The thermo-mechanical bending behavior of the antisymmetric cross-ply laminates is examined using a new simple four variable trigonometric plate theory. The proposed theory utilizes a novel displacement field which introduces undetermined integral terms and needs only four variables. The validity of the present model is proved by comparison with solutions available in the literature.

In this paper, wave propagation of double-bonded Cooper-Naghdi micro sandwich cylindrical shells with porous core and carbon nanotube reinforced composite (CNTRC) face sheets are investigated subjected to multi-physical loadings with temperature dependent material properties. The governing equations of motion are derived by Hamilton's principle. Then, the influences of various parameters such as wave number, CNT volume fraction, temperature change, Skempton coefficient, material length scale parameter, porosity coefficient on the phase velocity of double-bonded micro sandwich shell are taken into account. It is seen that by increasing of Skempton coefficient, the phase velocity decreases for higher wave number and the results become approximately the constant. Also, by increasing of the material length scale parameter, the cut of frequency increases, because the stiffness of micro structure increases. The obtained results for this article can be used to detect, locate and quantify crack.

In this work, an experimental research has been performed on Steel Fiber-Steel Reinforced Concrete (SFSRC)specimens subjected to four-point bending tests to evaluate the feasibility of mutual replacement of steel fibers and conventional reinforcement through studying failure modes, load-deflection curves, stiffness of characteristic points, stiffness degradation curves and damage analysis. The variables considered in this experiment included steel fiber volume percentage with and without conventional reinforcements (stirrups or steel fibers) with shear span depth ratios of S/D=2.5 and 3.5. Experimental results revealed that increasing the volume percentage of steel fiber decreased the creation and propagation of shear and bond cracks, just like shortening the stirrups spacing. Higher crack resistance and suturing ability of steel fiber can improve the stability of its bearing capacity. Both steel fibers and stirrups improved the stiffness and damage resistance of specimens where stirrups played an essential role and therefore, the influence of steel fibers was greatly weakened. Increasing S/D ratio also weakened the effect of steel fibers. An equation was derived to calculate the bending stiffness of SFSRC specimens, which was used to determine mid span deflection; the accuracy of the proposed equation was proved by comparing predicted and experimental results.

In this paper, the fracture characteristics of concrete specimens with different notch depths under three-point flexural loads are studied by finite element and fracture mechanics methods. Firstly, the concrete beams (the size is 700×100×150 mm) with different notch depths (a=30 mm, 45 mm, 60 mm and 75 mm respectively) are tested to study the influence of notch depths on the mechanical properties of concrete. Subsequently, the concrete beams with notch depth of 60 mm are loaded at different loading rates to study the influence of loading rates on the fracture characteristics, and digital image correlation (DIC) is used to monitor the strain nephogram at different loading rates. The test results show that the flexural characteristics of the beams are influenced by notch depths, and the bearing capacity and ductility of the concrete decrease with the increase of notch depths. Moreover, the peak load of concrete beam gradually increases with the increase of loading rate. Then, the fracture energy of the beams is accurately calculated by tail-modeling method and the bilinear softening constitutive model of fracture behavior is determined by using the modified fracture energy. Finally, the bilinear softening constitutive function is embedded into the finite element (FE) model for numerical simulation. Through the comparison of the test results and finite element analysis, the bilinear softening model determined by the tail-modeling method can be used to predict the fracture behavior of concrete beams under different notch depths and loading rates.

Alkali-silica reaction (ASR) in concrete can induce degradation in its mechanical properties, leading to compromised serviceability and even loss in load capacity of concrete structures. Compared to other properties, ASR often affects the modulus of elasticity more significantly. Several empirical models have thus been established to estimate elastic modulus reduction based on the ASR expansion only for condition assessment and capacity evaluation of the distressed structures. However, it has been observed from experimental studies in the literature that for any given level of ASR expansion, there are significant variations on the measured modulus of elasticity. In fact, many other factors, such as cement content, reactive aggregate type, exposure condition, additional alkali and concrete strength, have been commonly known in contribution to changes of concrete elastic modulus due to ASR. In this study, an artificial intelligent model using artificial neural network (ANN) is proposed for the first time to provide an innovative approach for evaluation of the elastic modulus of ASR-affected concrete, which is able to take into account contribution of several influence factors. By intelligently fusing multiple information, the proposed ANN model can provide an accurate estimation of the modulus of elasticity, which shows a significant improvement from empirical based models used in current practice. The results also indicate that expansion due to ASR is not the only factor contributing to the stiffness change, and various factors have to be included during the evaluation.

During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.

Understanding the realistic behavior of concrete up to failure under different loading conditions within the framework of damage mechanics and plasticity would lead to an enhanced design of concrete structures. In the present investigation, QR (Quick Response) code based random speckle pattern is used as a non-contact sensor, which is an innovative approach in the field of digital image correlation (DIC). A four-point bending test was performed on RC beams of size 1800 mm × 150 mm × 200 mm. Image processing was done using an open source Ncorr algorithm for the results obtained using random speckle pattern and QR code based random speckle pattern. Load-deflection curves of RC beams were plotted for the results obtained using both contact and non-contact (DIC) sensors, and further, Moment (M)-Curvature (κ) relationship of RC beams was developed. The loading curves obtained were used as input data for material model parameters in finite element analysis. In finite element method (FEM) based software, concrete damage plasticity (CDP) constitutive model is used to predict the realistic nonlinear quasi-static flexural behavior of RC beams for monotonic loading condition. The results obtained using QR code based DIC are observed to be on par with conventional results and FEM results.

This study aimed to investigate the permeable nature of pervious concretes (PC) through the moving particle simulation (MPS) method. In the simulation, the complex structure of a pervious concrete was virtually demonstrated as a lattice model (LM) of spherical beads, where the test of permeability was conducted. Results of the simulation were compared with the experimental ones for validation. As a result, MPS results showed the permeability index of the LM as almost twice as big as the actual PCs. A proposed virtual model was created to prevent the stuck of water flow in the MPS simulation of PC or LM. Successful simulation results were demonstrated with the model.

In this research paper, the free vibrational behavior of the simply supported FG nano-plate is studied using the nonlocal two variables integral refined plate theory. The present model takes into account the small scale effect. The effective's properties of the plate change according to the power law variation (P-FGM). The equations of motion of the system are determined and resolved via Hamilton's principle and Navier procedure, respectively. The validity and efficiency of the current model are confirmed by comparing the results with those given in the literature. At the last section, several numerical results are presented to show the various parameters influencing the vibrational behavior such as the small-scale effect, geometry ratio, material index and aspect ratio.