Prediction of the performance of impact hammer by adaptive neuro-fuzzy inference system modelling

Impact type excavators are widely used for excavations, performed in weak-laminated-foliated-anisotropic rocks. Therefore the prediction of the performance of impact hammer is very important in many mining and civil engineering projects.This paper describes the construction of adaptive neuro-fuzzy inference system model for predicting the performance of impact hammer type excavator by considering rock and excavating machine properties such as block punch strength index, geological strength index system and impact hammer power. Extensive field and laboratory studies were conducted in the tunnel construction route of the second stage of Izmir Metro Project, which excavated in laminated-foliated flysch rocks. The results of the constructed adaptive neuro-fuzzy inference system and traditional multiple regression models were compared. Although the prediction performance of traditional multiple regression model is high, it is seen that adaptive neuro-fuzzy inference model exhibits better prediction performance according to statistical performance indicators. By means of the developed model, the performance of impact type excavators can be predicted in terms of net excavation based on the selected rock and machine properties.     

Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers

In this research, a new wavelet artificial neural network (WANN) model was proposed for daily suspended sediment load (SSL) prediction in rivers. In the developed model, wavelet analysis was linked to an artificial neural network (ANN). For this purpose, daily observed time series of river discharge (Q) and SSL in Yadkin River at Yadkin College, NC station in the USA were decomposed to some sub-time series at different levels by wavelet analysis. Then, these sub-time series were imposed to the ANN technique for SSL time series modeling. To evaluate the model accuracy, the proposed model was compared with ANN, multi linear regression (MLR), and conventional sediment rating curve (SRC) models. The comparison of prediction accuracy of the models illustrated that the WANN was the most accurate model in SSL prediction. Results presented that the WANN model could satisfactorily simulate hysteresis phenomenon, acceptably estimate cumulative SSL, and reasonably predict high SSL values.