To explore the differences in carbon emissions over the whole life-cycle of different building structures, the published calculated carbon emissions from residential buildings in China and abroad were normalized. Embodied carbon emission flows, operations stage carbon emission flows, demolition and reclamation stage carbon emission flows and total life-cycle carbon emission flows from concrete, steel, and wood structures were obtained. This study is based on the theory of the social cost of carbon, with an adequately demonstrated social cost of carbon and social discount rate. Taking into consideration both static and dynamic situations and using a social discount rate of 3.5%, the total life-cycle carbon emission flows, absolute carbon emission and building carbon costs were calculated and assessed. The results indicated that concrete structures had the highest embodied carbon emission flows and negative carbon emission flows in the waste and reclamation stage. Wood structures that started the life-cycle with stored carbon had the lowest carbon emission flows in the operations stage and relatively high negative carbon emission flows in the reclamation stage. Wood structures present the smallest carbon footprints for residential buildings.

The global industries are progressively transitioning towards low-carbon development; however, construction remains a significant contributor to energy consumption and carbon emissions. In promoting industrialized construction, the use of prefabricated buildings emerges as a crucial strategy for achieving environmental sustainability. This study initially examines the development and current status of prefabricated concrete component factories in Fujian Province, focusing on regional distribution and production conditions. It also gathers data on carbon emissions, time, and costs to formulate a multi-objective optimization model. Utilizing ant colony algorithms, the model aims to minimize costs while adhering to low-carbon principles and fostering the sustainable development of prefabricated buildings. The optimization results for slabs indicate a minimum production cost of RMB 5.7023 million, with associated carbon emissions of 1154.85 tons. Notably, a cost variation of RMB 10,000 can lead to a maximum difference of 50 tons in carbon emissions, emphasizing the importance of cost minimization as a primary objective. In comparison to conventional production, the optimization of collaborative production demonstrates reductions in both costs and carbon emissions. Furthermore, when focusing on normal and rush modes, costs can be reduced by over 20%, resulting in a potential decrease of up to 50% in carbon emissions. Consequently, effectively mitigating carbon emissions in component production is essential for enhancing the sustainability of the construction industry.

Thermal printing is a prevalent method due to its advantages of rapid output, cost effectiveness, and ease of use. However, the quality of thermal printing is influenced by the printing speed, the temperature, and the concentration and characteristics of the materials. This research employs a BP neural network to forecast print quality, utilizing two activation functions. The findings indicate that a dual-layer hidden configuration utilizing the GeLU activation function yields a lower root-mean-square error (RMSE). The optimal configuration identified consists of six neurons in the first hidden layer and three neurons in the second hidden layer. To enhance the predictive performance, a PSO algorithm was integrated with the PSO-BP model to refine the parameter selection, which included ambient temperature, printing speed, and printing concentration, with iterative training and validation conducted via the gradient descent algorithm. The PSO-BP network achieved an MAE of 0.1108, an RMSE of 0.145, an MSE of 0.021, and an R² value of 0.9916 in predicting print quality. These results substantiate the stability and reliability of the neural network model developed with the PSO algorithm. Further validation with ten sets of test samples demonstrated that the model attained an average absolute error of 2.77% in print quality predictions, indicating robust generalization capabilities and precise forecasting.

The global industries are gradually moving towards the development of low-carbon, however, construction industry has significant energy consumption and carbon emissions. To making the promotion of industrialized construction with low-carbon and energy-efficient, prefabricated buildings are one of important ways to achieve environmental sustainability. This study first investigated the development and status of prefabricated concrete component factories in Fujian Province about regional distribution and production conditions and collecting data of carbon emissions, time and costs to create a multi-objective optimization model for intelligent and collaborative production. All while keeping costs as low as possible, using ant colony algorithms meets low-carbon and promotes sustainable development of prefabricated buildings. With the number of iterations, the overall cost shows a downward during the optimization process. According to optimization modes, the result of slabs indicates the minimum production cost (5.7023 million with carbon emissions 1154.85 tons), and a cost difference of 10000 can result in a maximum carbon emissions difference of 50 tons while minimizing costs to be the primary goal. Compared with general production, the optimization of collaborative production reduces both costs and carbon emissions. Meanwhile, focused on normal and rush mod, the cost reduced more than 20% can be down to 50% on carbon emissions. Therefore, effectively reducing carbon emissions in the component production will contribute to the sustainability of the construction industry.