Performance of bolted steel-beam to CFST-column joints using stiffened angles in column-removal scenario

This paper presents three experimental investigations on the performance of steel-beam to CFST-column joints using stiffened angle, long bolts and fin plate under a middle column removal scenario. Three specimens were designed and tested. The failure modes and catenary action are investigated in detail. The test results show that increasing the angle plate thickness at the joint could not only improve its performance significantly, but also trigger an early formation of catenary action. Increasing the length of short-limb had influence on the deformation ability of the proposed joint, rather than the load capacity. The buckling of stiffeners could prevent the brittle failure of the joints. With the contribution of catenary action, the joint shows much higher rotation capacities than that required in DoD design guidance. The initial stiffness of the joint was calculated using an analytical model with consideration of bolt pretension. Good agreement to the test results is achieved. A numerical analysis is also carried out, whose results show that adding additional row of bolts would improve the redundancy of the joint under column loss. An equivalent dynamic response evaluation of the joints was also performed. The results show that dynamic amplification coefficient should be worked out considering catenary action under large deformation

Properties of solutions of stochastic differential equations driven by the G-Brownian motion

In this paper, we study the differentiability of solutions of stochastic differential equations driven by the $G$-Brownian motion with respect to the initial data and the parameter. In addition, the stability of solutions of stochastic differential equations driven by the $G$-Brownian motion is obtained

On the Triality Theory for a Quartic Polynomial Optimization Problem

This paper presents a detailed proof of the triality theorem for a class of fourth-order polynomial optimization problems. The method is based on linear algebra but it solves an open problem on the double-min duality left in 2003. Results show that the triality theory holds strongly in a tri-duality form if the primal problem and its canonical dual have the same dimension; otherwise, both the canonical min-max duality and the double-max duality still hold strongly, but the double-min duality holds weakly in a symmetrical form. Four numerical examples are presented to illustrate that this theory can be used to identify not only the global minimum, but also the largest local minimum and local maximum.Comment: 16 pages, 1 figure; J. Industrial and Management Optimization, 2011. arXiv admin note: substantial text overlap with arXiv:1104.297

Combining Models of Approximation with Partial Learning

In Gold's framework of inductive inference, the model of partial learning requires the learner to output exactly one correct index for the target object and only the target object infinitely often. Since infinitely many of the learner's hypotheses may be incorrect, it is not obvious whether a partial learner can be modifed to "approximate" the target object. Fulk and Jain (Approximate inference and scientific method. Information and Computation 114(2):179--191, 1994) introduced a model of approximate learning of recursive functions. The present work extends their research and solves an open problem of Fulk and Jain by showing that there is a learner which approximates and partially identifies every recursive function by outputting a sequence of hypotheses which, in addition, are also almost all finite variants of the target function. The subsequent study is dedicated to the question how these findings generalise to the learning of r.e. languages from positive data. Here three variants of approximate learning will be introduced and investigated with respect to the question whether they can be combined with partial learning. Following the line of Fulk and Jain's research, further investigations provide conditions under which partial language learners can eventually output only finite variants of the target language. The combinabilities of other partial learning criteria will also be briefly studied.Comment: 28 page