Recent developments in Artificial Intelligence (AI) and computational intelligence have made it viable in the construction industry and structural analysis. This study usesthe Adaptive Network-based Fuzzy Inference System (ANFIS) as a modelling tool to predict the strain in tie section for High Strength Self Compacting Concrete (HSSCC) deep beams. 3773 experimental data were collected. The input data andits corresponding strains in tie section as output data were recorded at all loading stages. Results from ANFIS are compared with the classical linear regression (LR). The comparison shows that the ANFIS's results are highly accurate, precise and satisfactory.

This paper aims to contribute to the three-dimensional generalization of numerical prediction of crack propagation through the formulation of finite elements with embedded discontinuities. The analysis of crack propagation in two-dimensional problems yields lines of discontinuity that can be tracked in a relatively simple way through the sequential construction of straight line segments oriented according to the direction of failure within each finite element in the solid. In three-dimensional analysis, the construction of the discontinuity path is more complex because it requires the creation of plane surfaces within each element, which must be continuous between the elements. In the method proposed by Chaves (2003) the crack is determined by solving a problem analogous to the heat conduction problem, established from local failure orientations, based on the stress state of the mechanical problem. To minimize the computational effort, in this paper a new strategy is proposed whereby the analysis for tracking the discontinuity path is restricted to the domain formed by some elements near the crack surface that develops along the loading process. The proposed methodology is validated by performing three-dimensional analyses of basic problems of experimental fractures and comparing their results with those reported in the literature.

This paper concentrates on the results of experimental work on tensile strength of self-compacting concrete (SCC) caused by flexure, which is called rupture modulus. The work focused on concrete mixes having water/binder ratios of 0.35 and 0.45, which contained constant total binder contents of 500 $kg/m^3$ and 400 $kg/m^3$, respectively. The concrete mixes had four different dosages of a superplasticizer based on polycarboxylic with and without silica fume. The percentage of silica fume that replaced cement in this research was 10%. Based upon the experimental results, the existing equations for anticipating the rupture modulus of SCC according to its compressive strength were not exact enough. Therefore, it is decided to use artificial neural networks (ANN) for anticipating the rupture modulus of SCC from its compressive strength and workability. The conclusion was that the multi layer perceptron (MLP) networks could predict the tensile strength in all conditions, but radial basis (RB) networks were not exact enough in some circumstances. On the other hand, RB networks were more users friendly and they converged to the final networks quicker.

The purpose of this study is to present different composed material models for reinforced concrete structures (RC). For this aim a nonlinear finite element analysis program is coded in MATLAB. This program contains several yield criteria and stress-strain relationships for compression and tension behavior of concrete. In this study, the well-known criteria, Drucker-Prager, von Mises, Mohr Coulomb, Tresca, and two new criteria, Hsieh-Ting-Chen and Bresler-Pister, are taken into account. It is concluded that the coded program, the new yield criteria, and the models considered can be effectively used in the nonlinear analysis of reinforced concrete beams.

In this paper, the properties of the cement mortar modified with styrene acrylic ester copolymer were investigated. Expanded vermiculite as lightweight aggregate was used for making the polymer modified mortar test specimens. To study the effect of polymer-cement ratio and vermiculite-cement ratio on various properties, specimens were prepared by varying the polymer-cement and vermiculite-cement ratios. Tests of physical properties such as density, water absorption, thermal conductivity, three-point flexure and compressive tests were made on the specimens. Furthermore, a coupled thermal-structural finite element model of an entire corner wall was modelled in order to study the best material configuration. The wall is composed by a total of 132 bricks of $120{\times}242{\times}54$ size, joined by means of a contact-bonded model. The use of advanced numerical methods allows us to obtain the optimum material properties. Finally, comparisons of polymer-cement and vermiculite-cement ratios on physical properties are given and the most important conclusions are exposed.

Traditional concrete is effectively an insulator in the dry state. However, conductive concrete can attain relatively high conductivity by adding a certain amount of electronically conductive components in the regular concrete matrix. The main purpose of this study is to investigate the electrical and thermal properties of conductive concrete with various graphite contents, specimen dimensions and applied voltages. For this purpose, six different mixtures (the control mixtures and five conductive mixtures with steel fibers of 2% by weight of coarse aggregate and graphite as fine aggregate replacement at the levels of 0%, 5%, 10%, 15% and 20% by weight) were prepared and concrete blocks with two types of dimensions were fabricated. Four test voltage levels, 48 V, 60 V, 110 V, and 220 V, were applied for the electrical and thermal tests. Test results show that the compressive strength of specimens decreases as the amount of graphite increases in concrete. The rising applied voltage decreases electrical resistivity and increases heat of concrete. Meanwhile, higher electrical current and temperature have been obtained in small size specimens than the comparable large size specimens. From the results, it can be concluded that the graphite contents, applied voltage levels, and the specimen dimensions play important roles in electrical and thermal properties of concrete. In addition, the superior electrical and thermal properties have been obtained in the mixture adding 2% steel fibers and 10% graphite.

Post-punching resistance of a flat slab can help redistribute the gravity loads and resist progressive collapse of a structure following initial damage. One important difficulty with accounting for the post-punching strength of a slab is the discontinuity that develops following punching shear. A numerical simulation technique is proposed here to model and evaluate post-punching resistance of flat slabs. It is demonstrated that the simulation results of punching shear and post-punching response of the model of a slab on a single column are in good agreement with corresponding experimental data. It is also shown that progressive collapse due to a column removal (explosion) can lead to punching failure over an adjacent column. Such failure can propagate throughout the structure leading to the progressive collapse of the structure. Through post-punching modeling of the slab and accounting for the associated discontinuity, it is also demonstrated that the presence of an adequate amount of integrity reinforcement can provide an alternative load path and help resist progressive collapse.