istanbul.edu.tr © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Carbas et al. (eds.), Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications, Springer Tracts in Nature-Inspired Computing, https://doi.org/10.1007/978-981-33-6773-9_9 181