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Structural health monitoring of civil infrastructures is a difficult task, often impeded by the geometrical size of the monitored systems. Recent advances in conducting polymers enabled the fabrication of flexible sensors capable of covering large areas, a possible solution to the monitoring challenge of mesoscale systems. The authors have previously proposed a novel sensor consisting of a soft elastomeric capacitor (SEC) acting as a strain gauge. Arranged in a network configuration, the SECs have the potential to cover very large surfaces. In this paper, understanding of the proposed sensor is furthered by evaluating its performance at vibration-based monitoring of large-scale structures. The dynamic behavior of the SEC is characterized by subjecting the sensor to a frequency sweep, and detecting vibration modes of a full-scale steel beam. Results show that the sensor can be used to detect fundamental modes and dynamic input. Also, a network of SECs is used for output-only modal identification of a full-scale concrete beam, and results are benchmarked against off-theshelf accelerometers. The SEC network performs well at estimating both natural frequencies and mode shapes. The resolution of the sensor is currently limited by the available electronics to measure small changes in capacitance, which reduces its accuracy with increasing frequencies in both the time and frequency domain. The authors have previously proposed a novel sensor consisting of a soft elastomeric capacitor (SEC) acting 8 as a strain gauge. Arranged in a network configuration, the SECs have the potential to cover very large 9 surfaces. In this paper, we further the understanding of the proposed sensor by evaluating its performance at 12show that the sensor can be used to detect fundamental modes and dynamic input. Also, a network of

The authors have recently developed two novel solutions for strain sensing using nanocomposite materials. While they both aim at providing cost-effective solutions for the monitoring of local information on largescale structures, the technologies are different in their applications and physical principles. One sensor is made of a cementitious material, which could make it suitable for embedding within the core of concrete structures prior to casting, and is a resistor, consisting of a carbon nanotube cement-based transducer. The other sensor can be used to create an external sensing skin and is a capacitor, consisting of a flexible conducting elastomer fabricated from a nanocomposite mix, and deployable in a network setup to cover large structural surfaces. In this paper, we advance the understanding of nanocomposite sensing technologies by investigating the potential of both novel sensors for the dynamic monitoring of civil structures. First, an indepth dynamic characterization of the sensors using a uniaxial test machine is conducted. Second, their performance at dynamic monitoring of a full-scale concrete beam is assessed, and compared against off-theshelf accelerometers. Experimental results show that both novel technologies compare well against mature sensors at vibration-based structural health monitoring, showing the promise of nanocomposite technologies for the monitoring of large-scale structural systems. The authors have recently developed two novel solutions for strain sensing using nanocomposite materials. While they both aim at providing cost-eective solutions at monitoring local information on large-scale structures, both technologies are dierent in their applications and physical principles. One sensor is made of a cementitious material, which could make it suitable for embedding within the core of concrete structures prior to casting, and is a resistor, consisting of a carbon nanotube-cement based transducer. The other sensor can be used to create an external sensing skin and is a capacitor, consisting of a exible conducting elastomer fabricated from a nanocomposite mix, and deployable in a network setup to cover large structural surfaces. In this paper, we advance the understanding of nanocomposite sensing technologies by investigating the potential of both novel sensors at dynamic monitoring of civil structures. First, an in-depth dynamic characterization of the sensors using a uniaxial test machine is conducted. Second, their performance at dynamic monitoring of a full-scale concrete beam is assessed, and compared against othe-shelf accelerometers. Experimental results show that both novel technologies compare well against mature sensors at vibration-based structural health monitoring, showing the promise of nanocomposite technologies at monitoring large-scale structural systems.

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Structural health monitoring allows the automated condition assessment of civil infrastructure, leading to a cost-effective management of maintenance activities. However, there is still a debate in the literature about the effectiveness of available signal processing strategies to timely assess the health state of a structure. This paper is a contribution to this debate, by presenting the application of different vibration-based damage detection methods using up-to-date multivariate statistical analysis techniques applied to data acquired from a permanently monitored long-span arch bridge. Techniques based on dynamic regression models, linear and local principal component analysis, as well as on their combinations, including, in particular, the newly proposed method based on the combination of dynamic multiple linear regressions and local principal component analysis, and, finally, a method based on the recently proposed approach of cointegration, are considered. A first effort is made to formulate these methods within a unique mathematical framework, highlighting, in particular, the relevant parameters affecting their results and proposing objective criteria for their appropriate tuning and for choosing the length of the training period. Then, the considered damage detection methods are implemented and applied to field data, seeking for damage-sensitive features in the presence of variable environmental and operational conditions. The considered techniques are applied to time histories of identified modal frequencies of the bridge and their capability to reveal structural damage of varying severity is assessed using control charts. The case of an artificially imposed non-linear correlation between the features is also considered. The results provide, for the first time in the literature, an estimation of the minimum level of damage that can be realistically detected in the bridge using dynamic signatures and up-to-date signal processing algorithms, thus contributing to a more aware use of monitoring data and reliance over related health state assessment information.