PREDICTION OF CONCRETE STRENGTH WITH DATA MINING METHODS USING ARTIFICIAL BEE COLONY AS FEATURE SELECTOR
| dc.contributor.author | Kaya Keles, Mumine | |
| dc.contributor.author | Keles, Abdullah Emre | |
| dc.contributor.author | Kılıç, Umit | |
| dc.date.accessioned | 2025-01-06T17:36:26Z | |
| dc.date.available | 2025-01-06T17:36:26Z | |
| dc.date.issued | 2018 | |
| dc.description | International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEY | |
| dc.description.abstract | Concrete which is a highly complex material is the most basic input of the construction industry. Because of its strength, concrete is one of the most preferred structural building materials. In the ready-mixed concrete sector, there is an increasing need for earthquake resistant structures due to the fact that some producers produce out of control and poor quality. Ready-mixed concrete is a product whose quality can only be understood at the end of the 28th day if it is only controlled by taking the sample by the user. In this study, a data mining study was conducted on the factors affecting the 28-day compressive strength of concrete using the Concrete Slump Test Data Set from UCI Machine Learning Repository. The Artificial Bee Colony Algorithm is used as a feature selection method in order to determine the important ones of the concrete components, which are cement, slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate, affecting concrete strength and tried to predict the strength with data mining algorithms. As a result of the study, it was observed that Random Forest Algorithm gave the highest success rate with 91.2621% accuracy using only 3 features, which are cement, fly ash, and water. This means that it is possible to predict the compressive strength of concrete with a ratio above 90% by using a smaller number of concrete components. | |
| dc.description.sponsorship | Inonu Univ, Comp Sci Dept,IEEE Turkey Sect,Anatolian Sci | |
| dc.description.sponsorship | Scientific Research Projects Commission Unit of Adana Science and Technology University [18332001, 18103004] | |
| dc.description.sponsorship | This study was supported by Scientific Research Projects Commission Unit of Adana Science and Technology University under Grant Number: 18332001 and Grant Number: 18103004. Please send all your correspondence to [email protected] which is the e-mail address of our Corresponding Author. | |
| dc.identifier.isbn | 978-1-5386-6878-8 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14669/1881 | |
| dc.identifier.wos | WOS:000458717400182 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.language.iso | en | |
| dc.publisher | IEEE | |
| dc.relation.ispartof | 2018 International Conference on Artificial Intelligence and Data Processing (Idap) | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_20241211 | |
| dc.subject | Artificial Bee Colony | |
| dc.subject | Data Mining | |
| dc.subject | Feature Selection | |
| dc.subject | Prediction of concrete strength | |
| dc.subject | 28-Day compressive strength | |
| dc.title | PREDICTION OF CONCRETE STRENGTH WITH DATA MINING METHODS USING ARTIFICIAL BEE COLONY AS FEATURE SELECTOR | |
| dc.type | Conference Object |
