This paper presents an experimental study on the dynamic interaction effect of closely spaced square foundations under machine vibration. Under dynamic condition, a number of large-scale model tests were conducted in the field, which include a wide range of study on the isolated as well as the interacting footing response resting on the local soil available at Kanpur, India. The dynamic interaction of different combinations (size) of two-footing assembly was investigated by inducing vertical harmonic load on one of the footings (active footing), where the other footing (passive footing) was loaded with the static weight only. The active footing was excited with different Page 1 of 41 Can. Geotech. J. Downloaded from www.nrcresearchpress.com by CAMBRIDGE UNIVERSITY LIBRARY on 08/18/15 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.2 magnitudes of dynamic loading and the response was recorded for both the footings, placed at different clear spacing (S). The results are compiled and shown as the variation of displacement amplitude with frequency. The transmission ratio, which predicts the effect of dynamic excitation of the active footing on the passive one, is determined for the interacting footings and plotted with respect to the frequency ratio.

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The determination of heat transfer coefficient plays an important role in optimal designing of heat transfer equipments as it directly affects the heat surface area and thereby the weight and cost of the equipment. Thus, prediction of heat transfer coefficient with minimum error reduces the exhaustive experimental work. Therefore, the prime objective of the present work is the application of computational intelligence methods for improving the prediction accuracy of heat transfer coefficient in flow boiling over tube bundles. The adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) are applied to predict the flow boiling heat transfer coefficient as output, taking pressure, pitch of the bundle, heat flux, mass flux and vapour quality as input. The performance of different derivatives of neural network such as multilayer perceptron, general regression neural network and radial basis network (RBF) is studied with varying the parameter. The ANFIS is tested with different types and number of membership functions for the prediction of flow boiling heat transfer coefficient. These methods are found to be better for predicting the flow boiling heat transfer coefficient than conventional correlations.