Fuzzy Logic Based Built Environment Impact Assessment for Urban Regeneration Simulation

Abstract

Integration and adaptation of artificial intelligent designs with fuzzy inference techniques is an active area of research that can be used to meet the challenges of regeneration processes. Urban regeneration activities in built environment are complex and require a close collaboration between designers and planners in order to achieve a design plan that is environmentally robust and sustainable. Regeneration teams’ inability to manually analyze complex maps for various regeneration choices induces human errors in the regeneration process. This often results in a prolonged impact on the economic, social and environmental wellbeing and sustainability of the region. This paper, presents the development of an urban regeneration fuzzy inference system (FIS) aimed at tackling the issue of regeneration simulation in built environment through the measurement of impact of certain socioeconomic parameters to the regeneration plan. The impact assessment logic is embedded in the form of an expertly guided rule-base of an FIS. The impact is calculated using the four core principles of urban smart growth (crime, accessibility, employment and health), with respect to a measure of Euclidean intra-regional distance based accessibility function between the regeneration area(s) and the surrounding built environment neighborhood. The FIS evaluates the suitability of various regeneration sub-areas in terms of the aforementioned four principles affecting the concept of a walk-able, sustainable and eco-friendly neighborhood.