engleski

Earthquake performance of infilled frames using neural networks and experimental database

Reinforced-concrete frames with masonry wall infill, “framed-masonry”, is a composite structural system proven to be effective and efficient in the case of in plane horizontal excitations. Its behaviour depends on mechanical characteristics of its components but its performance is different than the sum of its components. Modelling and seismic design verifications of “framed-masonry” system that embraces all of the important aspects of behaviour, failure mechanism, shear strength and deformation capacity, are required. In this work we have tried to put the “frame–masonry” composite as a full-fledged building element whose performance could be determined quantitatively on the basis of data obtained from the performed tests. Frame–masonry composite was analyzed using neural networks trained on the experimental database that contains results of 113 published tests of one-story one-bay masonry infilled frames. In order to reduce the dimensionality of input data and achieve better performance of neural network, dimensionality reduction techniques: Principal Component Analysis, Forward stepwise sensitivity analysis and dimensionless modelling parameter approach were applied. A multilayered back propagation neural network with adaptive weight function was applied and the optimal network topology, for each required output value, was been chosen. The obtained results indicated that neural network, trained on the database, could be used for predicting the seismic behaviour of framed-masonry structural elements, with limitation of inputs according to the statistical range of input data. Sensitivity analysis of the important factors that affect the performance indicated that the most important ones were height/length ratio (a), material properties of masonry infill and frame (fk, fck), reinforcement ratio of columns (rc) and the amount of vertical loading (N).

Masonry infilled frames; Experimental database; Earthquake performance; Neural networks; Analysis

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