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Information
| Unit | FACULTY OF ENGINEERING |
| CIVIL ENGINEERING PR. | |
| Code | IMS481 |
| Name | Artificial Intelligence Applications in Civil Engineering |
| Term | 2026-2027 Academic Year |
| Semester | 7. Semester |
| Duration (T+A) | 1-2 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. AHMED KAMİL TANRIKULU |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to equip civil engineering students with the ability to identify, apply and evaluate artificial intelligence and machine learning technologies in their professional context. Within the scope of the course, students learn fundamental AI concepts, work with real civil engineering data in a Python programming environment, and develop original AI-based solutions in areas such as structural analysis, cost estimation, geotechnics, infrastructure monitoring and construction site safety. By the end of the course, students are expected to be equipped to contribute to the digital transformation of the construction industry.
Course Content
Fundamental concepts of artificial intelligence, machine learning and deep learning; data analysis and visualization with Python; supervised, unsupervised and reinforcement learning algorithms; machine learning applications in structural analysis and damage detection; cost estimation and risk analysis; integration of BIM data with artificial intelligence; soil classification in geotechnical applications; infrastructure monitoring and predictive maintenance; image processing and unmanned aerial vehicle applications; generative AI and design optimization; ethical and professional dimensions of artificial intelligence.
Course Precondition
None
Resources
Weekly lecture notes and laboratory materials prepared by the instructor will be shared via the course platform.
Notes
1. Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly Media. 2. Russell, S. & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. 3. Chollet, F. (2021). Deep Learning with Python (2nd ed.). Manning Publications. 4. McKinsey Global Institute. (2020). The Next Normal in Construction: How Disruption is Reshaping the World's Largest Ecosystem. McKinsey & Company. (Free PDF: mckinsey.com) 5. Autodesk & Deloitte. (2023). State of Data Capabilities in Construction. Autodesk. (Free PDF: autodesk.com) 6. Journal of Construction Engineering and Management (ASCE) — AI special issues 7. Automation in Construction (Elsevier) — Articles on AI and construction technology 8. Google Colab: colab.research.google.com 9. Kaggle datasets and competitions: kaggle.com 10. Roboflow image processing platform: roboflow.com 11. AFAD Open Data Portal: afad.gov.tr 12. Chamber of Civil Engineers (TMMOB): imo.org.tr
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains fundamental concepts of artificial intelligence, machine learning and deep learning; establishes their relationship with civil engineering. |
| LO02 | Compares machine learning types (supervised, unsupervised, reinforcement) and selects appropriate algorithms for civil engineering problems. |
| LO03 | Processes, analyzes and visualizes civil engineering data using Python programming language and Google Colab environment. |
| LO04 | Builds and validates machine learning models for civil engineering problems such as structural analysis, cost estimation and soil classification. |
| LO05 | Develops structural damage detection and construction site safety inspection applications using image processing methods. |
| LO06 | Creates project management and design optimization workflows by integrating BIM data with artificial intelligence. |
| LO07 | Identifies a real civil engineering problem, selects the appropriate AI method, and shares the solution as a technical report and presentation. |
| LO08 | Evaluates the ethical dimensions, limitations and future potential of artificial intelligence in civil engineering. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | “Sufficient knowledge in mathematics, science, and discipline-specific topics in Civil Engineering; the ability to use theoretical and applied knowledge in these fields to solve complex engineering problems.” | |
| PLO02 | Bilgi - Kuramsal, Olgusal | “The ability to identify, formulate, and solve complex Civil Engineering problems; and the ability to select and apply appropriate analysis and modeling methods for this purpose.” | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | “The ability to design a complex system, process, device, or product to meet specified requirements under realistic constraints and conditions; and the ability to apply modern design methods for this purpose.” | |
| PLO04 | Bilgi - Kuramsal, Olgusal | “The ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Civil Engineering applications; and the ability to use information technologies effectively.” | 5 |
| PLO05 | Bilgi - Kuramsal, Olgusal | “The ability to design experiments, conduct experiments, collect data, and analyze and interpret results for the investigation of complex Civil Engineering problems or discipline-specific research topics.” | 3 |
| PLO06 | Bilgi - Kuramsal, Olgusal | “The ability to work effectively in intra-disciplinary and multidisciplinary teams; and the ability to work independently.” | 3 |
| PLO07 | Bilgi - Kuramsal, Olgusal | “The ability to communicate effectively in both oral and written form; proficiency in at least one foreign language; the ability to write effective reports and understand written reports, prepare design and production reports, deliver effective presentations, and give and receive clear and understandable instructions.” | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | “Awareness of the necessity of lifelong learning; the ability to access information, follow developments in science and technology, and continuously improve oneself.” | 5 |
| PLO09 | Bilgi - Kuramsal, Olgusal | “Acting in accordance with ethical principles, having professional and ethical responsibility, and having knowledge of the standards used in engineering practices.” | |
| PLO10 | Bilgi - Kuramsal, Olgusal | “Knowledge of business-life practices such as project management, risk management, and change management; awareness of entrepreneurship and innovation; and knowledge about sustainable development.” | 3 |
| PLO11 | Bilgi - Kuramsal, Olgusal | “Knowledge of the universal and social impacts of Civil Engineering practices on health, environment, and safety, as well as the contemporary issues reflected in the field of engineering; and awareness of the legal consequences of engineering solutions.” |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction and Motivation: Impact of AI on Civil Engineering; Fundamental Concepts (AI, ML, DL, LLM); Tools to be Used | Create accounts on ChatGPT (chat.openai.com) and Claude.ai. Log in to Google Colab (colab.research.google.com) with your Gmail account and verify that it works. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Gösteri |
| 2 | Introduction to Machine Learning: Algorithms, Data Types; Introduction to Python and Google Colab; First Data Analysis | Review the "Python for Beginners" page (docs.python.org). Create a free account on Kaggle.com. | Öğretim Yöntemleri: Anlatım, Gösteri, Alıştırma ve Uygulama |
| 3 | Data Preprocessing and Preparation: Missing Data, Outliers, Feature Engineering; Cleaning a Real Construction Site Dataset | Review the Pandas library documentation (pandas.pydata.org). Download the construction site dataset shared on the course platform. | Öğretim Yöntemleri: Anlatım, Gösteri, Alıştırma ve Uygulama |
| 4 | Structural Analysis + ML: Beam Deflection Prediction, Regression Models; Lab: First ML Model with Python | Review the regression section of the Scikit-learn library (scikit-learn.org). Revise the concept of linear regression. | Öğretim Yöntemleri: Anlatım, Gösteri, Alıştırma ve Uygulama, Örnek Olay |
| 5 | Earthquake and Damage Detection: Image Processing Fundamentals, Pre-trained Models; Lab: Crack Detection from Building Damage Photos | Create a free account on Roboflow.com. Watch a basic introductory video on image processing and convolutional neural networks (CNN) (YouTube: "CNN explained"). | Öğretim Yöntemleri: Anlatım, Gösteri, Alıştırma ve Uygulama, Örnek Olay |
| 6 | Cost Estimation: Traditional vs. AI-Assisted Methods; Lab: Cost Estimation Model with Historical Project Data | Research Decision Tree and Random Forest algorithms. Download the historical project dataset shared on the course platform. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama, Örnek Olay |
| 7 | Schedule and Risk Analysis: Monte Carlo Simulation, Schedule Optimization; Lab: Risk Simulation with Python | Research the concept of Monte Carlo simulation. Review the basic functions of the NumPy library (numpy.org). | Öğretim Yöntemleri: Anlatım, Gösteri, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Review topics from weeks 1-7. Revise your lab assignments and notes. | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | BIM + Artificial Intelligence: IFC Data Structure, Extracting Data from BIM; Lab: Quantity Takeoff and Cost Analysis with BIM Data | Acquire basic knowledge about BIM and IFC format. Apply for the Autodesk Forma student license (autodesk.com/education). | Öğretim Yöntemleri: Anlatım, Gösteri, Alıştırma ve Uygulama, Örnek Olay |
| 10 | Soil Classification and Geotechnical Applications: ML with SPT/CPT Data; Lab: Soil Type Prediction Model | Review SPT and CPT test methods. Download the borehole log dataset shared on the course platform. | Öğretim Yöntemleri: Anlatım, Gösteri, Alıştırma ve Uygulama, Örnek Olay |
| 11 | Infrastructure Monitoring and Predictive Maintenance: Sensor Data, Anomaly Detection; Lab: Anomaly Analysis with Bridge Sensor Data | Research the concepts of time series data and anomaly detection. Download the simulated sensor dataset shared on the course platform. | Öğretim Yöntemleri: Anlatım, Gösteri, Alıştırma ve Uygulama |
| 12 | Computer Vision and Drone Applications: Object Detection with YOLO; Lab: Construction Site Safety Inspection | Research the YOLO (You Only Look Once) algorithm. Log in to your Roboflow account and familiarize yourself with the interface. | Öğretim Yöntemleri: Anlatım, Gösteri, Alıştırma ve Uygulama, Örnek Olay |
| 13 | Generative AI and Design Optimization: Generative Design, Sustainability; Lab: Multi-Criteria Design Optimization | Research the concept of generative design and the design optimization features of Autodesk Forma. | Öğretim Yöntemleri: Anlatım, Gösteri, Alıştırma ve Uygulama, Beyin Fırtınası, Tartışma |
| 14 | Ethical and Future Dimensions of AI: Bias, Accountability, Professional Ethics; Term Project Presentations (Group 1) | Finalize your term project for presentation. Read an article or news piece on ethics in artificial intelligence. | Öğretim Yöntemleri: Tartışma, Soru-Cevap, Alıştırma ve Uygulama |
| 15 | Term Project Presentations (Group 2); General Course Evaluation; Career and Future Directions | Finalize your term project for presentation. Prepare a note-taking format to evaluate other groups' presentations. | Öğretim Yöntemleri: Tartışma, Soru-Cevap, Beyin Fırtınası |
| 16 | Term Exams | Review all semester topics, lab assignments and course notes. | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Review all semester topics, lab assignments and course notes. | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 4 | 6 | 24 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 3 | 3 |
| Final Exam | 1 | 3 | 3 |
| Total Workload (Hour) | 128 | ||
| Total Workload / 25 (h) | 5,12 | ||
| ECTS | 5 ECTS | ||