Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm
Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In...
Gespeichert in:
| Veröffentlicht in: | Sensors (Basel, Switzerland) Jg. 22; H. 7; S. 2482 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Switzerland
MDPI AG
24.03.2022
MDPI |
| Schlagworte: | |
| ISSN: | 1424-8220, 1424-8220 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam’s structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition–processing workflow. |
|---|---|
| AbstractList | Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam’s structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition–processing workflow. Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam's structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition-processing workflow.Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam's structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition-processing workflow. |
| Author | Costa, Katherin Rocio Cano Bezerra da Dantas, Ana Paola do Nascimento Sousa, Patrícia Figuereido de Moreira, Gabriela Rodrigues Jorge, Rafael Mendes Albuquerque, Enzo Aldo Cunha Aquino, Fernando Rodrigo de Ozelim, Luan Carlos de Sena Monteiro Góis, Manuelle Santos Barros, Matheus Lima de Diniz, Mariana dos Santos Borges, Lucas Parreira de Faria Cavalcante, André Luís Brasil |
| AuthorAffiliation | Department of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, Brazil; lucaspdfborges@gmail.com (L.P.d.F.B.); abrasil@unb.br (A.L.B.C.); enzo.aldo@aluno.unb.br (E.A.C.A.); diniz.santos@aluno.unb.br (M.d.S.D.); manuellegeo@unb.br (M.S.G.); katherin.cano@aluno.unb.br (K.R.C.B.d.C.); figuereido.patricia@aluno.unb.br (P.F.d.S.); ana.paola@aluno.unb.br (A.P.d.N.D.); jorge.rafael@aluno.unb.br (R.M.J.); moreira.gabriela@aluno.unb.br (G.R.M.); barros.lima@aluno.unb.br (M.L.d.B.); fernando.aquino@aluno.unb.br (F.R.d.A.) |
| AuthorAffiliation_xml | – name: Department of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, Brazil; lucaspdfborges@gmail.com (L.P.d.F.B.); abrasil@unb.br (A.L.B.C.); enzo.aldo@aluno.unb.br (E.A.C.A.); diniz.santos@aluno.unb.br (M.d.S.D.); manuellegeo@unb.br (M.S.G.); katherin.cano@aluno.unb.br (K.R.C.B.d.C.); figuereido.patricia@aluno.unb.br (P.F.d.S.); ana.paola@aluno.unb.br (A.P.d.N.D.); jorge.rafael@aluno.unb.br (R.M.J.); moreira.gabriela@aluno.unb.br (G.R.M.); barros.lima@aluno.unb.br (M.L.d.B.); fernando.aquino@aluno.unb.br (F.R.d.A.) |
| Author_xml | – sequence: 1 givenname: Luan Carlos de Sena Monteiro orcidid: 0000-0002-2581-0486 surname: Ozelim fullname: Ozelim, Luan Carlos de Sena Monteiro – sequence: 2 givenname: Lucas Parreira de Faria orcidid: 0000-0003-4443-3310 surname: Borges fullname: Borges, Lucas Parreira de Faria – sequence: 3 givenname: André Luís Brasil orcidid: 0000-0003-4980-1450 surname: Cavalcante fullname: Cavalcante, André Luís Brasil – sequence: 4 givenname: Enzo Aldo Cunha surname: Albuquerque fullname: Albuquerque, Enzo Aldo Cunha – sequence: 5 givenname: Mariana dos Santos surname: Diniz fullname: Diniz, Mariana dos Santos – sequence: 6 givenname: Manuelle Santos orcidid: 0000-0002-6327-408X surname: Góis fullname: Góis, Manuelle Santos – sequence: 7 givenname: Katherin Rocio Cano Bezerra da surname: Costa fullname: Costa, Katherin Rocio Cano Bezerra da – sequence: 8 givenname: Patrícia Figuereido de orcidid: 0000-0002-4551-4940 surname: Sousa fullname: Sousa, Patrícia Figuereido de – sequence: 9 givenname: Ana Paola do Nascimento surname: Dantas fullname: Dantas, Ana Paola do Nascimento – sequence: 10 givenname: Rafael Mendes surname: Jorge fullname: Jorge, Rafael Mendes – sequence: 11 givenname: Gabriela Rodrigues surname: Moreira fullname: Moreira, Gabriela Rodrigues – sequence: 12 givenname: Matheus Lima de surname: Barros fullname: Barros, Matheus Lima de – sequence: 13 givenname: Fernando Rodrigo de surname: Aquino fullname: Aquino, Fernando Rodrigo de |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35408097$$D View this record in MEDLINE/PubMed |
| BookMark | eNplkk1v0zAYgCM0xD7gwB9AlriAtDJ_xUkuSKVjbFI3DqNny3HepC5OXGwHtB355XjtNnXjZMt-3kfv12G2N7gBsuwtwZ8Yq_BJoBQXlJf0RXZAOOWTMj3s7dz3s8MQVhhTxlj5KttnOcclroqD7O919KOOo1cWnYOycYku3WCi82bokGvRqeoD-qICNMgNaKrdGKLRO9AxOgVYoyvYOK4g_nH-ZzhGZ-Pt7Q2auy7RamiQQrPF9eISzdwQvbNoarsUH5f96-xlq2yAN_fnUbY4-_pjdj6Zf_92MZvOJ5qLKk4YUCoqXdaiJooUoDAVedOSltZ1kVNWCMFYDg0u66YCBrgGDk3L60JooLpiR9nF1ts4tZJrb3rlb6RTRm4enO-k8qk2C5ISnnykpkWpeVuRkjCihaobrZXgbZNcn7eu9Vj30GhINSn7RPr0ZzBL2bnfssKY4UIkwYd7gXe_RghR9iZosFYNkFosqeBVXhac44S-f4au3OiH1KoNRXDCWKLe7Wb0mMrDqBNwsgW0dyF4aKU2UUVzNw5lrCRY3i2TfFymFPHxWcSD9H_2H9LIySQ |
| CitedBy_id | crossref_primary_10_1111_mice_13431 crossref_primary_10_1016_j_jmsy_2025_01_007 crossref_primary_10_3390_buildings15152803 crossref_primary_10_1007_s42107_025_01474_w crossref_primary_10_1177_14759217231199569 crossref_primary_10_3390_s23094480 crossref_primary_10_3390_su151310543 crossref_primary_10_1109_TIM_2024_3428610 crossref_primary_10_3390_s23229292 crossref_primary_10_3390_s25051424 crossref_primary_10_3390_rs14225818 crossref_primary_10_1007_s11831_025_10244_5 |
| Cites_doi | 10.1139/t00-030 10.1134/S2075108717010035 10.1016/j.procs.2020.03.359 10.1098/rsta.2015.0202 10.1109/ICMLA.2015.9 10.1016/0013-4694(70)90143-4 10.1029/2010WR010247 10.1016/j.procs.2016.05.339 10.1111/j.2517-6161.1995.tb02052.x 10.1016/j.jocs.2016.11.016 10.5194/esurf-6-1219-2018 10.1016/0893-6080(89)90014-2 10.1016/j.enggeo.2018.02.015 10.1093/biomet/41.1-2.100 10.1007/BF00332918 10.1142/9789812562531_0015 10.1155/2021/6658575 10.1214/aoms/1177731118 10.1109/JSTSP.2012.2233713 10.1145/3097983.3098052 10.1016/B978-1-78548-229-8.50001-7 10.1785/0220180251 10.1145/235815.235821 10.3390/s20092517 10.1080/14786440109462720 10.1002/qre.2723 10.1214/aoms/1177693055 10.1680/jgeot.14.P.268 10.1016/j.ymssp.2021.108247 |
| ContentType | Journal Article |
| Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s22072482 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection (ProQuest) ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic CrossRef MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_2143761b278c4f918131c6abdcca64fd PMC9003076 35408097 10_3390_s22072482 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Neoenergia/CEB Distribuição S.A. grantid: PD-05160-1904/2019, CEBD782/2019 – fundername: Coordenação de Aperfeicoamento de Pessoal de Nível Superior grantid: 001 – fundername: National Council for Scientific and Technological Development grantid: 305484/2020-6 |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. ABJCF ALIPV ARAPS CGR CUY CVF ECM EIF HCIFZ KB. M7S NPM PDBOC 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c469t-3e2269c8b6b1a17ea0265df1f2bb7523766335ed08bd9e3e0be4edf4b76ce2c93 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 14 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000781905500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Tue Oct 14 18:56:34 EDT 2025 Tue Nov 04 01:54:42 EST 2025 Wed Oct 01 14:37:22 EDT 2025 Tue Oct 07 07:11:03 EDT 2025 Wed Feb 19 02:26:41 EST 2025 Tue Nov 18 22:07:14 EST 2025 Sat Nov 29 07:09:56 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Keywords | deep learning autoencoder structural monitoring fuzzy logic CUSUM algorithm geotechnical engineering dams |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. 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/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c469t-3e2269c8b6b1a17ea0265df1f2bb7523766335ed08bd9e3e0be4edf4b76ce2c93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-6327-408X 0000-0003-4980-1450 0000-0002-2581-0486 0000-0003-4443-3310 0000-0002-4551-4940 |
| OpenAccessLink | https://doaj.org/article/2143761b278c4f918131c6abdcca64fd |
| PMID | 35408097 |
| PQID | 2649108743 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_2143761b278c4f918131c6abdcca64fd pubmedcentral_primary_oai_pubmedcentral_nih_gov_9003076 proquest_miscellaneous_2649587440 proquest_journals_2649108743 pubmed_primary_35408097 crossref_citationtrail_10_3390_s22072482 crossref_primary_10_3390_s22072482 |
| PublicationCentury | 2000 |
| PublicationDate | 20220324 |
| PublicationDateYYYYMMDD | 2022-03-24 |
| PublicationDate_xml | – month: 3 year: 2022 text: 20220324 day: 24 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationTitleAlternate | Sensors (Basel) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Ikard (ref_8) 2012; 48 Page (ref_31) 1954; 41 Lafitte (ref_5) 1993; 45 Mooney (ref_12) 2016; 66 ref_13 ref_34 Chu (ref_36) 2019; 2019 ref_11 ref_33 Fisher (ref_14) 2017; 20 ref_17 ref_16 Grover (ref_25) 2020; 167 Jolliffe (ref_19) 2016; 374 Lai (ref_27) 1995; 57 Pearson (ref_20) 1901; 2 Sahki (ref_26) 2020; 36 Hjorth (ref_23) 1970; 29 Wald (ref_30) 1945; 16 Foster (ref_6) 2000; 37 Frongia (ref_10) 2018; 237 Lorden (ref_32) 1971; 42 Cocconcelli (ref_24) 2022; 164 ref_1 ref_3 Tartakovsky (ref_29) 2013; 7 ref_2 Fisher (ref_15) 2016; 80 Rastin (ref_18) 2021; 2021 Barber (ref_38) 1996; 22 ref_9 Bourlard (ref_21) 1988; 59 (ref_28) 2017; 8 ref_4 Baldi (ref_22) 1989; 2 ref_7 Manconi (ref_35) 2018; 6 Anthony (ref_37) 2018; 90 |
| References_xml | – volume: 37 start-page: 1000 year: 2000 ident: ref_6 article-title: The statistics of embankment dam failures and accidents publication-title: Can. Geotech. J. doi: 10.1139/t00-030 – ident: ref_7 – ident: ref_9 – volume: 8 start-page: 1 year: 2017 ident: ref_28 article-title: Comprehensive overview of quickest detection theory and its application to GNSS threat detection publication-title: Gyroscopy Navig. doi: 10.1134/S2075108717010035 – volume: 167 start-page: 1484 year: 2020 ident: ref_25 article-title: Rolling Element Bearing Fault Diagnosis using Empirical Mode Decomposition and Hjorth Parameters publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2020.03.359 – volume: 374 start-page: 20150202 year: 2016 ident: ref_19 article-title: Principal component analysis: A review and recent developments publication-title: Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. doi: 10.1098/rsta.2015.0202 – ident: ref_3 – ident: ref_13 doi: 10.1109/ICMLA.2015.9 – ident: ref_11 – volume: 29 start-page: 306 year: 1970 ident: ref_23 article-title: EEG analysis based on time domain properties publication-title: Electroencephalogr. Clin. Neurophysiol. doi: 10.1016/0013-4694(70)90143-4 – volume: 48 start-page: W04201 year: 2012 ident: ref_8 article-title: Saline pulse test monitoring with the self-potential method to nonintrusively determine the velocity of the pore water in leaking areas of earth dams and embankments publication-title: Water Resour. Res. doi: 10.1029/2010WR010247 – volume: 80 start-page: 577 year: 2016 ident: ref_15 article-title: Crack Detection in Earth Dam and Levee Passive Seismic Data Using Support Vector Machines publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2016.05.339 – volume: 57 start-page: 613 year: 1995 ident: ref_27 article-title: Sequential Changepoint Detection in Quality Control and Dynamical Systems publication-title: J. R. Stat. Soc. Ser. B (Methodol.) doi: 10.1111/j.2517-6161.1995.tb02052.x – volume: 20 start-page: 143 year: 2017 ident: ref_14 article-title: Anomaly detection in earth dam and levee passive seismic data using support vector machines and automatic feature selection publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2016.11.016 – volume: 6 start-page: 1219 year: 2018 ident: ref_35 article-title: Short Communication: Monitoring rockfalls with the Raspberry Shake publication-title: Earth Surf. Dyn. doi: 10.5194/esurf-6-1219-2018 – volume: 2 start-page: 53 year: 1989 ident: ref_22 article-title: Neural networks and principal component analysis: Learning from examples without local minima publication-title: Neural Netw. doi: 10.1016/0893-6080(89)90014-2 – volume: 237 start-page: 129 year: 2018 ident: ref_10 article-title: Internal characterization of embankment dams using ground penetrating radar (GPR) and thermographic analysis: A case study of the Medau Zirimilis Dam (Sardinia, Italy) publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2018.02.015 – volume: 41 start-page: 100 year: 1954 ident: ref_31 article-title: Continuous Inspection Schemes publication-title: Biometrika doi: 10.1093/biomet/41.1-2.100 – ident: ref_4 – ident: ref_33 – volume: 2019 start-page: H11H-1565 year: 2019 ident: ref_36 article-title: The Implementation of Debris Flow Seismic Detector With Raspberry Shake publication-title: AGU Fall Meet. Abstr. – ident: ref_2 – volume: 59 start-page: 291 year: 1988 ident: ref_21 article-title: Auto-association by multilayer perceptrons and singular value decomposition publication-title: Biol. Cybern. doi: 10.1007/BF00332918 – volume: 45 start-page: 13 year: 1993 ident: ref_5 article-title: Probabilistic risk analysis of large dams. Its value and limits publication-title: Int. Water Power Dam Constr. – ident: ref_34 doi: 10.1142/9789812562531_0015 – volume: 2021 start-page: 6658575 year: 2021 ident: ref_18 article-title: Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder publication-title: Shock Vib. doi: 10.1155/2021/6658575 – volume: 16 start-page: 117 year: 1945 ident: ref_30 article-title: Sequential Tests of Statistical Hypotheses publication-title: Ann. Math. Stat. doi: 10.1214/aoms/1177731118 – volume: 7 start-page: 4 year: 2013 ident: ref_29 article-title: Efficient Computer Network Anomaly Detection by Changepoint Detection Methods publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2012.2233713 – ident: ref_16 doi: 10.1145/3097983.3098052 – ident: ref_1 doi: 10.1016/B978-1-78548-229-8.50001-7 – volume: 90 start-page: 219 year: 2018 ident: ref_37 article-title: Do Low-Cost Seismographs Perform Well Enough for Your Network? An Overview of Laboratory Tests and Field Observations of the OSOP Raspberry Shake 4D publication-title: Seismol. Res. Lett. doi: 10.1785/0220180251 – volume: 22 start-page: 469 year: 1996 ident: ref_38 article-title: The Quickhull Algorithm for Convex Hulls publication-title: ACM Trans. Math. Softw. doi: 10.1145/235815.235821 – ident: ref_17 doi: 10.3390/s20092517 – volume: 2 start-page: 559 year: 1901 ident: ref_20 article-title: LIII. On lines and planes of closest fit to systems of points in space publication-title: Lond. Edinb. Dublin Philos. Mag. J. Sci. doi: 10.1080/14786440109462720 – volume: 36 start-page: 2699 year: 2020 ident: ref_26 article-title: Performance study of change-point detection thresholds for cumulative sum statistic in a sequential context publication-title: Qual. Reliab. Eng. Int. doi: 10.1002/qre.2723 – volume: 42 start-page: 1897 year: 1971 ident: ref_32 article-title: Procedures for Reacting to a Change in Distribution publication-title: Ann. Math. Stat. doi: 10.1214/aoms/1177693055 – volume: 66 start-page: 301 year: 2016 ident: ref_12 article-title: Time-lapse monitoring of internal erosion in earthen dams and levees using ambient seismic noise publication-title: Géotechnique doi: 10.1680/jgeot.14.P.268 – volume: 164 start-page: 108247 year: 2022 ident: ref_24 article-title: Detectivity: A combination of Hjorth’s parameters for condition monitoring of ball bearings publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2021.108247 |
| SSID | ssj0023338 |
| Score | 2.4381695 |
| Snippet | Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 2482 |
| SubjectTerms | 20th century Acoustics Algorithms Artificial Intelligence autoencoder Civil engineering Concrete Construction Control algorithms dams deep learning Engineering firms Failure Fuzzy Logic geotechnical engineering Neural Networks, Computer Sensors Soil erosion structural monitoring |
| SummonAdditionalLinks | – databaseName: Publicly Available Content Database dbid: PIMPY link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEB5BygEOUB4FQ4sWxIFDrax3Ha_3VKUpERyIIkGlcrK8D7eVWjvUAYke-eWdsR2ToIoTV-_YWntmZ3bWM98H8M4rpY1UPLSCWnIs6sII50KX24hLqxPPbUM2oWaz9OREz7v26Lorq1z5xMZRt2jPVLeNTnjoKksn5kMM4xjnUgx_B4vvIXFI0b_WjlDjLmwR8BYfwNb80-f5tz4Bk5iPtehCElP9YS0EVyJOxUZMaqD7b9tv_l02uRaHpo_-7xtsw8NuP8rGrQE9hju-fAIP1lAKn8LvLw3GLOFzsLZribWegIZZVbCj_LJmhxgOHatKNrZVwxC2JrTPjrxfMAICwWfM2srzep9Nf1xf_2JE92xZXjqWs8kxunk2aevn2fjiFO9fnl0-g-Pph6-Tj2FH3RBazLeXofS4rdM2NYmJ8kj5HFO9kSuiQhijRlSJk0g58o6nxmkvPTc-9q6IjSKGMqvlDgzKqvQvgEW-yBXXinNTxFZJnYhCR3EhEmdSZ6IA3q-Ul9kO15zoNS4yzG9Iz1mv5wDe9qKLFszjNqFDsoBegPC3mwvV1WnWLedM4DZTJZERKrUxTieNZGST3DhcEElcuAB2VzaQdU6hzv6oPIA3_TAuZ_pHk5cetdPIjIiSgAfwvDW3fiZ0RJfilwhAbRjixlQ3R8rzswYyXDfOPHn572m9gvuCuju4DEW8CwO0Lr8H9-zP5Xl99bpbTTfbOTNx priority: 102 providerName: ProQuest |
| Title | Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35408097 https://www.proquest.com/docview/2649108743 https://www.proquest.com/docview/2649587440 https://pubmed.ncbi.nlm.nih.gov/PMC9003076 https://doaj.org/article/2143761b278c4f918131c6abdcca64fd |
| Volume | 22 |
| WOSCitedRecordID | wos000781905500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection (ProQuest) customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwEB7BwgEOiDeBpTKIA4eN1rHTOD623VZw2KoCViqnKH7trrSbrkhBYg8c-OWM7TRq0UpcuPhgTxLHM-OZScbfALyzQkjFBU0180dyNPJCMWNSU-uMci0LS3UoNiHm83K5lIutUl8-JyzCA8eFO2Ro0DHUVkyUOncSDRLPdFErg48ucmf87kuF3ARTXajFMfKKOEIcg_rDliENy0u2Y30CSP9NnuXfCZJbFmf2EB50riIZxSk-glu2eQz3twAEn8DvzwH-1UNnkHigiEQl9cNk5chRfdmSMVoqQ1YNGelVKN61RXRAjqy9Ih6jA-8xj0nh7QGZfb--_kl8JWZN6saQmkxOcAcmk5jaTkYXp3j9-uzyKZzMpl8mH9KuqkKqMRRep9yixyV1qQqV1ZmwNUZhQ-Myx5QSQ58kU3A-tIaWykjLLVU2t8blSvjiYVryZ7DXrBr7AkhmXS2oFJQql2vBZcGQQ7ljhVGlUVkC7zerXekOctxXvrioMPTwjKl6xiTwtie9ijgbNxGNPct6Ag-NHTpQYKpOYKp_CUwC-xuGV52-thW6heg3lehOJfCmH0ZN879P6sYidwLN0FcLoAk8j_LRz8R_PStxJRIQO5KzM9Xdkeb8LKB5y7DPFi__x7u9gnvMH8-gPGX5PuyhDNrXcFf_WJ-33wZwWyxFaMsB3BlP54tPg6A22B7_mmLf4uPx4usfzW0eww |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB5VKRJw4P0wFFgQSBxqdR-O1z4glCZEjdpGkWil9mS8D7eVWjvUAdQe-UH8RmZtxySo4tYDV-_YWu1-O7Pj3fk-gLdWylgJSX3NXUmOxrlQ3BjfpJpRoePQUl2JTcjxODo4iCcr8GteC-OuVc59YuWoTaHdP_INDNwY2SIMeB-nX32nGuVOV-cSGjUstu3FD0zZyg-jAc7vO86Hn_b6W36jKuBrTAVnvrC444h1pELFUiZtillI12Qs40rJrrskEgrRtYZGysRWWKpsYE0WKOnEs7QjX0KXvxog2GkHViej3clhm-IJzPhq_iIhYrpRck4lDyK-FPUqcYCrdrR_X8xciHTDu__bGN2DO82emvTqRXAfVmz-AG4vMC0-hJ-fK55cxzFC6sorUnsz10yKjAzSs5JsYkg3pMhJTxeVytmC0ToZWDsljswEvzGub8-X62T47fLygjjJak3S3JCU9PcxVJF-XQNAeqdH-P7s-OwR7F_LIDyGTl7k9ikQZrNU0lhSqrJASxGHPItZkPHQqMgo5sH7OTwS3XCzO4mQ0wRzNIekpEWSB29a02lNSHKV0abDWGvgOMSrB8X5UdK4pITjVlmGTHEZ6QC7EzHBdJgqg4s6DDLjwdocZUnj2MrkD8Q8eN02o0ty50xpbnF2Kpuuk1WgHjypAd32xP1mjHAkPJBLUF_q6nJLfnJc0Z7HVUAKn_27W6_g5tbe7k6yMxpvP4db3FWrUOHzYA06iDT7Am7o77OT8vxls3YJfLnupfAbrmCGWw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB5VW4TgUN4QKGAQSBwarWNn4-SA0D66oiqKVkCl3tL4kbZSm2ybBdQe-Vn8OsZ5sYsqbj1wXU9Wlv15xpPMfB_AGyNEJLmgrmK2JUfhXkimtatT5VGuosBQVYlNiDgO9_ej2Rr8anthbFll6xMrR60LZd-R9zFwY2QLMeD1s6YsYjaZfpifuVZByn5pbeU0aojsmosfmL6V73cmuNdvGZtufx1_dBuFAVdhWrhwucHbR6RCGUgv9YRJMSMZ6MzLmJRiYAtGAs4HRtNQ6shwQ6Xxjc58KayQlrJETOj-1wXHpKcH66PtePa5S_c4Zn81lxHnEe2XjFHB_JCtRMBKKOCq2-3fRZpLUW96539er7uw0dy1ybA-HPdgzeT34fYSA-MD-Pml4s-13COk7sgitZezw6TIyCQ9LckIQ70mRU6GqqjUz5aMtsjEmDmxJCf4H3FdVV9ukem3y8sLYqWsFUlzTVIy3sMQRsZ1bwAZnhzi84uj04ewdy2L8Ah6eZGbJ0A8k6WCRoJSmfkKYRSwLPL8jAVahlp6DrxroZKohrPdSoecJJi7WVQlHaoceN2ZzmuikquMRhZvnYHlFq9-KM4Pk8ZVJQyv0CLwJBOh8nE6occ9FaRS42EP_Ew7sNkiLmkcXpn8gZsDr7phdFX2-1OaG9ydymZg5RaoA49rcHczsa8fQ1wJB8QK7FemujqSHx9VdOhRFaiCp_-e1ku4ifhPPu3Eu8_gFrNNLJS7zN-EHgLNPIcb6vviuDx_0RxjAgfXfRJ-A1CmjvU |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Structural+Health+Monitoring+of+Dams+Based+on+Acoustic+Monitoring%2C+Deep+Neural+Networks%2C+Fuzzy+Logic+and+a+CUSUM+Control+Algorithm&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Luan+Carlos+de+Sena+Monteiro+Ozelim&rft.au=Lucas+Parreira+de+Faria+Borges&rft.au=Andr%C3%A9+Lu%C3%ADs+Brasil+Cavalcante&rft.au=Enzo+Aldo+Cunha+Albuquerque&rft.date=2022-03-24&rft.pub=MDPI+AG&rft.eissn=1424-8220&rft.volume=22&rft.issue=7&rft.spage=2482&rft_id=info:doi/10.3390%2Fs22072482&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_2143761b278c4f918131c6abdcca64fd |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |