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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]

To date, no specific framework has been developed to guide composite structure designers to select the optimum fiber types and fabric weave patterns for a given application. This article aims to, first, investigate the effect of weighting methods in multiple criteria decision making (MCDM) and then arrive at a systematic framework for optimum weave pattern selection in fiber reinforced polymer (FRP) composites. Namely, via measured data from an industrial case study, the TOPSIS MCDM technique has been applied to choose the best candidate among different polypropylene/glass laminates. As an input to TOPSIS, different types of subjective and objective weighting methods were initially compared to assess the role of relative importance values (weights) of design criteria. These included the Entropy method, the modified digital logic (MDL) method, and the criteria importance through inter-criteria correlation (CRITIC) method. Next, two new subjective weighting methods, named ‘Numeric Logic (NL)’ and ‘Adjustable Mean Bars (AMB)’ methods, were introduced to give more practical and effective means to the decision makers during the weighting of criteria. In particular, compared to the MDL, the NL method increased the accuracy of assigned weights for an expert DM. On the other hand, the AMB provided a more interactive, visual approach through MCDM weighting process for less experienced DMs. Finally, a generalized combinative weighting framework is presented to show how different types of weightings may be combined to find more reliable rankings of alternatives. The combinative weighting could specifically accommodate different scenarios where a group of designers are involved and have different levels of experience, while given a large number of alternatives/criteria in highly nonlinear applications such as impact design of composite materials. [ABSTRACT FROM AUTHOR]

Traditional ceramic crowns occasionally experience chipping and cracking due to their brittle nature. Use of a low stiffness, biocompatible custom-design material and strengthening the crown in the direction of the loading are known as possible measures to help reducing these failure modes. This study aims to numerically analyze and compare a single implant supported crown made of two material options – ceramic and a new glass fiber dental composite (GFDC). Specifically, we aim to assess whether the fiber orientation in the composite crowns has a notable effect on the structural integrity of the implant under loading. It was observed that fibers aligned to the tooth long axis as well as through the thickness, can notably help in reducing the maximum stresses of the structure (up to 40% at high chewing angles). The addition of the veneering layer showed a higher stability of the GFDC crown, closer to that of the natural tooth. [ABSTRACT FROM AUTHOR]

X-ray computed tomography provides qualitative and quantitative structural and compositional information for a broad range of materials. Yet, its contribution to the field of advanced composites such as carbon fiber reinforced polymers is still limited by factors such as low imaging contrast, due to scarce X-ray attenuation features. This article, through a review of the state of the art, followed by an example case study on Micro-computed tomography (CT) analysis of low X-ray absorptive dry and prepreg carbon woven fabric composites, aims to highlight and address some challenges as well as best practices on performing scans that can capture key features of the material. In the case study, utilizing an Xradia Micro-CT-400, important aspects such as obtaining sufficient contrast, an examination of thin samples, sample size/resolution issues, and image-based modeling are discussed. The outcome of an optimized workflow in Micro-CT of composite fabrics can assist in further research efforts such as the generation of surface or volume meshes for the numerical modeling of underlying deformation mechanisms during their manufacturing processes. [ABSTRACT FROM AUTHOR]

In industrial composites applications, drilling is one of the most common operations and complex processes during the final assembly, which can generate undesirable damage to the manufactured part. Data collection from a given composite's fatigue life is often costly and time-consuming. To address this challenge, the current case study aims to adapt a hybrid machine learning framework to predict the fatigue life of the drilled Glass Fiber Reinforced Polymer composite laminates (with both unidirectional and woven lay-ups) under a limited and noisy data assumption. Composite specimens were drilled at various cutting speeds and feed rates. The size of the delamination around the hole was scanned by a microscopic camera. Cyclic three-point bending tests were conducted, and results indicated that the drilling-induced delamination size and the composite lay-up affect the specimens' fatigue lives. The latter were then modeled in two steps. In the first step, an offline deterministic model was established using the group method of data handling along with a singular value decomposition. Pareto multi-objective optimization was applied to prevent overfitting. In the second step, the Kalman filter was employed to update the polynomial of the deterministic model based on minimizing the mean and variance of error between the actual and modeled data. Results showed an excellent learning reliability, with a correlation coefficient of 97.6% and 96.5% in predicting the fatigue life of unidirectional and woven composite laminates, respectively. A sensitivity analysis was performed and indicated that the fatigue life of the samples has been more affected by the drilling feed rate, compared to the cutting speed. [ABSTRACT FROM AUTHOR]

Natural fibre-synthetic fibre hybrid composites provide desirable structural performance and durability, while lessening environmental impact of the final product, which is becoming increasingly important to government regulators and manufacturers. However, a major known challenge using natural fibers is their high moisture absorption, leading to fast degradation rates. Bamboo was recently shown to provide a decreased hydrophilic tendency when plastinated using the S-10 technique35—originally invented to preserve human remains. Building on this finding, this study explores the structural advantage of using plastinated bamboo as a filler material in a typical glass fiber/polypropylene woven composite. Results show that adding 10% (by weight) of the plastinated bamboo can increase the impact absorption capacity of the parent glass fibre composite, without lessening its elastic properties. Such hybrid biocomposites can bring new opportunities for structural applications, while addressing the environmental degradation issue of natural fibres. [ABSTRACT FROM AUTHOR]