نوع مقاله : یادداشت فنی
نویسندگان
پژوهشگاه بین المللی زلزله شناسی و مهندسی زلزله
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
The back-propagation neural network (BPNN) has been researched and applied as a convenient tool in a variety of application
areas in civil engineering. In particular, BPNN has been applied to many geotechnical engineering problems and has demonstrated
some degree of success. A review of the literature reveals that BPNN has been used successfully in pile capacity prediction,
modeling soil behavior and liquefaction, etc. However, learning algorithms, such as the BPNN, do not give information on the effect of each input parameter or influencing variable upon the predicted output variable. In other words, it is not possible to find out immediately how the weights of the network or the activation values of the hidden neurons are related to the set of data being handled. Instead, ANNs have been presented to the user as a kind of `black box', whose extremely complex work transforms
inputs into predetermined outputs. To deal with this problem, during the last 10 years, different interpretative methods for
analyzing the effect or importance of input variables on the output of a feedforward neural network have been proposed. In
this paper, six methods that give the relative contribution of the input factors were compared. The data used for training the
networks is based on the laboratory tests for determining the dynamic properties of aggregate-clay mixtures. Finally, the method
which best interprets the networks is introduced.
کلیدواژهها [English]
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