Structural Safety最新文献_第9页

Impact of structural information fidelity on reduced-order model development for regional risk assessment 结构信息保真度对区域风险评估降阶模型开发的影响

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-27 DOI: 10.1016/j.strusafe.2025.102602

Sang-ri Yi , Alexandros A. Taflanidis , Parisa Toofani Movaghar , Carmine Galasso

Reduced-order models (ROMs) are widely used for seismic vulnerability estimation, both for approximating the response of specific structures as well as for modeling a portfolio of buildings within regional risk assessment applications. There are different ROM modeling approaches with different degrees of complexity, and the modeling choice, as well as the accuracy of the estimated response, naturally depends on the fidelity of the available information for developing the ROM. For regional risk assessment applications, the ROM implementation is commonly established using an automated workflow that leverages generic information about basic building characteristics to derive the mechanical parameters of the simulation models. This paper investigates the influence of information fidelity on the downstream risk analysis when utilizing ROMs in such a context, focusing specifically on moment-resisting frames (MRFs). Initially, a framework for establishing multi-degree-of-freedom (MDoF) ROMs with hysteretic nonlinear behavior is presented, establishing rulesets to derive nominal values of ROM parameters from commonly available building descriptions such as number of stories, story height, design specifications, or structural system type and its material(s) (e.g., reinforced concrete or steel). The rulesets place emphasis on explicitly modeling differences across stories instead of relying on simplified approximations that utilize equivalence to inelastic single-degree-of-freedom systems. The fidelity of the information for developing the ROM is quantified by assigning probability distributions over the aforementioned nominal values, with different degrees of uncertainty across the different parameters. Parametric and global sensitivity analyses are then performed to investigate the importance of this information fidelity. A computational workflow leveraging resampling principles is discussed to promote computational efficiency in these analyses. The results provide unique insights into the parameters of critical importance for establishing ROMs for different MDoF archetypes and offer guidance for the type of data that needs to be collected with higher fidelity (degree of confidence) when deploying ROMs in regional scale seismic risk assessment, in order to improve the prediction accuracy.

降阶模型（ROMs）广泛用于地震易损性估计，既可用于近似特定结构的响应，也可用于在区域风险评估应用中对建筑物组合进行建模。不同的ROM建模方法具有不同程度的复杂性，建模选择以及估计响应的准确性自然取决于开发ROM的可用信息的保真度。对于区域风险评估应用，ROM实现通常使用自动化工作流来建立，该工作流利用有关基本建筑特征的通用信息来推导仿真模型的力学参数。本文研究了当在这种情况下使用rom时，信息保真度对下游风险分析的影响，特别关注抗矩框架（mrf）。首先，提出了一个建立具有滞后非线性行为的多自由度（mof） ROM的框架，建立规则集，从常用的建筑描述(如层数、层高、设计规范或结构系统类型及其材料（例如，钢筋混凝土或钢）中导出ROM参数的标称值。规则集强调显式地对故事之间的差异进行建模，而不是依赖于利用等效于非弹性单自由度系统的简化近似。通过在上述标称值上分配概率分布来量化用于开发ROM的信息的保真度，在不同参数上具有不同程度的不确定性。然后进行参数和全局敏感性分析，以调查该信息保真度的重要性。讨论了利用重采样原理的计算工作流，以提高这些分析的计算效率。研究结果为建立不同mof原型ROMs的关键参数提供了独特的见解，并为在区域尺度地震风险评估中部署ROMs时需要收集的数据类型提供了更高的保真度（置信度），以提高预测精度。

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引用次数: 0

Efficient reliability analysis for offshore wind turbines: Leveraging SVM and augmented oversampling technique 海上风力发电机的高效可靠性分析：利用支持向量机和增广过采样技术

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-27 DOI: 10.1016/j.strusafe.2025.102597

Xukai Zhang, Arash Noshadravan

This study develops an efficient reliability assessment method designed to optimize maintenance strategies for Offshore Wind Turbines (OWT), aiming for significant cost savings through reduced maintenance frequency and enhanced efficiency. Effective cost management requires a robust and accurate approach for reliability-based lifecycle management. Therefore, this paper introduces an improved predictive maintenance method, grounded on the reliability-based failure probability of OWT systems. To augment computational efficiency and diminish computational time, a surrogate model is proposed for the estimation of failure probability. This surrogate model integrates the classification strengths of Support Vector Machine (SVM) with an augmented Synthetic Minority Oversampling Technique (SMOTE), specifically adapted for extremely imbalanced data. The study’s contributions are twofold: firstly, it develops a novel reliability-based predictive maintenance method allowing for the quantitative assessment of OWTs’ current conditions; secondly, it presents a surrogate model adept at managing extreme data imbalance, thereby enhancing prediction accuracy. The effectiveness of the surrogate model is validated through a case study under two distinct weather conditions. The proposed predictive maintenance method serves as an efficient and effective tool for improved maintenance planning for OWTs.

本研究开发了一种有效的可靠性评估方法，旨在优化海上风力涡轮机（OWT）的维护策略，旨在通过减少维护频率和提高效率来显著节省成本。有效的成本管理需要基于可靠性的生命周期管理的稳健和准确的方法。因此，本文以OWT系统基于可靠性的故障概率为基础，提出了一种改进的预测性维修方法。为了提高计算效率和减少计算时间，提出了一种失效概率估计的替代模型。该代理模型将支持向量机（SVM）的分类优势与增强型合成少数过采样技术（SMOTE）相结合，特别适用于极度不平衡的数据。该研究的贡献有两个方面：首先，它开发了一种新的基于可靠性的预测性维护方法，允许对owt的当前状况进行定量评估；其次，提出了一种能够处理极端数据不平衡的代理模型，从而提高了预测精度。通过两种不同天气条件下的案例研究，验证了代理模型的有效性。提出的预测维修方法是改进维修计划的有效工具。

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引用次数: 0

The probabilistic inverse problem and its solving method based on probability density evolution theory and convex optimization algorithms 基于概率密度演化理论和凸优化算法的概率反问题及其求解方法

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-24 DOI: 10.1016/j.strusafe.2025.102600

Yuhan Zhu, Jie Li

A probabilistic inverse problem-solving method based on the framework of Probability Density Evolution Theory and convex optimization algorithms is proposed. This method reformulates the identification of the random source as a quadratic programming problem with linear constraints, identifying the probability density function of the random source in a physical stochastic system even when the distribution type of the random source is entirely unknown. Through singular value decomposition of the quadratic matrix, an error analysis is performed, revealing that the solvability of the probabilistic inverse problem fundamentally depends on the injectivity of the mapping from the random source space to the response space. Case studies confirm that the proposed method is not sensitive to prior information and does not require any predefined assumptions about the distribution type. Meanwhile, it can preliminarily determine whether the inverse problem is solvable before the computational process begins.

提出了一种基于概率密度演化理论和凸优化算法框架的概率逆问题求解方法。该方法将随机源的识别重新表述为具有线性约束的二次规划问题，即使在随机源的分布类型完全未知的情况下，也能识别物理随机系统中随机源的概率密度函数。通过对二次矩阵的奇异值分解进行误差分析，揭示了概率逆问题的可解性从根本上取决于随机源空间到响应空间的映射的注入性。案例研究证实，所提出的方法对先验信息不敏感，并且不需要对分布类型进行任何预定义的假设。同时，可以在计算过程开始前初步判断逆问题是否可解。

引用次数: 0

Frequency domain method for random vibration analysis of nonlinear systems under time-varying coherent nonstationary excitations 时变相干非平稳激励下非线性系统随机振动分析的频域方法

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-21 DOI: 10.1016/j.strusafe.2025.102601

Ning Zhao , Xu Wang , Yu Wu , Fengbo Wu , Shaomin Jia

Strong earthquakes, downbursts, and typhoons are extreme events that involve time-varying coherent excitations, which are crucial in accurately analyzing the structural response. However, most current methods for nonstationary random vibration analysis assume time-invariant coherence, which fails to capture the time-varying nature of real-world excitations. To address this gap, this study proposes an effective and efficient frequency domain analysis framework for nonlinear systems under time-varying coherent nonstationary excitations. This framework is grounded in the equivalent linearization technique and an enhanced evolutionary spectral method (EESM). Through the use of the equivalent linearization technique, a series of equivalent linear systems replaces the initial nonlinear system; with EESM, the highly efficient analysis of time-varying coherent nonstationary random vibrations in linear systems can be performed, requiring only a limited number of time history analyses and fast Fourier transform operations. For local nonlinear systems, the efficient frequency domain method is more favorable in terms of efficiency due to the explicit calculation advantages of EESM. The specific applications for Duffing system and hysteretic system are presented to demonstrate the reliable accuracy and exceptional efficiency of this method, thereby showcasing its potential in addressing large-scale nonlinear system problems.

强震、降爆和台风是涉及时变相干激励的极端事件，对准确分析结构响应至关重要。然而，目前大多数非平稳随机振动分析的方法都假设了时不变的相干性，这无法捕捉到现实世界激励的时变性质。为了解决这一差距，本研究提出了一种有效的时变相干非平稳激励下非线性系统的频域分析框架。该框架以等效线性化技术和改进的演化谱方法（EESM）为基础。利用等效线性化技术，用一系列等效线性系统代替初始非线性系统；利用EESM，可以对线性系统中的时变相干非平稳随机振动进行高效分析，只需要有限数量的时程分析和快速傅里叶变换操作。对于局部非线性系统，有效频域法由于EESM的显式计算优势，在效率上更有利。通过对Duffing系统和滞回系统的具体应用，证明了该方法的可靠精度和卓越的效率，从而展示了其在解决大规模非线性系统问题方面的潜力。

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引用次数: 0

Probabilistic life-cycle assessment of landslides exposed to both rainfall under nonstationary climate change effects and earthquakes 非平稳气候变化和地震下降雨作用下滑坡的概率生命周期评估

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-04-08 DOI: 10.1016/j.strusafe.2025.102599

Zhengying He , Mitsuyoshi Akiyama , Abdul Kadir Alhamid , Dan M. Frangopol , Yu Huang

Combined effects of rainfall and seismic hazards pose significant threats to structures and infrastructure systems. Additionally, climate change is projected to impact the intensity and frequency of future rainfall, increasing the likelihood of landslides. However, evaluating long-term landslide probability under the combined effects of rainfall and seismic hazards, while considering nonstationary climate change, presents significant challenges due to the distinct characteristics of their occurrence processes. This study introduces a novel framework for probabilistic life-cycle landslide assessment that systematically integrates climate change effects on rainfall hazard along with seismic hazard. Probabilistic nonstationary rainfall and seismic hazard models are developed by leveraging stochastic renewal process theory based on occurrence probability and the associated hazard intensity distribution. Slope fragility assessments are conducted for four event scenarios: individual rainfall, individual earthquake, rainfall followed by an earthquake, and an earthquake followed by rainfall, using seepage and equivalent linear analysis through Monte Carlo simulation. Finally, using the total probability theorem, life-cycle landslide probability is numerically evaluated by convolving nonstationary rainfall and seismic hazards with slope fragilities. An illustrative example is provided by applying the proposed framework to a slope in Hiroshima city, Japan, to explore how the combined effects between nonstationary rainfall and seismic hazards impact life-cycle landslide probability.

降雨和地震灾害的综合影响对建筑物和基础设施系统构成了重大威胁。此外，预计气候变化将影响未来降雨的强度和频率，增加发生山体滑坡的可能性。然而，在考虑非平稳气候变化的情况下，评估降雨和地震灾害联合作用下的长期滑坡概率，由于其发生过程的不同特征，提出了重大挑战。本文提出了一个新的滑坡概率生命周期评估框架，该框架系统地综合了气候变化对降雨灾害和地震灾害的影响。利用随机更新过程理论，建立了基于发生概率和相关灾害强度分布的概率非平稳降雨和地震灾害模型。采用渗流分析和蒙特卡罗模拟等效线性分析，对单次降雨、单次地震、先降雨后地震、先地震后降雨四种事件情景进行边坡脆弱性评价。最后，利用全概率定理，将非平稳降雨、地震灾害与边坡脆弱性进行卷积，对边坡生命周期滑坡概率进行数值计算。以日本广岛某边坡为例，探讨了非平稳降雨和地震灾害对边坡生命周期滑坡概率的综合影响。

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引用次数: 0

Seismic risk and failure modes assessment of steel BRB frames under earthquake sequences 地震序列下BRB钢框架的地震风险及破坏模式评估

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-03-27 DOI: 10.1016/j.strusafe.2025.102598

Fernando Gutiérrez-Urzúa , Fabio Freddi , Enrico Tubaldi

Buckling-Restrained Braces (BRBs) are characterized by steady and nearly symmetric hysteretic loops, providing large energy dissipation capacity under strong earthquakes. These devices are designed to sustain a specified maximum ductility demand and, if not properly designed, may fail due to excessive inelastic deformations. Moreover, their low post-yielding stiffness may lead the structure to large residual inter-story drifts at the end of the earthquake motion, and the cumulative ductility demand due to repeated plastic excursions may lead to low-cycle fatigue failure of the device core. The risk of reaching either of these failure modes is exacerbated when considering multiple earthquakes. Although BRBs are designed to function as a fuse element, there is a lack of consensus on the criteria for replacement, particularly when large residual deformations are not observed. Recent studies have suggested that BRBs can withstand several loading cycles before developing low-cycle fatigue rupture; thus, the decision to replace a BRB after a single ground motion may be overly conservative. The present study investigates the likelihood of BRBs reaching these failure modes within a stochastic framework that considers the probability of occurrence of multiple earthquakes during the structure’s lifetime. For this purpose, two steel Moment Resisting Frames (MRFs) retrofitted with BRBs are numerically modeled in OpenSees and subjected to the cumulative demand from hazard-consistent multiple earthquake sequences. The demand values are compared with multiple capacity models for low-cycle fatigue in the BRB core, as well as conventional limits for residual drifts and other failure modes. The outcomes of this study suggest that the risk of developing low-cycle fatigue in BRBs is negligible, even when multiple ground motions are considered, while other failure modes are significantly more likely to occur, particularly when the structures are subjected to pulse-like ground motions.

屈曲约束支撑结构具有稳定且近似对称的滞回环，在强震作用下具有较大的耗能能力。这些装置的设计是为了维持规定的最大延性需求，如果设计不当，可能会因过度的非弹性变形而失效。此外，它们的低屈服后刚度可能导致结构在地震运动结束时产生较大的残余层间漂移，并且由于反复塑性位移引起的累积延性需求可能导致装置核心的低周疲劳破坏。当考虑多次地震时，达到这些破坏模式中的任何一种的风险都加剧了。虽然brb被设计为保险丝元件，但在更换标准上缺乏共识，特别是在未观察到大残余变形的情况下。最近的研究表明，brb在发生低周疲劳断裂之前可以承受多次加载循环；因此，在一次地面运动后更换BRB的决定可能过于保守。本研究考察了brb在随机框架内达到这些破坏模式的可能性，该框架考虑了结构生命周期内发生多次地震的概率。为此，在OpenSees中对两个经brb改造的钢抗矩框架（mrf）进行了数值模拟，并对危险一致的多重地震序列的累积需求进行了研究。将需求值与BRB岩心低周疲劳的多种容量模型以及传统的残余漂移和其他失效模式的极限进行了比较。本研究的结果表明，即使考虑到多次地震动，brb中发生低周疲劳的风险也可以忽略不计，而其他失效模式更有可能发生，特别是当结构受到脉冲式地震动时。

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引用次数: 0

A phase-control-based method for the simulation of homogeneous random fields of fluctuating wind speed 基于相位控制的脉动风速均匀随机场模拟方法

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-03-20 DOI: 10.1016/j.strusafe.2025.102593

Jia-Hang Lyu , Jian-Bing Chen , Pol D. Spanos , Jie Li

The simulation of stochastic processes, and of time-variant random fields finds extensive applications across various scientific and engineering domains. Despite the existence of a variety of methods, including the well-developed spectral representation method, it is still necessary to study the representation of the correlation structure of time-variant random fields. This paper proposes a phase control method for simulating correlated stochastic processes and spatiotemporal random fields. First, by introducing an auxiliary random phase angle and controlling its amplitude, the correlation of two stochastic processes can be precisely reproduced by introducing the auxiliary phase angle to the original process. Further, for time-variant random field simulation, the correlation structure of the random field is converted into that of the random phase angle field, thereby making it possible for the random field simulation either by phase shifting from a single process or using the spectral representation method in a decoupled manner. The effectiveness of the proposed method is validated by two numerical examples of fluctuating wind field simulation. This method provides an alternative perspective on the correlation structure of random fields and could be used for conditional simulation of random fields in future work.

随机过程和时变随机场的模拟在各种科学和工程领域中有着广泛的应用。尽管存在多种方法，包括发展完善的谱表示方法，但仍有必要研究时变随机场相关结构的表示。本文提出了一种相位控制方法来模拟相关随机过程和时空随机场。首先，通过引入辅助随机相角并控制其幅值，通过在原过程中引入辅助相角，可以精确再现两个随机过程的相关性。此外，对于时变随机场模拟，将随机场的相关结构转换为随机相角场的相关结构，从而可以通过从单个过程移相或使用解耦的谱表示方法进行随机场模拟。通过两个脉动风场数值模拟实例验证了该方法的有效性。该方法为研究随机场的相关结构提供了另一种视角，可用于未来工作中随机场的条件模拟。

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引用次数: 0

Reliability-based quantification of the benefits of machine learning predictive models in seismic structural design and performance assessment 基于可靠性的量化机器学习预测模型在地震结构设计和性能评估中的益处

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-03-19 DOI: 10.1016/j.strusafe.2025.102596

Muneera A. Aladsani, Henry V. Burton

Machine Learning (ML) techniques have been used extensively in research within the field of structural engineering due to their high level of accuracy in predicting the behavior of different structural elements. In fact, the superior predictive performance relative to traditional statistical models is often suggested as the primary motivation for the adoption of ML models. However, the implications of such improvements in predictive accuracy in the design and performance of structural systems have not been studied. This paper presents a reliability-based investigation of the tangible benefits provided by ML models in terms of structural design and performance. To quantify these benefits, the increase in predictive accuracy is interpreted as a reduction in epistemic uncertainty. The specific focus is on a predictive model that estimates the drift capacity of reinforced concrete shear walls (RCSWs) with special boundary elements. The accuracy of an extreme gradient boosting (XGBoost) model relative to a basic linear regression equation is quantified in terms of reduced epistemic uncertainty. Then, using 36 RCSW archetype buildings, a Monte Carlo-based procedure is implemented to evaluate the implication of the improved predictive accuracy to seismic design and performance. The study provides insights into how much improvement in accuracy (i.e., ML relative to traditional model) is needed to have a tangible effect on the seismic design and performance.

机器学习（ML）技术在预测不同结构元素的行为方面具有很高的准确性，因此在结构工程领域的研究中得到了广泛应用。事实上，相对于传统统计模型的卓越预测性能往往被认为是采用 ML 模型的主要动机。然而，这种预测精度的提高对结构系统设计和性能的影响尚未得到研究。本文以可靠性为基础，探讨了 ML 模型在结构设计和性能方面带来的切实好处。为了量化这些优势，预测精度的提高被解释为认识不确定性的降低。具体重点是一个预测模型，该模型利用特殊边界元素估算钢筋混凝土剪力墙（RCSW）的漂移能力。相对于基本的线性回归方程，极端梯度提升（XGBoost）模型的准确性被量化为认识不确定性的降低。然后，使用 36 个 RCSW 原型建筑，实施基于 Monte Carlo 的程序，评估提高的预测精度对抗震设计和性能的影响。这项研究深入探讨了需要提高多少精度（即相对于传统模型的 ML）才能对抗震设计和性能产生切实影响。

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引用次数: 0

A refined TMCMC algorithm for adaptive model updating for the probabilistic analysis of complex engineering structures 一种用于复杂工程结构概率分析自适应模型更新的改进TMCMC算法

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-03-18 DOI: 10.1016/j.strusafe.2025.102582

Yu-Xiao Wu , De-Cheng Feng , Shi-Zhi Chen

Modelling complex engineering structures involves numerous parameters that are difficult to determine. Many uncertainties in the model parameters cannot be resolved through standards and experiments alone, necessitating model updating methods. The Bayesian model updating method is one of the most popular approaches for this purpose; and it has led to the development of numerous improved algorithms. However, the traditional Bayesian model updating algorithms are time-consuming and may not always yield the most likely posterior distributions of the model parameters in engineering applications. Therefore, this paper introduces a refined transitional Markov chain Monte Carlo (rTMCMC) algorithm based on the TMCMC algorithm and improved TMCMC (iTMCMC) algorithm. The rTMCMC algorithm is an adaptive Bayesian model updating method designed for engineering applications; it can adaptively find the most likely posterior distributions of model parameters without increasing the computation time. The efficiency of the rTMCMC algorithm is validated via a numerical example, which compares it with the TMCMC and iTMCMC algorithms. Finally, two examples at both the component and structural levels, updated by the rTMCMC algorithm, and compared with the iTMCMC algorithm, are presented, demonstrating the effectiveness of the rTMCMC algorithm in engineering applications.

复杂工程结构的建模涉及许多难以确定的参数。模型参数中的许多不确定性仅通过标准和实验是无法解决的，需要采用模型更新的方法。贝叶斯模型更新方法是最常用的方法之一；它还导致了许多改进算法的发展。然而，在工程应用中，传统的贝叶斯模型更新算法耗时长，且不一定能得到最可能的模型参数后验分布。因此，本文在TMCMC算法和改进的TMCMC （iTMCMC）算法的基础上，提出了一种改进的过渡马尔可夫链蒙特卡罗（rTMCMC）算法。rTMCMC算法是一种针对工程应用而设计的自适应贝叶斯模型更新方法；该方法可以在不增加计算时间的情况下自适应地找到模型参数最可能的后验分布。通过数值算例验证了rTMCMC算法的有效性，并将其与TMCMC和iTMCMC算法进行了比较。最后，给出了rTMCMC算法在构件和结构层面更新的两个实例，并与iTMCMC算法进行了比较，验证了rTMCMC算法在工程应用中的有效性。

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引用次数: 0

Physical-informed random field technique for virtual modelling based building probabilistic vulnerability assessment 基于虚拟建模的建筑物概率易损性评估的物理通知随机场技术

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-03-18 DOI: 10.1016/j.strusafe.2025.102595

Zhiyi Shi , Yuan Feng , Mark G. Stewart , Wei Gao

Developing a probabilistic vulnerability assessment framework for bushfire-prone buildings is a critical measure to reduce bushfire-induced risks to life safety and economic losses to an acceptable level. A reliable assessment approach should include multiple probability-based macro indicators by considering their inherent uncertainties. These macro indicators can incorporate the efficiency of bushfire-damaged transportation network at specified moments, the geographical position of buildings, among others. A Physics-Informed Random Field-Virtual Modelling (PIRF-VM) framework for probabilistic vulnerability assessment of bushfire-prone buildings in large-scale bushfire incidents is proposed. The PIRF generates a random field-based, multi-physical information-fusion model for the simulation of bushfire spread in a large-scale approximate natural environment. The integrated physical information includes the spatially varying vegetation characteristics, the Digital Elevation Model (DEM)-based terrain, the terrain-shaped time-dependent wind field, the geographical coordinates of roads and buildings. To mitigate the computational burden posed by stochastic bushfire simulations in PIRF, the VM is introduced. It can establish an explicit functional relationship between input physical information and output responses of interest, such as the remaining time for bushfire reaching a specified location. As a result, for any new input physical information, the output responses can be directly predicted without time-consuming simulations. Benefiting from the efficient predictions of the PIRF-VM, several probability-based macro indicators are simultaneously considered when assessing the probabilistic vulnerability for bushfire-prone buildings in large-scale bushfire incidents. The Australian community of Cowan serves as an example to illustrate the practical application of the proposed scheme, demonstrating potential in constructing more bushfire-resilient communities in the face of bushfire hazards.

为容易发生森林火灾的建筑物制定概率脆弱性评估框架是将森林火灾对生命安全和经济损失造成的风险降低到可接受水平的关键措施。一个可靠的评估方法应该包括多个基于概率的宏观指标，考虑到它们固有的不确定性。这些宏观指标可以包括特定时刻森林大火破坏的交通网络的效率、建筑物的地理位置等。提出了一种基于物理信息的随机场虚拟建模（PIRF-VM）框架，用于大规模林火事件中易发建筑物的脆弱性概率评估。PIRF生成了一个基于随机场的多物理信息融合模型，用于模拟大规模近似自然环境下的森林火灾蔓延。综合物理信息包括空间变化的植被特征、基于数字高程模型（DEM）的地形、地形形时变风场、道路和建筑物地理坐标。为了减轻pif中随机森林火灾模拟带来的计算负担，引入了虚拟机。它可以在输入物理信息和输出感兴趣的响应之间建立显式的函数关系，例如森林大火到达指定位置的剩余时间。因此，对于任何新的输入物理信息，可以直接预测输出响应，而无需耗时的模拟。受益于PIRF-VM的高效预测，在评估大规模林火事件中易发建筑物的概率脆弱性时，同时考虑了几个基于概率的宏观指标。澳大利亚Cowan社区作为一个例子，说明了所提出方案的实际应用，展示了在面对森林火灾危害时建设更具森林火灾弹性的社区的潜力。

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引用次数: 0