In the present paper, the destructive behavior of distilled water, salty water, sulfuric acid, and heat on glass/vinyl ester composites was investigated by experimental methods. Hetron 922 vinyl ester resin and two types of mat and woven glass fibers as the reinforcements were used to fabricate composite test samples. All samples were immersed in distilled water, salty water, and sulfuric acid with three different concentrations. The tests were performed at 20℃ and 70℃ for the exposure duration of 1, 2, 4, and 8 weeks. Bending tests were performed after aging for all composite samples to check the degradation of the bending modulus and strength. The results show that the effect of distilled water, in comparison with salty water, on the degradation of composite samples was significant. On the other hand, almost non-sensitivity of concentrations of salty water on the weight gain of specimens has been observed. In addition, it was also observed that the degradation of samples at 70℃ temperature is much more than that of at 20℃. Also, it was observed that the flexural modulus of virgin specimens exposed to salty water (2% concentration) has been recovered just after two weeks of immersion. Furthermore, in some cases, composite samples under the sulfuric acid solution have lost almost 80% of their mechanical properties.

A simple solution for free vibration of cross-ply and angle-ply laminated composite plates in a thermal environment is investigated using a basic trigonometric shear deformation theory. By application of trigonometric four variable plate theory, the transverse displacement is subdivided into bending and shear components, the present theory's number of unknowns and governing equations is reduced, making it easier to use. Hamilton's Principle is extended to derive the equations of motion of the plates using Navier's double trigonometric series, a closed-form solution is obtained; the primary conclusion is that simple solution is obtained with good results accuracy when compared with previously published results, and the natural frequency will differ depending on, environment temperature, thickness ratio, and lamination angle, as well as the aspect ratio of the plate.

In this study, potential of three machine learning techniques i.e., M5P, Support vector machines and Gaussian processes were evaluated to find the best algorithm for the prediction of flexural strength of concrete mix with steel fibre. The study comprises the comparison of results obtained from above-said techniques for given dataset. The dataset consists of 124 observations from past research studies and this dataset is randomly divided into two subsets namely training and testing datasets with (70-30)% proportion by weight. Cement, fine aggregates, coarse aggregates, water, super plasticizer/ high-range water reducer, steel fibre, fibre length and curing days were taken as input parameters whereas flexural strength of the concrete mix was taken as the output parameter. Performance of the techniques was checked by statistic evaluation parameters. Results show that the Gaussian process technique works better than other techniques with its minimum error bandwidth. Statistical analysis shows that the Gaussian process predicts better results with higher coefficient of correlation value (0.9138) and minimum mean absolute error (1.2954) and Root mean square error value (1.9672). Sensitivity analysis proves that steel fibre is the significant parameter among other parameters to predict the flexural strength of concrete mix. According to the shape of the fibre, the mixed type performs better for this data than the hooked shape of the steel fibre, which has a higher CC of 0.9649, which shows that the shape of fibers do effect the flexural strength of the concrete. However, the intricacy of the mixed fibres needs further investigations. For future mixes, the most favorable range for the increase in flexural strength of concrete mix found to be (1-3)%.

This paper presents a multiscale modeling method for sheet molding compound (SMC) composites through a novel bundle packing reconstruction algorithm based on a micro-CT (Computed Tomography) image processing. Due to the complex flow pattern during the compression molding process, the SMC composites show a spatially varying orientation and overlapping of fiber bundles. Therefore, significant inhomogeneity and anisotropy are commonly observed and pose a tremendous challenge to predicting SMC composites' properties. For high-fidelity modeling of the SMC composites, the statistical distributions for the fiber orientation and local volume fraction are characterized from micro-CT images of real SMC composites. After that, a novel bundle packing reconstruction algorithm for a high-fidelity SMC model is proposed by considering the statistical distributions. A method for evaluating specimen level's strength and stiffness is also proposed from a set of high-fidelity SMC models. Finally, the proposed multiscale modeling methodology is experimentally validated through a tensile test.

In this article, we studied the effect of two-temperature in a two-dimensional orthotropic thermoelastic media with fractional order heat transfer in generalized thermoelasticity with three-phase-lags due to thermomechanical sources. The boundary of the surface is subjected to linearly distributed and concentrated loads (mechanical and thermal source). The solution of the problem is obtained with the help of Laplace and Fourier transform techniques. The expressions for displacement components, stress components and conductive temperature are derived in transformed domain. Numerical inversion technique is used to obtain the results in physical domain. The effect of two-temperature on all the physical quantities has been depicted with the help graphs. Some special cases are also discussed in the present investigation.