/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10. 1016/j.ijheatmasstransfer.2010.03.024 International Journal of Heat and Mass Transfer, 53, pp. 3035-3044, 2010-05-01 Transient model for coupled heat, air and moisture transfer through multilayered porous media Tariku, F.; Kumaran, M. K.; Fazio, P. International Journal of Heat and Mass Transfer, 53, pp. 3035-3044, May 01, 2010, DOI: 10.1016/j.ijheatmasstransfer.2010 The material in this document is covered by the provisions of the Copyright Act, by Canadian laws, policies, regulations and international agreements. Such provisions serve to identify the information source and, in specific instances, to prohibit reproduction of materials without written permission.

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In the past few years, neural networks have emerged as a problem‐solving technique with capabilities suited to many civil engineering problems. Among the various neural network paradigms available, back‐propagation is by far the most utilized for its relatively simple mathematical proofs and good generalization capabilities. Despite its capabilities, back‐propagation suffers from several problems that hinder the development of practical neural network applications. These include slow training, ill‐defined knowledge representation and problem structuring, and nonguided design of an optimal network configuration for adequate generalization. This paper represents an effort to guide the process of developing practical neural network applications using back‐propagation. The paper starts with a brief description of back‐propagation mathematics. Some of the heuristics and techniques used to overcome back‐propagation problems, particularly lack of generalization, are identified and outlined, along with areas of potential improvements to the paradigm. An application development methodology is proposed utilizing the identified heuristics and techniques. The methodology provides a structured framework for designing and implementing practical neural network applications with less effort.

round motions arising during earthquakes create oscillating lateral loads on buildings, thereby causing them to sway back and forth with an amplitude proportional to the fed-in energy. If the input energy can be controlled, and its major portion dissipated during building motion, the level of distress can be significantly reduced.

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