## 5 Conclusions
* SD uncertainty modelling indicates large variations in reliability and risk levels of active systems due to the influence on HOEs over time. The results show the effect of perceived safety on the building management team. There is an initial period (0 to 5 years) in which the reliability declines slowly, thus causing the number of accidents to increase. * The reliability of active components gradually decreases with time and at about 5-7 years, there is a need to carry out maintenance activities. * It can be noted that the effect of the variations on the input variable deploy only after an initial period in which the input has no influence on the reliability outcome. This can be explained by the dynamic character of the high-level loop. Prior studies provide estimated effects of HOEs on risk in other industries such as nuclear plans and offshore oil platforms, and estimated effects of technical factors only on risks during a fire event in high-rise buildings, but existing literature does not address or quantify the impact of HOEs on risks during fire events in high-rise buildings. The T-H-O-Risk approach demonstrates how technical, human, and organizational risks can be quantified in a comprehensive probabilistic framework for high-rise residential buildings and is an important contribution to the development of the next generation building codes and risk assessment methods. Future work will focus on simplifying the model for wider applications to other building occupancies as well as on expanding the sensitivity and uncertainty analysis. In addition, cost-benefit analysis (CBA) should be carried out. The application of the model to the reliability of other safety systems is a further area of development. Another area is the research for other HOE variables applicable to the building sector. The following are available online at [[http://www.mdpi.com/2076-3417/10/24/8918/s1](http://www.mdpi.com/2076-3417/10/24/8918/s1)]([http://www.mdpi.com/2076-3417/10/24/8918/s1](http://www.mdpi.com/2076-3417/10/24/8918/s1)), Figures S1-S3: F-N Curves for TD05-TD16, Case #1 to #7; Figures S4-S10: Individual F-N Curves for TD01-16, Case #1-#7; Figures S11-S24: SD Individual ERL plots for all Trial Designs TD01-16, Case #1-#7. Conceptualization, S.T. and K.M. ; methodology, S.T. and Moinuddin; software, S.T., validation, S.T., D.W., P.J. and K.M. ; formal analysis, S.T. and K.M. ; investigation, S.T. ; resources, S.T. and K.M. ; data curation, S.T. ; writing--original draft preparation, S.T. ; writing--review and editing, S.T., D.W., P.J. and K.M. ; visualization, S.T. ; supervision, D.W., P.J. and K.M. ; project administration, K.M. ; funding acquisition, S.T. and K.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. The equations that govern the relationships among nodes in the BN are listed below:  
For HOE-variables, the equations represent the OR Boolean logic:  
P(Inefficient timely control) = P(deficient training) \(\cup\) P(inefficient emergency plan) \(\cup\)  
P(Deficient check) = P(no check rules) \(\cup\) P(not comply with instruction)  
P(Equipment ageing) = P(deficient maintenance) \(\cup\) P(wrong risk assessment)  
P(Adopt unsuitable equipment) = P(obeying standard) \(\cup\) P(improper safety organization)  
For other variables of the main net the Bayes' rule is used:  
P(A) = P(A\(\backslash\)B)*P(B)For multiple causal events, the rule can be extended as follows:  
\[P(A)=\sum_{i=1}^{N}P(A|C_{i})\]  
where \(C_{i}\) is the combination of states of causal events. In general:  
\[P(A)=\sum_{i=1}P(C_{i})*a_{i}\]  
where \(P(Ci)\) is the probability of occurrence of the \(i\)-th combination of states of the causative variables for the outcome \(A\), calculated as:  
\[P(C_{i})=\prod_{j=1}P\big{(}c_{j}\big{)}\]  
\(c_{j}\) is the state of the \(j\)-th root variable that constitutes the \(i\)-th combination.