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Construction of performance degradation model and development of performance estimation system for stock management of irrigation tank

灌溉水箱库存管理性能退化模型构建及性能评估系统开发

基本信息

批准号：
21380146
负责人：
KOBAYASHI Noriyuki
金额：
$ 9.07万
依托单位：
Ehime University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2009
资助国家：
日本
起止时间：
2009 至 2011
项目状态：
已结题

项目摘要

This research aimed to establish the method of functional diagnosis and to develop the performance estimation system for stock management of irrigation tanks. The field investigations by two types of the geophysical exploration methods, that is, the electrical resistivity method and the surface wave method, are carried out in three overage irrigation tanks. The spatial distributions of the electric resistivity and the S-wave velocity are individually obtained by the two methods, and the self-organizing map(SOM) is employed to combine the two results organically. SOM is a type of artificial neural network that is trained using unsupervised learning to produce a two dimensional discretized representation called a map. This makes SOM useful for visualizing low-dimensional views of high dimensional data. The soil types are classified automatically and objectively with the S-wave velocity, the electric resistivity and the change ratio of electric resistivity measured in full water level and low water level adopted as the input vectors. This procedure also enables us to estimate the grouting area. A computational method, incorporating the finite element model into identification using the Monte Carlo filter, is suggested in order to estimate deteriorated area within embankment of irrigation tank based on measured data on the settlement.

本研究旨在建立灌溉水箱库存管理之功能诊断方法，并开发其绩效评估系统。采用电阻率法和面波法两种物探方法，对三个超龄灌溉水池进行了现场勘察。利用这两种方法分别得到了电阻率和横波速度的空间分布，并利用自组织映射（SOM）将这两种方法的结果有机地联合收割机结合起来。SOM是一种人工神经网络，它使用无监督学习来训练，以产生称为映射的二维离散表示。这使得SOM对于可视化高维数据的低维视图非常有用。以满水位和低水位时实测的横波速度、电阻率和电阻率变化率为输入向量，实现了对土壤类型的自动客观分类。该程序还使我们能够估计灌浆面积。本文提出了一种将有限元模型与MonteCarlo滤波识别相结合的计算方法，用于根据实测沉降数据估算灌溉水池路堤的劣化范围。