Basic Study on Prediction of Differential Settlement by DMDc and Kalman Filter

Abstract

Improvements in measurement technology have enabled the acquisition of large-scale data, leading to the growing interest in the concept of Digital Twin. In Digital Twin, data science plays a crucial role, and Dynamic Mode Decomposition with Control (DMDc), a data-driven approach, has gained attention in recent years. The authors have previously explored the applicability of DMDc in predicting future settlement in a reclamation area. In this study, the method is improved by applying DMDc with a formulation of the Robbins-Monro algorithm and Kalman Filter to predict settlement. This paper demonstrates the potential improvement in prediction accuracy in the prediction period when observational data is available for some monitoring points after the learning phase. Additionally, it provides an example of quantifying the uncertainty of predicted settlements, which was not achievable with DMDc alone.