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In the context of Digital Construction (DC), collaborative innovation in the construction supply chain (CSC) is crucial for long-term competitiveness. However, transparent information flows and fickle market circumstances hinder enterprises from actively participating in collaborative innovation, making it challenging to establish effective incentive mechanisms. To achieve sustained and stable collaborative innovation, an evolutionary game model of collaborative innovation between core enterprises and member enterprises in the CSC under DC based on Prospect Theory is constructed. Five equilibrium scenarios and evolutionary stability strategies are analyzed, and the corresponding stability conditions are obtained. Finally, the impact of different parameters on strategy selection are analyzed by numerical simulation. The results indicate that the balance between knowledge sharing and knowledge leakage is the premise of the positive impact of DC technology on collaborative innovation. Moreover, the adjustment of gain sensitivity and loss sensitivity is the key to enhancing managerial enthusiasm for collaborative innovation. Furthermore, the design of income distribution and innovation incentives must adhere to the reciprocity principle, while subsidies from owners demonstrate a prominent positive impact on collaborative innovation. This paper systematically expounds the dynamic influence of DC technology application, knowledge spillover effects, and managerial cognitive structures while confirming the intrinsic effect of innovation incentive mechanisms. It provides substantial theoretical reference and management enlightenment for promoting the development of collaborative innovation in the CSC under DC. [ABSTRACT FROM AUTHOR]

In the wave of the digital era, Building Information Modelling (BIM) has emerged as the primary tool for intelligent construction and digital transformation in the Architecture, Engineering, and Construction (AEC) industry. China's focus on policy guidance for the dissemination and application of BIM technology has promoted high-quality development and technological innovation in the AEC industry. However, few studies have described the multi-dimensional evolution picture of BIM technology policy in China, and lack of effective grasp and prediction of policy evolution law and future direction. An improved policy quantitative analysis framework is built using natural language processing (NLP) based on deep learning in this paper. The dynamic characteristics of 242 Chinese BIM technology policy documents from 1998 to 2022 in the PKULAW database are quantitatively analyzed by the framework, and the four-stage integrated evolution of Chinese BIM technology policy is quantitatively mapped in a visual form through network analysis and dynamic topic model (DTM). Furthermore, this research conducts a comprehensive discussion from three dimensions, providing valuable insights for policy makers and industry participants in China. The findings contribute to the deep integration of policy optimization and the construction industry, while also aiming to share China's experience in BIM technology governance with other countries and regions. Ultimately, this study seeks to support the global AEC industry in its digital transformation and development by offering valuable knowledge and perspectives. [ABSTRACT FROM AUTHOR]