# Robust Simulation of Cyber-Physical Systems for Environmental Monitoring on Construction Sites
## 6 Results and Discussion
### Expert Score Fusion
This is inconsistent with the subjective judgment, which is consistent with [PERSON]'s findings [59]. [PERSON] [60] indicates that using actual belief functions in the combining rule confirms Bayesian theory and convergence. The result based on the [PERSON]'s method has the largest \"qualified\" trust value among the three results. In this study, the fusion result should be \"excellent\", which demonstrates the poor robustness of [PERSON]'s method utilized in conflicts in experts scores, although it is calculated simply when making decisions with incremental evidence. In this context, [PERSON] and [PERSON] [49] handled conflict evidence with different weighting factors through a new probabilistic dissimilarity to handle the issues due to unreliable evidence instead of Dempster rules. Based on this study, this study combined the fuzzy theory and D-S evidence theory to improve the credibility of evidence and better system robustness and effectiveness performance. The two fusion results based on the proposed method point to \"excellent\", and the trust degree remains mainly unchanged. This indicates that the output results maintain a satisfactory performance when the evidence is highly conflicting, and the evidence focal element slightly changes, which can be utilized in expert score fusion of evaluating green construction on construction sites. In summary, the abnormal-data-recognition algorithm based on the spatio-temporal correlation improves the system's anti-interference ability to prevent anomalous data entering the perception and transportation layer of the E-CPS and enhances the quality of the environmental monitoring data on construction sites. This can be used to identify and process the anomaly monitoring data on construction sites and lay a foundation for the stable and accurate output of early warning. The developed belief functions can detect the interfering data and improve the fusion of the monitoring data from different sensors on construction sites in the fusion layer of the E-CPS. This enhances the system robustness of the output data when witnessing the changes of individual data and helps the project managers deal with the problems of sensory data conflicts during the process of environmental management. Finally, the decision-fusion algorithm based on the improved D-S evidence theory can resolve the highly conflicting data and achieve stable results of the experts' decisions in the decision-making layer of the E-CPS. This indicates that the E-CPS model can maintain a robust performance during the construction period. In practice, different experts' decisions on the environmental monitoring results are collected on construction sites. The decision-fusion algorithm can be used to assess the project managers to better fuse the experts' decisions and efficiently evaluate the environmental performance and remote control of the environment on construction sites according to the experts' decisions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Excellent** & **Qualified** & **Unqualified** \\ \hline D-S evidence & \(m_{123}\) & 0 & 1 & 0 \\ theory & \(m_{123}\)’ & 0 & 1 & 0 \\ [PERSON] & \(m_{123}\)’ & 0.397 & 0.602 & 0.01 \\  & \(m_{123}\)’ & 0.551 & 0.449 & 0 \\ Improved D-S & \(m_{123}\)’ & 0.8544 & 0.1456 & 0 \\ evidence theory & \(m_{123}\)’ & 0.8837 & 0.1152 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Fusion results of three evidence-fusion methods.