National Institute of Technology Rourkela

राष्ट्रीय प्रौद्योगिकी संस्थान राउरकेला

ଜାତୀୟ ପ୍ରଯୁକ୍ତି ପ୍ରତିଷ୍ଠାନ ରାଉରକେଲା

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Syllabus

Course Details

Subject {L-T-P / C} : CE4010 : Application of AI and ML in Civil Engineering { 3-0-0 / 3}

Subject Nature : Theory

Coordinator : Bibekananda Mandal

Syllabus

Module 1 :

Fundamentals of Artificial Intelligence in Civil Engineering [8 hours]
Introduction to Artificial Intelligence, Machine Learning, and Deep Learning, Role of AI in Civil Engineering domains

Module 2 :

Module 2: Basics of Optimisation in Engineering Design [8 hours]
Optimisation problem formulation in civil engineering, Objective function, decision variables, Graphical optimisation, linear programming, simplex method

Module 3 :

Module 3: Constraints and unconstrained optimisation problems [6 hours]
Constraints and unconstrained optimisation methods, Single-variable and multivariable optimisation with applications

Module 4 :

Module 4: Classical and Metaheuristic Optimisation Algorithms [6 hours]
Traditional optimization algorithms (Direct search method, Gradient-based methods, Newton’s method, Levenberg-Marquardt method), Evolutionary algorithms (Genetic Algorithms (GA), Particle Swarm Optimisation (PSO)), with application examples

Module 5 :

Module 5: Artificial Neural Networks and Deep Learning in Civil Engineering [8 hours]
Introduction to Artificial Neural Networks (Network architecture, learning, Deep learning basics – CNN, RNN with Application to civil engineering

Course Objective

1 .

To introduce the fundamentals of Artificial Intelligence and its relevance to civil engineering.

2 .

To understand the optimal problem formulation for a given problem and learn to solve it using traditional optimisation methods.

3 .

To understand the working principle and application of Evolutionary algorithms for design applications.

4 .

To apply ANN to a civil engineering optimisation problem.

5 .

To apply AI and Deep learning techniques for civil engineering applications.

Course Outcome

1 .

Understand the basic concept of AI and Optimal Problem Formulation

2 .

The students will be able to understand the single and multi-variable optimisation methods.

3 .

The students will acquire knowledge regarding Direct search and Gradient-based optimisation methods.

4 .

The students will be able to understand the concept of dealing with optimisation problems with Evolutionary optimisation algorithms.

5 .

The students will be able to apply ANN and Deep learning techniques for civil engineering design and optimization problems.

Essential Reading

1 .

Kalyanmoy Deb, Optimization for Engineering Design: Algorithms & Examples, Prentice Hall India , 2nd Edition

Supplementary Reading

1 .

Rao,S.S, Engineering Optimization, Theory and Applications, New Age International publication , 3rd Edition

Journal and Conferences

1 .