Abstract Summary
Structural Health Monitoring of civil engineering structures is experiencing an increasing progress in the last decades. The present work focuses mainly on static behavior of a highway bridge due to environmental temperature effects or ground settlements. The first goal is to compare the results of the finite element simulations to the classical geodesy surveying measurements for deformation monitoring of a large, curved highway bridge. The second goal is to test the applicability of artificial intelligence methods to predict such deformations online without comprehensive computer simulations if a certain amount of measurement data is available for training purposes. This study is based on the work within the LEVANGO project funded by the German Federal Ministry of Research and Education in cooperation with the Airbus Defence and Space and the AllTerra Deutschland companies. The Wehretal bridge as a part the federal highway A44 was completed in 2019 and should be opened in 2022. This highway bridge is a prestressed concrete structure with a total length of approximately 670 m and a monolithic deck laying on a series of column pairs. A specific feature of the bridge is a relatively large curvature of the longitudinal axis with a constant radius of about 480 m and special supports with sliding bearings that enable a longitudinal and transversal displacement of the deck with a certain friction. A safe operation of the bridge requires a trouble-free behavior of sliding bearings under thermal deformations. The obtained results show a good correlation between simulation and measurement results as well as a good potential applicability of artificial neural networks to predict bridge deformation even in presence of several monitoring difficulties and data limitations. The advantages and problems of the applied approaches will be discussed in detail.
