Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning
The bidirectional long short-term memory (Bi-LSTM) network is an innovative computation paradigm that learns bidirectional long-term dependencies between time steps and sequence data to predict future occurrences. This study proposes a framework to incorporate Bi-LSTM and data sequencing to predict...
Gespeichert in:
| Veröffentlicht in: | Acta geotechnica 2021, Vol.16 (1), p.303-315 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 315 |
|---|---|
| container_issue | 1 |
| container_start_page | 303 |
| container_title | Acta geotechnica |
| container_volume | 16 |
| creator | Shen, Shui-Long Atangana Njock, Pierre Guy Zhou, Annan Lyu, Hai-Min |
| description | The bidirectional long short-term memory (Bi-LSTM) network is an innovative computation paradigm that learns bidirectional long-term dependencies between time steps and sequence data to predict future occurrences. This study proposes a framework to incorporate Bi-LSTM and data sequencing to predict diameter of jet grouted columns in soft soil in real time. The models are tested using a case study of jet grouting treatment of soft soil. The results show that the proposed strategies can efficiently predict the variation in column diameter with the depth. A comparative performance analysis among the Bi-LSTM, original long short-term memory (LSTM) and support vector regression (SVR) approaches is also conducted. The Bi-LSTM performs better than both the LSTM and SVR in root-mean-square error. This result substantiates the efficacy of modeling sequential step-by-step jet grouting process using the Bi-LSTM. Based on the analyzed results, some recommendations for improving the current design of jet grout columns are proposed. |
| doi_str_mv | 10.1007/s11440-020-01005-8 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2478665740</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2478665740</sourcerecordid><originalsourceid>FETCH-LOGICAL-a342t-a779d7efba608f847c19e4f229930df197a2edb210e151c8b3513a37b84f8e8a3</originalsourceid><addsrcrecordid>eNp9kDtPwzAUhS0EEqXwB5gsMQf8SuyMUJ5SEQNlYbGc5LpylTjBTob-ewxBsDHch67OOVf6EDqn5JISIq8ipUKQjLBU6ZBn6gAtqCpoRinnh787y4_RSYw7QgrORLFA77d7bzpX4yFA4-rR9R73Fu9gxNvQTyM0uO7bqfO4caaDEQJ2Hsfejqm5Fk_R-S2-cdn6dfOMG4ABt2CCT9dTdGRNG-HsZy7R2_3dZvWYrV8enlbX68xwwcbMSFk2EmxlCqKsErKmJQjLWFly0lhaSsOgqRglQHNaq4rnlBsuKyWsAmX4El3MuUPoPyaIo971U_DppWZCqqLIpSBJxWZVHfoYA1g9BNeZsNeU6C-GemaoE0P9zVCrZOKzKSax30L4i_7H9QmKxHO6</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2478665740</pqid></control><display><type>article</type><title>Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning</title><source>Springer journals</source><creator>Shen, Shui-Long ; Atangana Njock, Pierre Guy ; Zhou, Annan ; Lyu, Hai-Min</creator><creatorcontrib>Shen, Shui-Long ; Atangana Njock, Pierre Guy ; Zhou, Annan ; Lyu, Hai-Min</creatorcontrib><description>The bidirectional long short-term memory (Bi-LSTM) network is an innovative computation paradigm that learns bidirectional long-term dependencies between time steps and sequence data to predict future occurrences. This study proposes a framework to incorporate Bi-LSTM and data sequencing to predict diameter of jet grouted columns in soft soil in real time. The models are tested using a case study of jet grouting treatment of soft soil. The results show that the proposed strategies can efficiently predict the variation in column diameter with the depth. A comparative performance analysis among the Bi-LSTM, original long short-term memory (LSTM) and support vector regression (SVR) approaches is also conducted. The Bi-LSTM performs better than both the LSTM and SVR in root-mean-square error. This result substantiates the efficacy of modeling sequential step-by-step jet grouting process using the Bi-LSTM. Based on the analyzed results, some recommendations for improving the current design of jet grout columns are proposed.</description><identifier>ISSN: 1861-1125</identifier><identifier>EISSN: 1861-1133</identifier><identifier>DOI: 10.1007/s11440-020-01005-8</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Artificial intelligence ; Civil engineering ; Columns (structural) ; Complex Fluids and Microfluidics ; Computation ; Construction ; Deep learning ; Engineering ; Foundations ; Geoengineering ; Geotechnical Engineering & Applied Earth Sciences ; Grout ; Grouting ; Hydraulics ; Jet grouting ; Laboratories ; Long short-term memory ; Machine learning ; Neural networks ; Regression analysis ; Research Paper ; Soft and Granular Matter ; Soil ; Soil dynamics ; Soil Science & Conservation ; Soils ; Solid Mechanics ; Support vector machines ; Time series</subject><ispartof>Acta geotechnica, 2021, Vol.16 (1), p.303-315</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a342t-a779d7efba608f847c19e4f229930df197a2edb210e151c8b3513a37b84f8e8a3</citedby><cites>FETCH-LOGICAL-a342t-a779d7efba608f847c19e4f229930df197a2edb210e151c8b3513a37b84f8e8a3</cites><orcidid>0000-0002-9781-6019 ; 0000-0002-5610-7988 ; 0000-0001-5209-5169</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11440-020-01005-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11440-020-01005-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Shen, Shui-Long</creatorcontrib><creatorcontrib>Atangana Njock, Pierre Guy</creatorcontrib><creatorcontrib>Zhou, Annan</creatorcontrib><creatorcontrib>Lyu, Hai-Min</creatorcontrib><title>Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning</title><title>Acta geotechnica</title><addtitle>Acta Geotech</addtitle><description>The bidirectional long short-term memory (Bi-LSTM) network is an innovative computation paradigm that learns bidirectional long-term dependencies between time steps and sequence data to predict future occurrences. This study proposes a framework to incorporate Bi-LSTM and data sequencing to predict diameter of jet grouted columns in soft soil in real time. The models are tested using a case study of jet grouting treatment of soft soil. The results show that the proposed strategies can efficiently predict the variation in column diameter with the depth. A comparative performance analysis among the Bi-LSTM, original long short-term memory (LSTM) and support vector regression (SVR) approaches is also conducted. The Bi-LSTM performs better than both the LSTM and SVR in root-mean-square error. This result substantiates the efficacy of modeling sequential step-by-step jet grouting process using the Bi-LSTM. Based on the analyzed results, some recommendations for improving the current design of jet grout columns are proposed.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Civil engineering</subject><subject>Columns (structural)</subject><subject>Complex Fluids and Microfluidics</subject><subject>Computation</subject><subject>Construction</subject><subject>Deep learning</subject><subject>Engineering</subject><subject>Foundations</subject><subject>Geoengineering</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Grout</subject><subject>Grouting</subject><subject>Hydraulics</subject><subject>Jet grouting</subject><subject>Laboratories</subject><subject>Long short-term memory</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Regression analysis</subject><subject>Research Paper</subject><subject>Soft and Granular Matter</subject><subject>Soil</subject><subject>Soil dynamics</subject><subject>Soil Science & Conservation</subject><subject>Soils</subject><subject>Solid Mechanics</subject><subject>Support vector machines</subject><subject>Time series</subject><issn>1861-1125</issn><issn>1861-1133</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kDtPwzAUhS0EEqXwB5gsMQf8SuyMUJ5SEQNlYbGc5LpylTjBTob-ewxBsDHch67OOVf6EDqn5JISIq8ipUKQjLBU6ZBn6gAtqCpoRinnh787y4_RSYw7QgrORLFA77d7bzpX4yFA4-rR9R73Fu9gxNvQTyM0uO7bqfO4caaDEQJ2Hsfejqm5Fk_R-S2-cdn6dfOMG4ABt2CCT9dTdGRNG-HsZy7R2_3dZvWYrV8enlbX68xwwcbMSFk2EmxlCqKsErKmJQjLWFly0lhaSsOgqRglQHNaq4rnlBsuKyWsAmX4El3MuUPoPyaIo971U_DppWZCqqLIpSBJxWZVHfoYA1g9BNeZsNeU6C-GemaoE0P9zVCrZOKzKSax30L4i_7H9QmKxHO6</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Shen, Shui-Long</creator><creator>Atangana Njock, Pierre Guy</creator><creator>Zhou, Annan</creator><creator>Lyu, Hai-Min</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L.G</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-9781-6019</orcidid><orcidid>https://orcid.org/0000-0002-5610-7988</orcidid><orcidid>https://orcid.org/0000-0001-5209-5169</orcidid></search><sort><creationdate>2021</creationdate><title>Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning</title><author>Shen, Shui-Long ; Atangana Njock, Pierre Guy ; Zhou, Annan ; Lyu, Hai-Min</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a342t-a779d7efba608f847c19e4f229930df197a2edb210e151c8b3513a37b84f8e8a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Civil engineering</topic><topic>Columns (structural)</topic><topic>Complex Fluids and Microfluidics</topic><topic>Computation</topic><topic>Construction</topic><topic>Deep learning</topic><topic>Engineering</topic><topic>Foundations</topic><topic>Geoengineering</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Grout</topic><topic>Grouting</topic><topic>Hydraulics</topic><topic>Jet grouting</topic><topic>Laboratories</topic><topic>Long short-term memory</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Regression analysis</topic><topic>Research Paper</topic><topic>Soft and Granular Matter</topic><topic>Soil</topic><topic>Soil dynamics</topic><topic>Soil Science & Conservation</topic><topic>Soils</topic><topic>Solid Mechanics</topic><topic>Support vector machines</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shen, Shui-Long</creatorcontrib><creatorcontrib>Atangana Njock, Pierre Guy</creatorcontrib><creatorcontrib>Zhou, Annan</creatorcontrib><creatorcontrib>Lyu, Hai-Min</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database (ProQuest)</collection><collection>Engineering Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>ProQuest Central Basic</collection><jtitle>Acta geotechnica</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shen, Shui-Long</au><au>Atangana Njock, Pierre Guy</au><au>Zhou, Annan</au><au>Lyu, Hai-Min</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning</atitle><jtitle>Acta geotechnica</jtitle><stitle>Acta Geotech</stitle><date>2021</date><risdate>2021</risdate><volume>16</volume><issue>1</issue><spage>303</spage><epage>315</epage><pages>303-315</pages><issn>1861-1125</issn><eissn>1861-1133</eissn><abstract>The bidirectional long short-term memory (Bi-LSTM) network is an innovative computation paradigm that learns bidirectional long-term dependencies between time steps and sequence data to predict future occurrences. This study proposes a framework to incorporate Bi-LSTM and data sequencing to predict diameter of jet grouted columns in soft soil in real time. The models are tested using a case study of jet grouting treatment of soft soil. The results show that the proposed strategies can efficiently predict the variation in column diameter with the depth. A comparative performance analysis among the Bi-LSTM, original long short-term memory (LSTM) and support vector regression (SVR) approaches is also conducted. The Bi-LSTM performs better than both the LSTM and SVR in root-mean-square error. This result substantiates the efficacy of modeling sequential step-by-step jet grouting process using the Bi-LSTM. Based on the analyzed results, some recommendations for improving the current design of jet grout columns are proposed.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s11440-020-01005-8</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-9781-6019</orcidid><orcidid>https://orcid.org/0000-0002-5610-7988</orcidid><orcidid>https://orcid.org/0000-0001-5209-5169</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1861-1125 |
| ispartof | Acta geotechnica, 2021, Vol.16 (1), p.303-315 |
| issn | 1861-1125 1861-1133 |
| language | eng |
| recordid | cdi_proquest_journals_2478665740 |
| source | Springer journals |
| subjects | Algorithms Artificial intelligence Civil engineering Columns (structural) Complex Fluids and Microfluidics Computation Construction Deep learning Engineering Foundations Geoengineering Geotechnical Engineering & Applied Earth Sciences Grout Grouting Hydraulics Jet grouting Laboratories Long short-term memory Machine learning Neural networks Regression analysis Research Paper Soft and Granular Matter Soil Soil dynamics Soil Science & Conservation Soils Solid Mechanics Support vector machines Time series |
| title | Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T15%3A38%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Dynamic%20prediction%20of%20jet%20grouted%20column%20diameter%20in%20soft%20soil%20using%20Bi-LSTM%20deep%20learning&rft.jtitle=Acta%20geotechnica&rft.au=Shen,%20Shui-Long&rft.date=2021&rft.volume=16&rft.issue=1&rft.spage=303&rft.epage=315&rft.pages=303-315&rft.issn=1861-1125&rft.eissn=1861-1133&rft_id=info:doi/10.1007/s11440-020-01005-8&rft_dat=%3Cproquest_cross%3E2478665740%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2478665740&rft_id=info:pmid/&rfr_iscdi=true |