Predicting the chemical and mechanical properties of gypseous soils using different simulation technics
Gypseous soils are soils that contain sufficient quantities of gypsum that are considered collapsible soil. The present study's objective is to predict the shear strength parameters ( c , ϕ ), collapse potential (CP), and compression index (Cc) from the gypseous soils' physical properties...
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| Veröffentlicht in: | Acta geotechnica 2022-04, Vol.17 (4), p.1111-1127 |
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| creator | Mohammed, Ahmed Hummadi, Rizgar Ali Mawlood, Yousif Ismael |
| description | Gypseous soils are soils that contain sufficient quantities of gypsum that are considered collapsible soil. The present study's objective is to predict the shear strength parameters (
c
,
ϕ
), collapse potential (CP), and compression index (Cc) from the gypseous soils' physical properties using a wide range of 220 collected data from various published articles. The linear and nonlinear approaches were used in this study, and the outcomes of the models were compared with artificial neural network (ANN) performance. The developed models predicted the shear parameters, compression index, gypsum content, and collapse potential as a function of accessible laboratories measurable such as specific gravity, moisture content, density, and Atterberg limits with acceptable accuracy. The soils' gypsum content (Gc) was also correlated well based on the total soluble salts (TSS), sulfate (SO
3
), and pH values using the nonlinear Vipulanandan correlation model. Based on the adjusted (
R
2
), mean absolute error (MAE), and the root-mean-square error (RMSE), the linear and nonlinear models predicted the shear strength parameters, compression index, and collapse potential of the gypseous soils very well. The regression model predictions were comparable to the outcomes from the ANN model predicting. |
| doi_str_mv | 10.1007/s11440-021-01304-8 |
| format | Article |
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c
,
ϕ
), collapse potential (CP), and compression index (Cc) from the gypseous soils' physical properties using a wide range of 220 collected data from various published articles. The linear and nonlinear approaches were used in this study, and the outcomes of the models were compared with artificial neural network (ANN) performance. The developed models predicted the shear parameters, compression index, gypsum content, and collapse potential as a function of accessible laboratories measurable such as specific gravity, moisture content, density, and Atterberg limits with acceptable accuracy. The soils' gypsum content (Gc) was also correlated well based on the total soluble salts (TSS), sulfate (SO
3
), and pH values using the nonlinear Vipulanandan correlation model. Based on the adjusted (
R
2
), mean absolute error (MAE), and the root-mean-square error (RMSE), the linear and nonlinear models predicted the shear strength parameters, compression index, and collapse potential of the gypseous soils very well. The regression model predictions were comparable to the outcomes from the ANN model predicting.</description><identifier>ISSN: 1861-1125</identifier><identifier>EISSN: 1861-1133</identifier><identifier>DOI: 10.1007/s11440-021-01304-8</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Artificial neural networks ; Atterberg limits ; Collapse ; Complex Fluids and Microfluidics ; Compression ; Compression index ; Compressive strength ; Density ; Engineering ; Foundations ; Geoengineering ; Geotechnical Engineering & Applied Earth Sciences ; Gypsum ; Hydraulics ; Mechanical properties ; Moisture content ; Moisture effects ; Neural networks ; Parameters ; Physical properties ; Predictions ; Regression models ; Research Paper ; Root-mean-square errors ; Salts ; Shear strength ; Soft and Granular Matter ; Soil ; Soil mechanics ; Soil moisture ; Soil properties ; Soil Science & Conservation ; Soils ; Solid Mechanics ; Specific gravity ; Sulfur trioxide ; Water content</subject><ispartof>Acta geotechnica, 2022-04, Vol.17 (4), p.1111-1127</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a342t-205509551a9bc4d4d95332b437342909fb39d3cd008e2d324b456df1612a85cb3</citedby><cites>FETCH-LOGICAL-a342t-205509551a9bc4d4d95332b437342909fb39d3cd008e2d324b456df1612a85cb3</cites><orcidid>0000-0003-4306-3274</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/s11440-021-01304-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11440-021-01304-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Mohammed, Ahmed</creatorcontrib><creatorcontrib>Hummadi, Rizgar Ali</creatorcontrib><creatorcontrib>Mawlood, Yousif Ismael</creatorcontrib><title>Predicting the chemical and mechanical properties of gypseous soils using different simulation technics</title><title>Acta geotechnica</title><addtitle>Acta Geotech</addtitle><description>Gypseous soils are soils that contain sufficient quantities of gypsum that are considered collapsible soil. The present study's objective is to predict the shear strength parameters (
c
,
ϕ
), collapse potential (CP), and compression index (Cc) from the gypseous soils' physical properties using a wide range of 220 collected data from various published articles. The linear and nonlinear approaches were used in this study, and the outcomes of the models were compared with artificial neural network (ANN) performance. The developed models predicted the shear parameters, compression index, gypsum content, and collapse potential as a function of accessible laboratories measurable such as specific gravity, moisture content, density, and Atterberg limits with acceptable accuracy. The soils' gypsum content (Gc) was also correlated well based on the total soluble salts (TSS), sulfate (SO
3
), and pH values using the nonlinear Vipulanandan correlation model. Based on the adjusted (
R
2
), mean absolute error (MAE), and the root-mean-square error (RMSE), the linear and nonlinear models predicted the shear strength parameters, compression index, and collapse potential of the gypseous soils very well. The regression model predictions were comparable to the outcomes from the ANN model predicting.</description><subject>Artificial neural networks</subject><subject>Atterberg limits</subject><subject>Collapse</subject><subject>Complex Fluids and Microfluidics</subject><subject>Compression</subject><subject>Compression index</subject><subject>Compressive strength</subject><subject>Density</subject><subject>Engineering</subject><subject>Foundations</subject><subject>Geoengineering</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Gypsum</subject><subject>Hydraulics</subject><subject>Mechanical properties</subject><subject>Moisture content</subject><subject>Moisture effects</subject><subject>Neural networks</subject><subject>Parameters</subject><subject>Physical properties</subject><subject>Predictions</subject><subject>Regression models</subject><subject>Research Paper</subject><subject>Root-mean-square errors</subject><subject>Salts</subject><subject>Shear strength</subject><subject>Soft and Granular Matter</subject><subject>Soil</subject><subject>Soil mechanics</subject><subject>Soil moisture</subject><subject>Soil properties</subject><subject>Soil Science & Conservation</subject><subject>Soils</subject><subject>Solid Mechanics</subject><subject>Specific gravity</subject><subject>Sulfur trioxide</subject><subject>Water content</subject><issn>1861-1125</issn><issn>1861-1133</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kDlPxDAQhS0EEsvCH6CyRB0YXzlKtOKSVoICasuxnaxXubCdYv89XoKgo5oZ-X1vPA-hawK3BKC4C4RwDhlQkgFhwLPyBK1ImZOMEMZOf3sqztFFCHuAnFGer1D75q1xOrqhxXFnsd7Z3mnVYTUY3Fu9U8P3OPlxsj46G_DY4PYwBTvOAYfRdQHP4Ygb1zTW2yHi4Pq5U9GNA47JIjmES3TWqC7Yq5-6Rh-PD--b52z7-vSyud9minEaMwpCQCUEUVWtueGmEozRmrMiPVdQNTWrDNMGoLTUpBNqLnLTkJxQVQpdszW6WXzThz9nG6Lcj7Mf0kpJc5FzzosCkoouKu3HELxt5ORdr_xBEpDHQOUSqEyByu9AZZkgtkAhiYfW-j_rf6gvuJt5Pg</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Mohammed, Ahmed</creator><creator>Hummadi, Rizgar Ali</creator><creator>Mawlood, Yousif Ismael</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/0000-0003-4306-3274</orcidid></search><sort><creationdate>20220401</creationdate><title>Predicting the chemical and mechanical properties of gypseous soils using different simulation technics</title><author>Mohammed, Ahmed ; Hummadi, Rizgar Ali ; Mawlood, Yousif Ismael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a342t-205509551a9bc4d4d95332b437342909fb39d3cd008e2d324b456df1612a85cb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial neural networks</topic><topic>Atterberg limits</topic><topic>Collapse</topic><topic>Complex Fluids and Microfluidics</topic><topic>Compression</topic><topic>Compression index</topic><topic>Compressive strength</topic><topic>Density</topic><topic>Engineering</topic><topic>Foundations</topic><topic>Geoengineering</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Gypsum</topic><topic>Hydraulics</topic><topic>Mechanical properties</topic><topic>Moisture content</topic><topic>Moisture effects</topic><topic>Neural networks</topic><topic>Parameters</topic><topic>Physical properties</topic><topic>Predictions</topic><topic>Regression models</topic><topic>Research Paper</topic><topic>Root-mean-square errors</topic><topic>Salts</topic><topic>Shear strength</topic><topic>Soft and Granular Matter</topic><topic>Soil</topic><topic>Soil mechanics</topic><topic>Soil moisture</topic><topic>Soil properties</topic><topic>Soil Science & Conservation</topic><topic>Soils</topic><topic>Solid Mechanics</topic><topic>Specific gravity</topic><topic>Sulfur trioxide</topic><topic>Water content</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mohammed, Ahmed</creatorcontrib><creatorcontrib>Hummadi, Rizgar Ali</creatorcontrib><creatorcontrib>Mawlood, Yousif Ismael</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 Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>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</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>Mohammed, Ahmed</au><au>Hummadi, Rizgar Ali</au><au>Mawlood, Yousif Ismael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting the chemical and mechanical properties of gypseous soils using different simulation technics</atitle><jtitle>Acta geotechnica</jtitle><stitle>Acta Geotech</stitle><date>2022-04-01</date><risdate>2022</risdate><volume>17</volume><issue>4</issue><spage>1111</spage><epage>1127</epage><pages>1111-1127</pages><issn>1861-1125</issn><eissn>1861-1133</eissn><abstract>Gypseous soils are soils that contain sufficient quantities of gypsum that are considered collapsible soil. The present study's objective is to predict the shear strength parameters (
c
,
ϕ
), collapse potential (CP), and compression index (Cc) from the gypseous soils' physical properties using a wide range of 220 collected data from various published articles. The linear and nonlinear approaches were used in this study, and the outcomes of the models were compared with artificial neural network (ANN) performance. The developed models predicted the shear parameters, compression index, gypsum content, and collapse potential as a function of accessible laboratories measurable such as specific gravity, moisture content, density, and Atterberg limits with acceptable accuracy. The soils' gypsum content (Gc) was also correlated well based on the total soluble salts (TSS), sulfate (SO
3
), and pH values using the nonlinear Vipulanandan correlation model. Based on the adjusted (
R
2
), mean absolute error (MAE), and the root-mean-square error (RMSE), the linear and nonlinear models predicted the shear strength parameters, compression index, and collapse potential of the gypseous soils very well. The regression model predictions were comparable to the outcomes from the ANN model predicting.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s11440-021-01304-8</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0003-4306-3274</orcidid></addata></record> |
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| identifier | ISSN: 1861-1125 |
| ispartof | Acta geotechnica, 2022-04, Vol.17 (4), p.1111-1127 |
| issn | 1861-1125 1861-1133 |
| language | eng |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Artificial neural networks Atterberg limits Collapse Complex Fluids and Microfluidics Compression Compression index Compressive strength Density Engineering Foundations Geoengineering Geotechnical Engineering & Applied Earth Sciences Gypsum Hydraulics Mechanical properties Moisture content Moisture effects Neural networks Parameters Physical properties Predictions Regression models Research Paper Root-mean-square errors Salts Shear strength Soft and Granular Matter Soil Soil mechanics Soil moisture Soil properties Soil Science & Conservation Soils Solid Mechanics Specific gravity Sulfur trioxide Water content |
| title | Predicting the chemical and mechanical properties of gypseous soils using different simulation technics |
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