Syllabus

Module I: Optimization problem formulation [6 hours]
Optimal Problem Formulation, Engineering Optimization Problems, Classification of Optimization Algorithms, Graphical optimization, linear programming, Simplex method.
Module II: Constraints and unconstrained optimization problems [4 hours]
Constraints and unconstrained optimization methods Single and Multi variable optimization methods:
Module III: Traditional optimization algorithms [4 hours]
Direct search method, Gradient based methods, Newton’s method, Levenberg-Marquardt’s method.

Module IV: Structural Optimization with Evolutionary algorithms [10 hours]
Introduction to Evolutionary algorithms: Need for evolutionary algorithms, Type of evolutionary methods, Genetic algorithm (GA): Real-coded GA, Multi-objective GA, Particle Swarm optimization & Teaching Learning Based Optimization techniques for truss optimization.

Module V: Introduction to ANN & Deep Learning algorithms and applications [12 hours]
ANN based approaches for structural optimization problems- Introduction- basic concept of ANN- Architectures and learning methods of NN & structural applications, Introduction to Deep Learning, Convolution Neural Network Application of AI in Structural Design and Manufacturing Data Fitting and Regression Shape, geometry and material optimization for 2D and 3D truss and frame structures AI application in Structural Health Monitoring.

To understand the optimal problem formulation for a given structure and learn to solve by tradition optimization methods.

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

To apply ANN for structural design and optimization problem.

To Apply AI and Deep learning techniques for structural optimization and structural health monitoring problems.

After the completion of this course, students will be able to:

CO1: Understand the basic concept of Optimal Problem Formulation
CO2: The students will be able to understand the single and multi-variable optimization methods.
CO3: The students will acquire knowledge regarding Direct search and Gradient-based optimization methods.
CO4: The students will be able to understand the concept of dealing with structural optimization problems with Evolutionary optimization algorithms.
CO5: The students will be able to apply ANN and Deep learning techniques for structural design and optimization problems.

Arora,J.S., Introduction to Optimum Design, 2nd Edition,, McGraw-Hill Book Company, 2000

K. Deb, Optimization for Engineering Design: Algorithms & Examples, Prentice Hall India

K. Deb, Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons

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