# Occupational Risk Assessment of Wind Turbines in Bangladesh
## 6 Result and Discussions
### Risk Assessment and Risk Prioritizing
Thus, for the combined decision matrix, HIT1 (C) = (3, 5.67, 9) in Table 17.  
After that, the normalized fuzzy decision matrix and weighted fuzzy decision matrix were evaluated using Equations (6) and (7). For example, according to the Table 17, HIT1 (C) = (3, 5.67, 9), \(u_{j}^{+}=\max\Bigl{(}u_{ij(C)}\ \Bigr{)}=10\), and the fuzzy-weight (C) = (0.144, 0.189, 0.266). Therefore, the weighted normalized fuzzy decision matrix HIT1 (C) = (\(\frac{3}{10}\times 0.144,\ \frac{5.67}{10}\times 0.189,\ \frac{9}{10}\times 0.266 \Bigr{)}=\) (0.043, 0.107, 0.239) shown in Table 18.  
Then, the FPIS and _FNIS_ were computed by using the Equations (9) and (10). For example, \(\text{FPIS}\ of\ HIT_{i}^{C}(l,\ m,\ u)=\max\bigl{(}l_{ij},\ m_{ij},u_{ij}\bigr{)} =\) (0.072, 0.158, 0.266) and \(FNIS\ of\ HIT_{i}^{C}(l,\ m,\ u)=\min\bigl{(}l_{ij},\ m_{ij}u_{ij}\bigr{)} =\) (0.032, 0.133) in Table 18. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Maintenance** \\ **hazards HIM\(i\)** \\ \end{tabular} } & \multicolumn{6}{c}{**Consequence (C)**} & \multicolumn{6}{c}{**Exposure (\(E\))**} & \multicolumn{6}{c}{**Probability (\(P\))**} \\ \cline{2-10}  & **Exp1** & **Exp2** & **Exp3** & **Exp1** & **Exp2** & **Exp3** & **Exp1** & **Exp2** & **Exp3** \\ \hline HIM1 & VP & P & VP & F & MG & MG & MG & G & G \\ HIM2 & P & P & VP & MP & F & MP & MG & G & MG \\ HIM3 & MP & F & MP & G & ET & G & ET & ET & ET \\ HIM4 & VP & VP & P & G & MG & G & G & G & G \\ HIM5 & MG & F & MG & G & MG & G & G & ET & G \\ HIM6 & VP & P & VP & G & ET & ET & ET & G & ET \\ HIM7 & P & MP & P & F & MP & MP & MG & G & G \\ HIM8 & PR & PR & MP & MG & MG & G & G & G & G \\ HIM9 & P & VP & P & G & MG & MG & MG & MG & MG \\ HIM10 & MP & F & MP & MG & F & MG & G & MG & MG \\ HIM11 & FF & MG & F & G & ET & G & ET & ET & G \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Linguistic assessment of hazards associated with wind turbine maintenance. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **HIT\(i\)** \\ \end{tabular}  } & \multicolumn{3}{c}{**Consequence (C)**} & \multicolumn{3}{c}{**Exposure (\(E\))**} & \multicolumn{3}{c}{**Probability (\(P\))**} \\ \cline{2-10}  & \(l\) & \(m\) & \(u\) & \(l\) & \(m\) & \(u\) & \(l\) & \(m\) & \(u\) \\ \hline HIT1 & 3.00 & 5.67 & 9.00 & 5.00 & 7.67 & 10.00 & 7.00 & 9.00 & 10.00 \\ HIT2 & 0.00 & 1.67 & 5.00 & 5.00 & 7.67 & 10.00 & 5.00 & 7  
The distance from each alternative to the FPIS and to the FNIS was determined using the Equations (11)-(13). For example, HIT1 (\(C\), \(E\), \(P\)) =  
\[d_{c1}^{+} =\sqrt{\frac{1}{3}\times\left[\left(x_{1}-y_{1}\right)^{2}+\left(x_ {2}-y_{2}\right)^{2}+\left(x_{3}-y_{3}\right)^{2}\right]}\] \[=\sqrt{\frac{1}{3}\times\left[\left(0.072-0.043\right)^{2}+\left( 0.158-0.107\right)^{2}+\left(0.266-0.239\right)^{2}\right]}\] \[=0.037;\text{ similarly, }d_{c1}^{+}=0.072,\text{ and }d_{p1}^{+}=0.012.\]  
Therefore, \(d_{1}^{+}=\left(d_{c1}^{+}+d_{c1}^{+}+d_{p1}^{+}\right)=0.037+0.072+0.012=0.120\) and  
\(d_{c1}^{-}=\sqrt{\frac{1}{3}\times\left[\left(0.00-0.043\right)^{2}+\left(0.0 32-0.107\right)^{2}+\left(0.133-0.239\right)^{2}\right]}=0.079.\)  
In a similar way, we can determine the \(d_{1}^{-}\) value of \(0.684\). All values are shown in Table 19. The ranking of each hazard was measured by the \(CC_{i}\) using Equation (14) and shown in Table 19. Based on Table 19, the order of hazard source for the observed wind turbine in times of transportation found that \"Driving vehicles at night in dark weather conditions\" (HIT09) has the highest ranking among these 12-hazard sources. Because it has the largest CC\(i\), the second largest is \"Turbines not secured properly\" (HIT10), which is followed by \"Rough weather conditions (windy, rainy)\" (HIT07) and \"Uncoordinated movement by heavy vehicles\" (HIT8).