Course Details

COMPUTATIONAL AND EXPERIMENTAL METHODS IN CIVIL ENGINEERING RESEARCH

CE610

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
2CE610İNŞAAT MÜHENDİSLİĞİ ARAŞTIRMALARINDA DENEYSEL VE HESAPLAMALI YÖNTEMLER3+0+037,5

Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program CIVIL ENGINEERING
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course Description This course aims to bridge the gap between traditional civil engineering research and the evolving landscape of technological advancements. The course integrates programming skills with experimental methodologies to equip doctoral candidates with a diverse set of tools for cutting-edge research. Participants will delve into the world of programming languages, simulation techniques, and data analysis to enhance their ability to model and analyze complex civil engineering problems. Practical applications of computational tools in experimental settings will be explored, fostering a hands-on approach to problem-solving. The course aligns with doctorate level studies by integrating programming, simulation, and experimental methodologies, enabling candidates to tackle complex civil engineering problems through advanced computational tools and innovative research approaches.
Course Content Description This course aims to bridge the gap between traditional civil engineering research and the evolving landscape of technological advancements. The course integrates programming skills with experimental methodologies to equip doctoral candidates with a diverse set of tools for cutting-edge research. Participants will delve into the world of programming languages, simulation techniques, and data analysis to enhance their ability to model and analyze complex civil engineering problems. Practical applications of computational tools in experimental settings will be explored, fostering a hands-on approach to problem-solving. The course aligns with doctorate level studies by integrating programming, simulation, and experimental methodologies, enabling candidates to tackle complex civil engineering problems through advanced computational tools and innovative research approaches.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator Prof.Dr. burak UZAL burak.uzal@agu.edu.tr
Name of Lecturers Prof.Dr. BURAK UZAL
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources

Course Category
Mathematics and Basic Sciences %30
Engineering %25
Engineering Design %25
Science %20

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Ödev 3 % 30
Proje/Çizim 3 % 30
Sunum/Seminer 3 % 40
Total
9
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Belirsiz 5 5 25
Tartışma 5 5 25
Sunum için Hazırlık 3 5 15
Sunum 3 3 9
Rapor 3 5 15
Araştırma 3 5 15
İnceleme 3 5 15
Yüz Yüze Ders 14 3 42
Derse Devam 14 3 42
Final Sınavı 1 15 15
Total Work Load   Number of ECTS Credits 7,5 218

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Determine key programming concepts and syntax used in civil engineering research
2 Construct experimental and computational models for simulating complex civil engineering scenarios
3 Evaluate experimental data using computational tools.
4 Design an interdisciplinary research project that integrates computational methods with experimental works.


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Experimental and Computational Methods course
2 Fundamentals of Machine learning for Cİivl Engineering Materials
3 Data Processing and Feature Engineering
4 Machine Learning Model Development for Material Property Prediction
5 Case Studies & Integration of Machine Learning with Experimental Research
6 Introduction to computational modeling, fundamentals of continuum mechanics
7 Finite-Element Method (FEM) basics
8 Advanced numerical methods
9 Hybrid testing & simulation, digital twins
10 Structural case studies
11 Geotechnical & materials case studies
12 Construction Materials Case Studies
13 Artificial Intelligence and Experimental Methodologies
14 AI & ML for experimental data analytics


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
C1 5 4 4 5 3 5 5 5 3 3 4 4
C2 5 5 4 4 3 5 5 5 4 4 3
C3 3 4 5 4 3 3 5
C4 3 4 4 4 3 4 4 3 3 5

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant


https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77629&lang=en
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