The state of the quay walls in Amsterdam has deteriorated. In the past years, they have been monitored using tacheometry. The aim of this study is to forecast the time-series of the tacheometry data based on CPT and InSAR data. This was done by developing different configurations of BiLSTM models. The configurations conformed to three types of model fusion: early fusion, incremental fusion, and late fusion. These were compared to four baselines: a naive method and models taking input from each dataset separately. The models were compared based on their RMSE, MAE, and MASE scores. Incremental fusion yielded the significantly best results both on itself and in combination with late fusion. Early fusion did not benefit the performance. This is in line with previous studies using incremental and late model fusion, and contrary to a study using early fusion to predict deformation.
This research was conducted by Julian El-Fasih in collaboration with the Bridges and Quay Walls Program (Programma Bruggen en Kademuren) and the AI Team, Urban Innovation and R&D, City of Amsterdam.
Involved civil servants: Pantelis Karamitopoulos
Supervisors: Frank Nack & Pantelis Karamitopoulos
Icon image: Groene kademuur