Data-Interpretation Methodologies for Non-Linear Earthquake Response Predictions of Damaged Structures

Frontiers in Built Environment

Smith, Ian F. C.¹  Reuland, Yves¹  Lestuzzi, Pierino¹ 

[1] Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland

关键词: Nonlinear Data Interpretation;    Systematic model error;    Robust Model Extrapolation;    Prediction uncertainty;    Error-domain model falsification;    Bayesian model updating;    Aftershock Predictions.;   

DOI  :  10.3389/fbuil.2017.00043

学科分类：建筑学

来源: Frontiers

【 摘 要 】

Seismic exposure of buildings presents difficult engineering challenges. The principles of seismic design involve structures that sustain damage and still protect inhabitants. Precise and accurate knowledge of the residual capacity of damaged structures is essential for informed decision-making regarding clearance for occupancy after major seismic events. Unless structures are permanently monitored, modal properties derived from ambient vibrations are most likely the only source of measurement data that is available. However, such measurement data is linearly elastic and limited to a low number of vibration modes. Structural identification using hysteretic behavior models that exclusively relies on linear measurement data is a complex inverse engineering task that is further complicated by modeling uncertainty. Three structural identification methodologies that involve probabilistic approaches to data interpretation are compared: error-domain model falsification, Bayesian model updating with traditional assumptions as well as modified Bayesian model updating. While noting the assumptions regarding uncertainty definitions, the accuracy and robustness of identification and subsequent predictions are compared. A case study demonstrates limits on non-linear parameter identification performance and identification of potentially wrong prediction ranges for inappropriate model uncertainty distributions.

【 授权许可】

CC BY