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Filippo Ubertini published an article in March 2018.
Research Keywords & Expertise
0 Carbon Nanotubes
0 Reinforced Concrete
0 Structural Health Monitoring
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(2007 - 2018)
(2007 - 2018)
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Article 0 Reads 0 Citations An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Na... Published: 09 March 2018
Sensors, doi: 10.3390/s18030831
The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix mterials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNT contents. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both quasi-static and sine-sweep dynamic uni-axial compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.
Article 0 Reads 0 Citations Automated crack detection in conductive smart-concrete structures using a resistor mesh model Published: 19 February 2018
Measurement Science and Technology, doi: 10.1088/1361-6501/aa9fb8
Various nondestructive evaluation techniques are currently used to automatically detect and monitor cracks in concrete infrastructure. However, these methods often lack the scalability and cost-effectiveness over large geometries. A solution is the use of self-sensing carbon-doped cementitious materials. These self-sensing materials are capable of providing a measurable change in electrical output that can be related to their damage state. Previous work by the authors showed that a resistor mesh model could be used to track damage in structural components fabricated from electrically conductive concrete, where damage was located through the identification of high resistance value resistors in a resistor mesh model. In this work, an automated damage detection strategy that works through placing high value resistors into the previously developed resistor mesh model using a sequential Monte Carlo method is introduced. Here, high value resistors are used to mimic the internal condition of damaged cementitious specimens. The proposed automated damage detection method is experimentally validated using a $500 x 500 x 50 $ mm reinforced cement paste plate doped with multi-walled carbon nanotubes exposed to 100 identical impact tests. Results demonstrate that the proposed Monte Carlo method is capable of detecting and localizing the most prominent damage in a structure, demonstrating that automated damage detection in smart-concrete structures is a promising strategy for real-time structural health monitoring of civil infrastructure.
Article 0 Reads 0 Citations Smart bricks for strain sensing and crack detection in masonry structures Published: 30 November 2017
Smart Materials and Structures, doi: 10.1088/1361-665X/aa98c2
The paper proposes the novel concept of smart bricks as a durable sensing solution for structural health monitoring of masonry structures. The term smart bricks denotes piezoresistive clay bricks with suitable electronics capable of outputting measurable changes in their electrical properties under changes in their state of strain. This feature can be exploited to evaluate stress at critical locations inside a masonry wall and to detect changes in loading paths associated with structural damage, for instance following an earthquake. Results from an experimental campaign show that normal clay bricks, fabricated in the laboratory with embedded electrodes made of a special steel for resisting the high baking temperature, exhibit a quite linear and repeatable piezoresistive behavior. That is a change in electrical resistance proportional to a change in axial strain. In order to be able to exploit this feature for strain sensing, high-resolution electronics are used with a biphasic DC measurement approach to eliminate any resistance drift due to material polarization. Then, an enhanced nanocomposite smart brick is proposed, where titania is mixed with clay before baking, in order to enhance the brick's mechanical properties, improve its noise rejection, and increase its electrical conductivity. Titania was selected among other possible conductive nanofillers due to its resistance to high temperatures and its ability to improve the durability of construction materials while maintaining the aesthetic appearance of clay bricks. An application of smart bricks for crack detection in masonry walls is demonstrated by laboratory testing of a small-scale wall specimen under different loading conditions and controlled damage. Overall, it is demonstrated that a few strategically placed smart bricks enable monitoring of the state of strain within the wall and provide information that is capable of crack detection.
Article 0 Reads 0 Citations Experimental wind tunnel study of a smart sensing skin for condition evaluation of a wind turbine blade Published: 30 October 2017
Smart Materials and Structures, doi: 10.1088/1361-665X/aa9349
Condition evaluation of wind turbine blades is difficult due to their large size, complex geometry and lack of economic and scalable sensing technologies capable of detecting, localizing, and quantifying faults over a blade's global area. A solution is to deploy inexpensive large area electronics over strategic areas of the monitored component, analogous to sensing skin. The authors have previously proposed a large area electronic consisting of a soft elastomeric capacitor (SEC). The SEC is highly scalable due to its low cost and ease of fabrication, and can, therefore, be used for monitoring large-scale components. A single SEC is a strain sensor that measures the additive strain over a surface. Recently, its application in a hybrid dense sensor network (HDSN) configuration has been studied, where a network of SECs is augmented with a few off-the-shelf strain gauges to measure boundary conditions and decompose the additive strain to obtain unidirectional surface strain maps. These maps can be analyzed to detect, localize, and quantify faults. In this work, we study the performance of the proposed sensing skin at conducting condition evaluation of a wind turbine blade model in an operational environment. Damage in the form of changing boundary conditions and cuts in the monitored substrate are induced into the blade. An HDSN is deployed onto the interior surface of the substrate, and the blade excited in a wind tunnel. Results demonstrate the capability of the hybrid dense sensor network and associated algorithms to detect, localize, and quantify damage. These results show promise for the future deployment of fully integrated sensing skins deployed inside wind turbine blades for condition evaluation.
Article 0 Reads 1 Citation Damage detection, localization and quantification in conductive smart concrete structures using a resistor mesh model Published: 01 October 2017
Engineering Structures, doi: 10.1016/j.engstruct.2017.07.022
Article 0 Reads 3 Citations Assessment of a monumental masonry bell-tower after 2016 Central Italy seismic sequence by long-term SHM Published: 05 September 2017
Bulletin of Earthquake Engineering, doi: 10.1007/s10518-017-0222-7
The response of the San Pietro monumental bell-tower located in Perugia, Italy, to the 2016 Central Italy seismic sequence is investigated, taking advantage of the availability of field data recorded by a vibration-based SHM system installed in December 2014 to detect earthquake-induced damages. The tower is located about 85 km in the NW direction from the epicenter of the first major shock of the sequence, the Accumoli Mw6.0 earthquake of August 24th, resulting in a small local PGA of about 30 cm/s2, whereby near-field PGA was measured as 915.97 cm/s2 (E–W component) and 445.59 cm/s2 (N–S component). Similar PGA values also characterized the two other major shocks of the sequence (Ussita Mw5.9 and Norcia Mw6.5 earthquakes of October 26th and 30th, respectively). Despite the relatively low intensity of such earthquakes in Perugia, the analysis of long-term monitoring data clearly highlights that small permanent changes in the structural behavior of the bell-tower have occurred after the earthquakes, with decreases in all identified natural frequencies. Such natural frequency decays are fully consistent with what predicted by non-linear finite element simulations and, in particular, with the development of microcracks at the base of the columns of the belfry. Microcracks in these regions, and in the rest of tower, are however hardly distinguishable from pre-existing ones and from the physiological cracking of a masonry structure, what validates the effectiveness of the SHM system in detecting earthquake-induced damage at a stage where this is not yet detectable by visual inspections.