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INSL7710 | Optimization of Reinforced Concrete Elements | 3+0+0 | ECTS:7.5 | Year / Semester | Spring Semester | Level of Course | Third Cycle | Status | Elective | Department | DEPARTMENT of CIVIL ENGINEERING | Prerequisites and co-requisites | None | Mode of Delivery | Face to face | Contact Hours | 14 weeks - 3 hours of lectures per week | Lecturer | Doç. Dr. Hasan Tahsin ÖZTÜRK | Co-Lecturer | | Language of instruction | Turkish | Professional practise ( internship ) | None | | The aim of the course: | The aim of the course is to teach optimum design of reinforced concrete elements by using heuristic algorithms and the use of artificial neural networks trained with heuristic algorithms in the modeling of concrete experiments. |
Programme Outcomes | CTPO | TOA | Upon successful completion of the course, the students will be able to : | | | PO - 1 : | apply heuristic algorithms to a structural problem | 1,3,5 | | PO - 2 : | use heuristic algoritms to optimize reinforced concrete elements | 1,3,5 | | PO - 3 : | learn how to use heuristic algoritms for training of Artificial Neural Networks | 1,3,5 | | PO - 4 : | model experimental data with artificial neural networks trained with heuristic algorithms | 1,3,5 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | |
introduction, general structure of heuristic algorithms, objective functions for reinforced concrete, constraints for reinforced concrete, constraint handling methods, converting a RC design problem to an optimization problem, optimization of RC elements with Artificial Bee Colony Algorithm, optimization of RC elements with Cuckoo Search Algorithm, optimization of RC elements with Teaching-Learning Based Optimization Algorithm, optimization of RC elements with JAYA Algorithm, optimization of RC elements with Differential Search Algorithm, using Neural Networks with heuristic algorithms in concrete experiments. |
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Course Syllabus | Week | Subject | Related Notes / Files | Week 1 | Introduction to optimization | | Week 2 | General structure of heuristic algorithms | | Week 3 | Objective functions for reinforced concrete | | Week 4 | Constraints for reinforced concrete | | Week 5 | Constraint handling methods | | Week 6 | Converting a RC design problem to an optimization problem | | Week 7 | Optimization of RC elements with Artificial Bee Colony Algorithm | | Week 8 | Optimization of RC elements with Cuckoo Search Algorithm | | Week 9 | Mid-term exam | | Week 10 | Teaching-Learning Based Optimization Algorithm | | Week 11 | Optimization of RC elements with JAYA Algorithm | | Week 12 | Optimization of RC elements with Differential Search Algorithm | | Week 13 | General structure of ANN | | Week 14 | Training of ANN with heuristic algorithms | | Week 15 | Modeling of experiment data with ANN | | Week 16 | End-of-term exam | | |
1 | Öğretim üyesi ders notları | | |
1 | ÖZTÜRK H.T. 2013. "Deprem Bölgelerinde Yapılacak Betonarme Sığ Tünellerin Yapay Arı Koloni Algoritması Ve Genetik Algoritmayla Optimum Tasarımı", KTÜ Fen Bilimleri Enstitüsü İnşaat Mühendisliği Anabilim Dalı, Doktora Tezi | | 2 | SLOBBE, G. 2015. Optimisation of Reinforced Concrete Structures, Delft University of Technology, Main Report. | | |
Method of Assessment | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | Mid-term exam | 9 | | 2 | 50 | End-of-term exam | 16 | | 2 | 50 | |
Student Work Load and its Distribution | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | Yüz yüze eğitim | 3 | 14 | 42 | Sınıf dışı çalışma | 4 | 14 | 56 | Arasınav için hazırlık | 4 | 8 | 32 | Arasınav | 2 | 1 | 2 | Dönem sonu sınavı için hazırlık | 3 | 7 | 21 | Dönem sonu sınavı | 2 | 1 | 2 | Diğer 1 | 5 | 14 | 70 | Total work load | | | 225 |
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