A BIM-based Techno-Economic Framework and Tool for Evaluating and Comparing Building Renovation Strategies

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Abstract

Building renovation presents real challenges for project participants which frequently generate high cost and schedule overruns. The disruption caused to occupants is one of the main challenges for the planning and management of renovation works. To better manage occupant interference and enable the acceleration of renovation works, this study aims to develop a novel framework for the assessment and optimisation of renovation strategies using BIM. The concept of disruption is formalised through a renovation ontology using the UML language. To enable process automation, the renovation ontology is then populated, and knowledge related to renovation tasks, constraints, duration, cost, equipment, and disruption are captured, structured and validated with industry partners. A digital tool and a set of Key Performance Indicators are also developed so as to facilitate the identification, assessment and optimisation of renovation scenarios in terms of cost, project duration and disruptive potential. Using a step-by-step process, detailed descriptions of the methodologies and workflows of the proposed framework are finally provided and demonstrated on a live case study located in Greece. The findings show no spatial correlation exist for the disruption concept and also confirm the disruptive nature of building floor renovation which can lead to a low rate of retrofitting them. Furthermore, the findings question the general applicability of the Whiteman et al.’s heuristic suggesting to prioritise the planning and execution of the most disruptive renovation activities as early as possible in the renovation process, and of the preference of Fawcett for a one-off renovation strategy recommending to conduct renovation works in one go as quickly as possible. Ultimately, the TEA framework will be further demonstrated and tested by end-users on three additional European case studies within the RINNO project which will particularly help validating the added value and benefits of the TEA framework from a user perspective.

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Principal component analysis: a review and recent developments.

Ian Jolliffe, Jorge Cadima (2016)

Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.