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深基坑支护结构选型决策的Fisher判别分析模型
引用本文:李必红,周健,史秀志.深基坑支护结构选型决策的Fisher判别分析模型[J].重庆大学学报(自然科学版),2011,34(9):109-116.
作者姓名:李必红  周健  史秀志
作者单位:中南大学 资源与安全工程学院,湖南 长沙 410083;中南大学 资源与安全工程学院,湖南 长沙 410083;中南大学 资源与安全工程学院,湖南 长沙 410083
基金项目:“十一五”国家科技支撑计划资助项目(2006BAB02A02);中南大学学位论文创新资助项目(2009ssxt230);湖南省博士生科研创新项目(CX2011B119)
摘    要:针对传统的深基坑结构选型评价方法存在的问题,应用统计学理论并遵循“安全、经济、合理”的原则,选取了10个实测指标作为影响深基坑支护方案选型的判别因子,建立深基坑支护结构选型的Fisher判别分析模型 (FDA)。利用国内64组深基坑支护实例作为学习样本进行训练和检验,对水泥土重力式、土钉墙、桩锚、桩撑、地下连续墙等5种深基坑支护方案进行优选,并利用回代估计法对FDA模型进行检验。经过训练后的模型回判估计的误判率为14.1%。对另外10组待判样本进行测试,正确率100%。此外利用该模型对某市新世界中心工程深基坑支护结构选型进行决策,结果与实际情况符合较好,说明该模型在研究深基坑支护结构选型中具有良好的实用性和有效性,可为解决深基坑支护结构型式的优化选择提供一种新思路。

关 键 词:深基坑  支护结构  选型决策    Fisher判别分析  分类
收稿时间:2011/4/10 0:00:00

Fisher discriminant analysis model for selecting the retaining structure type of deep foundation pit
LI Bi hong,ZHOU Jian and SHI Xiu zhi.Fisher discriminant analysis model for selecting the retaining structure type of deep foundation pit[J].Journal of Chongqing University(Natural Science Edition),2011,34(9):109-116.
Authors:LI Bi hong  ZHOU Jian and SHI Xiu zhi
Institution:School of Resources and Safety Engineering,Central South University,Changsha,Hunan 410083,P.R.China;School of Resources and Safety Engineering,Central South University,Changsha,Hunan 410083,P.R.China;School of Resources and Safety Engineering,Central South University,Changsha,Hunan 410083,P.R.China
Abstract:Aiming at the problem of traditional evaluation methods of deep foundation pit for selecting the retaining structure type, based on the statistical theory and following the principle of security, economic and reasonable, a Fisher discriminant analysis(FDA) model for selecting the retaining structure type for deep foundation pit is established. 10 selected indicators which influence selection of deep excavation program are taken into account as discriminant factors, and the supporting schemes for deep foundation pit are classified into 5 groups, viz. gravity of the cement-soil type, soil nailing wall, pile anchors, pile supports and underground continuous wall. After training and testing 64 sets of measured data, the discriminant functions of FDA are solved, the re-substitution method is introduced to verify the stability of FDA model and the ratio of mis-discrimination is 14.1%. Another 10 groups of measured data are tested as forecast samples by the proposed model, and the correct rate is equal to 100%. Therefore, the feasibility of the proposed model is validated. Moreover, the proposed model is adopted for the New World Center Project in China, and the prediction results are in line with the artificial neural network(ANN) and the actual situation. The result shows that the deep foundation pit supporting structure lectotype decision of FDA model has excellent discriminant performance and the resubstitution error rate is low. It is easy and efficient to make discriminant analysis using this model and it provides efficient method to select deep excavation retaining structure and a practical new approach to choose the structural type of deep foundation pit optimization.
Keywords:deep foundation pit  supporting structure  lectotype decision  Fisher discriminant analysis  classification
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