Publication: Prediction of mechanical and penetrability properties of cement-stabilized clay exposed to sulfate attack by use of soft computing methods
dc.contributor.author | Sezer, Alper | |
dc.contributor.author | Sezer, Gözde Inan | |
dc.contributor.author | Mardani-Aghabaglou, Ali | |
dc.contributor.author | Altun, Selim | |
dc.contributor.buuauthor | Mardani-Aghabaglou, Ali | |
dc.contributor.department | Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü | |
dc.contributor.orcid | 0000-0003-0326-5015 | |
dc.contributor.researcherid | C-7860-2015 | |
dc.contributor.researcherid | AAJ-6415-2021 | |
dc.date.accessioned | 2024-07-04T10:34:40Z | |
dc.date.available | 2024-07-04T10:34:40Z | |
dc.date.issued | 2020-05-06 | |
dc.description.abstract | Similar to its effects on any type of cementitious composite, it is a well-known fact that sulfate attack has also a negative influence on engineering behavior of cement-stabilized soils. However, the level of degradation in engineering properties of the cement-stabilized soils still needs more scientific attention. In the light of this, a database including a total of 260 unconfined compression and chloride ion penetration tests on cement-stabilized kaolin specimens exposed to sulfate attack was constituted. The data include information about cement type (sulfate resistant-SR; normal portland (N) and pozzolanic-P), and its content (0, 5, 10 and 15%), sulfate type (sodium or magnesium sulfate) as well as its concentration (0.3, 0.5, 1%) and curing period (1, 7, 28 and 90 days). Using this database, linear and nonlinear regression analysis (RA), backpropagation neural networks and adaptive neuro-fuzzy inference techniques were employed to question whether these methods are capable of predicting unconfined compressive strength and chloride ion penetration of cement-stabilized clay exposed to sulfate attack. The results revealed that these methods have a great potential in modeling the strength and penetrability properties of cement-stabilized clays exposed to sulfate attack. While the performance of regression method is at an acceptable level, results show that adaptive neuro-fuzzy inference systems and backpropagation neural networks are superior in modeling. | |
dc.description.sponsorship | Ege Üniversitesi - 2014-BIL-009 | |
dc.identifier.doi | 10.1007/s00521-020-04972-x | |
dc.identifier.eissn | 1433-3058 | |
dc.identifier.endpage | 16722 | |
dc.identifier.issn | 0941-0643 | |
dc.identifier.issue | 21 | |
dc.identifier.startpage | 16707 | |
dc.identifier.uri | https://doi.org/10.1007/s00521-020-04972-x | |
dc.identifier.uri | https://link.springer.com/article/10.1007/s00521-020-04972-x | |
dc.identifier.uri | https://hdl.handle.net/11452/42895 | |
dc.identifier.volume | 32 | |
dc.identifier.wos | 000530789300002 | |
dc.indexed.wos | WOS.SCI | |
dc.language.iso | en | |
dc.publisher | Springer | |
dc.relation.journal | Neural Computing & Applications | |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | |
dc.relation.tubitak | 113M202 | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject | Strength development | |
dc.subject | Behavior | |
dc.subject | Density | |
dc.subject | Cement-stabilized soil | |
dc.subject | Strength | |
dc.subject | Penetrability | |
dc.subject | Bpnn | |
dc.subject | Anfis | |
dc.subject | Soft computing | |
dc.subject | Science & technology | |
dc.subject | Technology | |
dc.subject | Computer science, artificial intelligence | |
dc.title | Prediction of mechanical and penetrability properties of cement-stabilized clay exposed to sulfate attack by use of soft computing methods | |
dc.type | Article | |
dspace.entity.type | Publication |