AKTU Artificial Intelligence and Machine Learning in Civil Engineering (BCE071) Syllabus PDF – B.Tech 4th Year

Course Objectives

• To introduce Al and ML concepts and tools for analysing and solving civil engineering problems.
• To apply ML techniques on real-world data in areas like construction, SHM, and traffic.

Course Outcomes

Unit 1: Foundations of Al and ML in Civil Engineering (8 Hours)

• Introduction to Artificial Intelligence and Machine Learning
• Differences and relationships: Al, ML, Deep Learning
• Supervised, Unsupervised, and Reinforcement Learning
• Applications of Al/ML in Civil Engineering: planning, risk prediction, BIM
• Tools Overview: Python, Jupyter, Pandas, Scikit-learn

Unit 2: Regression and Classification Models (8 Hours)

• Regression: Linear, Polynomial, Ridge, Lasso
• Classification: Decision Tree, Random Forest, SVM
• Evaluation Metrics: MAE, RMSE, R?, Accuracy, Precision, Recall
• Overfitting, Cross-validation, Regularization

Unit 3: Al-Based Optimization Tools in Civil Engineering (8 Hours)

• Optimization in design: problem formulation and motivation
• Basics of Neural Networks (NN): input-output structure, practical use
• Basics of Genetic Algorithms (GA): natural selection and evolution in design
• Case studies: concrete strength prediction, foundation cost optimization
• Visual tools: Teachable Machine, Excel GA demos, Python examples

Unit 4: Construction and Infrastructure Applications (8 Hours)

• ML for project delay and cost prediction
• Al for construction planning and resource optimization
• Sensor-based infrastructure health monitoring
• Case Studies: traffic flow forecasting, bridge safety prediction

Unit 5: Interpretation, Deployment & Ethics (8 Hours)

• SHAP, LIME, and PDP: Model interpretation and visualization
• Simple deployment with Streamlit/Flask (conceptual overview only)
• Ethics in Al/ML: bias, transparency, fairness, and privacy
• Emerging trends: Al + FEM integration, Digital Twins in infrastructure

Suggested Readings

1. Machine Learning Applications in Civil Engineering — R. Mohan & V. Ravi, Notion Press
2. Internet of Things and Machine Learning — Dr. A, Suganthi, Charulatha Publications
3. Neural Networks, Fuzzy Logic, and Genetic Algorithms — S. Rajasekaran & G.A. Vijayalakshmi Pai, PHI
4. Big Data and Al in Smart Construction —John Soldatos, Edward Curry & Manfred Hauswirth, River Publishers
5. Alin Construction Management — Jay Yang & Simaan AbouRizk, Springer
6. Artificial Intelligence in Structural Engineering — Feng Chen & Baoming Sun, Springer
7. \IT Bombay — FOSSEE Project (Free Python ML tutorials and case studies); https://fossee.in