dc.contributor.author | Erdal, Halil Ibrahim | |
dc.contributor.author | Namli, Ersin | |
dc.contributor.author | Karakurt, Onur | |
dc.date.accessioned | 2021-03-06T09:35:51Z | |
dc.date.available | 2021-03-06T09:35:51Z | |
dc.date.issued | 2013 | |
dc.identifier.citation | Erdal H. I. , Karakurt O., Namli E., "High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform", ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.26, ss.1246-1254, 2013 | |
dc.identifier.issn | 0952-1976 | |
dc.identifier.other | av_e64d501a-f042-4638-bd2e-d712a929ec41 | |
dc.identifier.other | vv_1032021 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12627/151501 | |
dc.identifier.uri | https://doi.org/10.1016/j.engappai.2012.10.014 | |
dc.description.abstract | This paper investigates the use of wavelet ensemble models for high performance concrete (HPC) compressive strength forecasting. More specifically, we incorporate bagging and gradient boosting methods in building artificial neural networks (ANN) ensembles (bagged artificial neural networks (BANN) and gradient boosted artificial neural networks (GBANN)), first. Coefficient of determination (R-2), mean absolute error (MAE) and the root mean squared error (RMSE) statics are used for performance evaluation of proposed predictive models. Empirical results show that ensemble models (R-BANN(2)=0.9278, R-GBANN(2)=0.9270) are superior to a conventional ANN model (R-ANN(2)=0.9088). Then, we use the coupling of discrete wavelet transform (DWT) and ANN ensembles for enhancing the prediction accuracy. The study concludes that DWT is an effective tool for increasing the accuracy of the ANN ensembles (R-WBANN(2)=0.9397. R-WGBANN(2)=0.9528). (C) 2012 Elsevier Ltd. All rights reserved. | |
dc.language.iso | eng | |
dc.subject | Bilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği | |
dc.subject | Kontrol ve Sistem Mühendisliği | |
dc.subject | Sinyal İşleme | |
dc.subject | Bilgisayar Bilimleri | |
dc.subject | Algoritmalar | |
dc.subject | Mühendislik ve Teknoloji | |
dc.subject | MÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK | |
dc.subject | Harita Mühendisliği-Geomatik | |
dc.subject | MÜHENDİSLİK, MULTİDİSİPLİNER | |
dc.subject | Bilgisayar Bilimi | |
dc.subject | BİLGİSAYAR BİLİMİ, YAPAY ZEKA | |
dc.subject | Mühendislik, Bilişim ve Teknoloji (ENG) | |
dc.subject | Mühendislik | |
dc.subject | OTOMASYON & KONTROL SİSTEMLERİ | |
dc.title | High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform | |
dc.type | Makale | |
dc.relation.journal | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | |
dc.contributor.department | Prime Ministry Turkey , , | |
dc.identifier.volume | 26 | |
dc.identifier.issue | 4 | |
dc.identifier.startpage | 1246 | |
dc.identifier.endpage | 1254 | |
dc.contributor.firstauthorID | 77288 | |