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作 者:文畅平[1] 陈宗辉 孙政[1] 白银涌 苏伟[1]
机构地区:[1]中南林业科技大学土木工程与力学学院,长沙410018
出 处:《地下空间与工程学报》2017年第2期517-524,共8页Chinese Journal of Underground Space and Engineering
基 金:湖南省重点学科建设项目(2013ZDXK006);中南林业科技大学引进高层次人才科研启动基金(104-0094)
摘 要:基于未确知测度理论,建立了砂土地震液化判别和液化势分级的未确知均值聚类分析模型和方法。针对砂土地震液化评价中的许多不确定性影响因素,选用地震震级,地面地震加速度幅值,标准贯入击数,比贯入阻力,砂土相对密实度,砂土平均粒径和场地地下水位等7个评价指标作为判别因子;选用17个砂土样本作为训练样本,建立各评价指标的未确知测度函数,以样本中的各评价指标数据的平均值表示其分类中心;利用相似权赋权方法确定评价指标的权重,依据未确知测度距离判别地震液化等级;根据建立的模型对训练样本回判,回判正确率为94.12%。将建立的模型对20个测试样本进行判别,将判别结果与地震液化的实际情况、BP神经网络和SOFM神经网络等方法的评价结果进行了对比。研究表明,该模型的评价结果与实测结果,以及BP神经网络、SOFM神经网络等方法的评判结果一致性较高。Based on unascertained measurement theory,a unascertained average clustering model(UACM) was established for the identification and classification of seismic liquefaction of sandy soil. Aiming at unascertained factors in the analysis of sand liquefaction evaluate,earthquake magnitude,peak value of ground surface acceleration,standard penetration number, specific penetration resistance, relative compaction, mean particle diameter and underground water table were selected as discriminant indexes; 17 sets of sand soil were selected as training samples,the unascertained measure functions of the seven indexes were rigorously constructed. The classification center was indicated by using the average of classification of training samples. The indexes weight was determined by similar weight theory,and the identification and classification of sandy soil liquefaction potential were carried out using the unascertained measurement distance. Each of the training samples was back-discriminated according to UACM,and the correctness rate was 94.12%. The other 20 sets of sand soil samples were assessed by UACM. The study shows that the synthetic assessment results obtained through UACM agree well with the actual records and the results obtained by other methods such as BP neural network and SOFM neural network etc. So,the proposed model could be used widely in practical engineering.
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