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Preethichandra DMG, Suntharavadivel TG, Kalutara P, Piyathilaka L, Izhar U. Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices. SENSORS (BASEL, SWITZERLAND) 2023; 23:8279. [PMID: 37837109 PMCID: PMC10575112 DOI: 10.3390/s23198279] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023]
Abstract
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.
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Affiliation(s)
- D. M. G. Preethichandra
- School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4702, Australia; (T.G.S.); (P.K.); (L.P.)
| | - T. G. Suntharavadivel
- School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4702, Australia; (T.G.S.); (P.K.); (L.P.)
| | - Pushpitha Kalutara
- School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4702, Australia; (T.G.S.); (P.K.); (L.P.)
| | - Lasitha Piyathilaka
- School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4702, Australia; (T.G.S.); (P.K.); (L.P.)
| | - Umer Izhar
- School of Science, Technology and Engineering, Moreton Bay Campus, University of the Sunshine Coast, Moreton Parade, Petrie, QLD 4502, Australia;
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Pereira RCA, da Silva OS, de Mello Bandeira RA, dos Santos M, de Souza Rocha C, Castillo CDS, Gomes CFS, de Moura Pereira DA, Muradas FM. Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method. SENSORS (BASEL, SWITZERLAND) 2023; 23:4131. [PMID: 37112474 PMCID: PMC10146523 DOI: 10.3390/s23084131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
This paper proposes the use of the AHP-Gaussian method to support the selection of a smart sensor installation for an electric motor used in an escalator in a subway station. The AHP-Gaussian methodology utilizes the Analytic Hierarchy Process (AHP) framework and is highlighted for its ability to save the decision maker's cognitive effort in assigning weights to criteria. Seven criteria were defined for the sensor selection: temperature range, vibration range, weight, communication distance, maximum electric power, data traffic speed, and acquisition cost. Four smart sensors were considered as alternatives. The results of the analysis showed that the most appropriate sensor was the ABB Ability smart sensor, which scored the highest in the AHP-Gaussian analysis. In addition, this sensor could detect any abnormalities in the equipment's operation, enabling timely maintenance and preventing potential failures. The proposed AHP-Gaussian method proved to be an effective approach for selecting a smart sensor for an electric motor used in an escalator in a subway station. The selected sensor was reliable, accurate, and cost-effective, contributing to the safe and efficient operation of the equipment.
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Affiliation(s)
| | | | | | - Marcos dos Santos
- Department of Production Engineering, Faculty of Engineering, Praia Vermelha Campus, Federal Fluminense University, Niteroi 22040-036, Brazil
| | - Claudio de Souza Rocha
- Department of Production Engineering, Faculty of Engineering, Praia Vermelha Campus, Federal Fluminense University, Niteroi 22040-036, Brazil
| | - Cristian dos Santos Castillo
- Department of Production Engineering, Faculty of Engineering, Praia Vermelha Campus, Federal Fluminense University, Niteroi 22040-036, Brazil
| | - Carlos Francisco Simões Gomes
- Department of Production Engineering, Faculty of Engineering, Praia Vermelha Campus, Federal Fluminense University, Niteroi 22040-036, Brazil
| | | | - Fernando Martins Muradas
- Operational Research Department, Naval Systems Analysis Center (CASNAV), Rio de Janeiro 20091-000, Brazil
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Meoni A, D’Alessandro A, Saviano F, Lignola GP, Parisi F, Ubertini F. Strain Monitoring and Crack Detection in Masonry Walls under In-Plane Shear Loading Using Smart Bricks: First Results from Experimental Tests and Numerical Simulations. SENSORS (BASEL, SWITZERLAND) 2023; 23:2211. [PMID: 36850809 PMCID: PMC9960584 DOI: 10.3390/s23042211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 06/18/2023]
Abstract
A diffuse and continuous monitoring of the in-service structural response of buildings can allow for the early identification of the formation of cracks and collapse mechanisms before the occurrence of severe consequences. In the case of existing masonry constructions, the implementation of tailored Structural Health Monitoring (SHM) systems appears quite significant, given their well-known susceptibility to brittle failures. Recently, a new sensing technology based on smart bricks, i.e., piezoresistive brick-like sensors, was proposed in the literature for the SHM of masonry constructions. Smart bricks can be integrated within masonry to monitor strain and detect cracks. At present, the effectiveness of smart bricks has been proven in different structural settings. This paper contributes to the research by investigating the strain-sensitivity of smart bricks of standard dimensions when inserted in masonry walls subjected to in-plane shear loading. Real-scale masonry walls instrumented with smart bricks and displacement sensors were tested under diagonal compression, and numerical simulations were conducted to interpret the experimental results. At peak condition, numerical models provided comparable strain values to those of smart bricks, i.e., approximately equal to 10-4, with similar trends. Overall, the effectiveness of smart bricks in strain monitoring and crack detection is demonstrated.
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Affiliation(s)
- Andrea Meoni
- Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
| | - Antonella D’Alessandro
- Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
| | - Felice Saviano
- Department of Structures for Engineering and Architecture, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, Italy
| | - Gian Piero Lignola
- Department of Structures for Engineering and Architecture, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, Italy
| | - Fulvio Parisi
- Department of Structures for Engineering and Architecture, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, Italy
| | - Filippo Ubertini
- Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
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Elseady AAE, Lee I, Zhuge Y, Ma X, Chow CWK, Gorjian N. Piezoresistivity and AC Impedance Spectroscopy of Cement-Based Sensors: Basic Concepts, Interpretation, and Perspective. MATERIALS (BASEL, SWITZERLAND) 2023; 16:768. [PMID: 36676505 PMCID: PMC9865006 DOI: 10.3390/ma16020768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 06/17/2023]
Abstract
Cement-based sensors include conductive fillers to achieve a sensing capability based on the piezoresistivity phenomenon, in which the electrical resistivity changes with strain. The microstructural characterisation of cement-based sensors can be obtained using a promising non-destructive technique, such as AC impedance spectroscopy (ACIS), which has been recently used by many researchers. This paper reviews the fundamental concepts of piezoresistivity and ACIS in addition to the comparison of equivalent circuit models of cement-based sensors found in the literature. These concepts include piezoresistivity theory, factors affecting piezoresistivity measurement, resistance measurement methodology, strain/damage sensing, causes of piezoresistivity, theories of conduction, AC impedance spectroscopy theory, and the equivalent circuit model. This review aims to provide a comprehensive guide for researchers and practitioners interested in exploring and applying different techniques to self-sensing concrete.
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Affiliation(s)
- Amir A. E. Elseady
- Sustainable Infrastructure and Resource Management (SIRM), UniSA STEM, University of South Australia, Adelaide, SA 5095, Australia
| | - Ivan Lee
- Sustainable Infrastructure and Resource Management (SIRM), UniSA STEM, University of South Australia, Adelaide, SA 5095, Australia
| | - Yan Zhuge
- Sustainable Infrastructure and Resource Management (SIRM), UniSA STEM, University of South Australia, Adelaide, SA 5095, Australia
| | - Xing Ma
- Sustainable Infrastructure and Resource Management (SIRM), UniSA STEM, University of South Australia, Adelaide, SA 5095, Australia
| | - Christopher W. K. Chow
- Sustainable Infrastructure and Resource Management (SIRM), UniSA STEM, University of South Australia, Adelaide, SA 5095, Australia
| | - Nima Gorjian
- South Australian Water Corporation, Adelaide, SA 5095, Australia
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Ferreira PM, Machado MA, Carvalho MS, Vidal C. Embedded Sensors for Structural Health Monitoring: Methodologies and Applications Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8320. [PMID: 36366018 PMCID: PMC9654583 DOI: 10.3390/s22218320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
Sensing Technology (ST) plays a key role in Structural Health-Monitoring (SHM) systems. ST focuses on developing sensors, sensory systems, or smart materials that monitor a wide variety of materials' properties aiming to create smart structures and smart materials, using Embedded Sensors (ESs), and enabling continuous and permanent measurements of their structural integrity. The integration of ESs is limited to the processing technology used to embed the sensor due to its high-temperature sensitivity and the possibility of damage during its insertion into the structure. In addition, the technological process selection is dependent on the base material's composition, which comprises either metallic or composite parts. The selection of smart sensors or the technology underlying them is fundamental to the monitoring mode. This paper presents a critical review of the fundaments and applications of sensing technologies for SHM systems employing ESs, focusing on their actual developments and innovation, as well as analysing the challenges that these technologies present, in order to build a path that allows for a connected world through distributed measurement systems.
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Affiliation(s)
- Pedro M. Ferreira
- UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
| | - Miguel A. Machado
- UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
- Laboratório Associado de Sistemas Inteligentes, LASI, 4800-058 Guimarães, Portugal
| | - Marta S. Carvalho
- UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
- Laboratório Associado de Sistemas Inteligentes, LASI, 4800-058 Guimarães, Portugal
| | - Catarina Vidal
- UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
- Laboratório Associado de Sistemas Inteligentes, LASI, 4800-058 Guimarães, Portugal
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