Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices

D Preethichandra; T Suntharavadivel; Pushpitha Kalutara; Lasitha Piyathilaka; Umer Izhar

doi:10.3390/s23198279

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Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices

Journal article

Open access

Peer reviewed

Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices

D Preethichandra, T Suntharavadivel, Pushpitha Kalutara, Lasitha Piyathilaka and Umer Izhar

Sensors, Vol.23(19), pp.1-28

2023

DOI: https://doi.org/10.3390/s23198279

PMID: 37837109

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Abstract

smart sensors

sensor networks

asset management

Structural Health Monitoring

SHM

artificial intelligence

machine learning

digital twin

Recent developments in networked and smart sensors have significantly changed the way Structural Health Monitoring (SHM) and asset management are being carried out. Since the sensor networks continuously provide real-time data from the structure being monitored, they constitute a more realistic image of the actual status of the structure where the maintenance or repair work can be scheduled based on real requirements. This review is aimed at providing a wealth of knowledge from the working principles of sensors commonly used in SHM, to artificial-intelligence-based digital twin systems used in SHM and proposes a new asset management framework. The way this paper is structured suits researchers and practicing experts both in the fields of sensors as well as in asset management equally.

Details

Title: Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices
Authors: D Preethichandra (Corresponding Author) - Central Queensland University
T Suntharavadivel (Author) - Central Queensland University
Pushpitha Kalutara (Author) - Central Queensland University
Lasitha Piyathilaka (Author) - Central Queensland University
Umer Izhar (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Publication details: Sensors, Vol.23(19), pp.1-28
Publisher: MDPI AG
Date published: 2023
DOI: 10.3390/s23198279
ISSN: 1424-8220; 1424-8239
PMID: 37837109
Copyright note: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Organisation Unit: School of Science, Technology and Engineering
Language: English
Record Identifier: 99971189502621
Output Type: Journal article

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Collaboration types: Domestic collaboration
Web Of Science research areas: Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation

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