Incorporating safety risk into the design process is one of the most effective design sciences to enhance the safety of metro station construction. In such a case, the concept of Design for Safety (DFS) has attracted much attention. However, most of the current research overlooks the risk-prediction process in the application of DFS. Therefore, this paper proposes a hybrid risk-prediction framework to enhance the effectiveness of DFS in practice. Firstly, 12 influencing factors related to the safety risk of metro construction are identified by adopting the literature review method and code of construction safety management analysis. Then, a structured interview is used to collect safety risk cases of metro construction projects. Next, a developed support vector machine (SVM) model based on particle swarm optimization (PSO) is presented to predict the safety risk in metro construction, in which the multi-class SVM prediction model with an improved binary tree is designed. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. Finally, the proposed framework is applied to a case study of metro station construction. The prediction results show the PSO–SVM model is applicable and reasonable for safety risk prediction. This research also identifies the most important influencing factors to reduce the safety risk of metro station construction, which provides a guideline for the safety risk prediction of metro construction for design process.

Although the acceleration of urbanization brings social and economic development, it also produces various problems. Accurate evaluation of sustainable urbanization performance can help local governments summarize experiences and solve problems. Sustainable urban development should focus not only on modern construction, but also on original natural ecosystems and traditional cultural protection. This paper develops a holistic framework based on an “origin” and “modernization” perspectives and uses the multilevel extension method and the Analytic Hierarchy Process (AHP) method for accurately evaluating sustainable urbanization performance. A case study of Chongqing City in China demonstrates the process of using the holistic framework and evaluation method. The empirical results from this study indicate that Chongqing has a medium level of sustainable urbanization. The city is considered as a medium level in “origin” performance and the “modernization” performance is good, while uncoordinated. The case study reveals that the proposed framework and the method are effective theoretical bases for policy-makers and practitioners to understand the performance of urban sustainability and for promoting urbanization toward better sustainability. Beyond the application case, the holistic framework and method can be applied to other cities.