地下鉄豪雨災害連鎖に対する双方向閉ループ結合SD–BNモデル：持続可能な都市鉄道インフラのレジリエンス支援

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:複合災害連鎖に対するSD–BN双方向結合と小標本CPT較正の手法設計を参考にできる。

🏢実務担当者:鉄道・インフラ事業者が物理的気候リスクの連鎖評価と適応策の定量化に活用できる枠組み。

🏛政策担当者:都市インフラの気候適応政策において、災害連鎖を考慮した動的リスク評価の必要性を示す。

As cities advance toward the UN Sustainable Development Goals (SDGs) for resilient, low-carbon urban transport systems, protecting underground metro infrastructure from climate-driven flood risks has become a core priority for sustainable urban operation. Given the complex nature of metro rainstorm disaster chains—characterized by the interplay of continuous evolution, discrete transitions, and probabilistic uncertainty—a single model is insufficient to fully capture their evolutionary process. This study proposes a bidirectional closed-loop coupled model integrating System Dynamics (SD) with Bayesian Networks (BNs), where SD variables are discretized and fed into the BN at each time step, and BN inference results dynamically adjust SD control parameters in the form of risk levels. A three-tier recursive CPT calibration method, combining maximum likelihood estimation, Laplace smoothing, and physical logical constraints, is developed based on 47 historical accident cases with a 13-node network. The model is validated using the Qingdao Metro “July 23” flooding incident. Results show that, compared to the unidirectional coupling model, the closed-loop model dramatically reduces peak station hall water accumulation, shortens operational disruption, and lowers peak passenger retention under idealized instantaneous-response conditions. Specifically, peak station hall water accumulation is reduced by 14.7%, operational disruption duration is shortened from 5.55 h to 0.05 h (99.1% reduction), and peak passenger retention falls from 2790 to 40 passengers (98.6% reduction). These values are controlled model-internal scenario outputs, and are not directly generalizable to other metro systems without local recalibration and empirical validation. The closed-loop mechanism primarily accelerates post-peak risk decline and shortens high-risk exposure duration, rather than suppressing peak risk levels. The core contribution of this work is the transferable bidirectional coupling framework and small-sample CPT calibration workflow, which provides a quantitative basis for dynamic risk assessment and adaptive emergency response in metro flood management—supporting sustainable urban mobility aligned with SDG 11 (sustainable cities and communities) and SDG 13 (climate action).

• semanticscholar https://doi.org/10.3390/su182110713first seen 2026-10-28 05:41:57