@article{WavesSc,

title = {Automated operational modal analysis and ambient noise deconvolution interferometry for the full structural identification of historic towers: A case study of the Sciri Tower in Perugia, Italy},

author = {Enrique García Macías and Filippo Ubertini},

url = {https://drive.google.com/open?id=1kwKCCyetmXuk8AkAQ-2hDf76hFgmd7-j, Download},

year  = {2020},

date = {2020-04-17},

journal = {Engineering Structures},

abstract = {Structural Health Monitoring (SHM) based upon Operational Modal Analysis (OMA) constitutes an increasingly mature and widespread technology in the conservation of historic structures. Such techniques, while proved effective for assessing the global integrity of structures, may fail at detecting local damage with little influence on the modal features of the system. In this context, the analysis of propagating waves throughout the structure features a synergistic approach to OMA with superior capabilities for data-driven damage identification. Although some promising results have been reported in the literature on the application of Seismic Interferometry to reinforced-concrete structures, works concerning the continuous monitoring of ambient vibrations in historic structures are virtually non-existent. In this light, this paper proposes the coupled application of automated OMA and Ambient Noise Deconvolution Interferometry for the full structural system identification of historic structures, and evaluates the advantages of this technology through a validation case study of the Sciri Tower in Perugia, Italy. A continuous vibration-based monitoring system deployed in the tower during three weeks allows us to assess the effectiveness of the proposed approach. The reported results demonstrate the robustness of the monitoring system for identifying environmental effects on the spatial distribution of wave velocities, and shed light into the dispersion relation of the tower.},

keywords = {Ambient vibration testing, Automated Operational Modal Analysis, Seismic Interferometry, Structural Health Monitoring, Wave propagation},

pubstate = {published},

tppubtype = {article}

}

@article{MOVA_MOSS,

title = {MOVA/MOSS: Two integrated software solutions for comprehensive Structural Health Monitoring of structures},

author = {Enrique García-Macías and Filippo Ubertini},

url = {https://drive.google.com/open?id=19Kk3USGUHivrpGj_Eso8HLiULqrBpJCf, Download},

doi = {https://doi.org/10.1016/j.ymssp.2020.106830},

year  = {2020},

date = {2020-04-03},

journal = {Mechanical Systems and Signal Processing},

volume = {143},

pages = {106830},

abstract = {Recent ground-breaking advances in sensing technologies, data processing, and structural identification have made Structural Health Monitoring (SHM) occupy a central place in Structural Engineering. Although the technological transfer to the industry is still in the early development stages, there is clear evidence that SHM-enabled condition-based maintenance of structures will soon supersede traditional periodic maintenance strategies. Among the existing solutions, ambient vibration-based SHM has become particularly popular owing to its minimum intrusiveness and global damage assessment capabilities. Nevertheless, it is well documented that local pathologies with limited impact over the stiffness of structures can be hardly detected by such techniques. As a solution, recent studies advocate the use of integrated monitoring systems, where data from heterogeneous sensor networks are simultaneously processed to achieve a comprehensive structural assessment. Despite the great advances of these systems reported by researchers, practitioners still find many difficulties to bring them to practice. In this light, this paper reports the development of two novel software solutions for long-term SHM of structures, MOVA and MOSS, that are intended to bridge this gap while also introducing new methodological and scientific advances. The developed software enables the online system identification and damage detection of structures, including vibration-based SHM and data fusion of heterogeneous sensing systems with an innovative automated anomaly detection algorithm. A case study of a permanent static/dynamic/environmental monitoring system installed in a monumental masonry palace, the Consoli Palace in Gubbio (Italy), is presented to illustrate the capabilities of MOVA/MOSS.},

keywords = {Damage detection, Data fusion, Novelty analysis, Operational modal analysis, Structural Health Monitoring, Unsupervised learning},

pubstate = {published},

tppubtype = {article}

}