Time-Series Uncertainty Quantification of Foundation Settlement with Kernel Based Extreme Learning Machine

The dynamic building foundation settlement subsidence are threatening the urban business and residential communities. In the temporal domain, the building foundation settlement often suffers from high level dynamics and needs real-time monitoring. Accurate quantification of the uncertainty of foundation settlement in the near future is essential for the in-advance risk management for buildings. Traditional models for predicting foundation settlement mostly utilizing the point estimates approach which provide a single value that can be close or distant from the actual one. However, such estimation fails to offer the quantification of uncertainties of estimation. The interval prediction, as an alternative, can provide a prediction interval for the ground settlement with high confidence bands. In this paper, a lower upper bound estimation (LUBE) approach integrated with kernel based extreme learning machine (KELM) is proposed to predict the ground settlement levels with prediction intervals in the temporal domain. Comparison with the artificial neural network (ANN) and classical extreme learning machine (ELM) are conducted in this study. Building settlement data collected from Fuxing City, Liaoning Province in China has been used to validate the proposed approach. Comparative results show that the proposed approach can construct higher quality prediction intervals for the future foundation settlement.