International journal of disaster risk reduction最新文献_第7页

Prioritisation Recommendation Mapping (PrioReMap): A method for supporting relief coordination in flood disaster response 优先排序推荐映射（PrioReMap）：一种支持洪水救灾协调的方法

IF 4.5 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

International journal of disaster risk reduction

Pub Date : 2025-12-03 DOI: 10.1016/j.ijdrr.2025.105949

Moritz Schneider , Lukas Halekotte , Tina Comes , Frank Fiedrich

To effectively coordinate the response to a flood disaster, decision-makers have to prioritise areas that are in most urgent need of assistance. This prioritisation often has to be carried out under time pressure and on the basis of incomplete information, creating a high cognitive load for decision-makers. Methods that integrate Bayesian networks into GIS to draw spatial inference can inform this prioritisation process. However, existing approaches are not equipped to address the time pressure and unclear information-scape that is typical for a flood disaster. In this work, we present a novel spatial inference method for area prioritisation that is designed to address these time and information constraints. The core of this method is a GIS-informed Bayesian network, integrated into an expected loss framework, that can be set up during the preparation phase. The method can then quickly provide area prioritisation recommendations for disaster relief, which has the potential to support decisions-makers during the response phase. In this way, our method provides a means of shifting some of the most time-consuming aspects of the decision-making process from the time-critical disaster response phase to the less critical preparation phase. To illustrate how our method can support rapid and transparent area prioritisation, we present a case study of an extreme flood scenario in Cologne, Germany.

为了有效地协调对洪水灾害的反应，决策者必须优先考虑最迫切需要援助的地区。这种优先排序通常必须在时间压力和不完整信息的基础上进行，这给决策者带来了很高的认知负荷。将贝叶斯网络集成到GIS中以绘制空间推理的方法可以通知此优先级过程。然而，现有的方法无法解决洪水灾害中典型的时间压力和信息不清晰的问题。在这项工作中，我们提出了一种新的区域优先级空间推理方法，旨在解决这些时间和信息限制。该方法的核心是一个基于gis的贝叶斯网络，该网络集成到一个可在准备阶段建立的预期损失框架中。然后，该方法可以迅速为救灾提供地区优先级建议，这有可能在响应阶段支持决策者。通过这种方式，我们的方法提供了一种将决策过程中一些最耗时的方面从时间关键的灾难响应阶段转移到不那么关键的准备阶段的方法。为了说明我们的方法如何支持快速和透明的区域优先排序，我们提出了德国科隆极端洪水情景的案例研究。

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引用次数: 0

A nation-wide classification of the Italian Railway Network susceptibility to flood hazard 意大利铁路网对洪水灾害易感性的全国分类

IF 4.5 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

International journal of disaster risk reduction

Pub Date : 2025-12-02 DOI: 10.1016/j.ijdrr.2025.105946

Gianluca Lelli , Serena Ceola , Alessio Domeneghetti , Adriana Galli , Edmondo Elisei , Alessandro Rinaldi , Armando Brath

Floods pose significant threats to railway infrastructure, given their linear extension across diverse landscapes and frequent intersections with rivers. While European countries have developed Flood Risk Management Plans (FRMPs) following the EU Floods Directive, a comprehensive analysis of railway network flood susceptibility at national scale is still lacking for Italy. Here we develop a comprehensive flood hazard classification for the Italian Railway Network (IRN). Our methodology integrates flood hazard maps, railway infrastructure data, and digital elevation models to characterize flood hazard classes along flood-prone railway routes. The approach distinguishes between steep rapid, rapid and slow flood processes, based on topographical characteristics. Results demonstrate that, for the low probability flood hazard scenario (return period ≥500 years), 25.63 % of the IRN (4,523.4 km) exhibits flood susceptibility, with this proportion declining to 19.09 % and 9.77 % for medium and high flood hazard, respectively. By performing a regional analysis across seven hydrographic districts in Italy, a substantial spatial variability emerges, with the Po River district encompassing nearly half (47.5 %) of all flood-prone railway sections. Our analysis reveals also a marked predominance of rapid flood processes, characterized by values for the time of concentration <12 h. Our classification framework provides crucial insights for risk mitigation and resource allocation, relying exclusively on FRMPs and digital elevation models. The methodology presents a scalable approach applicable to other transportation networks and study areas, supporting infrastructure managers in developing targeted flood protection measures.

洪水对铁路基础设施构成了重大威胁，因为它们的线性延伸跨越了不同的景观，并且经常与河流相交。虽然欧洲国家已经根据欧盟洪水指令制定了洪水风险管理计划（FRMPs），但意大利仍然缺乏对全国范围内铁路网洪水易感性的全面分析。在这里，我们为意大利铁路网（IRN）开发了一个全面的洪水灾害分类。我们的方法集成了洪水灾害图、铁路基础设施数据和数字高程模型，以描述洪水易发铁路沿线的洪水灾害等级。该方法根据地形特征区分陡峭、快速和缓慢的洪水过程。结果表明：在低概率洪涝情景下（重现期≥500 a）， IRN （4523.4 km）有25.63%的区域具有洪涝敏感性，在中、高概率洪涝情景下，这一比例分别降至19.09%和9.77%。通过对意大利七个水文区进行区域分析，出现了大量的空间变化，波河地区包含了近一半（47.5%）的所有易受洪水影响的铁路路段。我们的分析还揭示了快速洪水过程的显著优势，其特征是集中时间（12小时）的值。我们的分类框架为风险缓解和资源分配提供了重要见解，完全依赖于frmp和数字高程模型。该方法提供了一种可扩展的方法，适用于其他交通网络和研究领域，支持基础设施管理者制定有针对性的防洪措施。

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引用次数: 0

A flexible simulation-based predictive approach to compare hazard and risk models: An example application to seismic hazard 一种灵活的基于模拟的预测方法，用于比较灾害和风险模型：地震灾害的应用示例

IF 4.5 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

International journal of disaster risk reduction

Pub Date : 2025-12-02 DOI: 10.1016/j.ijdrr.2025.105947

Francesco Pauli , Stefano Parolai

We propose a simulation-based approach to compare probabilistic hazard and risk models, exploiting the Bayesian prior/posterior predictive p-values (PPP) framework. The comparison can utilize an arbitrary summary statistic and can be customized to the aspects of interest, particularly the right tail, which is crucial in risk assessment. The primary benefits of our approach in comparison to existing alternatives are twofold. Firstly, it incorporates both aleatory and epistemic variability in a natural probabilistic framework, secondly, it produces interpretable measures of discrepancy. The method is demonstrated on synthetic data and two state-of-the-art seismic hazard models for Italy (MPS19, Modello di Pericolosità Sismica 2019, and ESHM20, European Seismic Hazard Model 2020). The method is applicable in any domain involving probabilistic hazard or risk models, including flood, volcanic, or multi-layer single hazard or single risk assessments.

我们提出了一种基于模拟的方法来比较概率危害和风险模型，利用贝叶斯先验/后验预测p值（PPP）框架。这种比较可以利用任意的汇总统计数据，并且可以根据感兴趣的方面进行定制，特别是在风险评估中至关重要的右尾。与现有的替代方案相比，我们的方法的主要好处是双重的。首先，它在自然概率框架中结合了选择性和认识性变异，其次，它产生了可解释的差异度量。该方法在意大利的合成数据和两个最先进的地震灾害模型（MPS19， Modello di pericolosito Sismica 2019和ESHM20，欧洲地震灾害模型2020）上进行了验证。该方法适用于任何涉及概率灾害或风险模型的领域，包括洪水、火山或多层单一灾害或单一风险评估。

引用次数: 0

The fundamental causes of disaster vulnerability: Subsistence agricultural land loss in rural Malawi 易受灾害影响的根本原因：马拉维农村生存农业用地的丧失

IF 4.5 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

International journal of disaster risk reduction

Pub Date : 2025-12-01 DOI: 10.1016/j.ijdrr.2025.105943

S. Livne , S. Chibvunde , M. Mwendera , M.B. Aron , N. Davidovitch , F. Munyaneza , A. Rosenthal

Land access is one of the strongest predictors of disaster vulnerability for extreme weather events in rural, low-income environments. In Southern Africa, smallholder farmers face accelerating land loss from both slow-onset stressors, such as declining soil fertility and acute stressors such as cyclones. However, the complex mechanisms through which land loss perpetuates disaster vulnerability require a deeper examination of the lived experience of rural farmers.

In this study, we examined how agricultural land loss functions as a fundamental cause of disaster vulnerability in rural Malawi, drawing on Blaikie's land degradation framework and using qualitative methods. Between 2020 and 2024, we conducted in-depth interviews with 49 community members and 44 disaster responders across Neno and Chikwawa Districts, following Cyclones Idai, Ana, and Freddy, supplemented by participant observations and spatial analysis.

Our analysis revealed that land degradation operates simultaneously as symptom, cause, and result of broader socioeconomic vulnerabilities. Land degradation reflects pre-existing rural-urban inequalities driven by increasing demands for charcoal and agricultural products. When intersecting with cyclones, degraded land accelerates soil loss and forces displacement. Subsequently, land degradation becomes a result of harmful adaptation strategies, as communities turn to illegal charcoal production, creating feedback loops that increase future disaster risk.

Climate change disrupts traditional coping mechanisms by compressing temporal patterns of disaster and recovery, creating double exposure to acute and slow-onset stressors that intersect with existing socioeconomic disparities. The findings demonstrate that disaster vulnerability persists because current policies fail to address land access as a fundamental cause, leaving underlying inequalities in resource access unaddressed.

在农村和低收入环境中，土地获取是极端天气事件的灾害脆弱性最强有力的预测因素之一。在南部非洲，由于土壤肥力下降等缓慢发生的压力因素和飓风等急性压力因素，小农面临着加速的土地流失。然而，土地损失使灾害脆弱性持续存在的复杂机制需要对农村农民的生活经验进行更深入的研究。在本研究中，我们借鉴Blaikie的土地退化框架并使用定性方法，研究了农业土地流失如何成为马拉维农村灾害脆弱性的根本原因。在2020年至2024年期间，在飓风“伊代”、“安娜”和“弗雷迪”之后，我们对尼奥和奇克瓦瓦地区的49名社区成员和44名救灾人员进行了深入访谈，并辅以参与者观察和空间分析。我们的分析表明，土地退化同时是更广泛的社会经济脆弱性的症状、原因和结果。土地退化反映了对木炭和农产品需求不断增加所导致的城乡不平等。当与飓风相交时，退化的土地加速了土壤流失，迫使人们流离失所。随后，土地退化成为有害的适应策略的结果，因为社区转向非法木炭生产，形成了增加未来灾害风险的反馈循环。气候变化通过压缩灾害和恢复的时间模式，破坏了传统的应对机制，造成双重暴露于急性和缓慢发作的压力源，这些压力源与现有的社会经济差距相交。研究结果表明，灾害脆弱性仍然存在，因为目前的政策未能将土地获取作为根本原因，导致资源获取方面的潜在不平等问题得不到解决。

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引用次数: 0

Seismic risk and resilience assessment for the G7 highway bridge in Greece 希腊G7公路桥地震风险及恢复力评估

IF 4.5 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

International journal of disaster risk reduction

Pub Date : 2025-12-01 DOI: 10.1016/j.ijdrr.2025.105882

Akrivi Chatzidaki, Christos Giannelos, Dimitrios Vamvatsikos

The seismic risk and resilience are assessed for the G7 reinforced concrete bridge of the Egnatia Odos highway in Greece. It comprises two structurally independent twin three-span balanced cantilever bridges. A component-based approach compatible to FEMA P-58 is followed for performance assessment that allows evaluating damage and consequence scenarios for individual critical components (i.e., piers and bearings) and propagate them to assess the asset-level performance. The consequences are quantified in terms of repair losses, downtime, and actions that the road operator shall take until repair actions have finished, i.e., number of lanes closed and/or speed limit in the remaining open ones. By combining the speed limit, number of lanes closed and downtime, the asset's recovery back to full functionality is determined. The aim for such data is to be integrated into a tool that can be used for pre-event risk assessment and rapid post-event inspection of critical road infrastructure. This allows tracing back the consequences after an event to individual bridge components and can help road operators establish bridge inspection prioritization protocols and manage associated incidents, facilitating the rapid assessment of the state of the bridge and optimal recovery to full functionality.

对希腊Egnatia Odos高速公路G7钢筋混凝土桥进行了地震危险性和恢复力评估。它由两座结构独立的双三跨平衡悬臂桥组成。性能评估采用与FEMA P-58兼容的基于组件的方法，允许评估单个关键组件（即桥墩和轴承）的损坏和后果情景，并将其传播到评估资产级性能。维修损失、停机时间和道路运营商在维修工作完成前应采取的行动（即关闭车道数和/或限制剩余开放车道的速度），对后果进行了量化。通过结合限速、关闭车道数量和停机时间，可以确定资产恢复到全部功能。收集这些数据的目的是将其纳入一种工具，用于事故发生前的风险评估和事故发生后对关键道路基础设施的快速检查。这使得事故发生后的后果可以追溯到单个桥梁部件，并可以帮助道路运营商建立桥梁检查优先级协议和管理相关事故，促进对桥梁状态的快速评估和最佳恢复功能。

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引用次数: 0

Economic impacts of recent and future coastal inundation in Vanuatu 瓦努阿图最近和未来沿海洪水对经济的影响

IF 4.5 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

International journal of disaster risk reduction

Pub Date : 2025-12-01 DOI: 10.1016/j.ijdrr.2025.105914

Anil Deo , Bryan Hally , Kevin Hennessy , Ron Hoeke , Leanne Webb , Savin Chand , Rebecca Gregory , Malcolm King , Nathan Eaton , Raviky Talae

Small Island Developing States (SIDS) are highly exposed to extreme sea level (ESL) events, which are expected to intensify under climate change. Understanding the exposure and economic implications of ESL-driven coastal inundation is essential for informing risk reduction and adaptation planning. This study presents a scalable methodological framework for assessing exposure and economic impacts of ESL-driven coastal inundation on critical assets. The framework integrates high-resolution geospatial data, probabilistic return interval analysis, and climate scenario projections to quantify present and future coastal risk. Applied to Vanuatu as a case study, the framework evaluates exposure and replacement costs for buildings and road networks under 50- and 100-year ESL events, across baseline (1981–2020) and future climate conditions (2041–2060 and 2081–2100), assuming low (RCP2.6) and high (RCP8.5) greenhouse gas emission scenarios. Results indicate a substantial increase in both exposed assets and associated economic losses under future scenarios. For instance, the total replacement cost of buildings exposed to 50-year ESL events rises from USD 59 million (5 % of GDP) under baseline conditions to USD 97 million (9 % of GDP) by mid-century under high emissions, and up to USD 163 million (15 % of GDP) by late century. Comparable increases are evident for road assets, particularly in low-lying local government areas adjacent to major waterways. Beyond quantifying local impacts, this case study demonstrates how the framework can be adapted for regional and national coastal risk assessments to inform climate adaptation planning, infrastructure investment, and disaster risk management in other SIDS in the Pacific.

小岛屿发展中国家（SIDS）极易受到极端海平面（ESL）事件的影响，预计这些事件将在气候变化的影响下加剧。了解esl驱动的沿海淹没的暴露和经济影响对于为减少风险和适应规划提供信息至关重要。本研究提出了一个可扩展的方法框架，用于评估esl驱动的沿海淹没对关键资产的暴露和经济影响。该框架集成了高分辨率地理空间数据、概率返回区间分析和气候情景预测，以量化当前和未来的沿海风险。该框架以瓦努阿图为例，评估了50年和100年ESL事件下建筑物和道路网络的暴露和重置成本，涵盖基线（1981-2020）和未来气候条件（2041-2060和2081-2100），假设温室气体排放低（RCP2.6）和高（RCP8.5）情景。结果表明，在未来情景下，暴露的资产和相关的经济损失都将大幅增加。例如，受50年ESL事件影响的建筑物的总重置成本从基线条件下的5900万美元（占GDP的5%）上升到本世纪中叶高排放下的9700万美元（占GDP的9%），到本世纪末高达1.63亿美元（占GDP的15%）。公路资产的可比增长也很明显，特别是在靠近主要水道的低洼地方政府区域。除了量化当地影响之外，本案例研究还展示了如何将该框架用于区域和国家沿海风险评估，从而为太平洋其他小岛屿发展中国家的气候适应规划、基础设施投资和灾害风险管理提供信息。

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引用次数: 0

Corrigendum to “Identification of influencing factors and risk assessment of underground space flooding in the mountain city” [Int. J. Disaster Risk Reduct. 113 (2024) 104807] “山城地下空间洪涝影响因素识别与风险评估”的勘误表[Int.]。[j] .灾害风险防范。113 (2024):104807]

IF 4.5 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

International journal of disaster risk reduction

Pub Date : 2025-12-01 DOI: 10.1016/j.ijdrr.2025.105925

Ximin Yuan , Minrui Wu , Fuchang Tian , Xiujie Wang , Rukai Wang

引用次数: 0

Driving behaviour during flood and bushfire emergency evacuations: Insights from observational and self-reported data 洪水和森林火灾紧急疏散期间的驾驶行为：来自观察和自我报告数据的见解

IF 4.5 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

International journal of disaster risk reduction

Pub Date : 2025-12-01 DOI: 10.1016/j.ijdrr.2025.105905

Sara Fazeli , Taha H. Rashidi , Mohammad Mojtahedi , Milad Haghani

Climate-related emergencies such as floods and bushfires are among the most prevalent natural hazards globally. During these events, individuals often drive to self-evacuate; however, doing so through floodwaters or bushfire-affected areas poses significant risks to both evacuees and emergency responders. Understanding the factors that influence driver decision-making in these situations is crucial, as it relates directly to pre-evacuation delays, compliance with evacuation orders, and the safety of volunteer rescue personnel. It also informs more effective risk communication and policy design. This study adopts a mixed-methods approach by integrating content analysis of self-recorded real-life driving videos with surveys and discrete choice experiments. It examines both strategic and operational dimensions of driver behaviour during flood and bushfire conditions. The video analysis captures driver actions, environmental cues, and emotional or verbal responses, while the choice experiment investigates how risk perception, environmental severity, social cues, and contextual factors shape the decision to proceed through hazardous routes. Findings suggest most participants prefer to avoid driving through flood or bushfire scenarios in hypothetical contexts. Environmental severity—such as floodwater depth or fire intensity—was the strongest deterrent. However, the perceived presence of other drivers emerged as a strong motivating factor. Observational data also show that driving mostly occurred when other vehicles were present. Younger and male participants reported greater willingness to drive in both hazards—a pattern also mirrored in the video observations. This dual-method approach offers new insights into emergency driving behaviour and holds practical value for shaping public messaging, emergency planning, and policy interventions during natural hazards.

洪水和森林大火等与气候有关的紧急情况是全球最常见的自然灾害之一。在这些事件中，个人经常开车自我疏散；然而，通过洪水或森林大火影响地区进行疏散对疏散人员和紧急救援人员都构成了重大风险。了解在这些情况下影响驾驶员决策的因素是至关重要的，因为它直接关系到疏散前的延迟、对疏散命令的遵守以及志愿救援人员的安全。它还为更有效的风险沟通和政策设计提供信息。本研究采用混合方法，将自录真实驾驶视频的内容分析与调查和离散选择实验相结合。它考察了在洪水和森林大火条件下驾驶员行为的战略和操作层面。视频分析捕捉司机的行为、环境线索、情绪或言语反应，而选择实验则调查风险感知、环境严重程度、社会线索和环境因素如何影响通过危险路线的决定。研究结果表明，在假设的环境中，大多数参与者更喜欢避免在洪水或丛林大火的场景中开车。环境的严重性——比如洪水深度或火灾强度——是最强大的威慑。然而，感知到其他司机的存在成为一个强大的激励因素。观察数据还显示，驾驶大多发生在其他车辆在场的情况下。年轻人和男性参与者报告说，他们更愿意在两种危险情况下开车——这一模式也反映在视频观察中。这种双重方法提供了对紧急驾驶行为的新见解，并对自然灾害期间形成公共信息、应急规划和政策干预具有实用价值。

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引用次数: 0

Assessing transport network resilience under flood scenarios using the capability approach: A GIS-Based case study of Ennis urban centre, Ireland 利用能力方法评估洪水情景下的交通网络弹性：基于gis的爱尔兰恩尼斯城市中心案例研究

IF 4.5 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

International journal of disaster risk reduction

Pub Date : 2025-12-01 DOI: 10.1016/j.ijdrr.2025.105909

Sangeeta , Hrishikesh Dev Sarma , Beatriz Martinez-Pastor , Helen McHenry , Rui Teixeira

Flooding disrupts transport networks, reducing access to essential services and disproportionately affecting vulnerable populations. Traditional assessments of transport resilience often emphasize infrastructure robustness, such as structural integrity and network connectivity, while overlooking human-centred aspects of accessibility during disruptions. This study introduces a Capability-Based Transport Resilience Framework that integrates Amartya Sen's Capability Approach (CA) with transport network analysis to evaluate accessibility loss and incorporates social vulnerability to assess resilience under flood scenarios. At the core of this framework is the Capability-Based Resilience Index (CRI), a composite index combining the Accessibility Deficit Index (ADI), which quantifies increases in travel time due to network disruption, and the Social Vulnerability Index (SoVI), which reflects variations in population sensitivity and adaptive capacity. By integrating these dimensions, CRI identifies locations where physical inaccessibility and social vulnerability intersect, highlighting areas most in need of intervention. Using Ennis urban centre, Ireland, as a case study, the framework simulates one current and two future fluvial flood scenarios (mid-range and high-end), which incorporate projected impacts of climate change and other developments, to assess impacts on access to healthcare. Findings show that under the high-end future scenario, mean CRI declines by approximately 16 % compared to current conditions, indicating increased climate-related resilience loss. Some areas experience complete collapse in resilience, while others with high travel disruption and social vulnerability show notably low scores. These results underscore the effects of inaccessibility and social disadvantage, emphasizing the need for equity-focused, resilient transport planning in flood-prone areas.

洪水破坏了交通网络，减少了获得基本服务的机会，对弱势群体造成了不成比例的影响。传统的交通弹性评估往往强调基础设施的稳健性，如结构完整性和网络连通性，而忽视了交通中断期间以人为本的可达性。本研究引入了一个基于能力的交通弹性框架，该框架将Amartya Sen的能力方法（CA）与交通网络分析相结合，以评估可达性损失，并将社会脆弱性纳入洪水情景下的弹性评估。该框架的核心是基于能力的弹性指数（CRI），这是一个综合指数，结合了可达性赤字指数（ADI）和社会脆弱性指数（SoVI），前者量化了由于网络中断而增加的旅行时间，后者反映了人口敏感性和适应能力的变化。通过整合这些维度，CRI确定了物理不可达性和社会脆弱性相交的位置，突出了最需要干预的区域。该框架以爱尔兰恩尼斯城市中心为例，模拟了一种当前和两种未来的河流洪水情景（中等和高端），其中包括气候变化和其他发展的预计影响，以评估对获得医疗保健的影响。研究结果表明，在高端未来情景下，与当前情况相比，平均CRI下降约16%，表明与气候相关的复原力损失增加。一些地区的恢复力完全崩溃，而其他旅行中断和社会脆弱性高的地区得分明显较低。这些结果强调了交通不便和社会劣势的影响，强调了在洪水易发地区进行以公平为重点、有弹性的交通规划的必要性。

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引用次数: 0

Mapping seismic risk of existing highway bridges at a regional scale using Artificial Neural Networks 利用人工神经网络在区域尺度上绘制既有公路桥梁的地震风险

IF 4.5 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

International journal of disaster risk reduction

Pub Date : 2025-11-29 DOI: 10.1016/j.ijdrr.2025.105937

Lorenzo Principi , Michele Morici , Valeria Leggieri , Andrea Dall’Asta

Bridges are critical components of transportation networks, whose failure can compromise public safety, economy, and mobility. As infrastructures are increasingly exposed to natural and anthropogenic hazards, effective risk assessment has become essential. Traditional Bridge Management Systems (BMSs), however, still rely heavily on visual inspections, making large-scale evaluations costly and time-consuming. A comprehensive assessment should also address multiple hazards, such as degradation, traffic loads, and earthquakes … Recent research has highlighted the potential of machine learning, and particularly Artificial Neural Networks (ANNs), to support bridge risk assessments. In a previous study, ANN-based models were developed to predict bridge degradation and traffic-related structural risk, although they had not yet been tested on a real case study. Building on this work, this work extends the framework by developing a predictive model for seismic risk and applying all three models to a transportation network in central Italy. The application provides predictions of degradation, traffic-related structural risk, and seismic risk, offering an overview of the network's multi-hazard risk profile. Finally, GIS-based spatial analyses were employed to map the three risk indicators across the region. This allowed the identification of patterns, clusters, and high-risk hotspots, offering information to support inspection prioritization and the planning of mitigation strategies. The novelty of this work lies in: the development of an ANN-based predictive model for seismic risk, the application of ANN models for degradation and traffic-related structural risk, together with the seismic risk model, to a real bridge network, andthe integration of all three predictions into a GIS-based maps.

桥梁是交通网络的重要组成部分，其故障会危及公共安全、经济和机动性。随着基础设施日益受到自然和人为灾害的影响，有效的风险评估变得至关重要。然而，传统的桥梁管理系统（bms）仍然严重依赖于目视检查，这使得大规模的评估既昂贵又耗时。综合评估还应考虑多种危害，如退化、交通负荷和地震……最近的研究强调了机器学习，特别是人工神经网络（ann）在支持桥梁风险评估方面的潜力。在之前的一项研究中，人们开发了基于人工神经网络的模型来预测桥梁退化和交通相关的结构风险，尽管这些模型尚未在实际案例研究中进行测试。在此基础上，本工作通过开发地震风险预测模型并将所有三种模型应用于意大利中部的交通网络，扩展了框架。该应用程序提供了退化、交通相关结构风险和地震风险的预测，提供了网络多灾害风险概况的概述。最后，采用基于gis的空间分析方法绘制了三种风险指标在区域范围内的分布图。这使得能够识别模式、集群和高风险热点，为支持检查优先次序和减灾战略规划提供信息。这项工作的新颖之处在于：开发了基于人工神经网络的地震风险预测模型，将人工神经网络模型应用于退化和交通相关的结构风险，连同地震风险模型一起应用于真实的桥梁网络，并将所有三种预测整合到基于gis的地图中。

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引用次数: 0