Assessment of Geotechnical Properties and Determination of Shear Strength Parameters
In this research, geotechnical properties and the relationship between cohesion (c) and internal friction angle (ϕ) with the SPT-N 60 were investigated in 120 boreholes in the sedimentary basin of Kerman. Laboratory tests such as direct shear, triaxial, consolidation, and physical tests were carried...
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| Veröffentlicht in: | Geotechnical and geological engineering 2021, Vol.39 (1), p.461-478 |
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| creator | Ghoreishi, Benyamin Khaleghi Esfahani, Mohammad Alizadeh Lushabi, Nargess Amini, Omid Aghamolaie, Iman Hashim, Nik Alif Amri Nik Alizadeh, Seyed Mehdi Seyed |
| description | In this research, geotechnical properties and the relationship between cohesion (c) and internal friction angle (ϕ) with the SPT-N
60
were investigated in 120 boreholes in the sedimentary basin of Kerman. Laboratory tests such as direct shear, triaxial, consolidation, and physical tests were carried out on soil samples extracted from the boreholes, and the SPT test was performed on all 120 boreholes. Since the soil in the area is CL, the SEM, XRD, XRF, physical, and mechanical properties of this soil were investigated. The artificial neural networks (ANN) and statistical analysis were used to estimate ϕ and c based on the SPT-N
60
. The petrography studies revealed that Quartz, Calcite, Dolomite, Albite, Illite, Clinochlore, and Microcline are the most plentiful minerals in this sedimentary basin. Also, the dominant clay is Illite. Illite clays, due to the low shear strength, have made some problems in the earth dams of the studied area. Results show that based on the SPT-N number, groundwater level, and soil texture the liquefaction hazard could not occur in this area. Previous equations are used to predict the c and ϕ and results are compared with this research. The obtained results from the ANN and statistical analysis showed that there is a good correlation between ϕ and c derived from the direct shear test with the SPT-N
60
. Based on
R
2
, RMSE,
P
-value and Durbin-Watson statistics the correlation between c and the SPT-N
60
is stronger than ϕ and the SPT-N
60
. Moreover, the ANN showed higher accuracy in predicting shear strength parameters compared to the simple regression. |
| doi_str_mv | 10.1007/s10706-020-01504-1 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2474981562</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2474981562</sourcerecordid><originalsourceid>FETCH-LOGICAL-a342t-56e4d8852134d27c4262cdc251e112677176bff7fe91d0ae59007b52a4900eaa3</originalsourceid><addsrcrecordid>eNp9kMtOwzAQRS0EEuXxA6wisTbMOH4ky6pAQapEpZa15SaTNlXjFNtd8PekFIkdq5nFuXc0h7E7hAcEMI8RwYDmIIADKpAcz9gIlck5KlGesxGUGniOhbhkVzFuAUBowBFbjmOkGDvyKeubbEp9omrj28rtsnno9xRSSzFzvs6eKFHoWu9S2_sjvNiQC9kiBfLrtMnmLrjuyMQbdtG4XaTb33nNPl6el5NXPnufvk3GM-5yKRJXmmRdFEpgLmthKim0qOpKKCREoY1Bo1dNYxoqsQZHqhxeXSnh5LCQc_k1uz_17kP_eaCY7LY_BD-ctEIaWRaotBgocaKq0McYqLH70HYufFkEe7RnT_bsYM_-2LM4hPJTKA6wX1P4q_4n9Q3X1HHV</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2474981562</pqid></control><display><type>article</type><title>Assessment of Geotechnical Properties and Determination of Shear Strength Parameters</title><source>SpringerLink Journals</source><creator>Ghoreishi, Benyamin ; Khaleghi Esfahani, Mohammad ; Alizadeh Lushabi, Nargess ; Amini, Omid ; Aghamolaie, Iman ; Hashim, Nik Alif Amri Nik ; Alizadeh, Seyed Mehdi Seyed</creator><creatorcontrib>Ghoreishi, Benyamin ; Khaleghi Esfahani, Mohammad ; Alizadeh Lushabi, Nargess ; Amini, Omid ; Aghamolaie, Iman ; Hashim, Nik Alif Amri Nik ; Alizadeh, Seyed Mehdi Seyed</creatorcontrib><description>In this research, geotechnical properties and the relationship between cohesion (c) and internal friction angle (ϕ) with the SPT-N
60
were investigated in 120 boreholes in the sedimentary basin of Kerman. Laboratory tests such as direct shear, triaxial, consolidation, and physical tests were carried out on soil samples extracted from the boreholes, and the SPT test was performed on all 120 boreholes. Since the soil in the area is CL, the SEM, XRD, XRF, physical, and mechanical properties of this soil were investigated. The artificial neural networks (ANN) and statistical analysis were used to estimate ϕ and c based on the SPT-N
60
. The petrography studies revealed that Quartz, Calcite, Dolomite, Albite, Illite, Clinochlore, and Microcline are the most plentiful minerals in this sedimentary basin. Also, the dominant clay is Illite. Illite clays, due to the low shear strength, have made some problems in the earth dams of the studied area. Results show that based on the SPT-N number, groundwater level, and soil texture the liquefaction hazard could not occur in this area. Previous equations are used to predict the c and ϕ and results are compared with this research. The obtained results from the ANN and statistical analysis showed that there is a good correlation between ϕ and c derived from the direct shear test with the SPT-N
60
. Based on
R
2
, RMSE,
P
-value and Durbin-Watson statistics the correlation between c and the SPT-N
60
is stronger than ϕ and the SPT-N
60
. Moreover, the ANN showed higher accuracy in predicting shear strength parameters compared to the simple regression.</description><identifier>ISSN: 0960-3182</identifier><identifier>EISSN: 1573-1529</identifier><identifier>DOI: 10.1007/s10706-020-01504-1</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Artificial neural networks ; Boreholes ; Calcite ; Civil Engineering ; Clay ; Clay minerals ; Clinochlore ; Correlation ; Dolomite ; Dolostone ; Earth and Environmental Science ; Earth dams ; Earth Sciences ; Geotechnical Engineering & Applied Earth Sciences ; Groundwater ; Groundwater levels ; Hydrogeology ; Illite ; Illites ; Internal friction ; Laboratory tests ; Liquefaction ; Mechanical properties ; Minerals ; Neural networks ; Original Paper ; Parameters ; Petrography ; Petrology ; Physical tests ; Regression analysis ; Sedimentary basins ; Shear strength ; Shear tests ; Soil investigations ; Soil mechanics ; Soil properties ; Soil texture ; Statistical analysis ; Statistical methods ; Statistical tests ; Statistics ; Terrestrial Pollution ; Waste Management/Waste Technology</subject><ispartof>Geotechnical and geological engineering, 2021, Vol.39 (1), p.461-478</ispartof><rights>Springer Nature Switzerland AG 2020. corrected publication 2020</rights><rights>Springer Nature Switzerland AG 2020. corrected publication 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a342t-56e4d8852134d27c4262cdc251e112677176bff7fe91d0ae59007b52a4900eaa3</citedby><cites>FETCH-LOGICAL-a342t-56e4d8852134d27c4262cdc251e112677176bff7fe91d0ae59007b52a4900eaa3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10706-020-01504-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10706-020-01504-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Ghoreishi, Benyamin</creatorcontrib><creatorcontrib>Khaleghi Esfahani, Mohammad</creatorcontrib><creatorcontrib>Alizadeh Lushabi, Nargess</creatorcontrib><creatorcontrib>Amini, Omid</creatorcontrib><creatorcontrib>Aghamolaie, Iman</creatorcontrib><creatorcontrib>Hashim, Nik Alif Amri Nik</creatorcontrib><creatorcontrib>Alizadeh, Seyed Mehdi Seyed</creatorcontrib><title>Assessment of Geotechnical Properties and Determination of Shear Strength Parameters</title><title>Geotechnical and geological engineering</title><addtitle>Geotech Geol Eng</addtitle><description>In this research, geotechnical properties and the relationship between cohesion (c) and internal friction angle (ϕ) with the SPT-N
60
were investigated in 120 boreholes in the sedimentary basin of Kerman. Laboratory tests such as direct shear, triaxial, consolidation, and physical tests were carried out on soil samples extracted from the boreholes, and the SPT test was performed on all 120 boreholes. Since the soil in the area is CL, the SEM, XRD, XRF, physical, and mechanical properties of this soil were investigated. The artificial neural networks (ANN) and statistical analysis were used to estimate ϕ and c based on the SPT-N
60
. The petrography studies revealed that Quartz, Calcite, Dolomite, Albite, Illite, Clinochlore, and Microcline are the most plentiful minerals in this sedimentary basin. Also, the dominant clay is Illite. Illite clays, due to the low shear strength, have made some problems in the earth dams of the studied area. Results show that based on the SPT-N number, groundwater level, and soil texture the liquefaction hazard could not occur in this area. Previous equations are used to predict the c and ϕ and results are compared with this research. The obtained results from the ANN and statistical analysis showed that there is a good correlation between ϕ and c derived from the direct shear test with the SPT-N
60
. Based on
R
2
, RMSE,
P
-value and Durbin-Watson statistics the correlation between c and the SPT-N
60
is stronger than ϕ and the SPT-N
60
. Moreover, the ANN showed higher accuracy in predicting shear strength parameters compared to the simple regression.</description><subject>Artificial neural networks</subject><subject>Boreholes</subject><subject>Calcite</subject><subject>Civil Engineering</subject><subject>Clay</subject><subject>Clay minerals</subject><subject>Clinochlore</subject><subject>Correlation</subject><subject>Dolomite</subject><subject>Dolostone</subject><subject>Earth and Environmental Science</subject><subject>Earth dams</subject><subject>Earth Sciences</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Groundwater</subject><subject>Groundwater levels</subject><subject>Hydrogeology</subject><subject>Illite</subject><subject>Illites</subject><subject>Internal friction</subject><subject>Laboratory tests</subject><subject>Liquefaction</subject><subject>Mechanical properties</subject><subject>Minerals</subject><subject>Neural networks</subject><subject>Original Paper</subject><subject>Parameters</subject><subject>Petrography</subject><subject>Petrology</subject><subject>Physical tests</subject><subject>Regression analysis</subject><subject>Sedimentary basins</subject><subject>Shear strength</subject><subject>Shear tests</subject><subject>Soil investigations</subject><subject>Soil mechanics</subject><subject>Soil properties</subject><subject>Soil texture</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistical tests</subject><subject>Statistics</subject><subject>Terrestrial Pollution</subject><subject>Waste Management/Waste Technology</subject><issn>0960-3182</issn><issn>1573-1529</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kMtOwzAQRS0EEuXxA6wisTbMOH4ky6pAQapEpZa15SaTNlXjFNtd8PekFIkdq5nFuXc0h7E7hAcEMI8RwYDmIIADKpAcz9gIlck5KlGesxGUGniOhbhkVzFuAUBowBFbjmOkGDvyKeubbEp9omrj28rtsnno9xRSSzFzvs6eKFHoWu9S2_sjvNiQC9kiBfLrtMnmLrjuyMQbdtG4XaTb33nNPl6el5NXPnufvk3GM-5yKRJXmmRdFEpgLmthKim0qOpKKCREoY1Bo1dNYxoqsQZHqhxeXSnh5LCQc_k1uz_17kP_eaCY7LY_BD-ctEIaWRaotBgocaKq0McYqLH70HYufFkEe7RnT_bsYM_-2LM4hPJTKA6wX1P4q_4n9Q3X1HHV</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Ghoreishi, Benyamin</creator><creator>Khaleghi Esfahani, Mohammad</creator><creator>Alizadeh Lushabi, Nargess</creator><creator>Amini, Omid</creator><creator>Aghamolaie, Iman</creator><creator>Hashim, Nik Alif Amri Nik</creator><creator>Alizadeh, Seyed Mehdi Seyed</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TN</scope><scope>7UA</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AEUYN</scope><scope>AFKRA</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>H96</scope><scope>HCIFZ</scope><scope>L.G</scope><scope>L6V</scope><scope>M7S</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope></search><sort><creationdate>2021</creationdate><title>Assessment of Geotechnical Properties and Determination of Shear Strength Parameters</title><author>Ghoreishi, Benyamin ; Khaleghi Esfahani, Mohammad ; Alizadeh Lushabi, Nargess ; Amini, Omid ; Aghamolaie, Iman ; Hashim, Nik Alif Amri Nik ; Alizadeh, Seyed Mehdi Seyed</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a342t-56e4d8852134d27c4262cdc251e112677176bff7fe91d0ae59007b52a4900eaa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural networks</topic><topic>Boreholes</topic><topic>Calcite</topic><topic>Civil Engineering</topic><topic>Clay</topic><topic>Clay minerals</topic><topic>Clinochlore</topic><topic>Correlation</topic><topic>Dolomite</topic><topic>Dolostone</topic><topic>Earth and Environmental Science</topic><topic>Earth dams</topic><topic>Earth Sciences</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Groundwater</topic><topic>Groundwater levels</topic><topic>Hydrogeology</topic><topic>Illite</topic><topic>Illites</topic><topic>Internal friction</topic><topic>Laboratory tests</topic><topic>Liquefaction</topic><topic>Mechanical properties</topic><topic>Minerals</topic><topic>Neural networks</topic><topic>Original Paper</topic><topic>Parameters</topic><topic>Petrography</topic><topic>Petrology</topic><topic>Physical tests</topic><topic>Regression analysis</topic><topic>Sedimentary basins</topic><topic>Shear strength</topic><topic>Shear tests</topic><topic>Soil investigations</topic><topic>Soil mechanics</topic><topic>Soil properties</topic><topic>Soil texture</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistical tests</topic><topic>Statistics</topic><topic>Terrestrial Pollution</topic><topic>Waste Management/Waste Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ghoreishi, Benyamin</creatorcontrib><creatorcontrib>Khaleghi Esfahani, Mohammad</creatorcontrib><creatorcontrib>Alizadeh Lushabi, Nargess</creatorcontrib><creatorcontrib>Amini, Omid</creatorcontrib><creatorcontrib>Aghamolaie, Iman</creatorcontrib><creatorcontrib>Hashim, Nik Alif Amri Nik</creatorcontrib><creatorcontrib>Alizadeh, Seyed Mehdi Seyed</creatorcontrib><collection>CrossRef</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</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>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</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><jtitle>Geotechnical and geological engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ghoreishi, Benyamin</au><au>Khaleghi Esfahani, Mohammad</au><au>Alizadeh Lushabi, Nargess</au><au>Amini, Omid</au><au>Aghamolaie, Iman</au><au>Hashim, Nik Alif Amri Nik</au><au>Alizadeh, Seyed Mehdi Seyed</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of Geotechnical Properties and Determination of Shear Strength Parameters</atitle><jtitle>Geotechnical and geological engineering</jtitle><stitle>Geotech Geol Eng</stitle><date>2021</date><risdate>2021</risdate><volume>39</volume><issue>1</issue><spage>461</spage><epage>478</epage><pages>461-478</pages><issn>0960-3182</issn><eissn>1573-1529</eissn><abstract>In this research, geotechnical properties and the relationship between cohesion (c) and internal friction angle (ϕ) with the SPT-N
60
were investigated in 120 boreholes in the sedimentary basin of Kerman. Laboratory tests such as direct shear, triaxial, consolidation, and physical tests were carried out on soil samples extracted from the boreholes, and the SPT test was performed on all 120 boreholes. Since the soil in the area is CL, the SEM, XRD, XRF, physical, and mechanical properties of this soil were investigated. The artificial neural networks (ANN) and statistical analysis were used to estimate ϕ and c based on the SPT-N
60
. The petrography studies revealed that Quartz, Calcite, Dolomite, Albite, Illite, Clinochlore, and Microcline are the most plentiful minerals in this sedimentary basin. Also, the dominant clay is Illite. Illite clays, due to the low shear strength, have made some problems in the earth dams of the studied area. Results show that based on the SPT-N number, groundwater level, and soil texture the liquefaction hazard could not occur in this area. Previous equations are used to predict the c and ϕ and results are compared with this research. The obtained results from the ANN and statistical analysis showed that there is a good correlation between ϕ and c derived from the direct shear test with the SPT-N
60
. Based on
R
2
, RMSE,
P
-value and Durbin-Watson statistics the correlation between c and the SPT-N
60
is stronger than ϕ and the SPT-N
60
. Moreover, the ANN showed higher accuracy in predicting shear strength parameters compared to the simple regression.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s10706-020-01504-1</doi><tpages>18</tpages></addata></record> |
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| subjects | Artificial neural networks Boreholes Calcite Civil Engineering Clay Clay minerals Clinochlore Correlation Dolomite Dolostone Earth and Environmental Science Earth dams Earth Sciences Geotechnical Engineering & Applied Earth Sciences Groundwater Groundwater levels Hydrogeology Illite Illites Internal friction Laboratory tests Liquefaction Mechanical properties Minerals Neural networks Original Paper Parameters Petrography Petrology Physical tests Regression analysis Sedimentary basins Shear strength Shear tests Soil investigations Soil mechanics Soil properties Soil texture Statistical analysis Statistical methods Statistical tests Statistics Terrestrial Pollution Waste Management/Waste Technology |
| title | Assessment of Geotechnical Properties and Determination of Shear Strength Parameters |
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