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In this research, the behavior of RC beams retrofitted with steel fiber-reinforced self-compacting concrete jacket (SFRSCC-J) is investigated. For this purpose, by constructing 25 different beams, flexural load-carrying capacity of beams retrofitted with different concrete jackets is examined. The variables studied are the type of concrete (conventional and self-compacting), the strength grade of concrete (C40, C50, and C60), and the presence or absence of reinforcement bar and steel fiber in the jacket. The beams were subjected to three-point flexural loading, and the response was evaluated in terms of load–displacement diagrams. The results show that by adding SFRSCC-J to the perimeter of the beams, their bearing capacity can be increased up to about 7.4 times to reach the yield load, and about 5.1 times to reach the ultimate load. Moreover, the combination of steel fibers (SFs) and steel rebar reduced the cracks more, and at the final moment, the cracks extended to the neutral axis of the cross-section. In the retrofitted beams with the combination of rebar and SFs, the bonding between the reinforcement and the concrete increased and the reinforcement bars slipped less into the specimen. Thus, with a better distribution of stress, besides the concentration of the cracks in the middle of the span, the diagonal cracks decreased, and as the load increased, crack lengths increased less.

We extend Vuong’s (1989) model-selection statistic to allow for complex survey samples. As a further extension, we use an M-estimation setting so that the tests apply to general estimation problems – such as linear and nonlinear least squares, Poisson regression and fractional response models, to name just a few – and not only to maximum likelihood settings. With stratified sampling, we show how the difference in objective functions should be weighted in order to obtain a suitable test statistic. Interestingly, the weights are needed in computing the model-selection statistic even in cases where stratification is appropriately exogenous, in which case the usual unweighted estimators for the parameters are consistent. With cluster samples and panel data, we show how to combine the weighted objective function with a cluster-robust variance estimator in order to expand the scope of the model-selection tests. A small simulation study shows that the weighted test is promising.

Wind-waves are the most important environmental parameter for the design of coastal and offshore structures, sediment transport, coastal erosion etc. Therefore, an accurate evaluation of the wave climate is of great importance. Due to the lack of long term measurements, nowadays numerically modeled wave data are widely used for determining the wave climate. The numerically simulated wave data are continuous in time and space, but generally inaccurate in enclosed and semi-enclosed basins mainly due to the inaccurate wind input data. The main goal of this study is to develop a new and efficient approach to improve the hindcasted wave parameters in the Persian Gulf. Hence, the third generation SWAN model was employed for the wave modeling forced by the 6-hourly ECMWF wind data with a resolution of 0.5°. A new methodology was introduced for the distribution of wave prediction errors from discrete observation points to the other points of interest. It was found that the proposed method which considers the wave generation physics, leads to a significant improvement in the predicted wave parameters. In addition, it was revealed that the improvements in prediction of waves with higher wave heights and longer periods are more than those of others. This was shown to be due to the higher correlation between high values of output parameters which contain larger errors. The influence radius in the error distribution procedure was found to be near 2° (~200 km).

Let $\Phi$ and $\Psi$ be Young functions, and let $L^{\Phi}(\Omega)$ and $L^{\Psi}(\Omega)$ be corresponding Orlicz spaces on a measure space $(\Omega,\mu)$ . Our aim in this paper is to prove that, under mild conditions on $\Phi$ and $\Psi$ , the multiplication from $L^{\Phi}(\Omega)\times L^{\Psi}(\Omega)$ onto $L^{1}(\Omega)$ is uniformly open. This generalizes an interesting recent result due to M. Balcerzak, A. Majchrzycki, and F. Strobin in 2013.

Liquefaction-induced lateral spreading has imposed severe damages to many important structures supported on pile foundations during past earthquakes. As a result, evaluation of pile response to lateral spreading is an important step towards safe and resistant design of pile foundations against this destructive phenomenon. Current paper aims to study the response of a group of piles subjected to liquefaction-induced lateral spreading using a large scale 1-g shake table test. General test results including time-histories of accelerations, pore water pressures, displacements and bending moments are presented and discussed in this paper. In addition, distribution of lateral soil pressure on piles and displacement patterns around the piles are investigated in detail examining two main concepts affecting the lateral soil pressures on a group of piles, namely shadow and neighboring effects. On the basis of the test results, a simple numerical method is implemented for predicting the behavior of single piles under lateral spreading. The results indicate that lateral soil pressures exerted from laterally spreading ground vary in individual piles of a group both in transverse and longitudinal directions depending on the pile position within the group. It was found that the behavior of a group of piles (without pile cap) in an infinite mild slope far from a free face is different from those located behind a quay wall or close to a free face which were reported by other investigators.

The main goal of this study is to develop an efficient approach for the assimilation of the hindcasted wave parameters in the Persian Gulf. Hence, the third generation SWAN model was employed for wave modeling forced by the 6-h ECMWF wind data with a resolution of 0.5°. In situ wave measurements at two stations were utilized to evaluate the assimilation approaches. It was found that since the model errors are not the same for wave height and period, adaptation of model parameter does not result in simultaneous and comprehensive improvement of them. Therefore, an approach based on the error prediction and updating of output variables was employed to modify wave height and period. In this approach, artificial neural networks (ANNs) were used to estimate the deviations between the simulated and measured wave parameters. The results showed that updating of output variables leads to significant improvement in a wide range of the predicted wave characteristics. It was revealed that the best input parameters for error prediction networks are mean wind speed, mean wind direction, wind duration, and the wave parameters. In addition, combination of the ANN estimated error with numerically modeled wave parameters leads to further improvement in the predicted wave parameters in contrast to direct estimation of the parameters by ANN.

This paper employs a back analysis method to determine soil strength parameters of the Mohr-Coulomb model from in situ geotechnical measurements. The lateral displacement of a soil nailed wall retaining an excavation in Tehran city used as a criterion for the back analysis. For this purpose, a genetic algorithm is applied as an optimization algorithm to minimize the error function, which can perform the back analysis process. When the accuracy of modeling is verified, the back analysis is performed automatically by creating a link between genetic algorithm in MATLAB and Abaqus software using Python programming language. This paper demonstrated that the genetic algorithm is a particularly suitable tool to determine 9 soil strength parameters simultaneously for 3 soil layers of the project site to decrease the difference of lateral displacement between the results of project monitoring and numerical analysis. The soil strength parameters have increased, with the most changes in Young's modulus of the first to third layers as the most effective parameter, 49.45%, 61.67% and 64.35% respectively. The results can be used in advanced engineering analyses and professional works.

Today, the back analysis methods are known as reliable and effective approaches for estimating the soil strength parameters in the site of project. The back analysis can be performed by genetic algorithm and particle swarm optimization in the form of an optimization process. In this paper, the back analysis is carried out using genetic algorithm and particle swarm optimization in order to determine the soil strength parameters in an excavation project in Tehran city. The process is automatically accomplished by linking between MATLAB and Abaqus software using Python programming language. To assess the results of numerical method, this method is initially compared with the results of numerical studies by Babu and Singh. After the verification of numerical results, the values of the three parameters of elastic modulus, cohesion and friction angle (parameters of the Mohr–Coulomb model) of the soil are determined and optimized for three soil layers of the project site using genetic algorithm and particle swarm optimization. The results optimized by genetic algorithm and particle swarm optimization show a decrease of 72.1% and 62.4% in displacement differences in the results of project monitoring and numerical analysis, respectively. This research shows the better performance of genetic algorithm than particle swarm optimization in minimization of error and faster success in achieving termination conditions.

This paper presents the results of an extensive numerical parametric study on seismic behavior of 2D homogenous hills subjected to vertically propagating incident SV waves. It is shown that the amplification potential of these hills is strongly influenced by the wavelength, by the shape ratio, by the shape of the hill and in a less order of importance, by the Poisson ratio of the media. The 2D topography effect could be ignored, only if the hill has a shape ratio of less than 0.1 or if it is subjected to incident waves with predominant dimensionless periods of greater than 13 times the shape ratio. In incidence of waves with wavelengths longer than the width of the hill, the amplification curve usually finds its maximum at the crest and decreases towards the base of the hill. Else, some de-amplification zones would occur along the hill. Among hills with similar shape ratios, those with intermediate cross section areas show intermediate seismic behavior, too. Estimated seismic site coefficients for the crest of a 2D rocky hill depend on its shape ratio and could reach even 1.7, which encourages one to classify it according to standard site categorization procedures as soil profile types SC or SD instead of the conventional SB type.