Information Package / Course Catalogue
Artificial Intelligence and Automation in Construction
Course Code: İNA275
Course Type: Required
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 1
Prt.: 1
Credit: 2
Lab: 0
ECTS: 2
Objectives of the Course

The objective of this course is to introduce students to the fundamental concepts and application areas of rapidly evolving artificial intelligence, machine learning, and digital automation technologies in the construction industry. Within the scope of this course, it is aimed for students to comprehend innovative approaches in modern civil engineering and construction technology—ranging from the optimization of construction materials to smart jobsite management, and from Building Information Modeling (BIM) to autonomous construction equipment. Furthermore, the course aims to equip students with the vision and technical infrastructure necessary to adapt to the digital transformation in the construction sector by analyzing the contributions of these technologies to occupational health and safety, productivity, and sustainability processes.

Course Content

This course covers the theoretical and practical foundations of digital transformation, artificial intelligence (AI), and automation applications in the construction industry. Throughout the course, the role of big data analytics and machine learning algorithms in construction technologies, AI integration with Building Information Modeling (BIM), smart jobsite management, autonomous construction equipment, and Internet of Things (IoT)-based safety systems will be examined. Additionally, the utilization of AI simulations in materials technology, such as industrial waste recycling and concrete mix optimization, alongside sustainable construction cycles will be emphasized. Students will analyze automation tools and intelligent decision-support systems in the modern construction sector through practical case studies.

Name of Lecturer(s)
Learning Outcomes
1.EN: To be able to define artificial intelligence, machine learning, and digital automation concepts used in the construction industry within the framework of Industry 4.0/Construction 4.0 dynamics.
2.EN: To be able to explain and analyze AI-based data analytics methods in the optimization, design, and laboratory simulation processes of construction materials.
3.EN: To be able to evaluate the integration of Building Information Modeling (BIM) with generative design and the operation of automation systems in smart buildings.
4.EN: To be able to examine the application areas of smart jobsite management, computer vision-assisted OHS tracking, and autonomous fault/damage detection systems in infrastructure networks.
5.EN: To be able to interpret the role of AI-driven scheduling and blockchain/smart contract technologies in construction project management, supply chain, and progress payment processes.
Recommended or Required Reading
1.Artificial Intelligence in Construction by Cem Kafadar - 2024 edition, OnOn Publishing House
2.Applications of Artificial Intelligence in the Construction Sector - AI / BIM / IT Group - https://www.youtube.com/watch?v=D7D2KZekRes
3.https://anthropic.skilljar.com/
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
Theoretical: Digital Transformation in the Construction Industry and Introduction: The concept of Construction 4.0, digitalization of traditional methods, and innovations brought by AI and automation to the sector. Practice: Case studies and analysis of successful digital construction and smart jobsite projects worldwide.
Week 2 - Theoretical & Practice
Theoretical: Big Data and Fundamentals of Data Analytics: Data types generated from jobsites and design processes, structured vs. unstructured data, and data collection methods. Practice: Practical data visualization and analysis using a sample construction dataset (e.g., concrete strength data or project delay durations).
Week 3 - Theoretical & Practice
Theoretical: Concepts of Artificial Intelligence and Machine Learning: Supervised, unsupervised, and reinforcement learning models; selecting the right algorithms for specific civil engineering problems. Practice: Running a basic predictive model on low-code/no-code AI platforms or visual interfaces.
Week 4 - Theoretical & Practice
Theoretical: Artificial Intelligence and Optimization in Construction Materials: AI in material design and R&D processes; optimization of concrete mix proportions using Artificial Neural Networks (ANN). Practice: Virtual laboratory application predicting the effects of industrial waste (fly ash, slag, etc.) on concrete strength using AI simulation tools.
Week 5 - Theoretical & Practice
Theoretical: Building Information Modeling (BIM) and AI Integration: Automation in BIM processes; data generation from 3D modeling, parametric design, and Generative Design concepts. Practice: Testing generative design tools in BIM software (e.g., generating optimal floor plans or structural system alternatives based on specific constraints).
Week 6 - Theoretical & Practice
Theoretical: Smart Jobsite Management and the Internet of Things (IoT): IoT architecture on jobsites; sensors, RFID tags, beacon technologies, and real-time data flow management. Practice: Modeling equipment and material tracking scenarios through smart jobsite simulation software or platforms.
Week 7 - Theoretical & Practice
Theoretical: Object Detection Systems and Computer Vision: Computer vision technology; processing visual data captured via jobsite cameras and drones through AI models. Practice: Hands-on demonstration of tracking site progress and detecting OHS violations (hard hat/safety vest checks) using pre-trained AI models.
Week 8 - Theoretical & Practice
Theoretical: Automation in Occupational Health and Safety (OHS) and Risk Management: AI-based risk analysis; systems learning from historical accident data, and geofencing management for hazardous zones. Practice: Configuring an AI-supported OHS checklist and risk score generation scenario using automation tools. -- MIDTERM EXAM
Week 9 - Theoretical & Practice
Theoretical: Autonomous Construction Equipment and Robotic Systems: Construction robots, 3D concrete printers, autonomous excavation machinery, and the working principles of unmanned aerial vehicles (UAVs) on site. Practice: Examining 3D printer production parameters or autonomous vehicle routes on simulation software.
Week 10 - Theoretical & Practice
Theoretical: Smart Infrastructure Systems and Automation in Urban Networks: Smart cities, autonomous infrastructure management; AI systems detecting leaks and failures in water, wastewater, and transportation networks.Practice: Reviewing a sample infrastructure management interface focused on the integration of Geographic Information Systems (GIS) and automation data.
Week 11 - Theoretical & Practice
Theoretical: Structural Health Monitoring and AI-Based Damage Detection: Sensor-based Structural Health Monitoring (SHM) methods for existing structures and bridges; crack and damage detection using AI algorithms.Practice: Analyzing AI models that automatically detect cracks on reinforced concrete surfaces using drone imagery or high-resolution photographs.
Week 12 - Theoretical & Practice
Theoretical: Smart Building Management Systems and Green Buildings: Automation in the operational phase; smart HVAC (heating, ventilation, air conditioning) control, energy efficiency optimization, and Building Management Systems (BMS). Practice: Analyzing AI-driven energy-saving scenarios based on real-time building energy consumption data.
Week 13 - Theoretical & Practice
Theoretical: AI-Powered Project Management and Scheduling: AI-based decision support systems in cost estimation, budget optimization, and time scheduling (CPM/PERT processes). Practice: Analyzing project delay risks using AI plugins or automated data workflows (e.g., Make) within project management software.
Week 14 - Theoretical & Practice
Theoretical: The Role of Blockchain and Smart Contracts in Construction: Transparency in the construction supply chain, smart contracts in progress payment processes, and token-based progress/reward models. Practice: Simulating a basic smart contract logic (e.g., "releasing payment automatically upon task completion approval") or designing its workflow chart.
Week 15 - Theoretical & Practice
Theoretical: Future Construction Technologies and Term Project Presentations: The future of the industry, ethical AI, data security, and the role of digital competencies in students' career planning. Practice: Presentation and evaluation of smart system designs, innovative ideas, or case study reports developed throughout the semester.
Week 16 - Final Exam
End-of-Term Exam (Final)
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%10
Quiz2%10
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory150115
Lecture - Practice150115
Assignment2048
Midterm Examination1066
Final Examination1066
TOTAL WORKLOAD (hours)50
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
PÇ-14
PÇ-15
PÇ-16
PÇ-17
PÇ-18
PÇ-19
OÇ-1
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
OÇ-2
5
5
5
5
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
OÇ-3
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
OÇ-4
5
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
OÇ-5
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Adnan Menderes University - Information Package / Course Catalogue
2026