## 4 Results
### Fuzzy-Based Model Sensitivity Analysis  
For the purpose of evaluating the robustness of the developed fuzzy-based decision making model that determines the future condition of a hospital building component taking into account its relative age, and its last observed condition rating into consideration, a sensitivity analysis was carried out to measure the fluctuation pertaining to the output parameter (deterioration rate) accompanying the modification of one of the input parameters (condition and age) which in turn evaluates the robustness of the develop framework. Fuzzy rules have been left to monitor the effect of the sole change of the membership functions associated with a single input parameter while the rest of the model-defining parameters remain unchanged. Accordingly, the following two figures were generated to show the variation of the component's estimated life expectancy with the incrementation of a single input parameter with +5, +10, +15, and -5, -10, and -15%, respectively. Figure 3 shows the output change corresponding to incrementing the age input, while the rest of the model remain unaltered. As illustrated in the previous graph in Figure 3, it can be noted that the positive and negative incremental changes in the fuzzy sets representing the membership functions of the age parameter of the hospital building component result in very little variation in the overall transition curve representing the building system moving from one condition state to a lower one. This also matches the initial hypothesis made about the life expectancy of the oxygen gas system in a hospital building as demonstrated in the graph. The mean absolute error (MAE) obtained from the comparison between every incremental value set applied to the age parameter, with the actual dataset of the hospital building inspection reports used for training purposes, can be visualized using the chart in Figure 4, where it can be noted that the minimum MAE is achieved by the base set that experienced no alteration nor incrementation in the age membership functions, contrarily, the maximum MAE value is obtained by the +15% incrementation set which validates the choice made for the membership functions of the age parameter. On the other hand, the life expectancy curves resulting from the positive and negative incremental changes done to the condition parameter only with the rest of the parameters as initially assumed is shown in Figure 5.  
Figure 3: Output alteration upon age parameter incrementation. Figure 5 shows that the output parameter which is the deterioration rate of the hospital building components tend to exhibit a higher sensitivity to the condition parameter than the age parameter as depicted in the figure. The figure shows that the negative incremental changes tend to have no or minimal difference in the life expectancy of the oxygen gas systems, while the positive incremental changes tend to lower the overall component's life expectancy as well as their respective condition ratings in different portions of their life cycle. The previous conclusions can be better visualized using the following bar chart in Figure 6, that shows the results arrived at by the comparison between the condition incrementation datasets with the actual datasets from real hospital buildings, and the mean absolute error obtained by each incrementation set. Figure 6 shows that the minimal MAE is obtained by the base initial hypothesis for the condition membership functions, while the highest MAE is associated with the positive 15% incrementation of the condition membership functions, which proves that the initial assumption is a better candidate for representing the datasets acquired from actual hospital building assets.