Why enroll

This course is highly beneficial for civil engineering students, postgraduate learners, and professionals who want to enhance analytical and decision-making skills. It helps learners design efficient and economical structures, improve project planning, and manage resources effectively. The concepts are widely applicable in structural design, transportation planning, water resources, and construction management, as well as in research and advanced engineering practice.

Course details

The Optimization Methods for Civil Engineering course introduces mathematical and computational techniques used to obtain optimal solutions for engineering problems involving cost, safety, performance, and resource efficiency. The course focuses on formulating civil engineering problems as optimization models and applying suitable solution methods to support better design and decision-making.

SOURCE- youtube [NPTEL IIT Guwahati]

Course suitable for

Key topics covered

Fundamentals of optimization and engineering decision-making
Linear and nonlinear optimization techniques
Unconstrained and constrained optimization
Gradient-based and search methods
Multi-objective optimization
Optimization in structural design
Cost and weight minimization of structures
Optimization in transportation and network systems
Applications in water resources and construction planning
Introduction to metaheuristic methods (genetic algorithms, etc.)

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

Optimization Methods for Civil Engineering

38 Lectures

1654 min

Lec 1: Introduction to Optimization
Preview
43 min
Lec 2: Classical Optimization
54 min
Lec 3: Introduction to Linear Problem
58 min
Lec 4: General system of equations
48 min
Lec 5: Simplex Method
55 min
Lec 6: Solution of Linear Problem using Excel Solver
43 min
Lec 7: Bracketing Method
26 min
Lec 8: Region Elimination Methods
40 min
Lec 9: Gradient Based Method and Examples
46 min
Lec 10: Convex Function
48 min
Lec 11: Line Search Methods for Multi-Variable Problems
36 min
Lec 12: Quadratic Approximation Method
25 min
Lec 13: Constrained Optimization I: Equality constraints
40 min
Lec 14: Constrained Optimization II:Inequality constraints
42 min
Lec 15: Constrained Optimization III: Penalty function methods
33 min
Lec 16: Introduction to Metaheuristic Optimization
48 min
Lec 17: Genetic Algorithms (Part I)
60 min
Lec 18: Genetic Algorithms (Part II)
56 min
Lec 19: Genetic Algorithms (Part III)
37 min
Lec 20: Real Coded Genetic Algorithms
32 min
Lec 21: Multi-modal optimization
21 min
Lec 22: Introductioin to R
71 min
Lec 23: GA using R (Unconstrained problem)
53 min
Lec 24: GA using R (Constrained problem)
45 min
Lec 25: Constraint Handling in GAs
41 min
Lec 26: Evolution Strategies (ESs)
29 min
Lec 27: Particle swarm optimization
33 min
Lec 28: Introduction to R (Part II)
35 min
Lec 29: Multi-objective Genetic Algorithms
44 min
Lec 30: Introduction to Differential Evolution
40 min
Lec 31: Introduction to Matlab
66 min
Lec 32: Optimization using Matlab (Classical methods)
51 min
Lec 33: A tutorial on Differential Evolution
20 min
Lec 34: NSGA II Using R
39 min
Lec 35: Optimization using MATLAB
56 min
Lec 36: Optimization using Excel Solver
52 min
Lec 37: Multi-objective Genetic Algorithms using MATLAB
38 min
Lec 38: Solution of a Design Problem Using MATLAB
50 min

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