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RISS 인기검색어
- 검색키워드
- 저자 : Moradi Parham (검색결과 3 건)
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Moradi, Gholam,Abdolmaleki, Arvin,Soltani, Parham,Ahmadvand, Masoud Techno-Press 2018 Geomechanics & engineering Vol.14 No.4
Currently, layered geogrid method (LGM) is the commonly practiced technique for reinforcement of slopes. In this paper the geogrid-box method (GBM) is introduced as a new approach for reinforcement of rock-soil slopes. To achieve the objectives of this study, a laboratory setup was designed and the slopes without reinforcements and reinforced with LGM and GBM were tested under the loading of a circular footing. The effect of vertical spacing between geogrid layers and box thickness on normalized bearing capacity and failure mechanism of slopes was investigated. A series of 3D finite element analysis were also performed using ABAQUS software to supplement the results of the model tests. The results indicated that the load-settlement behavior and the ultimate bearing capacity of footing can be significantly improved by the inclusion of reinforcing geogrid in the soil. It was found that for the slopes reinforced with GBM, the displacement contours are widely distributed in the rock-soil mass underneath the footing in greater width and depth than that in the reinforced slope with LGM, which in turn results in higher bearing capacity. It was also established that by reducing the thickness of geogrid-boxes, the distribution and depth of displacement contours increases and a longer failure surface is developed, which suggests the enhanced bearing capacity of the slope. Based on the studied designs, the ultimate bearing capacity of the GBM-reinforced slope was found to be 11.16% higher than that of the slope reinforced with LGM. The results also indicated that, reinforcement of rock-soil slopes using GBM causes an improvement in the ultimate bearing capacity as high as 24.8 times more than that of the unreinforced slope.
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Multi-label feature selection using density-based graph clustering and ant colony optimization
Kakarash Zana Azeez,Mardukhia Farhad,Moradi Parham 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1
Multi-label learning is a machine learning subclass that aims to assign more than one label simultaneously for each instance. Many real-world tasks include high-dimensional data which reduces the performance of machine learning methods. To solve this issue, a filter and multi-label feature selection is proposed in this paper. The main idea of the proposed method is to choose highly relevant and non-redundant features with the lowest information loss. The proposed method first uses a novel graph-based density peaks clustering to group similar features to reach this goal. It then uses the ant colony optimization search process to rank features based on their relevancy to a set of labels and also their redundancy with the other features. A graph first represents the feature space, and then a novel density peaks clustering is used to group similar features. Then, the ants are searched through the graph to select a set of non-similar features by remaining in the clusters with a low probability and jumping among the clusters with a high probability. Moreover, in this paper, to evaluate the solutions found by the ants, a novel criterion based on mutual information was used to assign a high pheromone value to highly relevant and non-redundant features. Finally, the final features are chosen based on their pheromone values. The results of experiments on a set of real-world datasets show the superiority of the proposed method over a set of baseline and state-of-the-art methods.
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