Information Package / Course Catalogue
Artificial Intelligence For Intelligent Built Environment Systems
Course Code: CE471
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: English
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 5
Objectives of the Course

The course aims to equip students with a foundational understanding of how Artificial Intelligence (AI) techniques can be applied to design, construct, operate, and maintain intelligent systems across the built environment. Students will explore data-driven methods for automating decision-making processes, optimizing lifecycle performance, and enhancing sustainability in civil infrastructure and facility operations.

Course Content

The course introduces students to Artificial Intelligence (AI) applications within the built environment, emphasizing both foundational knowledge and domain-specific implementations. It begins with an overview of AI in the construction and infrastructure sectors, followed by fundamental concepts of machine learning and deep learning. The course explores AI-driven solutions for construction automation, site monitoring, and predictive analytics in facility management. It covers intelligent infrastructure systems such as roads, bridges, and utilities, and delves into digital sensing and contextual data processing for real-time decision-making. Students will examine optimization strategies for lifecycle performance, proactive maintenance, and resilience of assets. Ethical considerations and responsible use of AI in civil engineering contexts are also addressed. The course concludes with discussions on smart cities and infrastructure intelligence, supported by real-world case studies and hands-on data analysis activities.

Name of Lecturer(s)
Assoc. Prof. Gözde Başak ÖZTÜRK ÖZERAY
Learning Outcomes
1.Explain AI/ML techniques and their application in civil systems. Assessed by midterm exam (MCQ + short answer).
2.Preprocess construction data (sensor, text, images). Assessed by Lab submissions (Python notebooks).
3.Develop an AI model (e.g., CV/NLP) for construction, facility, or infrastructure use cases. Assessed by Project demo + code repository.
4.Apply machine learning models to real-world built environment data
5.Analyze lifecycle performance through predictive analytics
6.Assess ethical and practical considerations of AI use in the built environment. Assessed by Ethics reflection report (500 words).
7.Present technical solutions to mixed audiences. Assessed by Final presentation.
8.Collaborate across disciplines to develop intelligent system prototypes
Recommended or Required Reading
1.Simeone, O. (2022). Machine learning for engineers. Cambridge university press.
2.Boiko, A. (2024). Data-Driven Construction: Navigating the Data Age in the Construction Industry.
3.Zhang, L., Pan, Y., Wu, X., and Skibniewski, M.J. (2021). Artificial Intelligence in Construction Engineering and Management (Lecture Notes in Civil Engineering, 163) 1st ed.
4.Russell, S. & Norvig, P. (2021). Artificial Intelligence: A Modern Approach.
5.Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning.
6.Zhang, J., & Teizer, J. (2020). AI in Construction and Infrastructure Management (Selected papers).
7.Parn, E., Sacks, R., Brilakis, I., Soibelman, L., and Enzer, M. (2024). Twin Systems: Digital Twins of the Built Environment. Twin Systems.
8.Yitmen, I. (Ed.). (2023). Cognitive digital twins for smart lifecycle management of built environment and infrastructure: challenges, opportunities and practices.
9.Yitmen, I., and Alizadehsalehi, S. (2021). BIM-Enabled Cognitive Computing for Smart Built Environment. Taylor & Francis.
10.Smith, D. (2020). Digital Construction: From BIM to IoT. Routledge.
11.Ozturk, G.B. and Ozen B. (2024). İNŞAAT 4.0-Teknoloji, Yöntem ve Uygulamalar, Nobel Akademik Yayıncılık.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to AI and the Built Environment. Overview of AI in civil and infrastructure systems; motivation and scope across the building/infrastructure lifecycle.
Week 2 - Theoretical
Digital Twins (Theory)
Week 3 - Theoretical
Fundamentals of AI: Machine Learning, Deep Learning, Reinforcement Learning. Key concepts, techniques, and algorithms with examples from built environment applications.
Week 4 - Theoretical
Data Acquisition and Pre-processing in Built Systems. Sources, types, and quality of data in construction and facility management; pre-processing pipelines.
Week 5 - Theoretical & Practice
Supervised Learning for Construction Operations and Safety. Predictive models for cost, schedule, safety incident prediction, and resource allocation. Team Projects.
Week 6 - Theoretical & Practice
Unsupervised Learning for Facility and Energy Analytics. Clustering, anomaly detection, and usage profiling for operational efficiency. Team Projects.
Week 7 - Theoretical & Practice
Deep Learning Applications in Vision-Based Site Monitoring. Object detection, image classification, and progress tracking from drone/vision data. Team Projects.
Week 8 - Theoretical & Practice
AI for Predictive Maintenance and Asset Lifecycle Modeling. Failure prediction, asset condition monitoring, and lifecycle performance optimization. Team Projects.
Week 9 - Theoretical & Practice
AI in Intelligent Infrastructure Systems. Application of AI in roads, bridges, water systems, and urban infrastructure monitoring. Team Projects.
Week 10 - Theoretical & Practice
Optimization Methods for Smart Facility and Construction Systems. Heuristic and AI-based optimization for scheduling, routing, and energy systems. Team Projects.
Week 11 - Theoretical & Practice
Sensor Fusion and Contextual Awareness in Intelligent Systems. Real-time integration of IoT, edge computing, and situational awareness for responsive environments. Team Projects.
Week 12 - Theoretical & Practice
AI for Resilience and Sustainability of the Built Environment. Using AI to model risk, improve resilience, and support environmental goals. Smart Cities and Urban-Scale Intelligence. City-scale sensing, decision support, mobility patterns, and system-of-systems thinking. Team Projects.
Week 13 - Practice
Ethics Workshop. Ethics, Privacy, and Bias in Built Environment AI Systems. Responsible AI use, privacy-preserving models, and implications of biased predictions in infrastructure contexts. Group Project Presentations and Knowledge Exchange. Students present AI solutions addressing real-world challenges in the built environment.
Week 14 - Practice
Group Project Presentations and Knowledge Exchange. Students present AI solutions addressing real-world challenges in the built environment.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%20
Project2%70
Report1%10
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142370
Assignment110111
Project215132
Midterm Examination110212
TOTAL WORKLOAD (hours)125
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
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PÇ-7
PÇ-8
PÇ-9
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PÇ-11
OÇ-1
5
5
3
5
3
2
3
5
3
4
4
OÇ-2
5
4
2
5
5
2
2
3
2
3
2
OÇ-3
5
5
5
5
5
3
3
4
3
5
5
OÇ-4
5
5
4
5
5
2
3
5
3
4
5
OÇ-5
5
5
4
5
4
2
2
4
3
5
5
OÇ-6
2
2
2
2
2
2
5
5
5
5
5
OÇ-7
2
2
2
2
2
2
5
3
2
2
2
OÇ-8
3
3
5
3
3
5
5
4
5
5
5
Adnan Menderes University - Information Package / Course Catalogue
2026