In this article, the primary resonance characteristic of magneto-electro-elastic plates is analyzed, in which the geometric imperfection, thermal effect and shear deformation are taken into account, Applying Hamilton's principle, derivation of nonlinear motion equations is performed. Through solving these equations according to the modified Lindstedt Poincare method, the impacts of external electric voltage, magnetic potential, boundary conditions, temperature changes, geometric imperfection and aspect ratio on the resonance behaviors of MEE plates are examined. It can be found that, as the electric potential rises, the resonance position will be advanced. As the magnetic potential goes up, the resonance frequency of the plates increases, thus delaying the resonance position. As the initial geometric imperfection rises, the resonance position does not change, and the hard spring properties of the plates gradually weaken.

In this study, ensemble machine learning (ML) models are employed to estimate the hardened properties of Self-Compacting Geopolymer Concrete (SCGC). The input variables affecting model development include the content of the SCGC such as the binder material, the age of the specimen, and the ratio of alkaline solution. On the other hand, the output parameters examined includes compressive strength, flexural strength, and split tensile strength. The ensemble machine learning models are trained and validated using a database comprising 396 records compiled from 132 unique mix trials performed in the laboratory. Diverse machine learning techniques, notably K-nearest neighbours (KNN), Random Forest, and Extreme Gradient Boosting (XGBoost), have been employed to construct the models coupled with Bayesian optimisation (BO) for the purpose of hyperparameter tuning. Furthermore, the application of nested cross-validation has been employed in order to mitigate the risk of overfitting. The findings of this study reveal that the BO-XGBoost hybrid model confirms better predictive accuracy in comparison to other models. The R² values for compressive strength, flexural strength, and split tensile strength are 0.9974, 0.9978, and 0.9937, respectively. Additionally, the BO-XGBoost hybrid model exhibits the lowest RMSE values of 0.8712, 0.0773, and 0.0799 for compressive strength, flexural strength, and split tensile strength, respectively. Furthermore, a SHAP dependency analysis was conducted to ascertain the significance of each parameter. It is observed from this study that GGBS, Flyash, and the age of specimens exhibit a substantial level of influence when predicting the strengths of geopolymers.

This study investigates the buckling behavior of CNTRC beams on a Winkler-Pasternak elastic foundation, considering their stiffness. To achieve the highest accuracy, the shear stiffness is taken into account based on the Higher-order Shear Deformation Theory (HSDT). A novel exponential power-law distribution of the CNT volume fraction across the beam thickness is employed to model CNTRC beams. Various reinforcement patterns are incorporated into the polymer matrix, featuring single-walled carbon nanotubes (SWCNT) that are both aligned and distributed. The effective mechanical properties of the CNTRC beam are predicted using the rule of mixtures. Hamilton's principle is applied to derive the differential equations of motion. This theoretical framework enables the validation of the approach by comparing numerical simulation results with previous studies. The impact of the exponent order (n), CNT volume fraction, geometrical ratio, and Winkler-Pasternak parameters on buckling analysis is thoroughly presented and discussed. The results indicate that, among the different types of analyzed CNTRC beams, the X-Beam pattern demonstrates the highest buckling load capacity.

The concrete damage plasticity (CDP) model is widely used to simulate concrete behaviour using either implicit or explicit analysis methods. To effectively execute the models and resolve convergence issues in implicit analysis, activating the viscosity parameter of this material model is a common practice. Despite the frequent application of implicit analysis to analyse concrete structures with the CDP model, the viscosity parameter significantly varies among available models and lacks consistency. The adjustment of the viscosity parameter at the element/structural level disregards its indirect impact on the material. Therefore, the accuracy of the numerical model is confined to the validated range and might not hold true for other values, often explored in parametric studies subsequent to validations. To address these challenges and develop a unified numerical model for varied conditions, a quasi-static analysis using the explicit solver was conducted in this study. Fifteen thick flat plates tested under load control with different geometries and different eccentric loads were considered to verify the accuracy of the model. The study first investigated various concrete material behaviours under compression and tension as well as the concrete tensile strength to identify the most reliable models from previous methodologies. The study compared the results using both implicit and explicit analysis. It was found that, in implicit analysis, the viscosity parameter should be as low as 0.0001 to avoid affecting material properties. However, at the structural level, the optimum value may need adjustment between 0.00001 to 0.0001 with changing geometries and loading type. This observation raises concerns about further parametric study if the specific value of the viscosity parameter is used. Additionally, activating the viscosity parameter in load control simulations confirmed its inability to capture the peak load. Conversely, the unified explicit model accurately simulated the behaviour of the test specimens under varying geometries, load eccentricities, and column sizes. This study recommends restricting implicit solutions to the viscosity values proposed in this research. Alternatively, for highly nonlinear problems under load control simulation, explicit analysis stands as an effective approach, ensuring unified parameters across a wide range of applications without convergence problems.

In this study, it is aimed to investigate the vertical seismic performance of reinforced concrete (R/C) frame buildings in two different building stocks, one of which consists of those designed as per the previous Turkish Seismic Code (TSC-2007) that does not consider the vertical earthquake load, and the other of which consists of those designed as per the new Turkish Seismic Code (TSCB-2018) that considers the vertical earthquake load. For this aim, three R/C buildings with heights of 15 m, 24 m and 33 m are designed separately as per TSC-2007 and TSCB-2018 based on some limitations in terms of seismic zone, soil class and structural behavior factor (Rx/Ry) etc. The vertical earthquake motion effects are identified according to the linear time-history analyses (LTHA) that are performed separately for only horizontal (H) and combined horizontal+vertical (H+V) earthquake motions. LTHA is performed to predict how vertical earthquake motion affects the response of the designed buildings by comparing the linear response parameters of the base shear force, the base overturning, the base axial force, top-story vertical displacement. Nonlinear time-history analysis (NLTHA) is generally required for energy dissipative buildings, not required for design of buildings. In this study, the earthquake records are scaled to force the buildings in the linear range. Since nonlinear behavior is not expected from the buildings herein, the nonlinear time-history analysis (NLTHA) is not considered. Eleven earthquake acceleration records are considered by scaling them to the design spectrum given in TSCB-2018. The base shear force is obtained not to be affected from the combined H+V earthquake load for the buildings. The base overturning moment outcomes underline that the rigidity of the frame system in terms of the dimensions of the columns can be a critical parameter for the influence of the vertical earthquake motion on the buildings. In addition, the building stock from TSC-2007 is estimated to show better vertical earthquake performance than that of TSCB-2018. The vertical earthquake motion is found out to be highly effective on the base axial force of 33 m building rather than 15 m and 24 m buildings. Thus, the building height is a particularly important parameter for the base axial force. The percentage changes in the top-story vertical displacement of the buildings designed for both codes show an increase parallel to that in the base axial force results. To extrapolate more general results, it is clear to state that many buildings should be analyzed.

This paper investigates the second-grade fluid between two parallel plates. Fluid is produced due to stretching. Convective heat and mass transfer features are elaborated with thermal and solutal stratification. Thermal radiation and chemical reactions are also assumed in heat and mass transport processes partial differential. Formulated non-linear partial differential equations are transformed into non-linear ordinary differential equations by utilizing the suitable transformation. Convergent series solutions are computed via Homotopy Analysis Method (HAM). Effects of Hartman number, temperature field, velocity distribution and Prandtl number are sketched and analyzed through graphs. It is noticed that velocity field first decreases and after some distance it shows increasing behavior by the increment.

TECSolverApp is an application that calculates the total equivalent seismic load (base shear) and shows the design spectra in accordance with the Turkish Earthquake Code (TEC). TECSolverApp software can present the spectral acceleration-period graph and the base shear (in terms of unit building weight) in MATLAB and .NET Core frameworks according to TEC 2007 and TEC 2018. In the software, three different building period evaluation options were provided, as entering the period directly, empirical calculation, and using the period calculation formula. In different period calculation scenarios, particular design input parameters such as site-specific spectral acceleration coefficients, local soil class, building importance coefficient, and structural system behavior coefficient are expected. TECSolverApp was produced in two different programming languages and published in MATLAB App Designer and ASP.NET Core MVC environments. To be shared in MATLAB App Designer, it was aimed at availability through the program and distributability as a desktop application. By deploying in ASP.NET Core MVC, open-source cross-platform coding and web-based accessibility were targeted. One of the strongest aspects of TECSolverApp is its developability thanks to software architecture. In this respect, it can be foreseen that other international seismic codes can be added to the calculations in the future.

This study evaluates the direct tensile strength of ultra-high-performance concrete (UHPC) using tests. A total of 45 dogbone-shaped specimens are tested, with the test variables being the fiber volume fraction and notch length. The test results showed that the material properties of UHPC were largely dependent on the fiber volume fraction and compressive strength. When steel fibers with more than 1% fiber volume fraction are mixed in the manufacturing of UHPC, the tensile strength can be more than twice that of plain UHPC. In addition, the incorporation of steel fibers enabled the significant improvement of the initial cracking strength. However, the effect of the notch length on the tensile behavior was insignificant. An assessment of the direct tensile strength is conducted using machine-learning algorithms (ML). For evaluation of the direct tensile strength of UHPC using ML, a total of 98 test data, including 53 data from other research works and 45 data from this experimental program, were collected. In total, 67 data with a 70% confidence interval on a normal distribution curve were selected, with 47 data among 67 used for ML training and 20 data used for ML testing. As a result, the machine-learning algorithm with a steel fiber volume fraction predicted that the tensile strength has an average of 0.98 and the lowest values of regression evaluation metrics among analytical and ML-based models. It is considered that an ML-based model can help to predict a more accurate tensile strength of UHPC.