Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm
Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SV...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ding, Xiaohua [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2020 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Engineering with computers - Springer London, 1985, 37(2020), 3 vom: 23. Jan., Seite 2273-2284 |
|---|---|
| Übergeordnetes Werk: |
volume:37 ; year:2020 ; number:3 ; day:23 ; month:01 ; pages:2273-2284 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00366-020-00937-9 |
|---|
| Katalog-ID: |
OLC2126475360 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2126475360 | ||
| 003 | DE-627 | ||
| 005 | 20230505115033.0 | ||
| 007 | tu | ||
| 008 | 230505s2020 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00366-020-00937-9 |2 doi | |
| 035 | |a (DE-627)OLC2126475360 | ||
| 035 | |a (DE-He213)s00366-020-00937-9-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |a 600 |q VZ |
| 100 | 1 | |a Ding, Xiaohua |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag London Ltd., part of Springer Nature 2020 | ||
| 520 | |a Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration. | ||
| 650 | 4 | |a Ground vibration | |
| 650 | 4 | |a Bagging algorithm | |
| 650 | 4 | |a SVR | |
| 650 | 4 | |a FA | |
| 700 | 1 | |a Hasanipanah, Mahdi |0 (orcid)0000-0001-7582-6745 |4 aut | |
| 700 | 1 | |a Nikafshan Rad, Hima |4 aut | |
| 700 | 1 | |a Zhou, Wei |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Engineering with computers |d Springer London, 1985 |g 37(2020), 3 vom: 23. Jan., Seite 2273-2284 |w (DE-627)129175404 |w (DE-600)51529-2 |w (DE-576)014455536 |x 0177-0667 |7 nnns |
| 773 | 1 | 8 | |g volume:37 |g year:2020 |g number:3 |g day:23 |g month:01 |g pages:2273-2284 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00366-020-00937-9 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_2018 | ||
| 912 | |a GBV_ILN_4277 | ||
| 951 | |a AR | ||
| 952 | |d 37 |j 2020 |e 3 |b 23 |c 01 |h 2273-2284 | ||
| author_variant |
x d xd m h mh r h n rh rhn w z wz |
|---|---|
| matchkey_str |
article:01770667:2020----::rdcighbatnuevbaineoiysnaagduprvcorgesoo |
| hierarchy_sort_str |
2020 |
| publishDate |
2020 |
| allfields |
10.1007/s00366-020-00937-9 doi (DE-627)OLC2126475360 (DE-He213)s00366-020-00937-9-p DE-627 ger DE-627 rakwb eng 004 600 VZ Ding, Xiaohua verfasserin aut Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration. Ground vibration Bagging algorithm SVR FA Hasanipanah, Mahdi (orcid)0000-0001-7582-6745 aut Nikafshan Rad, Hima aut Zhou, Wei aut Enthalten in Engineering with computers Springer London, 1985 37(2020), 3 vom: 23. Jan., Seite 2273-2284 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:37 year:2020 number:3 day:23 month:01 pages:2273-2284 https://doi.org/10.1007/s00366-020-00937-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 37 2020 3 23 01 2273-2284 |
| spelling |
10.1007/s00366-020-00937-9 doi (DE-627)OLC2126475360 (DE-He213)s00366-020-00937-9-p DE-627 ger DE-627 rakwb eng 004 600 VZ Ding, Xiaohua verfasserin aut Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration. Ground vibration Bagging algorithm SVR FA Hasanipanah, Mahdi (orcid)0000-0001-7582-6745 aut Nikafshan Rad, Hima aut Zhou, Wei aut Enthalten in Engineering with computers Springer London, 1985 37(2020), 3 vom: 23. Jan., Seite 2273-2284 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:37 year:2020 number:3 day:23 month:01 pages:2273-2284 https://doi.org/10.1007/s00366-020-00937-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 37 2020 3 23 01 2273-2284 |
| allfields_unstemmed |
10.1007/s00366-020-00937-9 doi (DE-627)OLC2126475360 (DE-He213)s00366-020-00937-9-p DE-627 ger DE-627 rakwb eng 004 600 VZ Ding, Xiaohua verfasserin aut Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration. Ground vibration Bagging algorithm SVR FA Hasanipanah, Mahdi (orcid)0000-0001-7582-6745 aut Nikafshan Rad, Hima aut Zhou, Wei aut Enthalten in Engineering with computers Springer London, 1985 37(2020), 3 vom: 23. Jan., Seite 2273-2284 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:37 year:2020 number:3 day:23 month:01 pages:2273-2284 https://doi.org/10.1007/s00366-020-00937-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 37 2020 3 23 01 2273-2284 |
| allfieldsGer |
10.1007/s00366-020-00937-9 doi (DE-627)OLC2126475360 (DE-He213)s00366-020-00937-9-p DE-627 ger DE-627 rakwb eng 004 600 VZ Ding, Xiaohua verfasserin aut Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration. Ground vibration Bagging algorithm SVR FA Hasanipanah, Mahdi (orcid)0000-0001-7582-6745 aut Nikafshan Rad, Hima aut Zhou, Wei aut Enthalten in Engineering with computers Springer London, 1985 37(2020), 3 vom: 23. Jan., Seite 2273-2284 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:37 year:2020 number:3 day:23 month:01 pages:2273-2284 https://doi.org/10.1007/s00366-020-00937-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 37 2020 3 23 01 2273-2284 |
| allfieldsSound |
10.1007/s00366-020-00937-9 doi (DE-627)OLC2126475360 (DE-He213)s00366-020-00937-9-p DE-627 ger DE-627 rakwb eng 004 600 VZ Ding, Xiaohua verfasserin aut Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration. Ground vibration Bagging algorithm SVR FA Hasanipanah, Mahdi (orcid)0000-0001-7582-6745 aut Nikafshan Rad, Hima aut Zhou, Wei aut Enthalten in Engineering with computers Springer London, 1985 37(2020), 3 vom: 23. Jan., Seite 2273-2284 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:37 year:2020 number:3 day:23 month:01 pages:2273-2284 https://doi.org/10.1007/s00366-020-00937-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 37 2020 3 23 01 2273-2284 |
| language |
English |
| source |
Enthalten in Engineering with computers 37(2020), 3 vom: 23. Jan., Seite 2273-2284 volume:37 year:2020 number:3 day:23 month:01 pages:2273-2284 |
| sourceStr |
Enthalten in Engineering with computers 37(2020), 3 vom: 23. Jan., Seite 2273-2284 volume:37 year:2020 number:3 day:23 month:01 pages:2273-2284 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Ground vibration Bagging algorithm SVR FA |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Engineering with computers |
| authorswithroles_txt_mv |
Ding, Xiaohua @@aut@@ Hasanipanah, Mahdi @@aut@@ Nikafshan Rad, Hima @@aut@@ Zhou, Wei @@aut@@ |
| publishDateDaySort_date |
2020-01-23T00:00:00Z |
| hierarchy_top_id |
129175404 |
| dewey-sort |
14 |
| id |
OLC2126475360 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2126475360</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505115033.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00366-020-00937-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2126475360</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00366-020-00937-9-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">600</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ding, Xiaohua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd., part of Springer Nature 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ground vibration</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bagging algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SVR</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">FA</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hasanipanah, Mahdi</subfield><subfield code="0">(orcid)0000-0001-7582-6745</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nikafshan Rad, Hima</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Wei</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Engineering with computers</subfield><subfield code="d">Springer London, 1985</subfield><subfield code="g">37(2020), 3 vom: 23. Jan., Seite 2273-2284</subfield><subfield code="w">(DE-627)129175404</subfield><subfield code="w">(DE-600)51529-2</subfield><subfield code="w">(DE-576)014455536</subfield><subfield code="x">0177-0667</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:37</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:3</subfield><subfield code="g">day:23</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:2273-2284</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00366-020-00937-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">37</subfield><subfield code="j">2020</subfield><subfield code="e">3</subfield><subfield code="b">23</subfield><subfield code="c">01</subfield><subfield code="h">2273-2284</subfield></datafield></record></collection>
|
| author |
Ding, Xiaohua |
| spellingShingle |
Ding, Xiaohua ddc 004 misc Ground vibration misc Bagging algorithm misc SVR misc FA Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm |
| authorStr |
Ding, Xiaohua |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129175404 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science 600 - Technology |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0177-0667 |
| topic_title |
004 600 VZ Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm Ground vibration Bagging algorithm SVR FA |
| topic |
ddc 004 misc Ground vibration misc Bagging algorithm misc SVR misc FA |
| topic_unstemmed |
ddc 004 misc Ground vibration misc Bagging algorithm misc SVR misc FA |
| topic_browse |
ddc 004 misc Ground vibration misc Bagging algorithm misc SVR misc FA |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Engineering with computers |
| hierarchy_parent_id |
129175404 |
| dewey-tens |
000 - Computer science, knowledge & systems 600 - Technology |
| hierarchy_top_title |
Engineering with computers |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 |
| title |
Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm |
| ctrlnum |
(DE-627)OLC2126475360 (DE-He213)s00366-020-00937-9-p |
| title_full |
Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm |
| author_sort |
Ding, Xiaohua |
| journal |
Engineering with computers |
| journalStr |
Engineering with computers |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2020 |
| contenttype_str_mv |
txt |
| container_start_page |
2273 |
| author_browse |
Ding, Xiaohua Hasanipanah, Mahdi Nikafshan Rad, Hima Zhou, Wei |
| container_volume |
37 |
| class |
004 600 VZ |
| format_se |
Aufsätze |
| author-letter |
Ding, Xiaohua |
| doi_str_mv |
10.1007/s00366-020-00937-9 |
| normlink |
(ORCID)0000-0001-7582-6745 |
| normlink_prefix_str_mv |
(orcid)0000-0001-7582-6745 |
| dewey-full |
004 600 |
| title_sort |
predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm |
| title_auth |
Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm |
| abstract |
Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration. © Springer-Verlag London Ltd., part of Springer Nature 2020 |
| abstractGer |
Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration. © Springer-Verlag London Ltd., part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration. © Springer-Verlag London Ltd., part of Springer Nature 2020 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 |
| container_issue |
3 |
| title_short |
Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm |
| url |
https://doi.org/10.1007/s00366-020-00937-9 |
| remote_bool |
false |
| author2 |
Hasanipanah, Mahdi Nikafshan Rad, Hima Zhou, Wei |
| author2Str |
Hasanipanah, Mahdi Nikafshan Rad, Hima Zhou, Wei |
| ppnlink |
129175404 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00366-020-00937-9 |
| up_date |
2024-07-04T07:08:32.155Z |
| _version_ |
1803631368240889856 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2126475360</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505115033.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00366-020-00937-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2126475360</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00366-020-00937-9-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">600</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ding, Xiaohua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd., part of Springer Nature 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ground vibration</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bagging algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SVR</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">FA</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hasanipanah, Mahdi</subfield><subfield code="0">(orcid)0000-0001-7582-6745</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nikafshan Rad, Hima</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Wei</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Engineering with computers</subfield><subfield code="d">Springer London, 1985</subfield><subfield code="g">37(2020), 3 vom: 23. Jan., Seite 2273-2284</subfield><subfield code="w">(DE-627)129175404</subfield><subfield code="w">(DE-600)51529-2</subfield><subfield code="w">(DE-576)014455536</subfield><subfield code="x">0177-0667</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:37</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:3</subfield><subfield code="g">day:23</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:2273-2284</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00366-020-00937-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">37</subfield><subfield code="j">2020</subfield><subfield code="e">3</subfield><subfield code="b">23</subfield><subfield code="c">01</subfield><subfield code="h">2273-2284</subfield></datafield></record></collection>
|
| score |
7.401388 |