Fabio Pioldi

Earthquake structural modal estimates of multi-storey frames by a refined Frequency Domain Decomposition algorithm

This paper targets the frequency domain identification of current structural modal properties under earthquake excitation. A new refined Frequency Domain Decomposition (rFDD) algorithm is implemented towards the output-only modal dynamic identification of heavy-damped frame structures, which are subjected to a wide set of strong ground motions. In fact, both seismic excitation and/or high damping values shall not fulfil traditional FDD assumptions. Despite that, with the present rFDD implementation quite limited errors in the modal parameter estimates have been achieved, including for the modal damping ratios (ranging from 1% to 10%). At first, the identification technique is formulated and explored analytically, by proving its potential effectiveness with seismic response input. Then, all strong motion modal parameters are consistently identified. As a fundamental necessary condition, synthetic response signals are adopted. These have been generated prior to dynamic identification from computed numerical seismic responses of a set of shear-type frames. The efficiency of the present original implementation is highlighted, by proving that consistent rFDD modal dynamic identification of structures at seismic input and simultaneous heavy damping looks feasible. Thus, the paper delivers a robust method for inspecting current structural modal properties of frame buildings under earthquake excitation and for observing their possible variation along experienced seismic histories.

A refined Frequency Domain Decomposition tool for structural modal monitoring in earthquake engineering

Output-only structural identification is developed by a refined Frequency Domain Decomposition (rFDD) approach, towards assessing current modal properties of heavy-damped buildings (in terms of identification challenge), under strong ground motions. Structural responses from earthquake excitations are taken as input signals for the identification algorithm. A new dedicated computational procedure, based on coupled Chebyshev Type II bandpass filters, is outlined for the effective estimation of natural frequencies, mode shapes and modal damping ratios. The identification technique is also coupled with a Gabor Wavelet Transform, resulting in an effective and self-contained time-frequency analysis framework. Simulated response signals generated by shear-type frames (with variable structural features) are used as a necessary validation condition. In this context use is made of a complete set of seismic records taken from the FEMA P695 database, i.e. all 44 “Far-Field” (22 NS, 22 WE) earthquake signals. The modal estimates are statistically compared to their target values, proving the accuracy of the developed algorithm in providing prompt and accurate estimates of all current strong ground motion modal parameters. At this stage, such analysis tool may be employed for convenient application in the realm of Earthquake Engineering, towards potential Structural Health Monitoring and damage detection purposes.

Fusion of wireless and non-contact technologies for the dynamic testing of a historic RC bridge

In this paper, a dynamic testing and corresponding signal processing methodology is presented for condition assessment of bridge structures, via use of a diverse and potentially dense grid of low-cost and easily deployable monitoring technologies. In particular, wireless and non-contact sensors are simultaneously deployed on a historic reinforced concrete bridge in order to record acceleration and dynamic displacement response, under operational loading conditions. An innovative monitoring approach is proposed on both the hardware (sensors) and software (algorithmic) front, in which an effective data fusion procedure is adopted for fusing these alternative technologies for vibration-based monitoring in terms of both acceleration and displacement information. The demonstrated efficacy of the fusion procedure on the case-study of an actual operating system, the historic Brivio bridge, reveals the potential of this approach within the context of structural monitoring, where acquisition of heterogeneous information certainly proves advantageous.

Heterogeneous sensor fusion for reducing uncertainty in Structural Health Monitoring

This work attempts a comprehensive processing of response signals acquired from an experimental campaign on a local historical bridge using four different types of sensory systems. A general analysis setup is developed, allowing for consistent modal dynamic identification via individual signal processing as well as via data fusion through dedicated Multi-Rate Kalman filtering approach. The investigation reveals the potential and limitations in terms of utilization of novel instrumentation systems to be adopted for Structural Health Monitoring (SHM) purposes.