As a main carrier mode for the sustainable development of the construction industry in China, prefabricated building may lead to problems such as cost overruns, project delays, and waste of resources due to unreasonable selection of prefabricated components. Therefore, we quantitatively analyze the contribution rate of quality optimization of prefabricated components using QFD-SEM. Under the constraints of prefabrication rate, quality optimization contribution rate, and expected values of various sub-goals, we propose a multi-objective optimization method for prefabricated component combinations based on cost, duration, and carbon emissions. By using NSGA-II to solve the model, we can obtain a set of optimal Pareto solutions for prefabricated component combinations. Based on the optimal Pareto solution set, we establish a multi-objective evaluation model using simulated annealing optimization projection tracing method, and select the optimal prefabricated component combination solution according to the projected eigenvalues of the solutions. An empirical study is conducted using an eleven-story framed building in Shenzhen, Guangdong Province, China as a case study. The results show that: (1) Using this method, optimal solutions can be obtained in an unbounded solution space, with the optimal solution having advantages over both fully cast-in-place and fully prefabricated solutions. Compared to the fully cast-in-place solution, the duration and carbon emissions are reduced by 36.62% and 12.74% respectively, while compared to the fully prefabricated solution, costs are reduced by 4.15%. (2) There is a certain negative correlation between the cost of prefabricated component combinations and duration, carbon emissions, and quality optimization, while there is a certain positive correlation with the prefabrication rate. (3) The size of the optimal projection direction vector based on the optimization objectives indicates that carbon emissions have the greatest impact on the evaluation results of the solutions.

Prefabricated buildings are gradually being promoted from pilot demonstration to scale, to combat climate change and improve energy conservation and emission reduction in the building sector. Despite the carbon emission of assembled structures being substantially lower than that of cast-in-place buildings due to the significant reduction in energy demand during the materialization process, there is still a lot of room for improvement. This study looks at the strategy choices made by manufacturers of prefabricated building components in relation to lowering carbon emissions from the standpoint of manufacturing prefabricated building components. By building a dynamic evolutionary game model between two parties, we investigate the evolutionary process of the strategy chosen by prefabricated building component manufacturers and the government, analyzing the evolutionary stability of each side’s strategy choice, and finally using Matlab tools to simulate the effectiveness of the evolutionary stability. The study findings indicate that (1) low-carbon production costs, local government incentives and sanctions, and corporate low-carbon production benefits are the main influencing factors for manufacturers of prefabricated building components to adopt low-carbon production techniques; and that (2) the cost of regulation under low-carbon production methods and the local government performance assessment system are the key elements affecting regulations by the local government. Based on this finding, we suggest corresponding countermeasures in three areas, including investigating new low-carbon technology options for businesses, developing a new carbon emission accounting subsidy mechanism, and improving the regulatory framework of the government, to provide an efficient pathway for the growth of a low-carbon economy.

There are problems with an inadequate quality assurance system and non-standard construction organization and administration while creating prefabricated buildings. There are currently fewer quality assessments employing prefabricated component combinations as the research focus, and the quality evaluation methodology is more subjective. We propose a method for evaluating the quality of prefabricated buildings using an ant colony algorithm and a simulated annealing algorithm to optimize the projection pursuit model: firstly, create a prefabricated building quality index system; secondly, questionnaires were distributed, tested for reliability and validity to avoid the influence of questionnaire subjectivity on the results, and structural equation modeling was used to calculate the weights of the quality influencing factors; thirdly, quantify the quality factors of prefabricated components by using the quality function development method, and construct a quality optimization model for the prefabricated component combinations; fourthly, use the ant colony algorithm to solve the quality optimization model to obtain a set of prefabricated component combinations to satisfy the quality requirements; and lastly, use a simulated annealing to optimize the projected pursuit method for evaluating the quality of prefabricated component combination solutions. The results show that (1) The use of optimization algorithms can successfully avoid the issue of a more subjective evaluation approach and increase the efficiency and accuracy of evaluation. (2) Residential Comfort (RC), Usage Durability (UD) and Structural Reliability (SR) have a substantially negative association, but Residential Comfort (RC) and Installation Stability (IS) have strong positive correlations. (3) Based on the magnitude of the vector of the ideal projection direction of the quality indicators, it was determined that the Installation Stability (IS) indicator had the greatest influence on the evaluation of the program, and the Structural Reliability (SR) indicator had the least influence on the program.

As an important part of the prefabricated building standard system, the evaluation standards for prefabricated buildings play an important guiding role in the construction and development of prefabricated buildings. However, at present, the national evaluation standards for prefabricated buildings are highly extensive and general, and some of their contents are not well considered, while local standards based on the national standard have been formulated in accordance with the actual situation in their respective regions, which makes the evaluation of prefabricated buildings in various regions more confusing, which also causes differences when calculating the prefabrication rate, restricting the development of prefabricated buildings. In order to promote the construction of a complete evaluation standard system for prefabricated buildings in China, this paper combs through the methods used for the calculation of the prefabrication rate of prefabricated buildings in 28 provincial-level administrative regions in China, conducts a comparative analysis and empirical research on the calculation of the prefabrication rate under different standards in four dimensions, and applies t-tests and simulated annealing algorithms to optimize the projection pursuit model to analyze the differences in the total scores of the prefabrication rate under different standards, as well as the key factors that affect them. The study shows that (1) the same building may have different prefabrication rates under different standards for prefabricated buildings, and the way of calculating the prefabrication rate and the scoring criteria of the evaluation items will result in significant differences in the calculated prefabrication rate. (2) Depending on the value of the optimal projection direction vector, it was found that among the evaluation criteria, the enclosure wall and the internal partition wall have the greatest influence on the total score value, while the main structure has the least influence on the total score value. (3) There are similarities as well as differences in the evaluation criteria of prefabricated buildings in each province. On this basis, by analyzing the reasons for these differences, corresponding suggestions are made for governments needing to formulate or revise local standards.

A multi-objective optimization design approach for prefabricated components such as columns, beams, slabs, walls and stairs in prefabricated buildings using ant colony algorithm is proposed to minimize cost, duration and carbon emissions in this paper. The proposed approach takes cost, duration, and carbon emissions as objective functions, the construction technologies of cast-in-place and prefabricated components as variables, prefabrication rate as constraints, and the ant colony algorithm as a solution tool, to minimize the cost, duration, and carbon emissions of prefabricated buildings. The validity of the proposed approach was verified by applying it to the multi-objective optimization design of a three-story frame structure. The results showed that:(1) Compared to all cast-in-place buildings, the prefabricated building under the baseline scenario achieves a reduction of 0.42% in cost, 19.05% in duration, and 13.49% in carbon emissions. (2) The main factor influencing the optimal combination of prefabricated building components is the incremental benefit of cost, duration, and carbon emissions resulting from changes in sub-target weight and prefabrication rate. The weight coefficients of each sub-objective determine “how” the construction technologies of the components is selected, while the prefabrication rate determines “how much” of the construction technologies is chosen. (3) Under four scenarios with different weighting coefficients, the optimized solution for prefabricated buildings compared to cast-in-place construction shows maximum reductions of 1.26% in cost, 27.89% in duration, and 18.4% in carbon emissions. The proposed approach provides an effective pathway for the transformation from cast-in-place construction to prefabricated construction and promotes sustainable development in the building industry.

Railway engineering projects exhibit complex internal structures and extensive external interconnections, making it difficult to establish definitive control objectives for duration and cost during construction. With the increasing severity of environmental pollution and energy consumption, carbon emissions have gradually become a critical factor that cannot be ignored in the construction process. This study proposes a multi-objective optimization method for construction based on cost-duration-carbon emissions under the carbon trading mechanism, employing an improved NSGA-II algorithm to solve the multi-objective optimization model and deriving the optimal construction solution based on projection characteristic values. An empirical study of a railway engineering project demonstrates the following results: (1) Compared to the baseline solution, the optimized construction solution achieves reductions of 1.87 million yuan in cost, 3 days in duration, and 137 tons in carbon emissions, with all optimization results being statistically significant; (2) Quantitative analysis using spacing metrics and one-sample t-tests confirms that the improved NSGA-II algorithm exhibits high solution stability and solution set uniformity in solving construction multi-objective optimization problems, along with good scalability; (3) The optimal projection vector [Duration, Cost, Carbon Emission] = [0.0120, 0.9821, 0.9958] indicates that carbon emissions are the most critical factor influencing the selection of the optimal construction solution. This study provides theoretical foundations and technical support for multi-objective optimization in railway engineering construction, offering significant value for promoting green and sustainable development in the industry.