## 3 Instance Validation
### Analysis of Results  
Since for some of the indicators it is difficult to accurately determine their similar risk levels by direct observation on the comparison cloud diagram, the formula for similarity (17) is introduced for quantitative analysis. The proximity formula allows the degree of proximity between each evaluation indicator and the different risk criteria to be derived, thus more precisely determining the risk level attribution of each indicator. The higher the degree of similarity, the stronger the match between the indicator and the risk level, thus providing a scientific basis for the determination of the risk level. The detailed calculation results and the finalized risk level can be seen in Table 6.  
From Figures 7 and 8, and from Table 6, it can be seen that the comprehensive risk level of the whole-process quality of this assembly building is level II, which indicates that the risk is acceptable and consistent with the actual situation of the project, and it also verifies the validity of the evaluation method of the three-dimensional cloud model proposed in this paper. The risk level of each level indicator is II, which means that the risk level at these stages is generally acceptable. Although there may be some challenges in risk  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Indicators** & **I** & **II** & **III** & **IV** & **Level of Affiliation** \\ \hline U & 0.200 & 1.559 & 0.217 & 0.124 & II \\ \hline U\({}_{1}\) & 0.174 & 1.679 & 0.265 & 0.138 & II \\ \hline U\({}_{2}\) & 0.194 & 2.143 & 0.227 & 0.127 & II \\ \hline U\({}_{3}\) & 0.158 & 0.833 & 0.311 & 0.150 & II \\ \hline U\({}_{4}\) & 0.238 & 0.675 & 0.181 & 0.112 & II \\ \hline U\({}_{5}\) & 0.298 & 0.421 & 0.154 & 0.101 & II \\ \hline U\({}_{11}\) & 0.175 & 0.751 & 0.240 & 0.133 & II \\ \hline U\({}_{12}\) & 0.232 & 0.725 & 0.185 & 0.113 & II \\ \hline U\({}_{13}\) & 0.116 & 0.291 & 1.123 & 0.230 & III \\ \hline U\({}_{14}\) & 0.221 & 1.373 & 0.199 & 0.118 & II \\ \hline U\({}_{21}\) & 0.190 & 1.617 & 0.230 & 0.128 & II \\ \hline U\({}_{22}\) & 0.157 & 0.516 & 0.264 & 0.142 & II \\ \hline U\({}_{23}\) & 0.234 & 0.752 & 0.185 management at each stage, these risks are still under control and do not require additional significant interventions. Refined to the secondary indicators, the risk level of all the secondary indicators is II, except for the number of design changes of prefabricated components U\({}_{13}\), and the quality of raw materials and the supply chain stability U\({}_{32}\), which have a risk level of III. It is analyzed that this is mainly due to the irrationality of the design scheme and changes in the construction site conditions in the assembly building project, which leads to an increase in prefabricated component design changes, and thus the overall quality and schedule of the project is greatly affected. In order to cope with such risks, research and demonstration work should be strengthened in the pre-design phase of assembly building projects to ensure the rationality and feasibility of the design scheme and to avoid rework as much as possible; at the same time, an efficient design change management process should be established to strengthen the communication and collaboration between the design team and the construction team, so as to minimize the adverse effects of design changes. In addition, the high risk of raw material quality and the supply chain stability issues is mainly due to factors such as poor supplier management, logistics and transportation issues, and market fluctuations in the project. It is recommended to select reputable suppliers and establish long-term and stable cooperative relationships; meanwhile, it is recommended to implement strict raw material quality testing and control measures to ensure that the raw materials for assembled buildings comply with standards and to further optimize supply chain management and to improve logistics and transportation efficiency in order to reduce losses and delays during transportation.