Field tests of cement fly-ash steel-slag pile composite foundation

Steel slag is one of the main waste materials in the steelmaking process. As a result, a tremendous amount of steel slag is produced and deposited into storing yards every year. Recycling of the abandoned steel slag is of great environmental and economic value. This study investigates the usage of steel-slag concrete with fly ash as a kind of composite foundation pile material, which can be applied to multi-pile composition foundations for ground improvement involving different pile types. The micromorphology of the concrete, which uses steel slag as aggregate, is analyzed using scanning electron microscopy (SEM). The bearing characteristics of cement fly-ash steel-slag pile (CFS pile) composite foundations are investigated via field load tests, including settlement of the foundation base, horizontal displacement at different depths, distribution of vertical stress increase of the composite foundation, and stress increase of the soil around the pile hole. In addition, the effect of soil squeezing caused by the construction of a CFS pile is studied. To accomplish this, the variation in the increase in stress of the foundation at different distances in the horizontal direction is measured. The results suggest that the usage of steel slag as an aggregate can effectively satisfy the strength requirement of the pile. CFS pile composite foundations have the advantages of high bearing capacity, small settlement deformation, and limited horizontal deformation. This study demonstrates the potential usage of steel slag as aggregate in pile composite foundations, which can bring significant economic and environmental benefits

Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification

In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative information; and (3) hierarchically extract patterns at different visual levels. Particularly, in each single layer of DDSFL, shareable filters are jointly learned for classes which share the similar patterns. Discriminative power of the filters is achieved by enforcing the features from the same category to be close, while features from different categories to be far away from each other. Furthermore, we also propose two exemplar selection methods to iteratively select training data for more efficient and effective learning. Based on the experimental results, DDSFL can achieve very promising performance, and it also shows great complementary effect to the state-of-the-art Caffe features.Comment: Pattern Recognition, Elsevier, 201