## 5 Conclusions  
The unsafe behavior of construction workers is the primary cause of engineering safety accidents. This study focuses on the construction workers group and evaluates 22 safety factors as research objects. An improved AHP and BP neural network model is developed to build a safety warning system. Furthermore, the study incorporates sensitivity analysis into safety monitoring and warning by comprehensively analyzing data from both construction workers and experts. The prewarning model is applied to a primary school project in Suzhou, where a sensitivity analysis of the four workers closest to the severe danger state is conducted, and suggestions for safety management measures are given. The conclusions are as follows: (1) experts and construction workers have different concerns regarding safety factors. Experts believe that protection measures, workers' age, group cohesion, safety atmosphere, and safety awareness significantly influence the unsafe states of construction workers. Conversely, workers believe that safety communication, safety awareness, protection measures, and group cohesion have a great impact. Nevertheless, both groups concur on the weight of construction workers' influencing factors, where protection measures, group cohesion, and safety awareness are highly significant, while safety compliance and security culture are insignificant. (2) The safety warning model established in this study effectively monitors the unsafe states of construction workers. (3) The safety prewarning model and sensitivity analysis enable targeted proposals for safety management measures. In theory, this study considers the opinions of workers and experts on the influencing factors. On the one hand, the results can guide real-world measures. On the other hand, \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Worker Number**} & \multirow{2}{*}{**Factor**} & \multicolumn{2}{c}{**500\% Change**} & \multicolumn{2}{c}{**700\% Change**} & \multicolumn{2}{c}{**900\% Change**} & \multicolumn{2}{c}{**1100\% Change**} \\ \cline{3-10}  & & **Score** & **State** & **Score** & **State** & **Score** & **State** & **Score** & **State** \\ \hline
1 & Safety climate & 0.674 & III & 0.6 & III & 0.497 & II & 0.406 & II \\
1 & Security leadership & 0.496 & II & 0.364 & II & 0.307 & II & 0.289 & II \\
1 & Safety communication & 0.673 & III & 0.604 & III & 0.516 & III & 0.43 & II \\
2 & Safety climate & 0.43 & II & 0.313 & II & 0.287 & II & 0.282 & II \\
2 & Security leadership & 0.581 & III & 0.477 & II & 0.38 & II & 0.32 & II \\
2 & Workmate impact & 0.632 & III & 0.554 & III & 0.488 & II & 0.438 & II \\
2 & Safety communication & 0.65 & III & 0.579 & III & 0.503 & III & 0.432 & II \\
2 & Protection measures & 0.652 & III & 0.588 & III & 0.524 & III & 0.471 & II \\
3 & Security leadership & 0.606 & III & 0.527 & III & 0.441 & II & 0.369 & II \\
3 & Workmate impact & 0.632 & III & 0.569 & III & 0.518 & III & 0.478 & II \\
4 & Safety climate & 0.679 & III & 0.618 & III & 0.521 & III & 0.42 & II \\
4 & Workmate impact & 0.632 & III & 0.569 & III & 0.508 & III & 0.456 & II \\
4 & Safety communication & 0.48 & II & 0.365 & II & 0.312 & II & 0.31 & II \\
4 & Protection measures & 0.643 & III & 0.595 & III & 0.544 & III & 0.495 & II \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Sensitivity analysis results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Worker Number**} & \multicolumn{2}{c}{**Before Training**} & \multicolumn{2}{c}{**After Training**} \\ \cline{2-7}  & **Score** & **Grade** & **State** & **Score** & **Grade** & **State** \\ \hline
1 & 0.735 & 3 & Moderate risk & 0.67 & 3 & Moderate risk \\
2 & 0.731 & 3 & Moderate risk & 0.671 & 3 & Moderate risk \\
3 & 0.724 & 3 & Moderate risk & 0.694 & 3 & Moderate risk \\
4 & 0.721 & 3 & Moderate risk & 0.672 & 3 & Moderate risk \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Comparison of safety states before and after improvement.