In this paper, the buckling of micro sandwich hollow circular plate is investigated with the consideration of the porous core and piezoelectric layer reinforced by functionally graded (FG)carbon nano-tube. For modeling the displacement field of sandwich hollow circular plate, the high-order shear deformation theory (HSDT) of plate and modified couple stress theory (MCST) are used. The governing differential equations of the system can be derived using the principle of minimum potential energy and Maxwell's equation that for solving these equations, the Ritz method is employed. The results of this research indicate the influence of various parameters such as porous coefficients, small length scale parameter, distribution of carbon nano-tube in piezoelectric layers and temperature on critical buckling load. The purpose of this research is to show the effect of physical parameters on the critical buckling load of micro sandwich plate and then optimize these parameters to design structures with the best efficiency. The results of this research can be used for optimization of micro-structures and manufacturing different structure in aircraft and aerospace.

Although concrete is the most widely used construction material, its deficiency in shrinkage and low tensile resistance is undeniable. However, the aforementioned defects can be partially modified by addition of fibers. On the other hand, possibility of adding waste materials in concrete has provided a new ground for use of recycled concrete aggregates in the construction industry. In this study, a constant combination of recyclable coarse and fine concrete aggregates was used to replace the corresponding aggregates at 50% substitution percentage. Moreover, in order to investigate the effects of fibers on mechanical and durability properties of recycled aggregate concrete, the amounts of 0.5%, 1%, and 1.5% steel fibers (ST) and 0.05%, 0.1% and 0.15% polypropylene (PP) fibers by volumes were used individually and in hybrid forms. Compressive strength, tensile strength, flexural strength, ultrasonic pulse velocity (UPV), water absorption, toughness, elastic modulus and shrinkage of samples were investigated. The results of mechanical properties showed that PP fibers reduced the compressive strength while positive impact of steel fibers was evident both in single and hybrid forms. Tensile and flexural strength of samples were improved and the energy absorption of samples containing fibers increased substantially before and after crack presence. Growth in toughness especially in hybrid fiber-reinforced specimens retarded the propagation of cracks. Modulus of elasticity was decreased by the addition of PP fibers while the contrary trend was observed with the addition of steel fibers. PP fibers decreased the ultrasonic pulse velocity slightly and had undesirable effect on water absorption. However, steel fiber caused negligible decline in UPV and a small impact on water absorption. Steel fibers reduce the drying shrinkage by up to 35% when was applied solely. Using fibers also resulted in increasing the ductility of samples in failure. In addition, mechanical properties changes were also evaluated by statistical analysis of MATLAB software and smoothing spline interpolation on compressive, flexural, and indirect tensile strength. Using shell interpolation, the optimization process in areas without laboratory results led to determining optimal theoretical points in a two-parameter system including steel fibers and polypropylene.

This paper has presented an effective and accurate meso-scale finite element model for simulating the fracture process of concrete under compression-shear loading. In the proposed model, concrete is parted into four important phases: aggregates, cement matrix, interfacial transition zone (ITZ), and the initial defects. Aggregate particles were modelled as randomly distributed polygons with a varying size according to the sieve curve developed by Fuller and Thompson. With regard to initial defects, only voids are considered. Cohesive elements with zero thickness are inserted into the initial mesh of cement matrix and along the interface between aggregate and cement matrix to simulate the cracking process of concrete. The constitutive model provided by ABAQUS is modified based on Wang's experiment and used to describe the failure behaviour of cohesive elements. User defined programs for aggregate delivery, cohesive element insertion and modified facture constitutive model are developed based on Python language, and embedded into the commercial FEM package ABAQUS. The effectiveness and accuracy of the proposed model are firstly identified by comparing the numerical results with the experimental ones, and then it is used to investigate the effect of meso-structure on the macro behavior of concrete. The shear strength of concrete under different pressures is also involved in this study, which could provide a reference for the macroscopic simulation of concrete component under shear force.

Experience of previous earthquakes shows that a considerable portion of concrete precast buildings sustain relatively large damages especially at the beam-column joints where the damages are mostly caused by bar slippage. Precast concrete buildings have a kind of discontinuity in their beam-column joints, so reinforcement details in this area is too important and have a significant effect on the seismic behavior of these structures. In this study, a relatively simple and efficient nonlinear model is proposed to simulate pre- and post-elastic behavior of the joints in usual practice of precast concrete building. In this model, beam and column components are represented by linear elastic elements, dimensions of the joint panel are defined by rigid elements, and effect of slip is taken into account by a nonlinear rotational spring at the end of the beam. The proposed method is validated by experimental results for both internal and external joints. In addition, the seismic behavior of the precast building damaged during Bojnord earthquake 13 May 2017, is investigated by using the proposed model for the beam-column joints. Damage unexpectedly inducing the precast building in the moderate Bojnord earthquake may confirm that bearing capacity of the precast building was underestimated without consideration of joint behavior effect.

The key issue for the finite element analysis (FEA) of section steel reinforced concrete (SRC) structure is how to consider the bond-slip performance. However, the bond-slip performance is hardly considered in the FEA of SRC structures because it is difficult to achieve in the finite element (FE) model. To this end, the software developed by Python can automatically add spring elements for the FE model in ABAQUS to considering bond-slip performance. The FE models of the push-out test were conducted by the software and calculated by ABAQUS. Comparing the calculated results with the experimental ones showed that: (1) the FE model of SRC structure with the bond-slip performance can be efficiently and accurately conducted by the software. For the specimen with a length of 1140 mm, 3565 spring elements were added to the FE model in just 6.46s. In addition, different bond-slip performance can also be set on the outer side, the inner side of the flange and the web. (2) The results of the FE analysis were verified against the corresponding experimental results in terms of the law of the occurrence and development of concrete cracks, the stress distribution on steel, concrete and steel bar, and the P-S curve of the loading and free end.

In order to investigate the strength recovery of fire-damaged concrete after post-fire curing, concrete specimens were heating at $2^{\circ}C/min$ or $5^{\circ}C/min$ to 400, 600 and $800^{\circ}C$, and these exposed specimens were soaked in the water for 24 hours and following by 29-day post-fire curing. The compressive strength and split tensile strength of the high-temperature-exposed specimens before and after post-fire curing were tested. The proportion of split aggregate in the split surfaces was analyzed to evaluate the mortar-aggregate interfacial strength. After the post-fire curing process, the split tensile strength of specimens exposed to all temperatures was recovered significantly, while the recovery of compressive strength was only obvious within the specimens exposed to $600^{\circ}C$. The tensile strength is more sensitive to the mortar-aggregate interfacial cracks, which caused that the split tensile strength decreased more after high-temperature exposure and recovery more after post-fire curing than the compressive strength. The mortar-aggregate interfacial strength also showed remarkable recovery after post-fire curing, and it contributed to the recovery of split tensile strength.

The work aims at the comparison among commonly used research programs concerning moment-curvature ($M-{\chi}$) diagrams of confined R.C. members. The software considered in this work are Sap2000, SeismoStruct and Opensees. The curves provided by these software, given the same modelling, have been compared to those provided by a theoretical fiber model. A parametric analysis has been led on rectangular column sections with different level of axial load and different stirrups spacing. The accuracy of the modelling of the considered structural programs has been investigated by comparing their results with those obtained by applying the theoretical fiber model.

Concrete undergoes a series of thermo-based physio-chemical changes once exposed to elevated temperatures. Such changes adversely alter the composition of concrete and oftentimes lead to fire-induced explosive spalling. Spalling is a multidimensional, complex and most of all sophisticated phenomenon with the potential to cause significant damage to fire-exposed concrete structures. Despite past and recent research efforts, we continue to be short of a systematic methodology that is able of accurately assessing the tendency of concrete to spall under fire conditions. In order to bridge this knowledge gap, this study explores integrating novel artificial intelligence (AI) techniques; namely, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA), together with traditional statistical analysis (multilinear regression (MLR)), to arrive at state-of-the-art procedures to predict occurrence of fire-induced spalling. Through a comprehensive datadriven examination of actual fire tests, this study demonstrates that AI techniques provide attractive tools capable of predicting fire-induced spalling phenomenon with high precision.