SAP.PY: AUTOMATED MODEL UPDATING USING SAP2000 OAPI AND PYTHON FOR STRUCTURAL HEALTH MONITORING

The paper presents an automated model updating strategy, named SAP.Py, which exploits the modelling potential of commercial software, enhanced by automatic tuning of variables featuring the numerical model. The structural analysis software SAP2000 was used, which presents the possibility to be modified through the OpenAPI. Using this advantage, the proposed procedure aims to implement an optimization tool allowing automated structural analysis and model modification according to the availability of real data. The first step of the proposed pipeline process consists in the traditional dynamic identification (i.e., frequency domain decomposition and singular value decomposition), which is performed to identify modal parameters that represent the terms of comparison for purpose of model updating. After, the structure is modelled in detail in SAP2000 environment, where the uncertain parameters to automatically tune are: (i) the mechanical properties of the structural materials; (ii) the boundary conditions and the constraint degrees applied on the numerical model. Last, the automated procedure performs the optimization task, in which the dynamic outputs of each automated analysis are compared with the real data, by minimising the distance between the latter and the simulated ones. The optimization procedure is enabled by an external Python script. The proposed methodology was tested on a real building in the port area of the city of Bari, (Southern Italy) for which a monitoring campaign is available from the beginning of 2021, by exploiting biaxial and triaxial accelerometers. The results of the investigation are presented, showing the automated procedure and the phase of model updating to achieve the best matching with the real data. In the end, some insights about this methodology are provided, considering the possibility to use this new tool as a support to existing techniques, by enhancing the central role of monitoring for the continuous health condition knowledge of structures and facility management.