Using a combination of nonlocal Eringen as well as classical beam theories, this research explores the thermal buckling of a bidirectional functionally graded nanobeam. The formulations of the presented problem are acquired by means on conserved energy as well as nonlocal theory. The results are obtained via generalized differential quadrature method (GDQM). The mechanical properties of the generated material vary in both axial and lateral directions, two-dimensional functionally graded material (2D-FGM). In nanostructures, porosity gaps are seen as a flaw. Finally, the information gained is used to the creation of small-scale sensors, providing an outstanding overview of nanostructure production history.

In this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freeze-thaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model.

Reinforced concrete bearing walls (RCBWs) are one of the most applicable structural systems. Therefore, vulnerability analysis and rehabilitation of the RCBW system are of great importance. In the present study, in order to the more precise investigation of the performance of this structural resistant system, pushover and nonlinear time history analyses based on several assumptions drawing upon experimental research were performed on several models with different stories. To validate the nonlinear analysis method, the analytical and experimental results are compared. Vulnerability evaluation was carried out on two seismic hazard levels and three performance levels. Eventually, the need for seismic rehabilitation with the basic safety objective (BSO) was investigated. The obtained results showed that the studied structures satisfied the BSO of the seismic rehabilitation guidelines. Consequently, according to the results of analyses and the desired performance, this structural system, despite its high structural weight and rigid connections and low flexibility, has integrated performance, and it can be a good option for earthquake-resistant constructions.

In this paper, a numerical procedure is proposed for achieving the minimum cost design of reinforced concrete polygonal column cross-sections under compression and biaxial bending. A methodology is developed to integrate the metaheuristic algorithm Quantum Particle Swarm Optimization (QPSO) with an algorithm for the evaluation of the strength of reinforced concrete cross-sections under combined axial load and biaxial bending, according to the design criteria of Brazilian Standard ABNT NBR 6118:2014. The objective function formulation takes into account the costs of concrete, reinforcement, and formwork. The cross-section dimensions, the number and diameter of rebar and the concrete strength are taken as discrete design variables. This methodology is applied to polygonal cross-sections, such as rectangular sections, rectangular hollow sections, and L-shaped cross-sections. To evaluate the efficiency of the methodology, the optimal solutions obtained were compared to results reported by other authors using conventional methods or alternative optimization techniques. An additional study investigates the effect on final costs for an alternative parametrization of rebar positioning on the cross-section. The proposed optimization method proved to be efficient in the search for optimal solutions, presenting consistent results that confirm the importance of using optimization techniques in the design of reinforced concrete structures.

In this paper, the non-linear mathematical problem is solved via numerical scheme by utilizing shooting method. Brownian diffusion and thermophoresis along mass and heat transfer are accounted for. Non-linear expression is reduced via non-dimensional variables. The simplified ordinary differential equations are tackled by shooting technique. Behavior of distinct influential parameters is investigated graphically and analyzed for temperature and concentration profile. Our finding indicates that temperature profile is enhanced for the thermophoresis, Brownian motion coefficient, Prandtl number, Eckert number and temperature slip parameter. Comparison of numerical technique with the extant literature is made and an acceptable agreement is attained. Graphs are plotted to examine the influence of these parameters.

This paper investigates the stability of the functionally graded cylindrical small-scale tube regarding the dynamic analysis and based on the modified nonclassical high-order nonlocal strain gradient theory. The nonlocal beam is modeled according to the high-order tube theory utilizing the energy method based on the Hamilton principle, then the nonlocal governing equations and also nonlocal boundary conditions equations are obtained. The tube structure is made of the new class of composite material composed of ceramic and metal phases as the functionally graded structures. The functionally graded (FG) tube structures rotate around the central axis, and the stability of this nanodevice is due to the centrifugal force which is used for the application of nanoelectromechanical systems (NEMS) is studied in detail.