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Prefabricated construction is an innovative technique for decreasing carbon emissions in the construction industry. However, as the investors of housing projects, a majority of developers are unwilling to adopt prefabricated housing in practice. To promote prefabricated housing, this study aims to develop an integrated framework of fuzzy-decision making trial and evaluation laboratory (fuzzy-DEMATEL) and system dynamics (SD) to understand the underlying influencing mechanism of developers' willingness.

Through literature review, a total of 17 influencing factors were identified. Then, the interrelationships among the factors were evaluated by 10 experienced professionals, and the impacts given and received by each factor were further analyzed through fuzzy-DEMATEL. Based on the technology acceptance model (TAM), a SD model was developed to explore the influencing mechanism.

The major cause factors were identified, including mandatory implementation policies, economic incentive policies, environmental protection policies, component standardization and developers' economic strength. This group of factors was expected to be given priority attention in the case of limited resources. On the other hand, the results indicated that economic incentive policies and mandatory implementation policies could affect the developers' willingness via perceived usefulness, while the others mainly influenced perceived ease of use.

Little research has focused on the interrelationships among the influencing factors of developers' willingness to adopt prefabricated housing. This study contributed to understanding the mechanism of developers' willingness from a systematic view and providing the priority of influencing factors. Several strategies were proposed to improve the practical implementation of prefabricated housing development.

Cross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors.

The concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established.

The application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors.

This is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.