研究目的:基坑工程开挖深度和规模越来越大,以及周边越来越复杂敏感的环境条件,给基坑工程的设计施工提出了更严格的变形控制要求,因此对基坑及周边环境变形的预测非常重要。研究结论:详细阐述了上海软土地区一邻近多幢6层砖混结构住宅...研究目的:基坑工程开挖深度和规模越来越大,以及周边越来越复杂敏感的环境条件,给基坑工程的设计施工提出了更严格的变形控制要求,因此对基坑及周边环境变形的预测非常重要。研究结论:详细阐述了上海软土地区一邻近多幢6层砖混结构住宅的深基坑工程支护设计方案及周边环境保护的技术措施。通过PLAXIS 3D Foundation软件建立三维有限元模型模拟了基坑开挖对邻近住宅的影响,与实测数据的对比表明,围护体变形和邻近住宅的沉降计算值与实测值较吻合,建立的模型和采用的分析方法可以较有效地预测基坑开挖对周边环境的影响,为设计和施工提供了重要依据。展开更多
A new algorithm named kernel bisecting k-means and sample removal(KBK-SR) is proposed as sampling preprocessing for support vector machine(SVM) training to improve the efficiency.The proposed algorithm tends to quickl...A new algorithm named kernel bisecting k-means and sample removal(KBK-SR) is proposed as sampling preprocessing for support vector machine(SVM) training to improve the efficiency.The proposed algorithm tends to quickly produce balanced clusters of similar sizes in the kernel feature space,which makes it efficient and effective for reducing training samples.Theoretical analysis and experimental results on three UCI real data benchmarks both show that,with very short sampling time,the proposed algorithm dramatically accelerates SVM sampling and training while maintaining high test accuracy.展开更多
文摘研究目的:基坑工程开挖深度和规模越来越大,以及周边越来越复杂敏感的环境条件,给基坑工程的设计施工提出了更严格的变形控制要求,因此对基坑及周边环境变形的预测非常重要。研究结论:详细阐述了上海软土地区一邻近多幢6层砖混结构住宅的深基坑工程支护设计方案及周边环境保护的技术措施。通过PLAXIS 3D Foundation软件建立三维有限元模型模拟了基坑开挖对邻近住宅的影响,与实测数据的对比表明,围护体变形和邻近住宅的沉降计算值与实测值较吻合,建立的模型和采用的分析方法可以较有效地预测基坑开挖对周边环境的影响,为设计和施工提供了重要依据。
基金National Natural Science Foundation of China (No. 60975083)Key Grant Project,Ministry of Education,China(No. 104145)
文摘A new algorithm named kernel bisecting k-means and sample removal(KBK-SR) is proposed as sampling preprocessing for support vector machine(SVM) training to improve the efficiency.The proposed algorithm tends to quickly produce balanced clusters of similar sizes in the kernel feature space,which makes it efficient and effective for reducing training samples.Theoretical analysis and experimental results on three UCI real data benchmarks both show that,with very short sampling time,the proposed algorithm dramatically accelerates SVM sampling and training while maintaining high test accuracy.
