Rapid post-earthquake damage assessment forms a critical element of resilience, ensuring a prompt and functional recovery of the built environment. Monitoring-based approaches have the potential to significantly improve upon current visual inspection-based condition assessment that is slow and potentially subjective. The large variety of sensing solutions that has become available at affordable cost in recent years allows the engineering community to envision permanent-monitoring applications even in conventional low-to-mid-rise buildings. When combined with adequate structural health monitoring (SHM) techniques, sensor data recorded during earthquakes have the potential to provide automated near-real-time identification of earthquake damage. Near-real time building assessment relies on the tracking of damage-sensitive features (DSFs) that can be directly and rapidly derived from dynamic monitoring data and scaled with damage. We here offer a comprehensive review of such damage-sensitive features in an effort to formally assess the capacity of such data-driven indicators to detect, localize and quantify the presence of nonlinearity in seismic-induced structural response. We employ both a parametric analysis on a simulated model and real data from shake-table tests to investigate the strengths and limitations of purely data-driven approaches, which typically involve a comparison against a healthy reference state. We present an array of damage-sensitive features which are found to be robust with respect to noise, to reliably detect and scale with nonlinearity, and to carry potential to localize the occurrence of nonlinear behavior in conventional structures undergoing earthquakes.

After a damaging earthquake, assessment of the residual seismic capacity is required for large parts of the building stock. Increased vulnerability of structures together with the threat of immediate aftershocks call for rapid and objective decision making. Structural identification has the potential to reduce parameter-value uncertainties of physics-based models through interpreting measurement data. Significant amounts of uncertainty are associated with the non-linear behaviour of structures during extreme events such as earthquakes. Therefore, a structural identification methodology that accommodates multiple sources of systematic modelling uncertainties is used. Error-domain model falsification (EDMF) enables structural identification through combining damage grades observed by visual inspection with fundamental frequencies that are derived from ambient vibrations. Parametric uncertainties of a hysteretic model are reduced with the two information sources in order to extrapolate the vulnerability of the building regarding future earthquakes. The applicability of the methodology is shown using measurements made on a mixed reinforced-concrete unreinforced-masonry building tested on a shaking table. Based on nonlinear time-history analyses involving single-degree-of-freedom models, EDMF leads to more precise, yet robust, vulnerability predictions of earthquake-damaged buildings when compared with prediction ranges that are obtained without data interpretation.

Sensor-based occupant tracking has the potential to enhance knowledge of the utilization of buildings. Occupancy-tracking strategies using footstep-induced floor vibrations may be beneficial for thermal-load prediction, security enhancement, and care-giving without undermining privacy. Current floor-vibration-based occupant-tracking methodologies are based on data-driven techniques that do not include a physics-based model of the structural behavior of the floor slab. These techniques suffer from ambiguous interpretations when signals are affected by complex configurations of structural and non-structural elements such as beams and walls. Using a physics-based model for data-interpretation enables deployment of sparse number of sensors in contexts of non-uniform structural configurations. In this paper, an application of physics-based data interpretation using error-domain model falsification (EDMF) is presented to track an occupant within an office environment through footstep-induced floor vibrations. EDMF is a population-based approach that incorporates various sources of uncertainty, including bias, arising from measurements and modeling. EDMF involves the rejection of simulated model responses that contradict footstep-induced floor vibration measurements. Thus, EDMF provides a set of candidate locations from an initial population of possible occupant locations. A sequential analysis that accommodates information from previous footsteps is then used to enhance candidate locations and identify trajectories among candidates. In this way, incorporating structural behavior in interpreting vibration measurements induced by occupant footsteps has the potential to identify accurately the trajectory of an occupant in buildings with complex configurations, thereby providing tracking information without undermining privacy.

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.