## 2 Literature Review  
Deterioration prediction is considered a fundamental process in healthcare facility management as, firstly, it helps ensure the smooth and continuous operation of the facility and its underlying assets while decreasing the number of unpredicted failures, and secondly, it is an aiding tool to prioritize the building systems and their components for capital renewal purposes [16]. An accurate deterioration prediction methodology is essential in successful and advantageous maintenance, rehabilitation and replacement (MR&R) decisions [17]. Deterioration prediction models have been priorly divided into three classes, namely: deterministic models, stochastic models, and artificial intelligence-based models [18]. The most commonly used models in healthcare facilities are the deterministic prediction ones that rely on a reference service life (FSL) for the assets under evaluation that is utilized to derive an estimated service life (ESL) by using statistics, building codes and expert surveys. Although these models are simple to develop and implement as they are not as data-intensive as other model types, they are often criticized for their involvement of high uncertainty levels which questions the robustness of the predicted estimates [19]. Several examples are available for this class of models where mathematical functions are employed to illustrate the deterioration pattern of assets ranging from straight-line extrapolation and polynomial models to more complex regression-based analyses including linear, nonlinear, multiple and step-wise regression models [20]. For instance, as part of their maintenance expenditure planning model, [21] adopted a deterministic deterioration prediction approach where they relied on expert surveys to determine the future condition of hospital building components. They suggested that concrete structural components experience a linear deterioration for 15 years in the beginning of their service life, followed by an exponential deterioration pattern for the rest of their lives. Additionally, [22] proposed a maintenance management methodology for healthcare facilities that included the deterioration prediction of hospital facility components using a deterministic approach where experts were surveyed to determine the P-F interval of each hospital building component representing the duration between the potential and functional points of failure. Alternatively, the stochastic deterioration prediction models involve higher degrees of accuracy and practicality than deterministic methodologies, as unlike deterministic approaches, the stochastic modeling of asset deterioration does not solely rely on age as the condition depreciation indicator. This led to the choice of stochastic deterioration prediction modeling as the most appropriate method to describe the degradation experienced by building asset components [23]. Additionally, stochastic models are more favorable than the artificial intelligence approaches in the specific cases of buildings and healthcare facilities due to the lack of timely condition assessments and rating and the scarcity of extensive data collection procedures [18]. Stochastic deterioration models are classified into either time-based models, or state-based Models. To start with, time-based models predict the probability distribution of the time spent on the change of an asset condition from one state to a lower one. Time-based strategies involve the use of parametric, semiparametric and nonparametric approaches to represent such a probability distribution for the transition time [24]. Most popularly, Weibull distributions are used to model asset condition depreciation as a function of time since construction or installation. The authors of [4] utilized Weibull distribution modeling to predict the deterioration of building components over time, using current and past condition inspection records to propose a sound projection of the service life of the component.