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

Yves Reuland,Ian F C Smith,Pierino Lestuzzi

doi:10.3389/fbuil.2017.00043

Abstract

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.

Highlights

Earthquakes still pose a major threat to the integrity of existing buildings
Prediction ranges can be reduced by 30% to 90% using EDMF
Prediction ranges can be reduced by 30% (for base moments) to 90% (for top displacements) using EDMF

Summary

Introduction

Significant progress has been made on earthquake-resistant design methodologies, large parts of the building stock continue to sustain damage from earthquake actions. Structural damage to buildings after an earthquake is inevitable, especially in the context of design specifications being generally limited to the protection of building occupants rather than guaranteeing structural integrity in regions with low to medium earthquake hazard (Priestley, 2000). Regions with low to medium seismicity are often characterized by large amounts of buildings that have been designed without consideration of the seismic limit state. Error-domain model falsification, traditional Bayesian model updating (TBMU), and modified Bayesian model updating (MBMU) are compared. These three methodologies result in populations of solution through taking uncertainties into account. The applicability of model falsification provides robust solutions to inverse engineering problems that are complicated by significant amounts of measurement and model uncertainty (Tarantola, 2006; Fernández-Martínez et al, 2013), in the presence of model bias (Pasquier and Smith, 2015a)

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Conclusion