Identification of reinforced concrete beam parameters using inverse modeling technique and measured dynamic responses for structure health monitoring

Abstract

Reinforced and prestressed concrete beams are widely employed in civil engineering structures, e.g. in simply supported spans using prestressed concrete beams (VIPP). To reduce the financial cost due to maintenance, structure damages have to be detected early. To achieve this purpose, one needs robust monitoring techniques. The paper deals with the determination of mechanical parameters, useful for Structure Health Monitoring, in a 2D beam using inverse modeling technique. The optimal control theory is employed. As an example, we aim to identify a reduction of the steel bar cross-section and a decrease of the concrete Young modulus in damaged area. In our strategy, the beam is instrumented with strain sensors, and a known dynamic load is applied. In the inverse technique, two space discretizations are considered: a fine dicretization (h) to solve the structural dynamic problem and a coarse discretization (H) for the beam parameter identification. To get the beam parameters, we minimize a classical data misfit functional using a gradient-like algorithm. A low-cost computation of the functional gradient is performed using the adjoint problem solution. The inverse problem is solved in a general way using engineer numerical tools: Python scripts and the free finite element software Code Aster. First results show that a local reduction of the steel bar cross-sections and a local decrease of concrete Young modulus can be detected using this inverse technique.