A Systematic Review of Structural Health Monitoring Systems to Strengthen Post-Earthquake Assessment Procedures
Structural health monitoring (SHM) is vital to ensuring the integrity of people and structures during earthquakes, especially considering the catastrophic consequences that could be registered in countries within the Pacific ring of fire, such as Ecuador. This work reviews the technologies, architectures, data processing techniques, damage identification techniques, and challenges in state-of-the-art results with SHM system applications. These studies use several data processing techniques such as the wavelet transform, the fast Fourier transform, the Kalman filter, and different technologies such as the Internet of Things (IoT) and machine learning. The results of this review highlight the effectiveness of systems aiming to be cost-effective and wireless, where sensors based on microelectromechanical systems (MEMS) are standard. However, despite the advancement of technology, these face challenges such as optimization of energy resources, computational resources, and complying with the characteristic of real-time processing.
Medienart: |
E-Artikel |
---|
Erscheinungsjahr: |
2022 |
---|---|
Erschienen: |
2022 |
Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
---|---|
Enthalten in: |
Sensors (Basel, Switzerland) - 22(2022), 23 vom: 26. Nov. |
Sprache: |
Englisch |
---|
Beteiligte Personen: |
López-Castro, Brian [VerfasserIn] |
---|
Links: |
---|
Themen: |
Accelerometers |
---|
Anmerkungen: |
Date Completed 16.12.2022 Date Revised 20.12.2022 published: Electronic Citation Status MEDLINE |
---|
doi: |
10.3390/s22239206 |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
NLM350141118 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | NLM350141118 | ||
003 | DE-627 | ||
005 | 20231226044315.0 | ||
007 | cr uuu---uuuuu | ||
008 | 231226s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3390/s22239206 |2 doi | |
028 | 5 | 2 | |a pubmed24n1167.xml |
035 | |a (DE-627)NLM350141118 | ||
035 | |a (NLM)36501906 | ||
035 | |a (PII)9206 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a López-Castro, Brian |e verfasserin |4 aut | |
245 | 1 | 2 | |a A Systematic Review of Structural Health Monitoring Systems to Strengthen Post-Earthquake Assessment Procedures |
264 | 1 | |c 2022 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ƒaComputermedien |b c |2 rdamedia | ||
338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
500 | |a Date Completed 16.12.2022 | ||
500 | |a Date Revised 20.12.2022 | ||
500 | |a published: Electronic | ||
500 | |a Citation Status MEDLINE | ||
520 | |a Structural health monitoring (SHM) is vital to ensuring the integrity of people and structures during earthquakes, especially considering the catastrophic consequences that could be registered in countries within the Pacific ring of fire, such as Ecuador. This work reviews the technologies, architectures, data processing techniques, damage identification techniques, and challenges in state-of-the-art results with SHM system applications. These studies use several data processing techniques such as the wavelet transform, the fast Fourier transform, the Kalman filter, and different technologies such as the Internet of Things (IoT) and machine learning. The results of this review highlight the effectiveness of systems aiming to be cost-effective and wireless, where sensors based on microelectromechanical systems (MEMS) are standard. However, despite the advancement of technology, these face challenges such as optimization of energy resources, computational resources, and complying with the characteristic of real-time processing | ||
650 | 4 | |a Systematic Review | |
650 | 4 | |a Journal Article | |
650 | 4 | |a Review | |
650 | 4 | |a accelerometers | |
650 | 4 | |a earthquakes | |
650 | 4 | |a signal processing methods | |
650 | 4 | |a structural health monitoring | |
650 | 4 | |a wireless network | |
700 | 1 | |a Haro-Baez, Ana Gabriela |e verfasserin |4 aut | |
700 | 1 | |a Arcos-Aviles, Diego |e verfasserin |4 aut | |
700 | 1 | |a Barreno-Riera, Marco |e verfasserin |4 aut | |
700 | 1 | |a Landázuri-Avilés, Bryan |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Sensors (Basel, Switzerland) |d 2007 |g 22(2022), 23 vom: 26. Nov. |w (DE-627)NLM187985170 |x 1424-8220 |7 nnns |
773 | 1 | 8 | |g volume:22 |g year:2022 |g number:23 |g day:26 |g month:11 |
856 | 4 | 0 | |u http://dx.doi.org/10.3390/s22239206 |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a GBV_NLM | ||
951 | |a AR | ||
952 | |d 22 |j 2022 |e 23 |b 26 |c 11 |