The advent of parallel computing capabilities, further boosted through the exploitation of graphics processing units, has resulted in the surge of new, previously infeasible, algorithmic schemes for structural health monitoring (SHM) tasks, such as the use of convolutional neural networks (CNNs) for vision-based SHM. This work proposes a novel approach for crack recognition in digital images based on coupling of CNNs and suited image processing techniques. The proposed method is applied on a dataset comprising images of the welding joints of a long-span steel bridge, collected via high-resolution consumer-grade digital cameras. The studied dataset includes photos taken in sub-optimal light and exposure conditions, with several noise contamination sources such as handwriting scripts, varying material textures, and, in some cases, under presence of external objects. The reference pixels representing the cracks, together with the crack width and length, are available and used for training and validating the proposed model. Although the proposed framework requires some knowledge of the "damaged areas", it alleviates the need for precise labeling of the cracks in the training dataset. Validation of the model by means of application on an unlabeled image set reveals promising results in terms of accuracy and robustness to noise sources.
Small-strain foundation response has mostly been studied analytically, with limited experimental verification against 1g physical model tests. This paper revisits the problem of small-strain foundation response, conducting a series of centrifuge model tests, aiming to eliminate the limitations of 1g testing. A centrifuge modelling technique is developed, combining static pushover and dynamic impulse testing for similar systems. To allow for derivation of meaningful insights, a novel procedure for in-flight measurement of the distribution of shear modulus with depth is also developed. The latter combines spectral analysis of surface waves (SASW) measurement of the shear modulus G0 at the surface, and estimation of the distribution of the shear modulus G with depth using acceleration measurements in shaking tests. A novel centrifuge tube–actuator is developed and employed to discharge spherical projectiles against single-degree-of-freedom models lying on shallow foundations on sand. This allows generating dynamic impulse excitation, which is used to measure the small-strain dynamic rocking stiffness. The developed actuator is versatile, and was also used for in-flight SASW testing. The centrifuge model tests are shown to confirm the widely used and well-known formulas. This good agreement can also be seen as a confirmation of the validity of the developed experimental techniques.
A significant part of the existing building stock in regions of low to moderate seismic hazard has been designed without modern seismic considerations and is, in the meantime, exceeding its design life span. The assessment of seismic performance poses an engineering challenge, due to unknown material properties, undocumented structural interventions and the scarcity of event-based information. Operational modal analysis has been applied in some cases to verify model assumptions beyond visual inspection. However, masonry buildings exhibit amplitude-dependent stiffness even at very low response amplitudes, raising questions about the validity of such methods. Planned demolitions provide engineers with the opportunity to leverage higher-amplitude vibrations generated during demolition activities to better understand the dynamic behaviour of existing buildings. This paper introduces a Bayesian model-updating framework, which aims at reducing uncertainty in seismic analysis, by fusing dynamic measurements with best-practice structural models. The proposed hybrid framework is applied to nine real masonry buildings, representative of existing residential buildings, as typically encountered in Switzerland, that have been monitored during controlled demolition. A vast reduction in prediction uncertainty is achieved through data-driven model updating, additionally exposing intra- and inter-typological differences in terms of seismic capacity and ductility. In addition, differences between updated model predictions and typical engineering assumptions and generic typological curves are discussed. Overall, this contribution demonstrates, applies and discusses the practical benefits of a straightforward methodology for fusing monitoring data into the seismic evaluation of existing masonry structures.
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.
Masonry buildings form a significant part of the central-European building stock. Despite significant efforts to standardize the seismic evaluation of such buildings, uncertainties pertaining to material properties and modeling assumptions introduce significant ambiguity. Operational modal analysis tools have been exploited to infer global structural stiffness properties, under the assumption of linear elastic behavior. However, measurements on real structures demonstrate nonlinear structural responses in the range of small strains, typically attributed to material cracking or to the soil. This work reports analysis of dynamic measurements on three real buildings at various amplitude levels, due to vibrations that are arbitrarily induced by construction works preceding planned demolition. The results show transient frequency drops that are attributed to increasing excitation amplitude, while the response remains in the commonly assumed linear elastic regime. This amplitude dependency remains poorly investigated, as vibrational data of higher amplitude for real masonry buildings are scarce. The evaluation of the impact of amplitude dependency on the, commonly assumed, linear elastic stiffness properties bears notable impact both in terms of model updating, as well as in terms of data-driven damage detection after disastrous events.
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