## 4 Results
### System Dynamics Modelling Results  
System dynamics modelling considers risk variation over time and examines the dynamic effects of human and organisational factors on component reliability. It is observed that the reliability of safety systems such as detection or suppression systems varies with time, as illustrated by Case #1 in the graph on the left in Figure 6. The curve shows 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 (Figure 6a). The excessive number of accidents compel the building management to implement new safety measures that improve the reliability from year 5 to 7. The peak of the reliability reassures the building management team so that safety measures are then relaxed, and reliability declines again from year 7 to year 10. So there is a bottom value for reliability before 5 years, then a reaction from the organization is expected with improvements in safety being implemented along the next 2 years, peaking at 0.95 before lax safety behaviour causes the downward trend again till year 10. The ERL curve in Figure 6b indicates an opposite behaviour to reliability over the 10-year period reaching a low risk level of \(5.9\times 10^{-6}\) in Year 7 before trending up. The detection probability curve in Figure 6c exhibits similar behaviour to the reliability curve over this period. The accident rate increases until year 5 when safety measures cause a downward dip until year 7 when it begins to slope up again. A sensitivity analysis for the SD model is then performed for the dynamic response of the system over 10 years. Sensitivity analysis is used to determine how the model behaves and responds to a change in a parameter. Each simulation with changed parameters and slope of the nonlinear relationship was compared with the base run simulation to determine whether the parameters and nonlinear relationships exhibited sensitive behavior. If the model behavior only changes numerically with the values of parameters, it indicates that the underlying behavior is not sensitive to changes in parameters. In fact, most of the input parameters will not have a great influence on the model behavior, except for critical variables in the model. The sensitivity of a parameter is given by the following equation:  
\[S(t)\left|\frac{(Y(t+1)-Y(t))/Y(t)}{(X(t+1)-X(t))/X(t)}\right. \tag{11}\]  
Figure 6: Sensitivity analysis: ERL and Reliability- TD01, Case #1. Note: SD Individual ERL sensitivity plots for all other Trial Designs for Case #1 to #7 are provided in Supplementary Material— Figures S11–S24. (a): reliability; (b): ERL; (c): FYDY; (d): accident rate. where \(S\) is the sensitivity function, \(Y\) is the output behavior variable, \(X\) is the model parameter and \(t\) is time. The sensitivity analysis tests are carried out in Vensim to provide a comparative graph of final results, which cause the simulation results to be displayed as confidence bounds ranging from 0 to 100 percentages. Confidence bounds are used to represent the sensitivity of the variable. The analysis is computed at each point in time by ordering and sampling all the simulation runs (1000 Monte Carlo simulation runs). The color area in the sensitivity graph indicates whether the specified variable may affect the simulation results to a great extent. For the confidence bounds color in Figure 6, yellow represents 50%, green represents 75%, blue represents 95% and grey represents 100%. The 'risk perception' variable of the socio-technical loop has been taken as the input variable and its range of variation is between 0.99999 to 1.00001 (the range is very small, in the order of \(1\times 10^{-5}\)). Monte Carlo simulation with 1000 iterations was run and the resulting behaviour of the reliability variable is represented by the sensitivity curve in Figure 6a.