This article investigates the thermal and post-buckling problems of graphene platelets reinforced metal foams (GPLRMF) beams with initial geometric imperfection. Three distribution forms of graphene platelet (GPLs) and foam are employed. This article utilizes the mixing law Halpin Tsai model to estimate the physical parameters of materials. Considering three different boundary conditions, we used the Euler beam theory to establish the governing equations. Afterwards, the Galerkin method is applied to discretize these equations. The correctness of this article is verified through data analysis and comparison with the existing articles. The influences of geometric imperfection, GPL distribution modes, boundary conditions, GPLs weight fraction, foam distribution pattern and foam coefficient on thermal post-buckling are analyzed. The results indicate that, perfect GPLRMF beams do not undergo bifurcation buckling before reaching a certain temperature, and the critical buckling temperature is the highest when both ends are fixed. At the same time, the structural stiffness of the beam under the GPL-A model is the highest, and the buckling response of the beam under the Foam-II mode is the lowest, and the presence of GPLs can effectively improve the buckling strength.

The present study pertains to the effects of variations in the location and size of drilled web openings on the behavior of fixed-fixed reinforced concrete (RC) beams. For this purpose, a reference bending beam with a transverse opening in each half span was tested to failure. Later, the same beam was modeled and analyzed with the help of finite element software using ABAQUS. Upon achieving close agreement between the experimental and numerical results, the location and size of the web opening were altered to uncover the effects of these factors on the shear strength and load-deflection behavior of RC beams. The experimental failure mode of the tested beam and the numerical results were also verified by theoretical calculations. In numerical analysis, when compared to the reference (D0) specimen, if the distance of the opening center from the support is 0 or h or 2h, reduction in load-bearing capacity of 1.5%-22.8% or 2.0%-11.3% or is 4.1%-40.7%. In other words, both the numerical analyses and theoretical calculations indicated that the beam behavior shifted from shear-controlled to flexure-controlled as the openings approached the supports. Furthermore, the deformation capacities, energy absorption values, and the ductilities of the beams with different opening diameters also increased with the decreasing distance of the opening from supports. Web compression failure was shown to be the predominant mode of failure of beams with large diameters due to the lack of sufficient material in the diagonal compression strut of the beam. The present study indicated that transverse openings with diameters, not exceeding about 1/3 of the entire beam depth, do not cause the premature shear failure of RC beams. Finally, shear damage should be prevented by placing special reinforcements in the areas where such gaps are opened.

Predicting fracture properties requires an understanding of structural failure behaviour in relation to specimen type, dimension, and notch length. Facture properties are evaluated using various testing methods, wedge splitting test being one of them. The wedge splitting test was numerically modelled three dimensionally using the finite element method on self compacting concrete specimens with varied specimen and notch depths in the current work. The load - Crack mouth opening displacement curves and the angle of rotation with respect to notch opening till failure are used to assess the fracture properties. Furthermore, based on the simulation results, failure curve was built to forecast the fracture behaviour of self-compacting concrete. The fracture failure curve revealed that the failure was quasi-brittle in character, conforming to non-linear elastic properties for all specimen depth and notch depth combinations.

This work presents a comprehensive investigation of buckling behavior of bidirectional functionally graded imperfect beams exposed to several thermal loading with general boundary conditions. The nonlinear governing equations are derived based on 2D shear deformation theory together with Von Karman strain-displacement relation. The beams are composed of two different materials. Its properties are porosity-dependent and are continuously distributed over the length and thickness of the beams following a defined law. The resulting equations are solved analytically in order to determine the thermal buckling characteristics of BDFG porous beams. The precision of the current solution and its accuracy have been proven by comparison with works previously published. Numerical examples are presented to explore the effects of the thermal loading, the elastic foundation parameters, the porosity distribution, the grading indexes and others factors on the nonlinear thermal buckling of bidirectional FG beam rested on elastic foundation.

Recently, many engineering computations have realized their digital transformation to Machine Learning (ML)-based systems. Predicting the behavior of a structure, which is mainly computed with structural analysis software, is an essential step before construction for efficient structural analysis. Especially in the seismic-based design procedure of the structures, predicting the lateral load capacity of reinforced concrete (RC) columns is a vital factor. In this study, a novel ML-based model is proposed to predict the maximum lateral load capacity of RC columns under varying axial loads or cyclic loadings. The proposed model is generated with a Deep Neural Network (DNN) and compared with traditional ML techniques as well as a popular commercial structural analysis software. In the design and test phases of the proposed model, 319 columns with rectangular and square cross-sections are incorporated. In this study, 33 parameters are used to predict the maximum lateral load capacity of each RC column. While some traditional ML techniques perform better prediction than the compared commercial software, the proposed DNN model provides the best prediction results within the analysis. The experimental results reveal the fact that the performance of the proposed DNN model can definitely be used for other engineering purposes as well.

This paper proposes a numerically-based methodology to implicitly model irreversible deformations in concrete through a damage model. Plasticity theory is not explicitly employed, although resemblances are still present. A scalar isotropic damage model is adopted and the damage variable is split in two: one contributing for stiffness degradation (cracking) and other contributing for irreversible deformations (plasticity). The proposed methodology is thermodynamically consistent as it consists in a damage model rewritten in different terms. Its Finite Element coding is presented, indicating that minor changes are necessary. It is also demonstrated that nonlinear algorithms are unnecessary to model concrete cracking and plasticity. Experimental data from direct tension and four-point bending tests under cyclic loading are compared to the proposed methodology. A numerical case study of a low-cycle fatigue is also presented. It can be concluded that the model is simple, feasible and capable to capture the essentials concerning cracking and plasticity.

Internal curing, a widely used method for mitigating early-age shrinkage in concrete, also offers notable advantages for concrete durability. This paper explores the potential of internal curing by partial replacement of sand with fine lightweight aggregate for enhancing the behavior of high-performance concrete at elevated temperatures. Such a technique may prove economical and safe for the construction of skyscrapers, where explosive spalling of high-performance concrete in fire is a potential hazard. To reach this aim, the physico-mechanical features of internally cured high-strength concrete specimens, including mass loss, compressive strength, strain at peak stress, modulus of elasticity, stress-strain curve, toughness, and flexural strength, were investigated under different temperature exposures; and to predict some of these mechanical properties, a number of equations were proposed. Based on the experimental results, an advanced stress-strain model was proposed for internally cured high-performance concrete at different temperature levels, the results of which agreed well with the test data. It was observed that the replacement of 10% of sand with pre-wetted fine lightweight expanded clay aggregate (LECA) not only did not reduce the compressive strength at ambient temperature, but also prevented explosive spalling and could retain 20% of its ambient compressive strength after heating up to 800℃. It was then concluded that internal curing is an excellent method to enhance the performance of high-strength concrete at elevated temperatures.

The growing demands for sustainable and high-performance construction materials necessitate a deep understanding of their physicochemical properties by that of these heterogeneities. This paper presents a comprehensive review of the state-of-the-art hierarchical multiscale modeling approach aimed at predicting the intricate physicochemical characteristics of construction materials. Emphasizing the heterogeneity inherent in these materials, the review briefly introduces single-scale analyses, including the ab initio method, molecular dynamics, and micromechanics, through a scale-bridging technique. Herein, the limitations of these models are also overviewed by that of effectively scale-bridging methods of length or time scales. The hierarchical multiscale model demonstrates these physicochemical properties considering chemical reactions, material defects from nano to macro scale, microscopic properties, and their influence on macroscopic events. Thereby, hierarchical multiscale modeling can facilitate the efficient design and development of next-generation construction.