Development of a method and intelligent decision support system for sustainable renovation of the built environment

Abstract

This dissertation examines the sustainable renovation of a built environment. The object of the dissertation research is the process of creating an analytical recommendation method with а knоwlеdgе-bаsеd dесіsіon suрроrt sуstеm іntеgrаtеd wіth а multі-lауеr аrtіfісіаl nеurаl nеtwоrk, proposing an approach that provides obtained data from the architectural environment for making decisions and analysing all life cycles of it. The dissertation includes an introduction, three chapters, a research generalisation, a literature summary and the author’s publications on the dissertation topic. The introduction discusses the research issues, the importance of the dissertation, the object of research, scientific novelty and research methodology. The introduction ends with a presentation of the author’s publications on the dissertation topic and the description of the dissertation structure. The First Chapter gives a review of the literature on the sustainable built environment covering the period between 2015 and 2021, analyses methods of sustainability certification systems (e.g., BREEAM, LEED) and their inclusion in research related to sustainable valuation, and widespread use in the construction industry, provides existing methods of sustainability assessment for more holistic perspective in the assessment following the recommendations for performing the sustainable renovation. The Second Chapter introduced a knowledge-based decision support system integrated with a multilayer artificial neural network for urbanisation in city construction. The chapter presents a method of a multivariant design and multiple criteria analysis of a building’s renovation (on the example of the building reconstruction in Kyiv, Ukraine). Multiple criteria analyses in the selected location were made to determine the emotional and rational market segments by demographic criteria (males and females), psycho-graphics and consumer behaviour criteria (e.g., happy, sad, angry, surprise and heart rate variability). The Third Chapter covers how to measure the segmentation of crowd composition effects (by age and gender) and emotional and physiological indicators of potential buyers. This allows offering stakeholders rational, environmentally friendly and energy-efficient building alternatives. To achieve this goal, the developed multi-criteria analysis of neuromarketing and video advertising was used to create the required conditions. More than 200 million multisensory pieces of data were analysed. This experiment was performed on the example of energy-efficient buildings to demonstrate the developed method. The results presented in this chapter are confirmed by the results of worldwide research. The research highlights were discussed at 5 scientific conferences; the key research study was presented in 3 research papers.