# Understanding Safety Performance of Prefabricated Construction Based on Complex Network Theory
## 6 Conclusions  
This paper applies complex network theory to reveal the patterns of underlying interactions among safety risks in prefabricated construction and understand its safety performance. An analytical framework comprised of three parts is developed to build prefabricated construction safety risk network (PCSRN) and to dissect its inherent characteristic at different topological scales. The results confirm that PCSRN is both a small-world and a scale-free network where a small fraction of key nodes exert a global impact and play vital roles in the process of accident formation and development. After incorporating different ranking criteria, a more comprehensive list of critical safety risks is achieved and several corresponding strategies instrumental in retaining a high level of prefabricated construction safety are proposed.  
The main original contributions of this study are twofold. The first one is the theoretical contribution. This paper provides a new perspective on prefabricated construction safety analysis by systematically exploring the correlation among safety risks rather than regarding safety risk factors as isolated elements. What is more, incorporating multiple relationships among safety risks into PCSRN is conducive to understanding the patterns of risk interactions, thereby broadening the quantitative application of complex network theory. The second one is practical contribution. Considering the capacity of diagnosing critical safety risks omitted in a single dimension criterion, the proposed method is supposed to be more scientific. It is feasible to extrapolate the method not only to other countries' prefabricated constructions but even to other types of constructions such as water conservancy and infrastructure. Simultaneously, the developed strategies specific to the pivotal safety risks will be instrumental in shaping the safety performance of this sector in the near future.  
Despite these contributions, there are several limitations in the current work that deserve further study. Firstly, qualitative measurement of the interactions, i.e., the way of unweighted edges, is adopted in this paper. The potential interactions from unobserved safety risks as well as the quantification of correlation intensity among risks are not taken into consideration, which could exert influence on the topological property of PCSRN. Along with the accumulation of the dataset, the edge's weight based on frequency can be incorporated into the network of PCSRN for reinforcing the reliability of network construction. Secondly, the network is manually constructed on the basis of the edge relationships obtained from the questionnaire survey. In the future, data mining technology could be combined to automatically mine the association relationships among safety risks in accidents and map them into complex networks to achieve this goal.  
Conceptualization, L.S. and H.L.; Formal analysis, L.S. and H.L.; Methodology, L.S. and H.L.; Supervision, L.S. and H.L.; Validation, L.S., Y.D. and H.L.; Writing--original draft, L.S. and H.L.; Writing--review & editing, L.S., Y.D. and H.L.; Investigation, H.L.; Software, H.L.; Visualization, H.L.; Resources, C.L.; Funding acquisition, L.S. and Y.D. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Social Science Fund Youth Project (No. 71801082 and No.71801214), and Humanity and Social Science Youth foundation of Ministry of Education of China (No. 18 YJCZH148). The authors declare no conflict of interest.