1996 Fiscal Year Final Research Report Summary
Development of design system of coastal and river structures using neural network
| Project/Area Number |
06555148
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 試験 |
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| Research Field |
水工水理学
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
MASE Hajime Disaster Prevention Research Institute, Kyoto University Assoc.Prof., 防災研究所, 助教授 (30127138)
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| Co-Investigator(Kenkyū-buntansha) |
TSUJIMOTO Tetsuro Department od Civil Engineering, Kyoto University Assoc.Prof., 工学部, 助教授 (20115885)
GOTOH Hitoshi Department od Civil Engineering, Kyoto University Lecturer, 大学院・工学研究科, 講師 (40243068)
SAKAI Tetsuo Department od Civil Engineering, Kyoto University Prof., 大学院・工学研究科, 教授 (30026182)
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| Project Period (FY) |
1994 – 1996
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| Keywords | neural network / rubble mound breakwater / armor layr |
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| Research Abstract |
The stability of rocky slope under wave attack have been investigated by mainly hydraulic model tests, and empirical formulae have been proposed based on the test results. Van der Meer (1988) proposed a formula for the rocky slope stability based on a large number of irregular wave experiments. However, the formula is insufficient to represent the damage level. Kaku et al. (1991) and Smith et al. (1992) newly proposed an empirical formula to improve the degree of agreement between the measured and predicted damage levels. The formula, however, is not universal ; which requires a change of the coefficients in the formula for different data source.
It is desired to make a universal formula or a methodology to arrange the experimental data having ambiguous mutual relationships between physical factors (Mase et al., 1995). A neural network modeled on the structure of brain is effective to deal with information having not clear relations between causes and effects. This study shows the efficiency of neural network method to assess the stability of rubble mound breakwaters and armor layrs. In addition to show the efficiency, sensitivity of some parameters governing the stability (taking up the damage level as a parameter representing stability) is evaluated by utilizing the neural network where the number of acting waves, the stability number, and the surf similarity parameter are always employed to represent at least the characteristics of breakwaters and waves. For the case of rubble mound breakwaters, the permeability coefficient and the dimensionless water depth were found to be significant. However, for the case of armor layr, the difference of sensitivity of input parameters was little.
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