The development of healthy building technology has become a major trend in the global construction industry, especially in China, owing to accelerating urbanization and increasing health awareness among residents. However, an effective evaluation framework to quantify and evaluate the maturity of healthy building technology is lacking. This paper proposes a novel maturity evaluation model for healthy building technology. After analyzing the Driver–Pressure–State–Impact–Response (DPSIR) framework for asserting the maturity of healthy building in China, it constructs an evaluation indicator system, comprising five and twenty-seven first- and second-class indicators, respectively. Subsequently, this paper constructs an improved projection pursuit model based on border collie optimization. The model obtains evaluation results by mining evaluation data, thus overcoming the limitations of traditional evaluation models in dealing with complex data. The empirical research results demonstrate that China is in the optimization stage in terms of the level of maturity of healthy building technology. The weight of impact is as high as 0.2743, which is the most important first-level indicator. Strict green energy utilization policy requirements are the most important secondary indicator, with a weight of 0.0513. Notably, the model is more advanced than other algorithms. In addition, this paper offers some countermeasures and suggestions to promote healthy building in China. Developing and applying this model can promote and popularize healthy building technology in China and even the globe and contribute to a healthier and more sustainable living environment.

In developing countries, especially China, large-scale commercial complexes are the current trend in commercial real estate. Compared with other similar buildings, scientific site selection is very important for the smooth construction and efficient operation of these complexes. However, there is still a lack of a targeted evaluation index system and quantitative evaluation methods. Therefore, this paper put forward the evaluation index system and method of large-scale commercial-complex location based on a projection pursuit model. First, this paper comprehensively considered the environmental, social, and economic factors, and used secondary and tertiary indicators to systematically establish an evaluation index system. This index system effectively dealt with the complex problem of its site selection. Compared with the traditional multi-attribute evaluation method based on expert advice, the evaluation method of the improved projection pursuit model based on a sparrow search algorithm constructed in this paper was to mine key information from the evaluation data, which could evaluate the site suitability of large commercial complexes more scientifically and objectively. In addition, this paper made a detailed empirical study of the Joy City project in Nanchang, China. The research results found the key factors affecting the site selection of the project and determined that the site-selection evaluation result of the project was medium. The research results of this paper provide the scientific and objective decision-making basis for the development enterprises of large commercial complexes to reduce site-selection risk and improve investment efficiency.

Traditional construction progress management is hindered by reliance on manual monitoring, delayed information feedback, and a lack of proactive correction capabilities. To address these issues, this study proposes a data-driven closed-loop framework for dynamic progress management leveraging Building Information Modeling (BIM) and Digital Twin (DT). The proposed framework is operationalized through three integrated modules: (i) a dynamic perception layer that synchronizes on-site conditions via IoT and digitized construction logs; (ii) a stochastic prediction engine coupling machine learning with Monte Carlo Simulation (MCS) to quantify delay risks; and (iii) an optimization module based on a Constraint Satisfaction Problem (CSP) model for automated strategy generation. The system’s efficacy was preliminarily evaluated through a prototype application on a primary school building project. Findings from this case indicate that the framework enables near real-time synchronization for schedule deviation warnings, effectively compressing the information latency from days to within a single management cycle. Furthermore, within the empirical scope of the case study, the implementation of DT-driven strategies was associated with a 3-day schedule advancement relative to the simulated baseline and a 15% reduction in the resource idle rate compared to the pre-deployment phase. This study provides a potential pathway for enhancing progress control precision in similar construction environments.