Probabilistic method for wind speed prediction and statistics distribution inference based on SHM data-driven
For wind-sensitive structures, such as long-span bridges, high-rise buildings, transmission towers, etc., the prediction of wind speed and its statistical distribution are vital steps in the design and operation stages. Specifically, wind speed prediction is directly related to the value of wind loa...
Saved in:
| Published in | Probabilistic engineering mechanics Vol. 73; p. 103475 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.07.2023
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0266-8920 |
| DOI | 10.1016/j.probengmech.2023.103475 |
Cover
| Abstract | For wind-sensitive structures, such as long-span bridges, high-rise buildings, transmission towers, etc., the prediction of wind speed and its statistical distribution are vital steps in the design and operation stages. Specifically, wind speed prediction is directly related to the value of wind load in the next occurrence; the statistical distribution of wind speed has regular characteristics, which can represent the random characteristics of wind field. In this paper, a probabilistic prediction model of wind speed based on Bayes’ theorem is proposed and verified based on structural health monitoring (SHM) data. Firstly, the Gaussian process is derived and used as an a priori function in Bayes’ theorem. In addition, the influence of six covariance functions on the prediction performance are discussed, that is, squared exponential (SE), Matern-3/2 (MA-3/2), Matern-5/2 (MA-5/2), automatic relevance determination SE (ARDSE), ARDMA-3/2, and ARDMA-5/2. Secondly, the correlation between the next wind speed and the previous wind speed is discussed by using the moving window method. Finally, the parameters in the three wind speed probability distribution functions (PDF), that is, Gumbel distribution, Weibull distribution, Rayleigh distribution, are updated in real time by increasing the SHM data based on Bayes’ theorem. |
|---|---|
| AbstractList | For wind-sensitive structures, such as long-span bridges, high-rise buildings, transmission towers, etc., the prediction of wind speed and its statistical distribution are vital steps in the design and operation stages. Specifically, wind speed prediction is directly related to the value of wind load in the next occurrence; the statistical distribution of wind speed has regular characteristics, which can represent the random characteristics of wind field. In this paper, a probabilistic prediction model of wind speed based on Bayes’ theorem is proposed and verified based on structural health monitoring (SHM) data. Firstly, the Gaussian process is derived and used as an a priori function in Bayes’ theorem. In addition, the influence of six covariance functions on the prediction performance are discussed, that is, squared exponential (SE), Matern-3/2 (MA-3/2), Matern-5/2 (MA-5/2), automatic relevance determination SE (ARDSE), ARDMA-3/2, and ARDMA-5/2. Secondly, the correlation between the next wind speed and the previous wind speed is discussed by using the moving window method. Finally, the parameters in the three wind speed probability distribution functions (PDF), that is, Gumbel distribution, Weibull distribution, Rayleigh distribution, are updated in real time by increasing the SHM data based on Bayes’ theorem. |
| ArticleNumber | 103475 |
| Author | Zhang, Ru Ma, Zhi Ding, Yang Ye, Xiao-Wei Guo, Yong |
| Author_xml | – sequence: 1 givenname: Yang orcidid: 0000-0002-1298-1710 surname: Ding fullname: Ding, Yang email: ceyangding@zju.edu.cn organization: Zhejiang Engineering Research Center of Intelligent Urban Infrastructure, Hangzhou City University, Hangzhou, 310015, China – sequence: 2 givenname: Xiao-Wei surname: Ye fullname: Ye, Xiao-Wei email: cexwye@zju.edu.cn organization: Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China – sequence: 3 givenname: Yong surname: Guo fullname: Guo, Yong email: 450437318@qq.com organization: Zhejiang Jiashao Bridge Investment and Development Co., Ltd., Shaoxing, 312366, China – sequence: 4 givenname: Ru surname: Zhang fullname: Zhang, Ru email: zhangru@hzcu.edu.cn organization: Zhejiang Engineering Research Center of Intelligent Urban Infrastructure, Hangzhou City University, Hangzhou, 310015, China – sequence: 5 givenname: Zhi surname: Ma fullname: Ma, Zhi email: mazhi@hzcu.edu.cn organization: Zhejiang Engineering Research Center of Intelligent Urban Infrastructure, Hangzhou City University, Hangzhou, 310015, China |
| BookMark | eNqNkM1OwzAQhH0oEm3hHcwDpNhO4qQnhCqgSEUgAWfLPxvqqrUj2xTx9jgtB8Spp9XO7ow03wSNnHeA0BUlM0oov97M-uAVuI8d6PWMEVZmvayaeoTGhHFetHNGztEkxg0htKHVfIx2L9kild3amKzGO0hrb3DnA_6yzuDYAxjcBzBWJ-sdloOYZDq8R2zyDFZ9Hm7WdRDAacBKxmzL0uvyCRuZZGGC3YO7QGed3Ea4_J1T9H5_97ZYFqvnh8fF7arQJaOpoLphvCwll5q1NeekolybirNWdVSB0lRxLluoeV2TRuW9JozojhEou8awcormx1wdfIwBOtEHu5PhW1AiBlZiI_6wEgMrcWSVvTf_vNoOhb1LQdrtSQmLYwLkinsLQURtBy7GBtBJGG9PSPkB6oSUPQ |
| CitedBy_id | crossref_primary_10_3390_su17020695 crossref_primary_10_1016_j_dibe_2024_100569 crossref_primary_10_1007_s13349_023_00714_4 crossref_primary_10_3390_app132312744 crossref_primary_10_1177_13694332241247923 crossref_primary_10_1016_j_istruc_2025_108650 crossref_primary_10_3390_electronics13183710 crossref_primary_10_3390_buildings14092718 crossref_primary_10_3390_fractalfract8110656 crossref_primary_10_1002_eng2_12781 crossref_primary_10_1002_eng2_12780 crossref_primary_10_1002_eng2_12782 crossref_primary_10_1080_13467581_2024_2345312 crossref_primary_10_3390_buildings14072054 crossref_primary_10_1088_1402_4896_ad398c crossref_primary_10_1631_jzus_A2200573 crossref_primary_10_3390_app132413138 crossref_primary_10_1007_s13349_024_00810_z crossref_primary_10_1088_2631_8695_ad5e34 crossref_primary_10_1016_j_probengmech_2023_103541 crossref_primary_10_1016_j_engstruct_2024_119523 crossref_primary_10_1016_j_heliyon_2024_e39383 crossref_primary_10_1016_j_probengmech_2023_103483 crossref_primary_10_1631_jzus_A2200599 crossref_primary_10_1002_eng2_12778 crossref_primary_10_1016_j_istruc_2023_104996 crossref_primary_10_3390_s24030866 crossref_primary_10_1038_s41598_025_90583_2 crossref_primary_10_1016_j_apenergy_2023_122015 crossref_primary_10_1007_s12205_024_0371_6 crossref_primary_10_1016_j_asoc_2024_112007 crossref_primary_10_3390_su16145843 crossref_primary_10_1016_j_dsp_2024_104838 crossref_primary_10_3390_ma16186341 crossref_primary_10_1080_13467581_2024_2378004 crossref_primary_10_1016_j_probengmech_2023_103502 crossref_primary_10_1088_2631_8695_ad45b6 crossref_primary_10_1088_2631_8695_ad681d |
| Cites_doi | 10.1177/1475921711424520 10.1093/biomet/asx075 10.1111/sjos.12046 10.1007/s13349-022-00662-5 10.1016/j.apenergy.2013.08.025 10.1007/BF02829088 10.1002/stc.2650 10.1016/S0167-4730(01)00016-9 10.1016/j.oceaneng.2014.09.029 10.1002/nme.5305 10.1016/j.engstruct.2020.110520 10.1002/stc.2258 10.1155/2023/4950487 10.1016/j.ymssp.2021.108204 10.1023/A:1014463014150 10.1016/j.jweia.2017.07.021 10.1016/j.renene.2003.11.009 10.1007/s13349-020-00430-3 10.1177/0309524X21999964 10.1016/S0266-8920(02)00013-9 10.1093/bioinformatics/btv267 10.1016/j.ymssp.2022.109624 10.1016/j.neucom.2020.06.114 10.1016/j.renene.2004.07.007 10.1002/we.2613 10.1061/(ASCE)BE.1943-5592.0000941 10.1002/rnc.4095 10.1016/j.renene.2012.07.041 10.1016/j.istruc.2022.12.028 10.1016/j.neucom.2020.09.002 10.1061/(ASCE)CP.1943-5487.0000429 10.1016/j.rser.2014.10.028 10.1016/j.jweia.2007.07.001 10.1002/stc.2699 10.1061/(ASCE)ST.1943-541X.0002085 |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier Ltd |
| Copyright_xml | – notice: 2023 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.probengmech.2023.103475 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences |
| ExternalDocumentID | 10_1016_j_probengmech_2023_103475 S0266892023000644 |
| GroupedDBID | --K --M -~X .~1 0R~ 123 1B1 1~. 1~5 29O 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXKI AAXUO ABEFU ABFNM ABJNI ABMAC ABXDB ACDAQ ACGFS ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AFJKZ AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HVGLF HZ~ IHE J1W JJJVA KOM LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SES SET SEW SPC SPCBC SST SSZ T5K TN5 WUQ XPP ZMT ~02 ~G- AATTM AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c321t-1c72633a6ac285660416cd4628bf1bebc1b66a8e565507bbc15020cf20e3f7d23 |
| IEDL.DBID | .~1 |
| ISSN | 0266-8920 |
| IngestDate | Thu Oct 09 00:32:47 EDT 2025 Thu Apr 24 23:13:01 EDT 2025 Tue Dec 03 03:45:23 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Bayes’ theorem Wind speed prediction Gaussian process Covariance functions Structural health monitoring Wind speed statistics |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c321t-1c72633a6ac285660416cd4628bf1bebc1b66a8e565507bbc15020cf20e3f7d23 |
| ORCID | 0000-0002-1298-1710 |
| ParticipantIDs | crossref_primary_10_1016_j_probengmech_2023_103475 crossref_citationtrail_10_1016_j_probengmech_2023_103475 elsevier_sciencedirect_doi_10_1016_j_probengmech_2023_103475 |
| PublicationCentury | 2000 |
| PublicationDate | July 2023 2023-07-00 |
| PublicationDateYYYYMMDD | 2023-07-01 |
| PublicationDate_xml | – month: 07 year: 2023 text: July 2023 |
| PublicationDecade | 2020 |
| PublicationTitle | Probabilistic engineering mechanics |
| PublicationYear | 2023 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Wang, Ni, Zhang, Zhang (b16) 2021 Ye, Ding, Wan (b12) 2021; 28 Feng, Huang, Li (b28) 2020; 414 Xu, Forde, Ren, Huang (b15) 2021 Davis, Hans, Santner (b29) 2021 Ye, Ding, Wan (b5) 2019; 24 Song, Renson, Noel, Moaveni, Kerschen (b23) 2018; 25 An, Choi, Kim (b25) 2013; 11 Ye, Hong, Wang (b7) 2015; 29 Ni, Wang, Zhang (b26) 2020; 212 Liao, Liu, Deng (b9) 2021; 24 Macdonald, Larose (b3) 2008; 96 Penfold, Millar, Wild (b35) 2015; 31 Ryoo, Lee (b20) 2004; 8 Bolin (b34) 2014; 41 Ding, Ye, Guo (b45) 2023; 47 Ericok, Zbek, Cemgil, Erturk (b37) 2019 Wan, Ni (b31) 2018; 18 Ding, Ye, Guo (b43) 2023 Mohandes, Halawani, Rehman, Hussain (b11) 2004; 29 Ding, Ye, Guo (b44) 2023; 13 Jiang, Song, Kusiak (b17) 2013; 50 Phoon, Huang, Quek (b32) 2002; 17 Ni, Li, Han, Xu, Du (b14) 2023; 183 Simiu, Heckert, Filliben, Johnson (b38) 2001; 23 Toure (b39) 2005; 30 Kang, Reich, Staicu (b36) 2016; 105 Faghih-Roohi, Xie, Ng (b41) 2014; 91 Wan, Ni (b30) 2018; 144 Pishgar-Komleh, Keyhani, Sefeedpari (b40) 2015; 42 Cai, Peng (b22) 2002; 33 Ni, Han, Du, Cheng (b13) 2022; 164 Ye, Yuan, Xi, Liu (b1) 2018; 21 Garbunoinigo, Diazdelao, Zuev (b42) 2016; 6 Wan, Ren, Todd (b27) 2017; 109 Ye, Ding, Wan (b18) 2020; 10 Chen, Jie (b8) 2014; 113 Yu, Li, Xu (b24) 2017; 28 Xu, Ying, Li, Zhang (b2) 2016; 21 Kumar, Kumar (b19) 2021; 45 Chang, Yamada (b33) 2008; 134 Chen, Zhao, Jia, Li (b6) 2021 Castillo-Barnes, Martinez-Murcia, Ramírez, Górriz, Salas-Gonzalez (b21) 2020; 413 Ye, Xi, Su, Chen (b4) 2017; 63 Huang, He, He, Zhu (b10) 2017; 170 Xu (10.1016/j.probengmech.2023.103475_b15) 2021 Castillo-Barnes (10.1016/j.probengmech.2023.103475_b21) 2020; 413 Ding (10.1016/j.probengmech.2023.103475_b45) 2023; 47 Chen (10.1016/j.probengmech.2023.103475_b6) 2021 Ni (10.1016/j.probengmech.2023.103475_b26) 2020; 212 Wan (10.1016/j.probengmech.2023.103475_b27) 2017; 109 Xu (10.1016/j.probengmech.2023.103475_b2) 2016; 21 Wan (10.1016/j.probengmech.2023.103475_b31) 2018; 18 Penfold (10.1016/j.probengmech.2023.103475_b35) 2015; 31 Ericok (10.1016/j.probengmech.2023.103475_b37) 2019 Pishgar-Komleh (10.1016/j.probengmech.2023.103475_b40) 2015; 42 Ding (10.1016/j.probengmech.2023.103475_b43) 2023 Ni (10.1016/j.probengmech.2023.103475_b14) 2023; 183 An (10.1016/j.probengmech.2023.103475_b25) 2013; 11 Ye (10.1016/j.probengmech.2023.103475_b4) 2017; 63 Song (10.1016/j.probengmech.2023.103475_b23) 2018; 25 Chang (10.1016/j.probengmech.2023.103475_b33) 2008; 134 Macdonald (10.1016/j.probengmech.2023.103475_b3) 2008; 96 Ye (10.1016/j.probengmech.2023.103475_b5) 2019; 24 Ni (10.1016/j.probengmech.2023.103475_b13) 2022; 164 Davis (10.1016/j.probengmech.2023.103475_b29) 2021 Kumar (10.1016/j.probengmech.2023.103475_b19) 2021; 45 Phoon (10.1016/j.probengmech.2023.103475_b32) 2002; 17 Huang (10.1016/j.probengmech.2023.103475_b10) 2017; 170 Wan (10.1016/j.probengmech.2023.103475_b30) 2018; 144 Bolin (10.1016/j.probengmech.2023.103475_b34) 2014; 41 Wang (10.1016/j.probengmech.2023.103475_b16) 2021 Jiang (10.1016/j.probengmech.2023.103475_b17) 2013; 50 Simiu (10.1016/j.probengmech.2023.103475_b38) 2001; 23 Garbunoinigo (10.1016/j.probengmech.2023.103475_b42) 2016; 6 Ye (10.1016/j.probengmech.2023.103475_b12) 2021; 28 Toure (10.1016/j.probengmech.2023.103475_b39) 2005; 30 Cai (10.1016/j.probengmech.2023.103475_b22) 2002; 33 Ryoo (10.1016/j.probengmech.2023.103475_b20) 2004; 8 Faghih-Roohi (10.1016/j.probengmech.2023.103475_b41) 2014; 91 Feng (10.1016/j.probengmech.2023.103475_b28) 2020; 414 Liao (10.1016/j.probengmech.2023.103475_b9) 2021; 24 Ye (10.1016/j.probengmech.2023.103475_b18) 2020; 10 Mohandes (10.1016/j.probengmech.2023.103475_b11) 2004; 29 Yu (10.1016/j.probengmech.2023.103475_b24) 2017; 28 Ye (10.1016/j.probengmech.2023.103475_b7) 2015; 29 Chen (10.1016/j.probengmech.2023.103475_b8) 2014; 113 Ye (10.1016/j.probengmech.2023.103475_b1) 2018; 21 Kang (10.1016/j.probengmech.2023.103475_b36) 2016; 105 Ding (10.1016/j.probengmech.2023.103475_b44) 2023; 13 |
| References_xml | – volume: 144 year: 2018 ident: b30 article-title: Bayesian modeling approach for forecast of structural stress response using structural health monitoring data publication-title: J. Struct. Eng. – volume: 113 start-page: 690 year: 2014 end-page: 705 ident: b8 article-title: Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach publication-title: Appl. Energy – volume: 33 start-page: 61 year: 2002 end-page: 71 ident: b22 article-title: Cooperative coevolutionary adaptive genetic algorithm in path planning of cooperative multi-mobile robot systems publication-title: J. Intell. Robot. Syst. – volume: 24 start-page: 991 year: 2021 end-page: 1012 ident: b9 article-title: Short-term wind speed multistep combined forecasting model based on two-stage decomposition and lstm publication-title: Wind Energy – volume: 6 start-page: 341 year: 2016 end-page: 359 ident: b42 article-title: Slice sampling publication-title: Int. J. Uncertain. Quantif. – start-page: 1 year: 2021 end-page: 14 ident: b15 article-title: A Bayesian approach for site-specific extreme load prediction of large scale bridges publication-title: Struct. Infrastr. Eng. – volume: 21 start-page: 591 year: 2018 end-page: 600 ident: b1 article-title: SHM-based probabilistic representation of wind properties: statistical analysis and bivariate modeling publication-title: Smart Struct. Syst. – volume: 11 start-page: 293 year: 2013 end-page: 303 ident: b25 article-title: Identification of correlated damage parameters under noise and bias using Bayesian inference publication-title: Struct. Health Monit. – volume: 31 start-page: 97 year: 2015 end-page: 105 ident: b35 article-title: Inferring orthologous gene regulatory networks using interspecies data fusion publication-title: Bioinformatics – volume: 63 start-page: 809 year: 2017 end-page: 824 ident: b4 article-title: Analysis and probabilistic modeling of wind characteristics of an arch bridge using structural health monitoring data during typhoons publication-title: Struct. Eng. Mech. – volume: 164 year: 2022 ident: b13 article-title: Bayesian model updating of civil structures with likelihood-free inference approach and response reconstruction technique publication-title: Mech. Syst. Signal Process. – volume: 25 year: 2018 ident: b23 article-title: Bayesian model updating of nonlinear systems using nonlinear normal modes publication-title: Struct. Control Health Monit. – volume: 96 start-page: 308 year: 2008 end-page: 326 ident: b3 article-title: Two-degree-of-freedom inclined cable galloping-part 2: analysis and prevention for arbitrary frequency ratio publication-title: J. Wind Eng. Ind. Aerodyn. – volume: 10 start-page: 987 year: 2020 end-page: 1000 ident: b18 article-title: Statistical evaluation of wind properties based on long-term monitoring data publication-title: J. Civ. Struct. Health Monit. – volume: 13 start-page: 579 year: 2023 end-page: 589 ident: b44 article-title: Data set from wind, temperature, humidity and cable acceleration monitoring of the Jiashao bridge publication-title: J. Civ. Struct. Health Monit. – year: 2023 ident: b43 article-title: A multistep direct and indirect atrategy for predicting wind direction based on the EMD-LSTM model publication-title: Struct. Control Health Monit. – volume: 91 start-page: 363 year: 2014 end-page: 370 ident: b41 article-title: Accident risk assessment in marine transportation via Markov modelling and Markov chain Monte Carlo simulation publication-title: Ocean Eng. – volume: 8 start-page: 129 year: 2004 end-page: 133 ident: b20 article-title: Genetic algorithm and simultaneous parameter estimation of the nested logit model publication-title: KSCE J. Civ. Eng. – volume: 45 start-page: 1544 year: 2021 end-page: 1556 ident: b19 article-title: Application of differential evolution for wind speed distribution parameters estimation publication-title: Wind Eng. – volume: 413 start-page: 210 year: 2020 end-page: 216 ident: b21 article-title: Expectation–maximization algorithm for finite mixture of publication-title: Neurocomputing – volume: 30 start-page: 511 year: 2005 end-page: 521 ident: b39 article-title: Investigations on the eigen-coordinates method for the 2-parameter weibull distribution of wind speed publication-title: Renew. Energy – volume: 170 start-page: 1 year: 2017 end-page: 17 ident: b10 article-title: Prediction of wind loads on high-rise building using a bp neural network combined with pod publication-title: J. Wind Eng. Ind. Aerodyn. – volume: 29 start-page: 939 year: 2004 end-page: 947 ident: b11 article-title: Support vector machines for wind speed prediction publication-title: Renew. Energy – volume: 47 start-page: 2074 year: 2023 end-page: 2080 ident: b45 article-title: Wind load assessment with the JPDF of wind speed and direction based on SHM data publication-title: Structures – year: 2019 ident: b37 article-title: Gaussian process and design of experiments for surrogate modeling of optical properties of fractal aggregates publication-title: J. Quant. Spectrosc. Radiat. Transfer – volume: 21 year: 2016 ident: b2 article-title: Experimental explorations of the torsional vortex-induced vibrations of a bridge deck publication-title: J. Bridge Eng. – volume: 41 start-page: 557 year: 2014 end-page: 579 ident: b34 article-title: Spatial matern fields driven by non-gaussian noise publication-title: Scand. J. Statist. – volume: 414 start-page: 346 year: 2020 end-page: 355 ident: b28 article-title: Ultrasound image de-speckling by a hybrid deep network with transferred filtering and structural prior publication-title: Neurocomputing – volume: 109 start-page: 739 year: 2017 end-page: 760 ident: b27 article-title: An efficient metamodeling approach for uncertainty quantification of complex systems with arbitrary parameter probability distributions publication-title: Internat. J. Numer. Methods Engrg. – start-page: 165 year: 2021 ident: b6 article-title: Multi-step wind speed forecast based on sample clustering and an optimized hybrid system publication-title: Renew. Energy – volume: 29 year: 2015 ident: b7 article-title: Comparison of spatial interpolation methods for extreme wind speeds over Canada publication-title: J. Comput. Civ. Eng. – start-page: 154 year: 2021 ident: b29 article-title: Prediction of non-stationary response functions using a Bayesian composite Gaussian process publication-title: Comput. Statist. Data Anal. – volume: 183 year: 2023 ident: b14 article-title: Substructure approach for Bayesian probabilistic model updating using response reconstruction technique publication-title: Mech. Syst. Signal Process. – volume: 134 start-page: 1013 year: 2008 end-page: 1020 ident: b33 article-title: Bayesian learning using automatic relevance determination prior with an application to earthquake early warning publication-title: J. Eng. Mech. – volume: 17 start-page: 293 year: 2002 end-page: 303 ident: b32 article-title: Implementation of karhunen-loeve expansion for simulation using a wavelet-Galerkin scheme publication-title: Probab. Eng. Mech. – volume: 18 year: 2018 ident: b31 article-title: Bayesian multi-task learning methodology for reconstruction of structural health monitoring data publication-title: Struct. Health Monit. – volume: 42 start-page: 313 year: 2015 end-page: 322 ident: b40 article-title: Wind speed and power density analysis based on weibull and rayleigh distributions (a case study: firouzkooh county of iran) publication-title: Renew. Sustain. Energy Rev. – volume: 28 year: 2021 ident: b12 article-title: Probabilistic forecast of wind speed based on Bayesian emulator using monitoring data publication-title: Struct. Control Health Monit. – volume: 23 start-page: 221 year: 2001 end-page: 229 ident: b38 article-title: Extreme wind load estimates based on the gumbel distribution of dynamic pressures: an assessment publication-title: Struct. Saf. – volume: 28 start-page: 3475 year: 2017 end-page: 3500 ident: b24 article-title: Robust adaptive algorithm for nonlinear systems with unknown measurement noise and uncertain parameters by variational Bayesian inference publication-title: Internat. J. Robust Nonlinear Control – year: 2021 ident: b16 article-title: Bayesian approaches for evaluating wind-resistant performance of long-span bridges using structural health monitoring data publication-title: Struct. Control Health Monit. – volume: 50 start-page: 637 year: 2013 end-page: 647 ident: b17 article-title: Very short-term wind speed forecasting with Bayesian structural break model publication-title: Renew. Energy – volume: 212 year: 2020 ident: b26 article-title: A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data publication-title: Eng. Struct. – volume: 24 start-page: 733 year: 2019 end-page: 744 ident: b5 article-title: Machine learning approaches for wind speed forecasting using long-term monitoring data: a comparative study publication-title: Smart Struct. Syst. – volume: 105 start-page: 165 year: 2016 end-page: 184 ident: b36 article-title: Scalar-on-image regression via the soft-thresholded Gaussian process publication-title: Biometrika – volume: 11 start-page: 293 issue: 3 year: 2013 ident: 10.1016/j.probengmech.2023.103475_b25 article-title: Identification of correlated damage parameters under noise and bias using Bayesian inference publication-title: Struct. Health Monit. doi: 10.1177/1475921711424520 – volume: 105 start-page: 165 issue: 1 year: 2016 ident: 10.1016/j.probengmech.2023.103475_b36 article-title: Scalar-on-image regression via the soft-thresholded Gaussian process publication-title: Biometrika doi: 10.1093/biomet/asx075 – volume: 41 start-page: 557 issue: 3 year: 2014 ident: 10.1016/j.probengmech.2023.103475_b34 article-title: Spatial matern fields driven by non-gaussian noise publication-title: Scand. J. Statist. doi: 10.1111/sjos.12046 – volume: 13 start-page: 579 issue: 3 year: 2023 ident: 10.1016/j.probengmech.2023.103475_b44 article-title: Data set from wind, temperature, humidity and cable acceleration monitoring of the Jiashao bridge publication-title: J. Civ. Struct. Health Monit. doi: 10.1007/s13349-022-00662-5 – volume: 113 start-page: 690 issue: 1 year: 2014 ident: 10.1016/j.probengmech.2023.103475_b8 article-title: Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach publication-title: Appl. Energy doi: 10.1016/j.apenergy.2013.08.025 – start-page: 1 year: 2021 ident: 10.1016/j.probengmech.2023.103475_b15 article-title: A Bayesian approach for site-specific extreme load prediction of large scale bridges publication-title: Struct. Infrastr. Eng. – volume: 8 start-page: 129 issue: 1 year: 2004 ident: 10.1016/j.probengmech.2023.103475_b20 article-title: Genetic algorithm and simultaneous parameter estimation of the nested logit model publication-title: KSCE J. Civ. Eng. doi: 10.1007/BF02829088 – volume: 28 issue: 1 year: 2021 ident: 10.1016/j.probengmech.2023.103475_b12 article-title: Probabilistic forecast of wind speed based on Bayesian emulator using monitoring data publication-title: Struct. Control Health Monit. doi: 10.1002/stc.2650 – volume: 18 issue: 8 year: 2018 ident: 10.1016/j.probengmech.2023.103475_b31 article-title: Bayesian multi-task learning methodology for reconstruction of structural health monitoring data publication-title: Struct. Health Monit. – volume: 23 start-page: 221 issue: 3 year: 2001 ident: 10.1016/j.probengmech.2023.103475_b38 article-title: Extreme wind load estimates based on the gumbel distribution of dynamic pressures: an assessment publication-title: Struct. Saf. doi: 10.1016/S0167-4730(01)00016-9 – volume: 91 start-page: 363 issue: 11 year: 2014 ident: 10.1016/j.probengmech.2023.103475_b41 article-title: Accident risk assessment in marine transportation via Markov modelling and Markov chain Monte Carlo simulation publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2014.09.029 – volume: 134 start-page: 1013 issue: 12 year: 2008 ident: 10.1016/j.probengmech.2023.103475_b33 article-title: Bayesian learning using automatic relevance determination prior with an application to earthquake early warning publication-title: J. Eng. Mech. – volume: 63 start-page: 809 issue: 6 year: 2017 ident: 10.1016/j.probengmech.2023.103475_b4 article-title: Analysis and probabilistic modeling of wind characteristics of an arch bridge using structural health monitoring data during typhoons publication-title: Struct. Eng. Mech. – volume: 109 start-page: 739 issue: 5 year: 2017 ident: 10.1016/j.probengmech.2023.103475_b27 article-title: An efficient metamodeling approach for uncertainty quantification of complex systems with arbitrary parameter probability distributions publication-title: Internat. J. Numer. Methods Engrg. doi: 10.1002/nme.5305 – volume: 212 year: 2020 ident: 10.1016/j.probengmech.2023.103475_b26 article-title: A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2020.110520 – volume: 25 issue: 12 year: 2018 ident: 10.1016/j.probengmech.2023.103475_b23 article-title: Bayesian model updating of nonlinear systems using nonlinear normal modes publication-title: Struct. Control Health Monit. doi: 10.1002/stc.2258 – year: 2023 ident: 10.1016/j.probengmech.2023.103475_b43 article-title: A multistep direct and indirect atrategy for predicting wind direction based on the EMD-LSTM model publication-title: Struct. Control Health Monit. doi: 10.1155/2023/4950487 – volume: 164 year: 2022 ident: 10.1016/j.probengmech.2023.103475_b13 article-title: Bayesian model updating of civil structures with likelihood-free inference approach and response reconstruction technique publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2021.108204 – year: 2019 ident: 10.1016/j.probengmech.2023.103475_b37 article-title: Gaussian process and design of experiments for surrogate modeling of optical properties of fractal aggregates publication-title: J. Quant. Spectrosc. Radiat. Transfer – volume: 21 start-page: 591 issue: 5 year: 2018 ident: 10.1016/j.probengmech.2023.103475_b1 article-title: SHM-based probabilistic representation of wind properties: statistical analysis and bivariate modeling publication-title: Smart Struct. Syst. – volume: 33 start-page: 61 issue: 1 year: 2002 ident: 10.1016/j.probengmech.2023.103475_b22 article-title: Cooperative coevolutionary adaptive genetic algorithm in path planning of cooperative multi-mobile robot systems publication-title: J. Intell. Robot. Syst. doi: 10.1023/A:1014463014150 – volume: 170 start-page: 1 year: 2017 ident: 10.1016/j.probengmech.2023.103475_b10 article-title: Prediction of wind loads on high-rise building using a bp neural network combined with pod publication-title: J. Wind Eng. Ind. Aerodyn. doi: 10.1016/j.jweia.2017.07.021 – volume: 29 start-page: 939 issue: 6 year: 2004 ident: 10.1016/j.probengmech.2023.103475_b11 article-title: Support vector machines for wind speed prediction publication-title: Renew. Energy doi: 10.1016/j.renene.2003.11.009 – volume: 10 start-page: 987 issue: 5 year: 2020 ident: 10.1016/j.probengmech.2023.103475_b18 article-title: Statistical evaluation of wind properties based on long-term monitoring data publication-title: J. Civ. Struct. Health Monit. doi: 10.1007/s13349-020-00430-3 – volume: 45 start-page: 1544 issue: 6 year: 2021 ident: 10.1016/j.probengmech.2023.103475_b19 article-title: Application of differential evolution for wind speed distribution parameters estimation publication-title: Wind Eng. doi: 10.1177/0309524X21999964 – volume: 17 start-page: 293 issue: 3 year: 2002 ident: 10.1016/j.probengmech.2023.103475_b32 article-title: Implementation of karhunen-loeve expansion for simulation using a wavelet-Galerkin scheme publication-title: Probab. Eng. Mech. doi: 10.1016/S0266-8920(02)00013-9 – volume: 31 start-page: 97 issue: 12 year: 2015 ident: 10.1016/j.probengmech.2023.103475_b35 article-title: Inferring orthologous gene regulatory networks using interspecies data fusion publication-title: Bioinformatics doi: 10.1093/bioinformatics/btv267 – volume: 6 start-page: 341 issue: 4 year: 2016 ident: 10.1016/j.probengmech.2023.103475_b42 article-title: Slice sampling publication-title: Int. J. Uncertain. Quantif. – volume: 183 year: 2023 ident: 10.1016/j.probengmech.2023.103475_b14 article-title: Substructure approach for Bayesian probabilistic model updating using response reconstruction technique publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2022.109624 – volume: 24 start-page: 733 issue: 6 year: 2019 ident: 10.1016/j.probengmech.2023.103475_b5 article-title: Machine learning approaches for wind speed forecasting using long-term monitoring data: a comparative study publication-title: Smart Struct. Syst. – volume: 413 start-page: 210 issue: 9 year: 2020 ident: 10.1016/j.probengmech.2023.103475_b21 article-title: Expectation–maximization algorithm for finite mixture of α-stable distributions publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.06.114 – volume: 30 start-page: 511 issue: 4 year: 2005 ident: 10.1016/j.probengmech.2023.103475_b39 article-title: Investigations on the eigen-coordinates method for the 2-parameter weibull distribution of wind speed publication-title: Renew. Energy doi: 10.1016/j.renene.2004.07.007 – volume: 24 start-page: 991 issue: 9 year: 2021 ident: 10.1016/j.probengmech.2023.103475_b9 article-title: Short-term wind speed multistep combined forecasting model based on two-stage decomposition and lstm publication-title: Wind Energy doi: 10.1002/we.2613 – start-page: 154 year: 2021 ident: 10.1016/j.probengmech.2023.103475_b29 article-title: Prediction of non-stationary response functions using a Bayesian composite Gaussian process publication-title: Comput. Statist. Data Anal. – volume: 21 issue: 12 year: 2016 ident: 10.1016/j.probengmech.2023.103475_b2 article-title: Experimental explorations of the torsional vortex-induced vibrations of a bridge deck publication-title: J. Bridge Eng. doi: 10.1061/(ASCE)BE.1943-5592.0000941 – volume: 28 start-page: 3475 issue: 10 year: 2017 ident: 10.1016/j.probengmech.2023.103475_b24 article-title: Robust adaptive algorithm for nonlinear systems with unknown measurement noise and uncertain parameters by variational Bayesian inference publication-title: Internat. J. Robust Nonlinear Control doi: 10.1002/rnc.4095 – volume: 50 start-page: 637 issue: 12 year: 2013 ident: 10.1016/j.probengmech.2023.103475_b17 article-title: Very short-term wind speed forecasting with Bayesian structural break model publication-title: Renew. Energy doi: 10.1016/j.renene.2012.07.041 – volume: 47 start-page: 2074 issue: 1 year: 2023 ident: 10.1016/j.probengmech.2023.103475_b45 article-title: Wind load assessment with the JPDF of wind speed and direction based on SHM data publication-title: Structures doi: 10.1016/j.istruc.2022.12.028 – volume: 414 start-page: 346 issue: 24 year: 2020 ident: 10.1016/j.probengmech.2023.103475_b28 article-title: Ultrasound image de-speckling by a hybrid deep network with transferred filtering and structural prior publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.09.002 – start-page: 165 year: 2021 ident: 10.1016/j.probengmech.2023.103475_b6 article-title: Multi-step wind speed forecast based on sample clustering and an optimized hybrid system publication-title: Renew. Energy – volume: 29 issue: 6 year: 2015 ident: 10.1016/j.probengmech.2023.103475_b7 article-title: Comparison of spatial interpolation methods for extreme wind speeds over Canada publication-title: J. Comput. Civ. Eng. doi: 10.1061/(ASCE)CP.1943-5487.0000429 – volume: 42 start-page: 313 year: 2015 ident: 10.1016/j.probengmech.2023.103475_b40 article-title: Wind speed and power density analysis based on weibull and rayleigh distributions (a case study: firouzkooh county of iran) publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2014.10.028 – volume: 96 start-page: 308 issue: 3 year: 2008 ident: 10.1016/j.probengmech.2023.103475_b3 article-title: Two-degree-of-freedom inclined cable galloping-part 2: analysis and prevention for arbitrary frequency ratio publication-title: J. Wind Eng. Ind. Aerodyn. doi: 10.1016/j.jweia.2007.07.001 – year: 2021 ident: 10.1016/j.probengmech.2023.103475_b16 article-title: Bayesian approaches for evaluating wind-resistant performance of long-span bridges using structural health monitoring data publication-title: Struct. Control Health Monit. doi: 10.1002/stc.2699 – volume: 144 issue: 9 year: 2018 ident: 10.1016/j.probengmech.2023.103475_b30 article-title: Bayesian modeling approach for forecast of structural stress response using structural health monitoring data publication-title: J. Struct. Eng. doi: 10.1061/(ASCE)ST.1943-541X.0002085 |
| SSID | ssj0017149 |
| Score | 2.5566945 |
| Snippet | For wind-sensitive structures, such as long-span bridges, high-rise buildings, transmission towers, etc., the prediction of wind speed and its statistical... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 103475 |
| SubjectTerms | Bayes’ theorem Covariance functions Gaussian process Structural health monitoring Wind speed prediction Wind speed statistics |
| Title | Probabilistic method for wind speed prediction and statistics distribution inference based on SHM data-driven |
| URI | https://dx.doi.org/10.1016/j.probengmech.2023.103475 |
| Volume | 73 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Baden-Württemberg Complete Freedom Collection (Elsevier) issn: 0266-8920 databaseCode: GBLVA dateStart: 20110101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: true ssIdentifier: ssj0017149 providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier ScienceDirect Freedom Collection Journals issn: 0266-8920 databaseCode: AIKHN dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: true ssIdentifier: ssj0017149 providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier SD Complete Freedom Collection [SCCMFC] issn: 0266-8920 databaseCode: ACRLP dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: true ssIdentifier: ssj0017149 providerName: Elsevier – providerCode: PRVESC databaseName: Science Direct issn: 0266-8920 databaseCode: .~1 dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: true ssIdentifier: ssj0017149 providerName: Elsevier – providerCode: PRVLSH databaseName: Elsevier Journals issn: 0266-8920 databaseCode: AKRWK dateStart: 19860301 customDbUrl: isFulltext: true mediaType: online dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017149 providerName: Library Specific Holdings |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwFA5jgvjiXZyXEcHXuDVJsxR8GcMxHRviHO6tNGkqE1fLNvHN325Om-oEQcGnktBDy0l6zpeey4fQuZdIqWWsSKwSTjiPNQmosUAuagqZKE9HDIqTB0PRG_ObiT-poE5ZCwNplc72FzY9t9ZupuG02cim08bInh6EDID-O3es0BOU8xawGFy8f6Z5AL93UPxnEQTuXkdnXzleQNpi0seZyeMSlEEJOoeUw5981Irf6W6jTQcYcbt4px1UMeku2nLgEbtPc7GHZrf2KXm7XOi8jAtmaGwhKX6zx268yKybwtkc4jKwFjiCSYjD542acQwNdB33FZ6WVYAYnFyM7dSoN8CQTUriOdjHfTTuXt13esRxKRDNqLcknm5RwVgkIk2lhXBNC8R0DIWpKvGUUdpTQkTSWHxnEaKyY98CSZ3QpmFJK6bsAFXTl9QcImzXkPo0SgKdaA6FrypgwofDrg648lgNyVJ7oXaNxoHv4jksM8qewhXFh6D4sFB8DdFP0azotvEXoctyicJvWye0XuF38aP_iR-jDRgVGbwnqLqcv5pTi1OWqp5vxDpaa1_3e0O49u8e-h-gCeyC |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwED_mBPXFb3F-RvC1bk3SrAVfZDimbkPYBnsrTZrKxNWxTXzzbzfXpnOCoOBj0x4tl_Tud8nd_QAu3cT3lR9LJ5YJdziPlRNQbYBcVBN-Il0VMSxO7nRFa8Dvh96wBI2iFgbTKq3tz216Zq3tSNVqszoZjao9Ez0IP0D678yx8hVY5R6tYwR29bHI80CC7yDfaBEOPr4GF19JXsjaotOnsc4OJijDGnSOOYc_Oaklx9Pchk2LGMlN_lE7UNLpLmxZ9Ejsvznbg_GjeUvWLxdbL5OcGpoYTEreTdxNZhPjp8hkigczOBkkwkE8iM86NZMYO-ha8isyKsoACXq5mJihXqtDMJ3UiadoIPdh0LztN1qOJVNwFKPu3HFVnQrGIhEp6hsMVzNITMVYmSoTV2qpXClE5GsD8AxElObaM0hSJbSmWVKPKTuAcvqa6kMgZhKpR6MkUIniWPkqAyY8jHZVwKXLKuAX2guV7TSOhBcvYZFS9hwuKT5ExYe54itAF6KTvN3GX4SuiykKv62d0LiF38WP_id-Duutfqcdtu-6D8ewgXfydN4TKM-nb_rUgJa5PMsW5Se9zOx0 |
| 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=Probabilistic+method+for+wind+speed+prediction+and+statistics+distribution+inference+based+on+SHM+data-driven&rft.jtitle=Probabilistic+engineering+mechanics&rft.au=Ding%2C+Yang&rft.au=Ye%2C+Xiao-Wei&rft.au=Guo%2C+Yong&rft.au=Zhang%2C+Ru&rft.date=2023-07-01&rft.pub=Elsevier+Ltd&rft.issn=0266-8920&rft.volume=73&rft_id=info:doi/10.1016%2Fj.probengmech.2023.103475&rft.externalDocID=S0266892023000644 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0266-8920&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0266-8920&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0266-8920&client=summon |