Bibliographic record
Abstract
With increasing urbanization and population density, cities are facing significant challenges, including land scarcity, traffic congestion, and environmental degradation. The utilization of underground space presents a viable solution to these problems, enabling the integration of transportation, commercial, industrial, and residential infrastructure while preserving above-ground areas for green spaces and public use. This study examines the challenges, methods, and prospects of underground space utilization based on global experience and research. A key issue associated with underground construction is its high financial cost due to the complexity of excavation, structural reinforcement, waterproofing, ventilation, and lighting systems. Additionally, geological and hydrogeological conditions significantly impact the feasibility and safety of underground projects, necessitating advanced geotechnical analysis and engineering solutions. To ensure the successful integration of underground facilities into the urban environment, psychological and social factors, such as public perception and accessibility, must also be considered. The article analyzes various approaches to underground construction, including tunnel boring machines and multifunctional underground complexes. Examples from cities such as Singapore, Tokyo, and Montreal illustrate a wide range of underground space applications – from large-scale subway systems and logistics hubs to underground shopping centers and data storage facilities. The study highlights the importance of integrating underground infrastructure into overall city master plans to optimize functionality and cost-effectiveness. Furthermore, innovative solutions such as underground cooling systems, renewable energy integration, and smart underground transport corridors are considered essential strategies for sustainable urban development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".