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.

本研究旨在建立功能诊断方法并开发灌溉水箱库存管理的性能评估系统。采用电阻率法和表面波法两种地球物理勘探方法在3个超量灌水池进行了现场调查。两种方法分别得到了电阻率和横波速度的空间分布，并利用自组织图（SOM）将两种结果有机地结合起来。 SOM 是一种人工神经网络，使用无监督学习进行训练，生成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。以横波速度、电阻率以及满水位和低水位测得的电阻率变化率作为输入向量，自动、客观地对土壤类型进行分类。此过程还使我们能够估计灌浆面积。提出了一种利用蒙特卡罗滤波器将有限元模型纳入识别的计算方法，以便根据沉降实测数据来估计灌溉水箱路堤内的恶化面积。