Cementitious-based strain sensors can be used as robust monitoring systems for civil engineering applications, such as road pavements and historic structures. To enable large-scale deployments, the fillers used in creating a conductive material must be inexpensive and easy to mix homogeneously. Carbon black (CB) particles constitute a promising filler due to their low cost and ease of dispersion. However, a relatively high quantity of these particles needs to be mixed with cement in order to reach the percolation threshold. Such level may influence the physical properties of the cementitious material itself, such as compressive and tensile strengths. In this paper, we investigate the possibility of utilizing a polymer to create conductive chains of CB more quickly than in a cementitious-only medium. This way, while the resulting material would have a higher conductivity, the percolation threshold would be reached with fewer CB particles. Building on the principle that the percolation threshold provides great sensing sensitivity, it would be possible to fabricate sensors using less conducting particles. We present results from a preliminary investigation comparing the utilization of a conductive paint fabricated from a poly-Styrene-co-Ethylene-co-Butylene-co-Styrene (SEBS) polymer matrix and CB, and CB-only as fillers to create cementitious sensors. Preliminary results show that the percolation threshold can be attained with significantly less CB using the SEBS+CB mix. Also, the study of the strain sensing properties indicates that the SEBS+CB sensor has a strain sensitivity comparable to the one of a CB-only cementitious sensor when comparing specimens fabricated at their respective percolation thresholds.
The authors have developed a capacitive-based thin film sensor for monitoring strain on mesosurfaces. Arranged in a network configuration, the sensing system is analogous to a biological skin, where local strain can be monitored over a global area. The measurement principle is based on a measurable change in capacitance provoked by strain. In the case of bi-directional in-plane strain, the sensor output contains the additive measurement of both principal strain components. In this paper, we present an algorithm for retrieving the directional strain from measurements. The algorithm leverages the dense network application of the thin film sensor to reconstruct the surface strain map. A bi-directional shape function is assumed, and it is differentiated to obtain expressions for planar strain. A least square estimator (LSE) is used to reconstruct the planar strain map from the sensors measurement’s, after the system’s boundary conditions have been enforced in the model. The coefficients obtained by the LSE can be used to reconstruct the estimated strain map or the deflection shape directly. Results from numerical simulations and experimental investigations show good performance of the algorithm, in particular for monitoring surface strain on cantilever plates.
A novel thin film sensor consisting of a soft elastomeric capacitor (SEC) for meso-scale monitoring has been developed by the authors. Each SEC transduces surface strain into a measurable change in capacitance. In previous work, the authors have shown that the performance of the SEC compares well with conventional resistive strain gauges, providing a resolution of 25 με using an inexpensive off-the-shelf data acquisition system for capacitance measurements. Here, we further the understanding of the thin film sensor by characterizing its dynamic behavior. The SEC is subjected to dynamic loads in bending mode. The study of Fourier and wavelet transforms indicates that the sensor can be used to identify dynamic inputs. Overall results demonstrate the promising capabilities of the thin film sensor at dynamic monitoring of civil structures.
Existing sensing solutions facilitating continuous condition assessment of wind turbine blades are limited by a lack of scalability and clear link signal-to-prognosis. With recent advances in conducting polymers, it is now possible to deploy networks of thin film sensors over large areas, enabling low cost sensing of large-scale systems. Here, we propose to use a novel sensing skin consisting of a network of soft elastomeric capacitors (SECs). Each SEC acts as a surface strain gage transducing local strain into measurable changes in capacitance. Using surface strain data facilitates the extraction of physics-based features from the signals that can be used to conduct condition assessment. We investigate the performance of an SEC network at detecting damages. Diffusion maps are constructed from the time series data, and changes in point-wise diffusion distances evaluated to determine the presence of damage. Results are benchmarked against time-series data produced from off-the-shelf resistive strain gauges. This paper presents data from a preliminary study. Results show that the SECs are promising, but the capability to perform damage detection is currently reduced by the presence of parasitic noise in the signal.
Damages in composite components of wind turbine blades and large-scale structures can lead to increase in maintenance and repair costs, inoperability, and structural failure. The vast majority of condition assessment of
composite structures is conducted by visual inspection and non-destructive evaluation (NDE) techniques. NDE
techniques are temporally limited, and may be further impeded by the anisotropy of the composite materials,
conductivity of the fibers, and the insulating properties of the matrix. In previous work, the authors have proposed a
novel soft elastomeric capacitor (SEC) sensor for monitoring of large surfaces, applicable to composite materials. This
soft capacitor is fabricated using a highly sensitive elastomer sandwiched between electrodes. It transduces strain into
changes in capacitance. Here, we present a fabrication method for fabricating the SEC. Different surface treatment
techniques for the nanoparticles are investigated and the effects on the mechanical and the electrical properties of the
produced film are studied. Results show that using melt mixing fabrication method was successful at dispersing the
nanoparticles without using any surface treatment, including coating the particles with PDMS oil or the use of Si-69
coupling agent. Yet, treating the surface would result in increasing the stiffness of the matrix as well as improving the
interaction between the filler particles and the matrix.
Health monitoring of civil structures is a process that aims at diagnosing and localizing structural damages. It
is typically conducted by visual inspections, therefore relying vastly on the monitoring frequency and individual
judgement of the inspectors. The automation of the monitoring process would be greatly beneficial by increasing
life expectancy of civil structures via timely maintenance, thus improving their sustainability. In this paper, we
present a sensing method for automatically localizing strain over large surfaces. The sensor consists of several
soft capacitors arranged in a matrix form, which can be applied over large areas. Local strains are converted
into changes in capacitance among a soft capacitors matrix, permitting damage localization. The proposed
sensing method has the fundamental advantage of being inexpensive to apply over large-scale surfaces. which
allows local monitoring over large regions, analogous to a biological skin. In addition, its installation is simple,
necessitating only limited surface preparation and deployable utilizing off-the-shelf epoxy. Here, we demonstrate
the performance of the sensor at measuring static and dynamic strain, and discuss preliminary results from
an application on a bridge located in Ames, IA. Results show that the proposed sensor is a promising health
monitoring method for diagnosing and localizing strain on a large-scale surface.