Time Series Prediction of Chimney Foundation Settlement by Neural Networks

Abstract

Neural network (NN) models for time series forecasting were initially used in economic fields. In this paper, NN models for time series forecasting are introduced for use in forecasting the settlement of chimney foundations. The data sets used in the NN models were measured in the field. Seven models with different input series are developed to determine the optimal structure of the network. In evaluating the network performance, the network model that uses the previous nine months’ settlement values as input is selected as the optimal model. The analysis results demonstrate that the settlement values predicted by the optimal model are in good agreement with the field measurements. In addition, as the number of data points in the input series increases, the NN performance clearly improves, and this improvement stops after the input series has increased to a certain extent. This demonstrates that the time-series-based NN model can also be successfully applied to predict foundation settlement.