Date of Award

May 2021

Document Type


Degree Name

Master of Science (MS)


Civil Engineering

Committee Member

Brandon Ross

Committee Member

Dustin Albright

Committee Member

M. Z. Naser


The relationships between the physical features of buildings and the adaptability of those buildings have become an intriguing topic for many researchers in recent years. Although much research has been published about the theme of building adaptability, evaluating these relationships quantitatively is a relatively new subject. The current thesis aims to create a quantitative model of adaptability by identifying physical features of buildings that have strong, positive relationships with building adaptability. Chapter Two of the current thesis presents a design-based adaptability scoring system that evaluates college campus buildings. Eight physical features of buildings were scored and these scores were calculated into an overall adaptability score for four Clemson University buildings. The overall adaptability scores were compared to scores from a previous study of the same buildings conducted through expert evaluations and an Analytic Hierarchy Process (Becker et al., 2020). The results show that both systems rank the buildings in the same order with respect to their adaptability. Chapter Three utilizes three different tools, Artificial Neural Networks, Logistic Regression, and Linear Regression, to analyze the relationships between a building’s physical features and the building’s adaptability. The data on the 59 buildings used in the study were taken from a previous study conducted in the Netherlands. Both adapted buildings and demolished buildings were included in the study. All three tools were processed through a series of sensitivity studies to evaluate the relationships between physical parameters and both adaptation and demolition outcomes. A final Linear Regression model for adaptability was created and all of the buildings were scored on this model. The results from this model were compared to similar, previous models of adaptability and the Linear Regression model proved to be more accurate in predicting adaptation and demolition outcomes. The research in this field contains a great deal of uncertainty and variability in what makes buildings adaptable. This thesis identifies a few key parameters that professionals should consider when designing future buildings to be more adaptable.



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