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Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

The soft stabilized slab and pile-supported (SSPS) embankment is an improvement technique to increase the efficiency of resources in road construction. To capture the effects of stabilized slabs on the stress transfer mechanism, the differential settlements and the lateral displacement of the embankment completely. A theoretical model of SSPS is proposed by considering the effect of soil arching and the interaction between the embankment fill, stabilized soil, pile, foundation soil and bearing stratum.

In the theoretical model, the stress and strain coordination relationship of the system was analyzed in view of the minimum potential energy theory and equal settlement plane theory. Subsequently, the theoretical method was applied to field tests for comparison. Finally, the influence of the elastic modulus and the thickness of the stabilized slab on the stress concentration ratio and foundation settlement were examined.

In addition to the experimental findings, the method has been revealed to be reasonable and feasible, considering its ability to effectively exploit the stabilized slab effect and improve the bearing capacity of soil and piles. An economical and reasonable arrangement scheme for the thickness and strength of stabilized slabs was obtained. The results reveal that the optimum elastic modulus was chosen as 28 MPa–60 MPa, and the optimum thickness of the stabilized slab was selected as 1.5 m–2.1 m using the parameters of field tests, which can provide guidance to engineering design.

An optimization calculation method is established to analyze the load transfer mechanics of the SSPS embankment based on a double-equal settlement plane. The model’s rationality was analyzed by comparing the settlement and stress concentration ratios in the field tests. Subsequently, the influence of the elastic modulus and the thickness of the stabilized slab on the stress concentration ratio and settlement were examined. An economical and reasonable arrangement scheme for the thickness and elastic modulus of stabilized slabs was obtained, which can provide a novel approach for engineering design.

College student’s mental health issues have emerged as a significant public health concern. The urban campus environment, being the primary habitat for college students, plays a crucial role in influencing their mental health.

Based on survey data from 34 Chinese universities and 1173 college students in 2021, this study utilized deep learning and street view images to explore the relationship between various urban campus landscapes, college students' exercise participation, and mental health.

The study revealed substantial variations in campus landscape features, particularly in terms of spatial openness. While green campus landscapes (measured by the Green View Index and Normalized Difference Vegetation Index) showed no significant impact on exercise participation or mental health, the Sky View Factor did. Higher levels of campus openness and exercise frequency were associated with better mental health. The study also underscored that the influence of urban campus landscapes on college students' mental health was mediated by their exercise participation. Notably, spatial openness emerged as the most prominent differentiating factor among urban campus landscape attributes, significantly affecting students' exercise participation and mental health.

Thus, fostering open campus environments and reducing spatial constraints are vital steps in creating a sustainable urban landscape that can help alleviate potential negative effects on college students' mental health issues.

This paper explores whether fintech paves the way for the transition to carbon neutrality in the context of China’s climate policy uncertainty (CCPU) and the influence of the ocean carbon sink market.

We apply a novel wavelet analysis technique to investigate the time-frequency dependence between the CCPU index, the CSI (China Securities Index) Fintech Theme Index (CFTI) and the Carbon Neutral Concept Index (CNCI).

The empirical results show that CCPU and CFTI have a detrimental effect on CNCI in high-frequency bands. Furthermore, in low-frequency domains, the development of CFTI can effectively promote the realization of carbon neutrality.

Our findings show that information from the CCPU and CFTI can be utilized to forecast the movement of CNCI. Therefore, the government should strike a balance between fintech development and environmental regulation and, hence, promote the use of renewable energy to reduce carbon emissions, facilitating the orderly and regular development of the ocean carbon sink market.

The development of high-quality fintech and positive climate policy reforms are crucial for achieving carbon neutrality targets and promoting the growth of the marine carbon sink market.

Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.