## 1 Introduction  
Incessantly aging healthcare facilities often experience complications regarding the estimation and quantification of the probable deterioration within asset components due to a number of reasons. First, the scarcity of condition assessments and rating data made available for research and analysis purposes imposes a difficulty in producing more relatable models and frameworks to the actual hospital building environments, and thus obscures the process of making rational renewal decisions [1]. Second, the inspection, assessment and upkeep activities of hospital building assets are frequently interrupted or delayed due to the dynamic nature of healthcare environments, the high cost associated with performing such activities as well as the shortage in nonmedical-related funding allowances, which in turn contribute to unexpected asset failures that end up costing the hospital extensive amounts of time and funding necessary to reactively respond to the occurring failures as opposed to the original values required to proactively upkeep the assets [2]. This entails the need for formulating a more efficient deterioration model and prediction framework that represent the actual condition progression of assets within healthcare facilities over time to aid decision-makers in achieving more sound maintenance and renewal verdicts [3]. A reliable and precise measurement of the current condition of building assets is considered a fundamental process to be able to accurately forecast the deterioration expected within building assets and accordingly produce reliable asset management frameworks. This is mainly achieved by involving experts and trained personnel to evaluate the condition and performance levels achieved by building assets over a certain planning horizon [4]. However, making such estimations is generally a complex task to perform especially in cases where a dearth of real evidence-based data to back up the decisions made is experienced along with the presence of relative imprecisions and uncertainties [5]. Imprecisions and uncertainties become dominant in frameworks that integrate human experts' knowledge for the purpose of identifying, rating or ranking a number of alternative solutions to a given problem. This led to the categorization of the decision-making process by [6] into two different categories. First is the \"Risk Decision-Making\" where the expert playing the decision maker role can provide a single accurate estimate for the problem in hand. The other category represents the situations where the decision-maker delivers an imprecise or uncertain estimate based on their own experience, and this is called \"Fuzzy Decision-Making\". Maintenance and renewal decision-making models have been developed over the years to provide a most-likely or near-optimum estimate of the condition development pattern of assets throughout their lifecycle, with respective applications on various infrastructure categories including: sewer pipelines [7], pavements [8], buildings [9], bridges [10], water distribution pipelines [11], tunnels [12] and railways [13]. However, in comparison to infrastructure-based models that tackled the areas of condition prediction and deterioration modeling, the development of studies that are particularly tailored to healthcare facilities' reliability, deterioration modeling and prediction remains an under-studied and a limitedly explored research area [14]. In a healthcare setting, the asset failures can be rather catastrophic given the fact that such failures can put plenty of lives at stake [15]. Therefore, this study develops a more efficient deterioration prediction framework that is capable of modeling the current condition degradation pattern in hospital building assets and forecasting the expected future asset conditions on a factual-basis rather than the subjective deterministic approaches to deterioration prediction currently utilized within healthcare facilities.