Tabarak Ballal

Knowledge communication and translation - a knowledge transfer model

Purpose – The purpose of this paper is to propose a process model for knowledge transfer in using theories relating knowledge communication and knowledge translation.Design/methodology/approach – Most of what is put forward in this paper is based on a research project titled “Procurement for innovation and knowledge transfer (ProFIK)”. The project is funded by a UK government research council – The Engineering and Physical Sciences Research Council (EPSRC). The discussions are mainly grounded on a thorough review of literature accomplished as part of the research project.Findings – The process model developed in this paper has built upon the theory of knowledge transfer and the theory of communication. Knowledge transfer, per se, is not a mere transfer of knowledge. It involves different stages of knowledge transformation. Depending on the context of knowledge transfer, it can also be influenced by many factors; some positive and some negative. The developed model of knowledge transfer attempts to encapsu...

Artificial neural network for the selection of buildable structural systems

In this study, artificial neural networks have been developed to acquire construction knowledge from past projects to integrate buildability considerations into the preliminary structural design process. Four artificial neural network models are presented. These allow the generation of an expeditious solution for given sets of design and buildability constraints. Once information is entered into the models, a recommendation of which structural scheme to choose is generated instantaneously. Thus, valuable design time is released, allowing designers the opportunity to invest in other equally important design tasks. The information entered into the models consists of site‐related information including site access; availability of working space; and speed of erection, and conceptual design information including type of building; number of storeys and gross floor area. The results show that artificial neural networks can be successfully used for the implementation of buildability at the preliminary stage of design.