Abstract. Numerical simulations were conducted to investigate the bearing behaviour of steel foot pipe. Main mechanical affections occur at the lateral zone, and the maximal stress occurs at the lateral end of the pipe. With the increase of cross direction load, the maximal stress increases and exceeds the strength of material, and the pipe is pulled out. The influencing factors of bearing capacity of the steel foot pipe were studied. Some important results different from engineering experiences are as following: 1) to steel foot pipe that can not penetrate the soft base, its reasonable dip angle is very small for its low longitudinal bearing capacity; 2) steel pipe with diameter of 42mm is unsuitable for foot steel pipe because its low cross bearing capacity; 3) to a given geological condition and pipe parameter, the reasonable length of steel pipe is certain, extra longer pipes can’t obtain larger capacity; 4) surrounding rock near the support structure should be especially reinforced by drilling grouting holes along the whole steel pipe. Steel foot pipe and its application Lots of tunnels built in soft surround rock underwent great settlement for the poor bearing capacity of the base rock [1-2]. To prevent or treat the immoderate settlement, a kind of reinforcement named steel foot pipe are widely used in China for its great rigidity, quick and convenient construction.

We describe a system for representing moving images with sets of overlapping layers. Each layer contains an intensity map that defines the additive values of each pixel, along with an alpha map that serves as a mask indicating the transparency. The layers are ordered in depth and they occlude each

, and demonstrate that most of the steps are polynomial, typically bounded by O (N 2 ). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work. This paper

Most active learning approaches select either informative or representative unla-beled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usu-ally ad hoc in finding unlabeled instances

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Most active learning approaches select either informative or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both

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