The limited availability of raw materials and increasing service demands for pavements pose a unique challenge in terms of pavement design and concrete material selection. The self-compacting rubberized concrete (SCRC) can be used in pavement design. The SCRC pavement slab has advantages of excellent toughness, anti-fatigue and convenient construction. On the premise of satisfying the strength, the SCRC can increase the ductility of pavement slab. The aim of this investigation is proposing a new method to predict the crack growth and flexural capacity of large-scale SCRC slabs. The mechanical properties of SCRC are obtained from experiments on small-scale SCRC specimens. With the increasing of the specimen depth, the bearing capacity of SCRC beams decreases at the same initial crack-depth ratio. By constructing extended finite element method (XFEM) models, crack growth and flexural capacity of large-scale SCRC slabs with different fracture types and force conditions can be predicted. Considering the diversity of fracture types and force conditions of the concrete pavement slab, the corresponding test was used to verify the reliability of the prediction model. The crack growth and flexural capacity of SCRC slabs can be obtained from XFEM models. It is convenient to conduct the experiment and can save cost.

Evolutionary algorithms based on conventional statistical methods such as regression and classification have been widely used in data mining applications. This work involves application of gene expression programming (GEP) for predicting compressive strength of fly ash geopolymer concrete, which is gaining increasing interest as an environmentally friendly alternative of Portland cement concrete. Based on 56 test results from the existing literature, a model was obtained relating the compressive strength of fly ash geopolymer concrete with the significantly influencing mix design parameters. The predictions of the model in training and validation were evaluated. The coefficient of determination ($R^2$), mean (${\mu}$) and standard deviation (${\sigma}$) were 0.89, 1.0 and 0.12 respectively, for the training set, and 0.89, 0.99 and 0.13 respectively, for the validation set. The error of prediction by the model was also evaluated and found to be very low. This indicates that the predictions of GEP model are in close agreement with the experimental results suggesting this as a promising method for compressive strength prediction of fly ash geopolymer concrete.

The depth of superstructure is the summation of the height of girders and the thickness of the deck floor. In this study, it is aim to determine the maximum span length of girders and minimum depth of the superstructure of prestressed concrete I-girder bridge. For this purpose the superstructure of the bridge with the width of 10m and the thickness of the deck floor of 0.175m, which the girders length was changed by two meter increments between 15m and 35m, was taken into account. Twelve different girders with heights of 60, 75, 90, 100, 110, 120, 130, 140, 150, 160, 170 and 180 cm, which are frequently used in Turkey, were chosen as girder type. The analyses of the superstructure of prestressed concrete I girder bridge was conducted with I-CAD software. In the analyses AASHTO LRFD (2012) conditions were taken into account a great extent. The dead loads of the structural and non-structural elements forming the bridge superstructure, prestressing force, standard truck load, equivalent lane load and pedestrian load were taken into consideration. HL93, design truck of AASHTO and also H30S24 design truck of Turkish Code were selected as vehicular live load. The allowable concrete stress limit, the number of prestressed strands, the number of debonded strands and the deflection parameters obtained from analyses were compared with the limit values found in AASHTO LRFD (2012) to determine the suitability of the girders. At the end of the study maximum span length of girders and equation using for calculation for minimum depth of the superstructure of prestressed concrete I-girder bridge were proposed.

The purpose of the present study is to present a new approach to designing and selecting the details of multidimensional continuous RC beam by applying all strength, serviceability, ductility and other constraints based on ACI318-14 using Teaching Learning Based Optimization (TLBO) algorithm. The optimum reinforcement detailing of longitudinal bars is done in two steps. in the first stage, only the dimensions of the beam in each span are considered as the variables of the optimization algorithm. in the second stage, the optimal design of the longitudinal bars of the beam is made according to the first step inputs. In the optimum shear reinforcement, using gradient-based methods, the most optimal possible mode is selected based on the existing assumptions. The objective function in this study is a cost function that includes the cost of concrete, formwork and reinforcing steel bars. The steel used in the objective function is the sum of longitudinal and shear bars. The use of a catalog list consisting of all existing patterns of longitudinal bars based on the minimum rules of the regulation in the second stage, leads to a sharp reduction in the volume of calculations and the achievement of the best solution. Three example with varying degrees of complexity, have been selected in order to investigate the optimal design of the longitudinal and shear reinforcement of continuous beam.

Despite the extensive use of mortar materials in constructions over the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict mortar strength based on its mix components. This limitation is due to the highly nonlinear relation between the mortar's compressive strength and the mixed components. In this paper, the application of artificial neural networks for predicting the compressive strength of mortars has been investigated. Specifically, surrogate models (such as artificial neural network models) have been used for the prediction of the compressive strength of mortars (based on experimental data available in the literature). Furthermore, compressive strength maps are presented for the first time, aiming to facilitate mortar mix design. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of mortars in a reliable and robust manner.

This work investigates the effect of Winkler/Pasternak/Kerr foundation and porosity on dynamic behavior of FG plates using a simple quasi-3D hyperbolic theory. Four different patterns of porosity variations are considered in this study. The used quasi-3D hyperbolic theory is simple and easy to apply because it considers only four-unknown variables to determine the four coupled vibration responses (axial-shear-flexion-stretching). A detailed parametric study is established to evaluate the influences of gradient index, porosity parameter, stiffness of foundation parameters, mode numbers, and geometry on the natural frequencies of imperfect FG plates.

This paper aims to present an analytical model to predict the static analysis of laminated reinforced composite plates subjected to sinusoidal and uniform loads by using a simple first-order shear deformation theory (SFSDT). The most important aspect of the present theory is that unlike the conventional FSDT, the proposed model contains only four unknown variables. This is due to the fact that the inplane displacement field is selected according to an undetermined integral component in order to reduce the number of unknowns. The governing differential equations are derived by employing the static version of principle of virtual work and solved by applying Navier's solution procedure. The non-dimensional displacements and stresses of simply supported antisymmetric cross-ply and angle-ply laminated plates are presented and compared with the exact 3D solutions and those computed using other plate theories to demonstrate the accuracy and efficiency of the present theory. It is found from these comparisons that the numerical results provided by the present model are in close agreement with those obtained by using the conventional FSDT.

Steel-concrete composite beams are widely employed in constructions and their performance at the serviceability stage is of concern among practitioners and design regulations. In this context, an accurate evaluation of long-term deflections via various rheological concrete models is needed. In this work, the performance and predict capability of some concrete creep and shrinkage models ACI, CEB, B3, FIB and GL2000 are ascertained, and compared by using statistical bias indicators. Ten steel-concrete composite beams with existing experimental and numerical results are then modeled for this purpose. The proposed modeling technique uses the finite element method, where the concrete slab and steel beam are modeled with shell finite elements. Concrete is considered as an aging viscoelastic material and cracking is treated with the common smeared approach. The results show that when the experimental ultimate shrinkage strain is used for calibration, all studied rheological models predict nearly similar deflections, which agree with the experimental data. In contrast, significance differences are encountered for some models, when none calibration is made prior to. A value between twenty and thirty times the cracking strain is recommended for the ultimate tensile strain in the tension stiffening model. Also, increasing the relative humidity and decreasing the ambient temperature can lead to a substantial reduction of slab cracking for beams under negative flexure. Finally, there is not a unique rheological model that clearly excels in all scenarios.