ULTIMATE STRENGTH OF H-COLUMNS UNDER BIAXIAL BENDING (Part II)

Ultimate strength analysis of composite sections under biaxial bending and axial load

A new generic fiber model algorithm is presented that allows for the efficient analysis of arbitrary composite sections under biaxial bending and axial load. The geometry of the cross section is described by multi-nested curvilinear polygons i.e. closed polygons with edges that are straight lines or circular arcs. The polygons may be convex or concave and may contain openings. The stress–strain...

PREDICTION OF BIAXIAL BENDING BEHAVIOR OF STEEL-CONCRETE COMPOSITE BEAM-COLUMNS BY ARTIFICIAL NEURAL NETWORK

In this study, the complex behavior of steel encased reinforced concrete (SRC) composite beam–columns in biaxial bending is predicted by multilayer perceptron neural network. For this purpose, the previously proposed nonlinear analysis model, mixed beam-column formulation, is verified with biaxial bending test results. Then a large set of benchmark frames is provided and P-Mx-My triaxial ...

"ultimate Strength of Members of Reinforced Concrete Frames in Combined Bending and Torsion

Glass-reinforced hydroxyapatite composites: secondary phase proportions and densification effects on biaxial bending strength.

CaO-P(2)O(5) glasses with additions of MgO and CaF(2) were used as a sintering aid of hydroxyapatite, and glass-reinforced hydroxyapatite composites obtained. Glasses promoted significant changes in the microstructure of the composites, namely with the formation of tricalcium phosphate secondary phases, beta and alpha-TCP. Quantitative phase analysis was performed by the Rietveld method using G...

Ultimate Intelligence Part II: Physical Measure and Complexity of Intelligence

We investigate physical measures and limits of intelligence that are objective and useful. We propose a universal measure of operator induction fitness, and show how it can be used in a reinforcement learning model, and a self-preserving agent model based on the free energy principle. We extend logical depth and conceptual jump size measures to stochastic domains, and elucidate their relations....