This article examines the capability of Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM) for determination of compressive strength (f _c ) of self compacting concrete. The input variables of LSSVM and RVM are Cement (kg/m³)(C), Fly ash (kg/m³)(F), Water/powder (w/p), Superplasticizer dosage (%)(SP) Sand (kg/m³)(S) and Coarse Aggregate (kg/m³)(CA). The output of LSSVM and RVM is f _c . The developed LSSVM and RVM give equations for prediction of f _c . A comparative study has been done between the developed LSSVM, RVM and ANN models. Experiments have been conducted to verify the developed RVM and LSSVM. The developed RVM gives variance of the predicted f _c . The results confirm that the developed RVM is a robust model for prediction of f _c of self compacting concrete.

In recent years steel-concrete composite shear walls have been widely used in enormous high-rise buildings. Due to high strength and ductility, enhanced stiffness, stable cycle characteristics and large energy absorption, such walls can be adopted in the auxiliary building; surrounding the reactor containment structure of nuclear power plants to resist lateral forces induced by heavy winds and severe earthquakes. This paper demonstrates a set of nonlinear numerical studies on I-shaped composite steel-concrete shear walls of the nuclear power plants subjected to reverse cyclic loading. A three-dimensional finite element model is developed using ABAQUS by emphasizing on constitutive material modeling and element type to represent the real physical behavior of complex shear wall structures. The analysis escalates with parametric variation in steel thickness sandwiching the stipulated amount of concrete panels. Modeling details of structural components, contact conditions between steel and concrete, associated boundary conditions and constitutive relationships for the cyclic loading are explained. Later, the load versus displacement curves, peak load and ultimate strength values, hysteretic characteristics and deflection profiles are verified with experimental data. The convergence of the numerical outcomes has been discussed to conclude the remarks

The compressive strength of concrete is a commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time consuming. More importantly, it is too late to make improvements even if the test result does not satisfy the required strength, since the test is usually performed on the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is very important. This study presents the probabilistic technique for predicting the compressive strength of concrete on the basis of concrete mix proportions. The estimation of the strength is performed using the probabilistic neural network which is an effective tool for the pattern classification problem and provides a probabilistic viewpoint as well as a deterministic classification result. Application of probabilistic neural networks in the compressive strength estimation of concrete is performed using the mix proportion ...