Szerző dc.contributor.author | Hemn Unis Ahmed | hu_HU |
Szerző dc.contributor.author | Aso A. Abdalla | hu_HU |
Szerző dc.contributor.author | Ahmed S. Mohammed | hu_HU |
Szerző dc.contributor.author | Azad A. Mohammed | hu_HU |
Szerző dc.contributor.author | Mosavi Amir | hu_HU |
Elérhetőség dátuma dc.date.accessioned | 2022-04-11T10:31:17Z | |
Rendelkezésre állás dátuma dc.date.available | 2022-04-11T10:31:17Z | |
Kiadás dc.date.issued | 2022 | hu_HU |
Issn dc.identifier.issn | 1996-1944 | hu_HU |
Uri dc.identifier.uri | http://hdl.handle.net/20.500.12944/17571 | |
Kivonat dc.description.abstract | In recent years, geopolymer has been developed as an alternative to Portland cement (PC) because of the significant carbon dioxide emissions produced by the cement manufacturing industry. A wide range of source binder materials has been used to prepare geopolymers; however, fly ash (FA) is the most used binder material for creating geopolymer concrete due to its low cost, wide availability, and increased potential for geopolymer preparation. In this paper, 247 experimental datasets were obtained from the literature to develop multiscale models to predict fly-ash-based geopolymer mortar compressive strength (CS). In the modeling process, thirteen different input model parameters were considered to estimate the CS of fly-ash-based geopolymer mortar. The collected data contained various mix proportions and different curing ages (1 to 28 days), as well as different curing temperatures. The CS of all types of cementitious composites, including geopolymer mortars, is one of the most important properties; thus, developing a credible model for forecasting CS has become a priority. Therefore, in this study, three different models, namely, linear regression (LR), multinominal logistic regression (MLR), and nonlinear regression (NLR) were developed to predict the CS of geopolymer mortar. The proposed models were then evaluated using different statistical assessments, including the coefficient of determination (R2), root mean squared error (RMSE), scatter index (SI), objective function value (OBJ), and mean absolute error (MAE). It was found that the NLR model performed better than the LR and MLR models. For the NLR model, R2, RMSE, SI, and OBJ were 0.933, 4.294 MPa, 0.138, 4.209, respectively. The SI value of NLR was 44 and 41% lower than the LR and MLR models’ SI values, respectively. From the sensitivity analysis result, the most effective parameters for predicting CS of geopolymer mortar were the SiO2 percentage of the FA and the alkaline liquid-to-binder ratio of the mixture. | hu_HU |
Nyelv dc.language | en | hu_HU |
Kiadó dc.publisher | MDPI | hu_HU |
Kulcsszó dc.subject | Alkaline activator | hu_HU |
Kulcsszó dc.subject | Compressive strength | hu_HU |
Kulcsszó dc.subject | Construction materials | hu_HU |
Kulcsszó dc.subject | Fly ash | hu_HU |
Kulcsszó dc.subject | Geopolymer | hu_HU |
Kulcsszó dc.subject | Geopolymer concrete | hu_HU |
Kulcsszó dc.subject | Machine learning | hu_HU |
Kulcsszó dc.subject | Mortar | hu_HU |
Kulcsszó dc.subject | Prediction | hu_HU |
Kulcsszó dc.subject | Regression | hu_HU |
Cím dc.title | Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar | hu_HU |
Típus dc.type | folyóiratcikk | hu_HU |
Változat dc.description.version | published | hu_HU |
Hozzáférés dc.rights.accessRights | nyílt hozzáférésű | hu_HU |
Doi azonosító dc.identifier.doi | 10.3390/ma15051868 | hu_HU |
Tudományág dc.subject.discipline | Műszaki tudományok | hu_HU |
Tudományterület dc.subject.sciencebranch | Műszaki tudományok/Anyagtudományok és technológiák | hu_HU |
Mtmt azonosító dc.identifier.mtmt | 32716535 | hu_HU |
Folyóirat dc.identifier.journalTitle | Materials | hu_HU |
Évfolyam dc.identifier.journalVolume | 15 | hu_HU |
Füzetszám dc.identifier.journalIssueNumber | 5 | hu_HU |
Wos azonosító dc.identifier.wos | 000767776700001 | hu_HU |
Scopus azonosító dc.identifier.scopus | 2-s2.0-85126323405 | hu_HU |
Szerző intézménye dc.contributor.department | Információs Társadalom Kutatóintézet | hu_HU |
Kar dc.contributor.faculty | Eötvös József Kutatóközpont | hu_HU |