An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures

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.Ministerio de Educación FPU13/0489

An Experimental Study on Static and Dynamic Strain Sensitivity of Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures

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 materials 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 MWCNTs content. 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 static and dynamically varying 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

Capacitance-Based Sensor with Layered Carbon-Fiber Reinforced Polymer and Titania-Filled Epoxy

Advances in intelligent infrastructure can be achieved through the use of novel materials for increased system-level efficiency and multifunctionality. Carbon Fiber-Reinforced Polymer (CFRP) has been widely used in strengthening, rehabilitating, and retrofitting of existing structures because of its speed of deployment, low maintenance requirement, and high strength-to-weight ratio. In this work, the authors propose a novel method to augment CFRP with self-sensing capabilities. The sensor consists of two CFRP layers separated by a titania-filled epoxy dielectric layer, therefore forming a parallel plate capacitor. Sensing capability can be achieved through variations in the sensor’s capacitance provoked by strain, therefore providing an additional function that could be leveraged for structural health monitoring and structural health management purposes. Comprehensive testing, including (1) sensing properties on sensors with and without titania-doped epoxy and (2) electromechanical test on tension specimens subjected to both static and dynamic loading, was conducted. The test results show that doping the titania filler within the epoxy matrix can improve the sensor’s sensitivity. The gauge factor is 0.92 under static load and decreases with the increasing frequency up to 1 Hz. Therefore, it can be concluded that CFRP can be used as a self-sensing sensor without affecting its mechanical properties

Nanomaterials in Structural Engineering

Development of structural engineering, daring structures with record spans or heights, meets two serious obstacles—the limitations of traditionally used materials and the need of continuous monitoring of new structures subjected to complex loads, including those of dynamic nature. Considering the responsibility for the life of people and the budget of new structures, the need of constant monitoring is inevitable. This is why structural engineers seek for new solutions; among them, smart structures based on self-monitoring materials seem to be one of the most attractive proposals. It is still an unexplored area, but current research shows a high potential of the use of composites reinforced by carbon-based nanomaterials as self-sensing structural materials. Nanomaterials also influence other important features of structural materials, such as microstructure, mechanical, and transport-related properties. In this chapter, we present the state of art of the use of nanomaterials in structural engineering in various areas including mechanical and electrical properties as well as issues referring to durability

Effect of carbonation on bulk resistivity of cement/carbon nanofiber composites

The conductivity of cement/carbon nanofiber (CNF) composite materials has previously been shown to be affected by parameters such as e.g. CNF content or water to cement (w/c) ratios, water saturation and temperature. However, whether and to what extent chemical processes like cement carbonation can affect the electrical conductivity of cement/CNF materials remains unexplored. To investigate this the resistivity changes upon carbonation of Portland G cement/CNF composites were followed for more than 4 months. An increase in resistivity was observed within the first weeks of carbonation followed by a plateau and a subsequent decrease after 4 months. The changes in resistivity were correlated with the progress of the carbonation front followed using X-ray tomography. The magnitude of the resistivity changes was found to be related to w/c ratio. Volumetric changes affecting the connectivity between the CNFs can explain the resistivity changes.publishedVersio

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

Smart materials are promising technologies for reducing the instrumentation cost required to continuously monitor road infrastructures, by transforming roadways into multifunctional elements capable of self-sensing. This study investigates a novel algorithm empowering smart pavements with weigh-in-motion (WIM) characterization capabilities. The application domain of interest is a cementitious-based smart pavement installed on a bridge over separate sections. Each section transduces axial strain provoked by the passage of a vehicle into a measurable change in electrical resistance arising from the piezoresistive effect of the smart material. The WIM characterization algorithm is as follows. First, basis signals from axles are generated from a finite element model of the structure equipped with the smart pavement and subjected to given vehicle loads. Second, the measured signal is matched by finding the number and weights of appropriate basis signals that would minimize the error between the numerical and measured signals, yielding information on the vehicle’s number of axles and weight per axle, therefore enabling vehicle classification capabilities. Third, the temporal correlation of the measured signals are compared across smart pavement sections to determine the vehicle weight. The proposed algorithm is validated numerically using three types of trucks defined by the Eurocodes. Results demonstrate the capability of the algorithm at conducting WIM characterization, even when two different trucks are driving in different directions across the same pavement sections. Then, a noise study is conducted, and the results conclude that a given smart pavement section operating with less than 5% noise on measurements could yield good WIM characterization results

Electrically conductive concrete heated pavement system: Challenges and solution

In regions characterized by harsh winter climates, transportation networks face substantial challenges due to ice and snow accumulation, disrupting air and ground travel. These disruptions can result in significant financial losses for the aviation industry and a worrying, problematic increase in weather-related road accidents. Traditional snow and ice removal methods involving mechanical equipment and chemical de-icers are costly and raise ecological concerns by threatening soil and water quality and global food supplies. Heated Pavement Systems (HPS), particularly Electrically Conductive Concrete (ECON) HPS, have emerged as a promising technology that, particularly Electrically Conductive Concrete (ECON) HPS, which shows potential in for effectively alleviating snow and ice accumulation in critical infrastructure areas such as airports and busy roadways. Despite its promise, the widespread implementation of ECON HPS technology remains limited, warranting a closer investigation into the barriers hindering its adoption. This research paper reviews the existing literature on ECON HPS technology to identify key aspects and challenges. Among the challenges highlighted is the elevated electrical resistivity of ECON during field implementation, increasing its operational costs. Subsequently, the study delves into efforts to produce low-resistivity ECON, presenting findings that provide valuable insights and directions for future research, with the ultimate goal of promoting the widespread adoption of ECON HPS technology and enhancing the resilience of transportation infrastructure in frigid climates

An Experimental Study on Static and Dynamic Strain Sensitivity of Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures

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 materials 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 MWCNTs content. 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 static and dynamically varying 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.This article is published as Meoni, Andrea, Antonella D’Alessandro, Austin Downey, Enrique García-Macías, Marco Rallini, A. Luigi Materazzi, Luigi Torre, Simon Laflamme, Rafael Castro-Triguero, and Filippo Ubertini. "An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures." Sensors 18, no. 3 (2018): 831. DOI: 10.3390/s18030831. Posted with permission.</p