Tehnički vjesnik, Vol. 28 No. 2, 2021.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20200422014902
Prediction of Rebound Amount in Dry Mix Shotcrete by a Fast Adaboosting Neural Network
Mert Alkan
; Burdur Mehmet Akif Ersoy University, Faculty of Engineering and Architecture, 15550 Burdur, Turkey
Hüseyin Hakan Ince
; Burdur Mehmet Akif Ersoy University, Faculty of Engineering and Architecture, 15550 Burdur, Turkey
Melda Alkan Çakiroğlu
; Department of Civil Engineering, Isparta University of Applied Sciences, 32200 Isparta, Turkey
Ahmet Ali Süzen*
orcid.org/0000-0002-5871-1652
; Department of Information Security Technology, Isparta University of Applied Sciences, 32050 Isparta, Turkey
Sažetak
In this study, a new machine learning approach has been proposed to predict the rebound causing loss of material in shotcrete using the ensemble learning method. In shotcrete application, the amount of rebound material was obtained for use in a dataset. In this study, the shotcrete mixes that contain an additive of fly-ash, silica fume, and polypropylene fiber were produced besides simple shotcrete. Each mix was sprayed onto 2 wooden panels measuring 45 × 45 × 15 cm in size. The rebound material resulting from the spraying process was collected, weighed and recorded as data. The highest rebound was observed for the plain sample and the lowest for samples with substituted silica fume. Dependent and independent parameters were identified in the dataset produced as a result of experimental studies. Hyperparameters producing optimum results in the training of the model were identified for the model and boosting method. The dataset was split into training and testing sets by 80% and 20%, respectively. As a result, the model achieved a prediction performance of 84.25%. To test the performance of the proposed model, traditional machine learning algorithms were compared on the same dataset. Consequently, the proposed model was observed to have the highest accuracy.
Ključne riječi
adaboosting; dry-mix shotcrete; ensemble learning; neural network; rebound
Hrčak ID:
255808
URI
Datum izdavanja:
17.4.2021.
Posjeta: 1.139 *