Description:

Structural health monitoring i.e. to detect damage in the structure at the earliest possible state is crucial in civil, mechanical, aerospace and other engineering communities. Most of the existing damage detection methods are either visual or experimental such as acoustic or ultrasonic methods, magnet field methods, radiographs, eddy-current methods and thermal field methods. However, these experimental techniques require that the vicinity of the damage is known a priori and that the portion of the structure being inspected is readily accessible. These experimental methods can detect damage on or near the surface of the structure. The objective of the project is to investigate various global damage detection methods that can be applied to complex structures using advanced methods of signal processing. At the completion of this project the students must have learnt various machine learning methods of fault/damage detection in structures.

Type:

Undergraduate

Outcome:

Prerequisites

None

Specialisations

• Electrical and Electronic Engineering

Categories

• AI & Machine learning

Supervisor

• Akshya Swain

Co-supervisor

• Udaya Madawala

Team

• Muhammad Arif Md Isa
• Muhammad Afiq Mohmad Nazari

Lab

Lab allocations have not been finalised