The industry chain of industrialized construction is a key strategy for promoting the sustainable performance of China’s construction industry. Its risk identification is the fundamental step to promote the development of the industry chain. The study was conducted in two phases. The first phase included an extensive literature review and case study analysis to document 32 key factors affecting the process of the industry chain of industrialized construction. In the second phase, 22 key factors influencing the development of the industry chain of industrialized construction in Shandong Province were screened through data collection and expert consultation. A complex network of industrialized construction risk associations (CNICRA) was developed to assess these risks by considering the interrelationship among risks, network nodes, and network edges, and the comprehensive degree indicators for improving the model’s accuracy and resolution. The results show that enterprise collaboration level is the most important factor in the industry chain of industrialized construction. The industrialized system is the most transmittable factor of risk. This study investigated a list of risks in the industrialization of construction, optimized a complex network of risk association, and provided theoretical support for risk management of the industry chain of industrialized construction and understanding of risk response strategies for decision makers.

Prefabricated building construction is an important method of enhancing construction productivity and promoting sustainable development in the construction industry. Evaluating and predicting the development performance of prefabricated buildings will contribute to identifying and implementing the most effective responses to promote prefabricated building technologies. Based on the Drives–Pressures–States–Impacts–Responses (DPSIR) framework, 14 evaluation indexes are determined to evaluate the development level of prefabricated buildings. The entropy weight method was used to determine the weight of the evaluation index, and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method with improved grey correlation was applied to comprehensively evaluate the trend of the index. The grey model (GM(1,1)) was developed to predict the development trend of prefabricated buildings. The development of prefabricated buildings in Shandong province, China, is employed as a case to apply the developed method and investigate development experiences. The results demonstrate that the case has achieved significant progress and has great potential in promoting the use of prefabricated buildings. The development recommendations include developing a policy and regulation system, strengthening a prefabricated building talent pool, and enhancing the investment in technological innovation. This study innovatively formulated the evaluation and prediction system based on the DPSIR, TOPSIS and GM(1,1) models, which could be used for evaluating development performance between social and environmental factors among various cause-effect relationships.

The duration of a residential building project will directly influence its successful implementation; hence, it is essential to estimate a reasonable timeframe. In this study, a genetic algorithm (GA) was employed to optimize and refine the weights and thresholds of a back propagation (BP) neural network, thereby creating a GA‐BP neural network model. A dataset comprising 111 instances of residential building durations was gathered, segmented into 90 training sets and 21 test sets. The model was validated and assessed through root mean square error (RMSE), correlation coefficient (R), and average error rate, demonstrating that the GA‐BP neural network model is effective in predicting the duration of residential buildings. To enhance the predictive accuracy of the GA‐BP neural network model, this research utilized an artificial bee colony (ABC)‐improved K‐means clustering algorithm to categorize 111 experimental datasets and 33 new datasets. The results indicated that the ABC‐K‐means‐GA‐BP model exhibited robust generalization capabilities and high predictive accuracy, with the fitness function showing optimal performance after 10, 15, and 35 generations, and the best validation performances recorded as 0.0019156, 0.00035905, and 0.0036914. This validates that the proposed ABC‐K‐means‐GA‐BP neural network model significantly aids in forecasting the construction period of residential buildings, which holds substantial practical value for enhancing construction efficiency.

This Managing and evaluating large engineering project clusters, especially those collaborating multiple heterogeneous functional units or subcontractors, represents one of the biggest challenges in Project Management, Operation and Service science. To effectively manage and evaluate the performance during the project life cycle, managers need to have clear project architecture of functional units and comprehensive evaluation architecture. Here we introduce a two-way dynamic interactive mapping model based three-layer four-pipeline service-driven organization architecture for evaluating performance of large engineering project clusters. We had developed it from our practical applications. We argue that our architecture is comprehensive and effective from our empirically application to five large engineering project clusters.