Prediction of Rebound Amount in Dry Mix Shotcrete by a Fast Adaboosting Neural Network

Abstract

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.

Keywords

• adaboosting
• dry-mix shotcrete
• ensemble learning
• neural network
• rebound