The number of safety accidents caused by excavation of deep foundation pits in subway stations has been increasing rapidly in recent years. Thus, precisely predicting the safety risks for subway deep foundation pits bears importance. Existing methods, such as machine learning models, have been established for predicting such risks. However, these methods are unable to provide accurate results for deep foundation pits in subway stations due to small and unbalanced data samples. In this research, an intelligent model based on random forest (RF) was established for risk prediction of deep foundation pits in subway stations. To achieve such a goal, different types of monitoring data and risk level monitoring were introduced to the RF for training the model and estimating unknown relationships between monitoring values and safety risks of pits. An actual deep foundation pit in a subway station of the Wuhan Metro was used to demonstrate the applicability of the developed RF risk prediction model. The results showed that the superiority of the proposed RF risk prediction model can be used as a basis to implement a decision-making tool for predicting safety risks of subway foundation pits. The importance evaluation function of the model provides the ability to aid onsite engineers in determining the causes of safety risks, thus facilitating the implementation of emergency measures in advance.

Microbe-driven bioconstruction offers a promising approach to sustainable deep-space habitats as missions shift from short-term exploration to long-term human presence. This review presents the first full-lifecycle bioconstruction paradigm that integrates Earth-derived extremophilic microorganisms, space station validation, and in-situ utilization of Lunar and Martian resources. It addresses gaps in extraterrestrial bioconstruction strategies. The proposed four-stage paradigm begins with biological pioneering using extremophilic microorganisms to establish survival conditions. This is followed by biological conversion of extraterrestrial resources for metal extraction, thereby enabling the synthesis of building materials through biological fabrication and their in-situ assembly into integrated habitats. The final stage involves integration into a self-sustaining life support system for long-term habitation. Key challenges include limited adaptability under compound space stresses, slow screening processes, limited in-orbit validation, and uncertain material longevity. Recommended directions include AI-driven strain optimization, expanded space and lunar experiments, and predictive modeling of material behaviour to support the development of resilient and resource-efficient extraterrestrial habitats.

Digital modeling of tunnels plays a critical role in the refined management of urban metro infrastructure. Backpack LiDAR systems, due to their portability and operational efficiency, show great poten...

Sustainable, high-power energy infrastructure is essential for lunar bases yet must withstand the Moon’s extreme environment. This review compares power-generation, storage, and grid technologies across performance, environmental adaptation, in-situ resource utilization (ISRU) compatibility, and technology readiness; adds a first-order economic cost–benefit analysis of integrated architectures; and proposes a staged, milestone-driven development roadmap. Together, these provide a framework for resilient, ISRU-enabled lunar energy systems supporting habitation and deep-space exploration.