Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste
Pervious concrete is a kind of concrete used for storm-water management due to its high porosity and permeability. However, its’ flexural strength as the most desirable mechanical properties was predicted in this study. The paper aims to demonstrate three soft-computing models, i.e., artificial neur...
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| Veröffentlicht in: | Modeling earth systems and environment 2022-06, Vol.8 (2), p.1933-1947 |
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| container_title | Modeling earth systems and environment |
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| creator | Malami, Salim Idris Musa, A. A. Haruna, S. I. Aliyu, U. U. Usman, A. G. Abdurrahman, M. I. Bashir, Abba Abba, S. I. |
| description | Pervious concrete is a kind of concrete used for storm-water management due to its high porosity and permeability. However, its’ flexural strength as the most desirable mechanical properties was predicted in this study. The paper aims to demonstrate three soft-computing models, i.e., artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) were applied for the prediction of flexural strength (
σ
f
)
of pervious Concrete (PC) incorporated with calcium carbide waste (CCW) and rice husk ash (RHA) as supplementary cementation materials. The models were trained on the experimental data obtained by replacing cement content from 0 to 10% RHA and 0 to 20% CCW in the PC at 3-, 7-, and 28-days curing age. The results indicated that three AI-based models ANN, SVM, and ANFIS have predicted the flexural strength with high accuracy in both the testing and training stages, following the performance evaluation involving; Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), mean square error (MSE), and root mean square error (RMSE). All the input variables contribute to the accuracy of the model. The ANFIS-M5 (NSE = 0.9997 and RMSE = 0.0044 in testing phase) proved merit despite the acceptable accuracy obtained in the ANN model. The hybrid ANFIS predicts the flexural strength of hybridized pervious concrete with the highest accuracy compared to the other two soft-computing models. |
| doi_str_mv | 10.1007/s40808-021-01195-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2667622297</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2667622297</sourcerecordid><originalsourceid>FETCH-LOGICAL-c249t-53e4d746f0515b283de10308abf93623b063ce608350559fa3cec06cc23a288a3</originalsourceid><addsrcrecordid>eNp9kctOxDAMRSsEEiPgB1hFYl1wkjbTLhHiJSGxgXWUps5MhrYpScowfAmfS2B47FjZvjrXlnyz7JjCKQWYn4UCKqhyYDQHSusyL3ayGeOC54JRuvvbA9_PjkJYAQAVTIi6nmXvt_3YYY9DVNG6gThDgjMx164fp2iHBeldi10gxnkyemyt_uFMh6-TVx0J0eOwiMtPcUT_Yt0UiHaD9hiRLDeNt619w5asbYK81UmcwhNRYUnU0BKtOm2nPlXf2BbJWoWIh9meUV3Ao-96kD1eXT5c3OR399e3F-d3uWZFHfOSY9HOC2GgpGXDKt4iBQ6VakzNBeMNCK5RQMVLKMvaqDRpEFozrlhVKX6QnWz3jt49TxiiXLnJD-mkTC-aC8ZYPU8U21LauxA8Gjl62yu_kRTkZwhyG4JMIcivEGSRTHxrCgkeFuj_Vv_j-gAYh4zU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2667622297</pqid></control><display><type>article</type><title>Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste</title><source>SpringerNature Journals</source><creator>Malami, Salim Idris ; Musa, A. A. ; Haruna, S. I. ; Aliyu, U. U. ; Usman, A. G. ; Abdurrahman, M. I. ; Bashir, Abba ; Abba, S. I.</creator><creatorcontrib>Malami, Salim Idris ; Musa, A. A. ; Haruna, S. I. ; Aliyu, U. U. ; Usman, A. G. ; Abdurrahman, M. I. ; Bashir, Abba ; Abba, S. I.</creatorcontrib><description>Pervious concrete is a kind of concrete used for storm-water management due to its high porosity and permeability. However, its’ flexural strength as the most desirable mechanical properties was predicted in this study. The paper aims to demonstrate three soft-computing models, i.e., artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) were applied for the prediction of flexural strength (
σ
f
)
of pervious Concrete (PC) incorporated with calcium carbide waste (CCW) and rice husk ash (RHA) as supplementary cementation materials. The models were trained on the experimental data obtained by replacing cement content from 0 to 10% RHA and 0 to 20% CCW in the PC at 3-, 7-, and 28-days curing age. The results indicated that three AI-based models ANN, SVM, and ANFIS have predicted the flexural strength with high accuracy in both the testing and training stages, following the performance evaluation involving; Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), mean square error (MSE), and root mean square error (RMSE). All the input variables contribute to the accuracy of the model. The ANFIS-M5 (NSE = 0.9997 and RMSE = 0.0044 in testing phase) proved merit despite the acceptable accuracy obtained in the ANN model. The hybrid ANFIS predicts the flexural strength of hybridized pervious concrete with the highest accuracy compared to the other two soft-computing models.</description><identifier>ISSN: 2363-6203</identifier><identifier>EISSN: 2363-6211</identifier><identifier>DOI: 10.1007/s40808-021-01195-4</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Accuracy ; Adaptive systems ; Artificial neural networks ; Ashes ; Calcium ; Cementation ; Cemented carbides ; Chemistry and Earth Sciences ; Computation ; Computer Science ; Concrete ; Correlation coefficient ; Correlation coefficients ; Earth and Environmental Science ; Earth Sciences ; Earth System Sciences ; Ecosystems ; Environment ; Flexural strength ; Fuzzy logic ; Math. Appl. in Environmental Science ; Mathematical Applications in the Physical Sciences ; Mean square errors ; Mechanical properties ; Model accuracy ; Neural networks ; Original Article ; Performance evaluation ; Permeability ; Physics ; Porosity ; Root-mean-square errors ; Statistics for Engineering ; Storms ; Stormwater ; Stormwater management ; Strength ; Support vector machines ; Testing ; Water management</subject><ispartof>Modeling earth systems and environment, 2022-06, Vol.8 (2), p.1933-1947</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021</rights><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c249t-53e4d746f0515b283de10308abf93623b063ce608350559fa3cec06cc23a288a3</citedby><cites>FETCH-LOGICAL-c249t-53e4d746f0515b283de10308abf93623b063ce608350559fa3cec06cc23a288a3</cites><orcidid>0000-0001-9356-2798</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/s40808-021-01195-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40808-021-01195-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids></links><search><creatorcontrib>Malami, Salim Idris</creatorcontrib><creatorcontrib>Musa, A. A.</creatorcontrib><creatorcontrib>Haruna, S. I.</creatorcontrib><creatorcontrib>Aliyu, U. U.</creatorcontrib><creatorcontrib>Usman, A. G.</creatorcontrib><creatorcontrib>Abdurrahman, M. I.</creatorcontrib><creatorcontrib>Bashir, Abba</creatorcontrib><creatorcontrib>Abba, S. I.</creatorcontrib><title>Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste</title><title>Modeling earth systems and environment</title><addtitle>Model. Earth Syst. Environ</addtitle><description>Pervious concrete is a kind of concrete used for storm-water management due to its high porosity and permeability. However, its’ flexural strength as the most desirable mechanical properties was predicted in this study. The paper aims to demonstrate three soft-computing models, i.e., artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) were applied for the prediction of flexural strength (
σ
f
)
of pervious Concrete (PC) incorporated with calcium carbide waste (CCW) and rice husk ash (RHA) as supplementary cementation materials. The models were trained on the experimental data obtained by replacing cement content from 0 to 10% RHA and 0 to 20% CCW in the PC at 3-, 7-, and 28-days curing age. The results indicated that three AI-based models ANN, SVM, and ANFIS have predicted the flexural strength with high accuracy in both the testing and training stages, following the performance evaluation involving; Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), mean square error (MSE), and root mean square error (RMSE). All the input variables contribute to the accuracy of the model. The ANFIS-M5 (NSE = 0.9997 and RMSE = 0.0044 in testing phase) proved merit despite the acceptable accuracy obtained in the ANN model. The hybrid ANFIS predicts the flexural strength of hybridized pervious concrete with the highest accuracy compared to the other two soft-computing models.</description><subject>Accuracy</subject><subject>Adaptive systems</subject><subject>Artificial neural networks</subject><subject>Ashes</subject><subject>Calcium</subject><subject>Cementation</subject><subject>Cemented carbides</subject><subject>Chemistry and Earth Sciences</subject><subject>Computation</subject><subject>Computer Science</subject><subject>Concrete</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earth System Sciences</subject><subject>Ecosystems</subject><subject>Environment</subject><subject>Flexural strength</subject><subject>Fuzzy logic</subject><subject>Math. Appl. in Environmental Science</subject><subject>Mathematical Applications in the Physical Sciences</subject><subject>Mean square errors</subject><subject>Mechanical properties</subject><subject>Model accuracy</subject><subject>Neural networks</subject><subject>Original Article</subject><subject>Performance evaluation</subject><subject>Permeability</subject><subject>Physics</subject><subject>Porosity</subject><subject>Root-mean-square errors</subject><subject>Statistics for Engineering</subject><subject>Storms</subject><subject>Stormwater</subject><subject>Stormwater management</subject><subject>Strength</subject><subject>Support vector machines</subject><subject>Testing</subject><subject>Water management</subject><issn>2363-6203</issn><issn>2363-6211</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kctOxDAMRSsEEiPgB1hFYl1wkjbTLhHiJSGxgXWUps5MhrYpScowfAmfS2B47FjZvjrXlnyz7JjCKQWYn4UCKqhyYDQHSusyL3ayGeOC54JRuvvbA9_PjkJYAQAVTIi6nmXvt_3YYY9DVNG6gThDgjMx164fp2iHBeldi10gxnkyemyt_uFMh6-TVx0J0eOwiMtPcUT_Yt0UiHaD9hiRLDeNt619w5asbYK81UmcwhNRYUnU0BKtOm2nPlXf2BbJWoWIh9meUV3Ao-96kD1eXT5c3OR399e3F-d3uWZFHfOSY9HOC2GgpGXDKt4iBQ6VakzNBeMNCK5RQMVLKMvaqDRpEFozrlhVKX6QnWz3jt49TxiiXLnJD-mkTC-aC8ZYPU8U21LauxA8Gjl62yu_kRTkZwhyG4JMIcivEGSRTHxrCgkeFuj_Vv_j-gAYh4zU</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Malami, Salim Idris</creator><creator>Musa, A. 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I.</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>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>L.G</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><orcidid>https://orcid.org/0000-0001-9356-2798</orcidid></search><sort><creationdate>20220601</creationdate><title>Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste</title><author>Malami, Salim Idris ; Musa, A. A. ; Haruna, S. I. ; Aliyu, U. U. ; Usman, A. G. ; Abdurrahman, M. I. ; Bashir, Abba ; Abba, S. 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Appl. in Environmental Science</topic><topic>Mathematical Applications in the Physical Sciences</topic><topic>Mean square errors</topic><topic>Mechanical properties</topic><topic>Model accuracy</topic><topic>Neural networks</topic><topic>Original Article</topic><topic>Performance evaluation</topic><topic>Permeability</topic><topic>Physics</topic><topic>Porosity</topic><topic>Root-mean-square errors</topic><topic>Statistics for Engineering</topic><topic>Storms</topic><topic>Stormwater</topic><topic>Stormwater management</topic><topic>Strength</topic><topic>Support vector machines</topic><topic>Testing</topic><topic>Water management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Malami, Salim Idris</creatorcontrib><creatorcontrib>Musa, A. A.</creatorcontrib><creatorcontrib>Haruna, S. I.</creatorcontrib><creatorcontrib>Aliyu, U. U.</creatorcontrib><creatorcontrib>Usman, A. G.</creatorcontrib><creatorcontrib>Abdurrahman, M. 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A.</au><au>Haruna, S. I.</au><au>Aliyu, U. U.</au><au>Usman, A. G.</au><au>Abdurrahman, M. I.</au><au>Bashir, Abba</au><au>Abba, S. I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste</atitle><jtitle>Modeling earth systems and environment</jtitle><stitle>Model. Earth Syst. Environ</stitle><date>2022-06-01</date><risdate>2022</risdate><volume>8</volume><issue>2</issue><spage>1933</spage><epage>1947</epage><pages>1933-1947</pages><issn>2363-6203</issn><eissn>2363-6211</eissn><abstract>Pervious concrete is a kind of concrete used for storm-water management due to its high porosity and permeability. However, its’ flexural strength as the most desirable mechanical properties was predicted in this study. The paper aims to demonstrate three soft-computing models, i.e., artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) were applied for the prediction of flexural strength (
σ
f
)
of pervious Concrete (PC) incorporated with calcium carbide waste (CCW) and rice husk ash (RHA) as supplementary cementation materials. The models were trained on the experimental data obtained by replacing cement content from 0 to 10% RHA and 0 to 20% CCW in the PC at 3-, 7-, and 28-days curing age. The results indicated that three AI-based models ANN, SVM, and ANFIS have predicted the flexural strength with high accuracy in both the testing and training stages, following the performance evaluation involving; Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), mean square error (MSE), and root mean square error (RMSE). All the input variables contribute to the accuracy of the model. The ANFIS-M5 (NSE = 0.9997 and RMSE = 0.0044 in testing phase) proved merit despite the acceptable accuracy obtained in the ANN model. The hybrid ANFIS predicts the flexural strength of hybridized pervious concrete with the highest accuracy compared to the other two soft-computing models.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s40808-021-01195-4</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-9356-2798</orcidid></addata></record> |
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| subjects | Accuracy Adaptive systems Artificial neural networks Ashes Calcium Cementation Cemented carbides Chemistry and Earth Sciences Computation Computer Science Concrete Correlation coefficient Correlation coefficients Earth and Environmental Science Earth Sciences Earth System Sciences Ecosystems Environment Flexural strength Fuzzy logic Math. Appl. in Environmental Science Mathematical Applications in the Physical Sciences Mean square errors Mechanical properties Model accuracy Neural networks Original Article Performance evaluation Permeability Physics Porosity Root-mean-square errors Statistics for Engineering Storms Stormwater Stormwater management Strength Support vector machines Testing Water management |
| title | Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste |
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