Enrique García-Macías

Static and Dynamic Strain Monitoring of Reinforced Concrete Components through Embedded Carbon Nanotube Cement-Based Sensors

The paper presents a study on the use of cement-based sensors doped with carbon nanotubes as embedded smart sensors for static and dynamic strain monitoring of reinforced concrete (RC) elements. Such novel sensors can be used for the monitoring of civil infrastructures. Because they are fabricated from a structural material and are easy to utilize, these sensors can be integrated into structural elements for monitoring of different types of constructions during their service life. Despite the scientific attention that such sensors have received in recent years, further research is needed to understand (i) the repeatability and accuracy of sensors’ behavior over a meaningful number of sensors, (ii) testing configurations and calibration methods, and (iii) the sensors’ ability to provide static and dynamic strain measurements when actually embedded in RC elements. To address these research needs, this paper presents a preliminary characterization of the self-sensing capabilities and the dynamic properties of a meaningful number of cement-based sensors and studies their application as embedded sensors in a full-scale RC beam. Results from electrical and electromechanical tests conducted on small and full-scale specimens using different electrical measurement methods confirm that smart cement-based sensors show promise for both static and vibration-based structural health monitoring applications of concrete elements but that calibration of each sensor seems to be necessary.

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.

Continuous and embedded solutions for SHM of concrete structures using changing electrical potential in self-sensing cement-based composites

Interest in the concept of self-sensing structural materials has grown in recent years due to its potential to enable continuous low-cost monitoring of next-generation smart-structures. The development of cement-based smart sensors appears particularly well suited for monitoring applications due to their numerous possible field applications, their ease of use and long-term stability. Additionally, cement-based sensors offer a unique opportunity for structural health monitoring of civil structures because of their compatibility with new or existing infrastructure. Particularly, the addition of conductive carbon nanofillers into a cementitious matrix provides a self-sensing structural material with piezoresistive characteristics sensitive to deformations. The strain-sensing ability is achieved by correlating the external loads with the variation of specific electrical parameters, such as the electrical resistance or impedance. Selection of the correct electrical parameter for measurement to correlate with features of interest is required for the condition assessment task. In this paper, we investigate the potential of using altering electrical potential in cement-based materials doped with carbon nanotubes to measure strain and detect damage in concrete structures. Experimental validation is conducted on small-scale specimens including a steel-reinforced beam of conductive cement paste. Comparisons are made with constant electrical potential and current methods commonly found in the literature. Experimental results demonstrate the ability of the changing electrical potential at detecting features important for assessing the condition of a structure.

Multi-scale model updating of a timber footbridge using experimental vibration data

Purpose The purpose of this paper is to capture the actual structural behavior of the longest timber footbridge in Spain by means of a multi-scale model updating approach in conjunction with ambient vibration tests. Design/methodology/approach In a first stage, a numerical pre-test analysis of the full bridge is performed, using standard beam-type finite elements with isotropic material properties. This approach offers a first structural model in which optimal sensor placement (OSP) methodologies are applied to improve the system identification process. In particular, the effective independence (EFI) method is used to determine the optimal locations of a set of sensors. Ambient vibration tests are conducted to determine experimentally the modal characteristics of the structure. The identified modal parameters are compared with those values obtained from this preliminary model. To improve the accuracy of the numerical predictions, the material response is modeled by means of a homogenization-based multi-scale computational approach. In a second stage, the structure is modeled by means of three-dimensional solid elements with the above material definition, capturing realistically the full orthotropic mechanical properties of wood. A genetic algorithm (GA) technique is adopted to calibrate the micromechanical parameters which are either not well-known or susceptible to considerable variations when measured experimentally. Findings An overall good agreement is found between the results of the updated numerical simulations and the corresponding experimental measurements. The longitudinal and transverse Youngs moduli, sliding and rolling shear moduli, density and natural frequencies are computed by the present approach. The obtained results reveal the potential predictive capabilities of the present GA/multi-scale/experimental approach to capture accurately the actual behavior of complex materials and structures. Originality/value The uniqueness and importance of this structure leads to an intensive study of its structural behavior. Ambient vibration tests are carried out under environmental excitation. Extraction of modal parameters is obtained from output-only experimental data. The EFI methodology is applied for the OSP on a large-scale structure. Information coming from several length scales, from sub-micrometer dimensions to macroscopic scales, is included in the material definition. The strong differences found between the stiffness along the longitudinal and transverse directions of wood lumbers are incorporated in the structural model. A multi-scale model updating approach is carried out by means of a GA technique to calibrate the micromechanical parameters which are either not well-known or susceptible to considerable variations when measured experimentally.

Uncertainty Analysis of Mechanical Behavior of Functionally Graded Carbon Nanotube Composite Materials

The remarkable mechanical and sensing properties of carbon nanotubes (CNTs) suggest that they are ideal candidates for high performance and self-sensing cementitious composites. However, there is still a lack of deeper knowledge of the uncertainty associated with their incorporation, concretely in functionally graded composite materials (FGM). The influence of these uncertainties can be critical for future applications in the field of Structural Health Monitoring (SHM), techniques that usually require high accuracy modeling. Most researches restrict the aim of their studies to the analysis of composite materials with uniform or linear grading profiles. This study throws light on the basis of stochastic representation of the grading profiles and analyzes the propagation of its uncertainty into the response of some structural elements. The finite element method (FEM) is employed to study the individual and interactive effects of the mechanical properties (Young’s modulus, density, Poisson’s ratio and CNT’s waviness) and grading profiles. The effects of stochastic uncertainties on the overall properties of the composite material are represented using the probability theory. Numerical results show the influence of these variables in several benchmark cases such as cylindrical, spherical and doubly curved shells, in terms of their static and dynamic characteristics by performing modal analysis.

Ambient Vibration Testing of Historic Steel-Composite Bridge, the E. Torroja Bridge, for Structural Identification and Finite Element Model Updating

The E. Torroja Bridge is a steel-composite bridge which combines inverted steel arch trusses with a concrete deck. This bridge crosses the Guadalquivir River in the small town of Posadas, 30 km far from Cordoba, Spain. It was E. Torroja, the renowned civil engineer who designed and built it until its inauguration in 1951 with an original deck 7 m width. Nevertheless, in 1995 it was remodeled by the author’s grandson, Jose Antonio Torroja, who raised the width of the deck to 11 m and added two new inverted steel arches beside the original ones. In order to assess the structural health condition of the bridge, ambient vibration tests were carried out in June 2014.