Wireless sensor network for structural health monitoring

Excessive loads can lead to cracks which result in the failure of any engineering structure if timely detection is absent. There are various types of traditional sensors which can be used to detect such cracks or excessive loads with huge wiring, human investment, considerable time and error. This...

Full description

Saved in:
Bibliographic Details
Main Author: Lee, Mei Shuang
Other Authors: Soh Chee Kiong
Format: Final Year Project
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/71722
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:Excessive loads can lead to cracks which result in the failure of any engineering structure if timely detection is absent. There are various types of traditional sensors which can be used to detect such cracks or excessive loads with huge wiring, human investment, considerable time and error. This Final Year Project (FYP) aims to develop an alternative strategy to existing traditional wired sensors using wireless sensor network (WSN), which is a last decade discovery. However, WSN is still relatively new that needs validation. Hence, experimental studies were done and results were compared with theoretical analysis. A representative numerical modelling was also carried out. This FYP involves the measurement of signals using both the wired and wireless sensors. The WSN was used to monitor the aluminium specimens for vibrations, compression and tensile forces. This project also aimed to identify the location of strains or damage on structures when they were subjected to random loading. This random load was a function of load and the area of structural contact. Lastly, this report explains how WSN worked together with kid’s radio control car to study: (1) the obstacles along the path and motion (2) speed breaker detection, and (3) vehicle identification. In this report, seven experimental investigations have been done to explore the functionality of WSN. Some data were analysed using root mean square deviation (RMSD).