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Piezoelectric, piezoresistive, and optical fiber sensors have attracted considerable attention in structural health monitoring (SHM) applications for strain/temperature measurements in advanced material structures such as composites. Issues pertaining to the fabrication process and integration of these sensors, however, continue to limit their efficiency in detecting SHM parameters in sensitive applications such as aerospace and medical devices, where accuracy and reliability are prime concerns. This paper provides a critical review of recent developments on sensing materials and their integration methods into composites. For piezoelectric sensors, ferroelectric and piezoelectric characteristics of different material compositions are described and the evolution of ferroelectric properties over a range of temperatures and mechanical loadings is addressed. Fabrication processes and characterization of silicon- and carbon-based piezoresistive materials are presented to assess the electromechanical performance of such sensors. Furthermore, the methods developed for concurrent measurement of strain and temperature using optical fiber sensors are reviewed. The implantation of different sensing mechanisms for detecting physical parameters during and after composite manufacturing processes is described. Since composite delamination due to the embedded sensors is a reoccurring issue, mechanical compatibility of different reported sensors with the base material interface is highlighted. Finally, potential future directions of the reviewed sensing mechanisms are outlined. [ABSTRACT FROM AUTHOR]

A mesoscopic analytical model of wrinkling of Plain-Woven Composite Preforms (PWCPs) under the bias extension test is presented, based on a new instability analysis. The analysis is aimed to facilitate a better understanding of the nature of wrinkle formation in woven fabrics caused by large in-plane shear, while it accounts for the effect of fabric and process parameters on the onset of wrinkling. To this end, the mechanism of wrinkle formation in PWCPs in mesoscale is simplified and an equivalent structure composed of bars and different types of springs is proposed, mimicking the behavior of a representative PWCP element at the post-locking state. The parameters of this equivalent structure are derived based on geometric and mechanical characteristics of the PWCP. The principle of minimum total potential energy is employed to formluate the model, and experimental validation is carried out to reveal the effectiveness of the derived wrinkling prediction equation. [ABSTRACT FROM AUTHOR]

Structural health monitoring is widely applied in industrial sectors as it reduces costs associated with maintenance intervals and manual inspections of damage in sensitive structures, while enhancing their operation safety. A major concern and current challenge in developing “robust” structural health monitoring systems, however, is the impact of uncertainty in the input training parameters on the accuracy and reliability of predictions. The aim of this article is to adapt an advanced statistical pattern recognition technique capable of considering variations in input parameters and arriving at a new structural health monitoring system more immune to the effect of uncertainty. Gaussian processes have been implemented to predict the state of damage in a typical composite airfoil structure. Different covariance functions were evaluated during the training stage of structural health monitoring. Results through a case study showed a remarkable capability of the Gaussian process–based approach to deal with uncertainty in the pattern recognition problem in structural health monitoring of a multi-layer composite airfoil structure. To illustrate robustness advantage of the approach as compared to conventional neural network models, the damage size and location prediction accuracy of the Gaussian process structural health monitoring has been compared to multi-layer perceptron neural networks. Some practical insights and limitations of the approach have also been outlined. [ABSTRACT FROM AUTHOR]

This paper explores a set of new equations to predict the bond strength between fiber reinforced polymer (FRP) rebar and concrete. The proposed equations are based on a comprehensive statistical analysis and existing experimental results in the literature. Namely, the most effective parameters on bond behavior of FRP concrete were first identified by applying a factorial analysis on a part of the available database. Then the database that contains 250 pullout tests were divided into four groups based on the concrete compressive strength and the rebar surface. Afterward, nonlinear regression analysis was performed for each study group in order to determine the bond equations. The results show that the proposed equations can predict bond strengths more accurately compared to the other previously reported models. [ABSTRACT FROM AUTHOR]