This paper mainly deals with the simulation on the strength of the concrete armor block in model test. According to the requirement for the strength of blocks in models with various scales, the components of materials...This paper mainly deals with the simulation on the strength of the concrete armor block in model test. According to the requirement for the strength of blocks in models with various scales, the components of materials for model blocks and their proportions are determined. The failure of armor blocks on rubble-mound breakwaters is reproduced by model tests.展开更多
In this study, an advanced probabilistic neural network （APNN） method is proposed to reflect the global probability density function （PDF） by summing up the heterogeneous local PDF which is automatically determine...In this study, an advanced probabilistic neural network （APNN） method is proposed to reflect the global probability density function （PDF） by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables. The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of＇ van der Meet, and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network （ANN） model. The APNN shows better results in predicting the stability number of armor bilks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.展开更多
文摘This paper mainly deals with the simulation on the strength of the concrete armor block in model test. According to the requirement for the strength of blocks in models with various scales, the components of materials for model blocks and their proportions are determined. The failure of armor blocks on rubble-mound breakwaters is reproduced by model tests.
基金This work was supported by grant PM484400 PM41500 from"High-Tech Port Research Program"founded by Ministry of Maritime Affairs and Fisheries of Korean Government.
文摘In this study, an advanced probabilistic neural network （APNN） method is proposed to reflect the global probability density function （PDF） by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables. The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of＇ van der Meet, and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network （ANN） model. The APNN shows better results in predicting the stability number of armor bilks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.
