## 2 Literature Review
The previously presented diversity within the prediction models and techniques for building assets' deterioration leads us to the conclusion proposed by [21], which suggests that the process of predicting the deterioration of building components and healthcare facility assets is a rather complicated process due to the variability of the users and stakeholders along with their varying needs, interests and measures of satisfaction that several researchers have tried to ease by using various modeling methodologies. However, the reliance in almost all the previous models on estimates or experience of experts surveyed makes the data collected imprecise requiring the integration of fuzziness when dealing with such prediction problems. Additionally, the fact that the previous stochastic models, especially Markov models, were all initiated based on guesses and assumptions made for deriving the transition probabilities that, if not made or estimated wisely, can possibly impose errors in calibrations that can eventually lead to an inaccurate prediction of the future condition of facility system components. Moreover, the limited amount of healthcare facilities condition data available for utilization in research purposes mandated the use of a method that is less data-intensive and can be efficiently used with limited datasets and still provide an efficient prediction performance. Considering the previously presented limitations obtained by searching the relevant literature, the main contribution of this paper is combining the analytical precision of stochastic deterioration prediction models, with the hands-on approach and practical knowledge of field experts within the facility maintenance management personnel in the healthcare sector, along with an integration of fuzzy set theory to minimize the potential uncertainties, subjectivity and imprecision pertaining to expert opinions and decisions. Another fundamental aspect of the developed model is the calibration process as per real datasets collected from hospital building assets that helps derive a more factual-based prediction methodology. Given that the deterioration of the building asset components occurs on a probabilistic and stochastic pattern, a Markovian process has been implemented in this paper to model the deterioration of hospital building components [27]. The proposed model exhibits two main functions: (1) a deterioration modeling function which starts with a fuzzy expert-based derivation of the initial transition probabilities relying on the age, condition and expected deterioration rate of hospital building components that are then fed into the Markov model in order to derive the future condition of the components under study. Moreover, a genetic algorithm is employed to train the Markov model by optimizing the transition probabilities to reflect the training datasets collected from the actual hospital building; (2) a deterioration prediction function that utilizes datasets collected for validation purposes in order to measure the capability of the developed model to estimate the future condition of hospital building components as opposed to state-of-art deterioration prediction models. These functions help in producing a more precise, relatable and representative model while preserving the advantage of not being as data-demanding as other artificial intelligence models.