Prefabricated buildings that are more environmentally friendly have been vigorously promoted by the Chinese government because of the reduced waste and carbon emissions during the construction process. Most of the construction processes of prefabricated buildings are completed in the prefabricated component factory, but the safety risks during the hoisting process cannot be ignored. In this paper, the initial framework of a Bayesian Network (BN) is obtained from the combination of the improved Human Factors Analysis and Classification System Model (HFACS) and BN. The improved similarity aggregation method (SAM) is used to calculate the prior probability of BN, which can better summarize and deal with the fuzzy judgment of experts on risk accidents. The improved SAM can consider both the weight of experts and the relative consistency of their opinions, which is of great significance for improving the reliability of BN inputted data. This paper uses the construction project in Sanya, Hainan Province, to verify the validity of the model. The results show that the calculation results of the model are basically consistent with the actual situation. The safety risk of this project is relatively low, and the premise of unsafe behaviors and unsafe supervision are the key risk factors of the project. In addition to maintaining good construction conditions and workers’ healthy states, it is also necessary to carefully check the performance of tower cranes and equipment such as spreaders. During the operation process of the tower crane, workers should avoid walking or staying within the hoisting range.

Prefabricated building constitutes the development trend of the construction industry in the future. However, many uncertainties in the construction process will surely lead to a higher cost. Therefore, it is necessary to study the cost risk evolution and transfer mechanism in the implementation process of this project. A dynamic evolution model for the cost risk of prefabricated buildings has been established in this paper. First of all, a matrix for cost risk of prefabricated buildings was established based on the WSR (Wuli-Shili-Renli) model, and all risk factors in the implementation stage were classified in accordance with the WSR principle. Second, a DBN-based dynamic evolution model was established based on the risk matrix, and the structure and node parameters of the Dynamic Bayesian Network were determined with the aid of the K2 structure learning algorithm and parameter learning method. In view of the probability change process of risks over time, the dynamic evolution path of risks was predicted in different cases through causal reasoning and diagnostic reasoning. Eventually, the model was applied into construction projects. The research results show that: because prefabricated components need to be made by prefabricated component factories, the management systems of prefabricated component factories are usually not perfect, and the probability of management risks is higher. The occurrence of management risks not only has an impact on other risks at the current time node, but also causes other risks to occur in the subsequent transportation and construction phases at the next moment, which eventually leads to the occurrence of risk events.

With prefabricated construction method deemed as an effective way to improve the environmental performance and sustainable development of the building industry, it is inevitably adopted in the scaled residence in the process of residential industrialization. However, the development of prefabricated residential buildings is still immature under the current market economy system, because the stakeholders involved in the process are not yet able to form a good cooperation mechanism and they are more inclined to keep their own interests. As a result, the market share of prefabricated residential buildings is relatively low. Therefore, it is necessary to conduct research on the stakeholders involved. By analyzing their costs and benefits, the reasons that really impede the population of prefabricated residential buildings can be found. In this paper, incremental cost allocation coefficient is introduced, the incremental cost difference under different assembly rates is considered, and the allocation ratio of the incremental cost input of the prefabricated building is analyzed based on game theory. The evolutionary game theory for government and real estate companies is established under the condition of bounded rationality with consumer participation. Then the effectiveness of the game theory is verified using empirical analysis, so as to provide reference for the authorities to promote the large-scale development of prefabricated residential buildings.

With growing concern about environmental pollution and occupational safety in construction industry globally, prefabricated building has become a popular building model in sustainable society. In China, management specifications of prefabricated buildings are far from mature, and safety accidents occur frequently in construction. In order to comprehensively analyze risks in hoisting construction of prefabricated buildings, this study, in view of characteristics of hoisting construction process and correlations in complex system, summarizes risk factors and classifies them according to Wuli-Shili-Renli (WSR) system. From perspective of multiple correlations, evolution mechanism of multi-system correlation and multi-risk correlation is carried out, so as to explore risk probability of hoisting construction of prefabricated buildings. At the same time, this study extends Two Additive Choquet Integral (TACI) operator and Decision-making Trial and Evaluation Laboratory (DEMATEL) in dynamic stochastic environment to construct a two-stage model for risk probability research of hoisting construction, hoping to profoundly reveal influence of risk factors and their dynamic evolution. The results show that: (1) risk probability presented a seasonal, dynamic change trend, which meant rising first, then falling, and finally keeping rising, thus regular inspection and dynamic monitoring are required in hoisting construction in these regions in the first three quarters. (2) the influence of each risk factor demonstrated dynamic changes, and risk sources that need to prevent and defuse at different time points are varied, thus targeted measures catering to different risk sources are required. (3) the degree of risk controllability is in dynamic change, but classification of cause or result in the region at the period remains the same, thus necessitating targeted response measures aimed at various risk types. (4) Individual risks like hoisting job climated break out periodically, so the law of risk occurrence should be mastered and relative precautionary measures should be taken in advance.

The supply chain for prefabricated buildings (PB) is vulnerable to the operation failure of node enterprises, with frequent damage occurring. Therefore, it is vital to establish an evaluation model of supply chain resilience (SCRE) to improve the ability to resist unanticipated risks. However, existing research falls short of explaining the hierarchy of the influential components. To fill this gap, this paper established an element-based system of PBSCRE affecting factors. The DEMATEL-ISM method, which combines Pythagorean fuzzy sets, was utilized to analyze the factors. The effectiveness of this framework was then verified via a case study. The results showed the following: the top six elements in terms of centrality were risk management level, inventory management, emergency response plan, visibility, environmental risk, and information technology level; all factors were divided into six levels: (1) factors in level 1 are surface direct influence factors, (2) factors in levels 2 to 5 are intermediate transfer factors, and (3) factors in level 6 are deep root factors. There are 4 root factors, namely, supplier level, environmental risk, information technology level, and visibility. The results indicate that the proposed model will assist managers in identifying critical aspects and achieving sustainable management.