Structural Safety最新文献

A probabilistic framework to construct tropical cyclone loss models for building portfolios 为建筑投资组合构建热带气旋损失模型的概率框架

IF 6.3 1区工程技术

Structural Safety Pub Date : 2025-10-13 DOI: 10.1016/j.strusafe.2025.102665

Yu Liang , Hao Zhang , Cao Wang , Diqi Zeng

{"title":"A probabilistic framework to construct tropical cyclone loss models for building portfolios","authors":"Yu Liang , Hao Zhang , Cao Wang , Diqi Zeng","doi":"10.1016/j.strusafe.2025.102665","DOIUrl":"10.1016/j.strusafe.2025.102665","url":null,"abstract":"<div><div>Tropical cyclones (TCs) evolve over time and space and can cause substantial damage to building portfolios. Therefore, timely and accurate TC damage assessment is essential for effective risk management. One practical approach is to establish a relationship between hazard intensity (e.g., wind speeds) and regional damage. However, when the study area is large, spatial heterogeneity, such as clustered building distributions, terrain variability, and spatial variations in wind speeds, can hinder accurate modelling of the hazard-damage relationship. To address this challenge, the present study employs a spatial clustering algorithm to divide the entire area into multiple sub-regions with relatively homogeneous characteristics. For each sub-region, a TC loss model is developed as a function of wind speed at the sub-regional centroid and the corresponding building portfolio loss ratio. In practice, losses in all sub-regions are first assessed individually and then aggregated to estimate the total regional loss. This divide-and-aggregate approach significantly improves the accuracy and applicability of TC loss modelling and can be readily applied to various contexts, such as long-term risk management in large-scale communities.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"119 ","pages":"Article 102665"},"PeriodicalIF":6.3,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LSTM-augmented probability-informed neural network-driven evolution estimation for time-dependent reliability analysis 时变可靠性分析的lstm增强概率神经网络驱动进化估计

IF 6.3 1区工程技术

Structural Safety Pub Date : 2025-09-22 DOI: 10.1016/j.strusafe.2025.102652

Hongyuan Guo , Jiaxin Zhang , You Dong , Dan M. Frangopol

{"title":"LSTM-augmented probability-informed neural network-driven evolution estimation for time-dependent reliability analysis","authors":"Hongyuan Guo , Jiaxin Zhang , You Dong , Dan M. Frangopol","doi":"10.1016/j.strusafe.2025.102652","DOIUrl":"10.1016/j.strusafe.2025.102652","url":null,"abstract":"<div><div>For time-dependent dynamic systems, the inputs include not only random variables but also stochastic processes, posing significant challenges to traditional Time-Dependent Reliability Analysis (TDRA) methods regarding efficiency, accuracy, and generality. To address these challenges, this paper develops a Long Short-Term Memory (LSTM)-Augmented Probability-Informed Neural Network Evolution (LPNE) framework for TDRA of dynamic systems. A set of local performance functions is introduced by selecting representative points for time-independent random variables. Subsequently, an LSTM network is trained to learn the time-dependent behavior of the dynamic system for each local limit state function. Multiple local surrogate LSTM models are then employed to assemble an enhanced dataset accordingly. Based on the enriched dataset, point-evolution estimation is performed with a more ample sample size, integrating Deep Neural Networks (DNN) with the physical equation information of the generalized probability density evolution equation (GDEE). The proposed framework can effectively compensate for the limitations of existing point-evolution approaches that struggle to consider scenarios with stochastic process inputs. The proposed LPNE is validated through four benchmark cases: a simple numerical example, scenarios involving corroded steel beams, corrosion-induced deterioration of steel structures, and the seismic response of multi-story shear frame structure. The results demonstrate that LPNE can accurately and efficiently estimate time-dependent failure probabilities with a limited number of representative points without requiring additional samples.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"118 ","pages":"Article 102652"},"PeriodicalIF":6.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Branch-and-bound algorithm for efficient reliability analysis of general coherent systems 一般相干系统高效可靠性分析的分支定界算法

IF 6.3 1区工程技术

Structural Safety Pub Date : 2025-09-22 DOI: 10.1016/j.strusafe.2025.102653

Ji-Eun Byun , Hyeuk Ryu , Daniel Straub

{"title":"Branch-and-bound algorithm for efficient reliability analysis of general coherent systems","authors":"Ji-Eun Byun , Hyeuk Ryu , Daniel Straub","doi":"10.1016/j.strusafe.2025.102653","DOIUrl":"10.1016/j.strusafe.2025.102653","url":null,"abstract":"<div><div>Branch-and-bound algorithms, also known as bounding or decomposition algorithms, have been developed for reliability analysis of coherent systems. They can find a computationally efficient representation of a system failure or survival event, which can be re-used when the input probability distributions or reliabilities change, for example with time or when new data is available. Existing branch-and-bound algorithms can handle only a limited set of system performance functions, mostly network connectivity and maximum flow. Furthermore, they run redundant analyses on component vector states whose system state can be inferred from previous analysis results. We address these limitations by proposing the <em><strong>b</strong>ranch-and-bound for <strong>r</strong>eliability analysis of general <strong>c</strong>oherent systems</em> (BRC) algorithm: an algorithm that automatically finds minimal representations of failure/survival events of general coherent systems. Computational efficiency is attained by dynamically inferring importance of component events from hitherto obtained results. We demonstrate advantages of the BRC method as a real-time risk management tool by application to the Eastern Massachusetts highway benchmark network.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"118 ","pages":"Article 102653"},"PeriodicalIF":6.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seismic risk assessment methodology for large-span CFST arch bridges in near-fault areas based on fragility analysis 基于易损性分析的近断裂带大跨度钢管混凝土拱桥地震风险评价方法

IF 6.3 1区工程技术

Structural Safety Pub Date : 2025-09-18 DOI: 10.1016/j.strusafe.2025.102656

Lihan Xu , Lueqin Xu , Dong Xie , Jianting Zhou

{"title":"Seismic risk assessment methodology for large-span CFST arch bridges in near-fault areas based on fragility analysis","authors":"Lihan Xu , Lueqin Xu , Dong Xie , Jianting Zhou","doi":"10.1016/j.strusafe.2025.102656","DOIUrl":"10.1016/j.strusafe.2025.102656","url":null,"abstract":"<div><div>Large-span concrete-filled steel tube (CFST) arch bridges are widely built in high-seismicity mountainous areas in China due to their low maintenance costs and high adaptability to the challenging construction environments. The dynamic response of such bridges under seismic loading is highly complex, and their seismic performance is a major concern for multiple stakeholders. This study proposes a seismic risk assessment method for large-span CFST arch bridges from a risk perspective, based on seismic fragility analysis. The method begins with seismic hazard analysis of the bridge site, followed by seismic risk scenario identification of the bridge through fragility analysis, then quantifies the seismic risk scenarios from the perspective of economic losses, and finally evaluates the quantified results of discrete risk scenarios based on tolerance theory. A CFST arch bridge located in a near-fault area is analyzed as a case study, with two design schemes and five annual earthquake frequencies considered to validate the feasibility of the proposed method. The research results show that the seismic risk assessment method effectively identifies risk scenarios and their characteristics across different design schemes and seismic frequencies. Additionally, as the method presents results through macro risk tolerance zone divisions, it offers more intuitive and stakeholder-friendly outputs compared to traditional engineering-technology-based assessments (e.g., seismic fragility curves). Overall, the proposed method serves as a robust decision-making tool for the design, operation, and maintenance of large-span CFST arch bridges and similar structures with complex seismic responses.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"118 ","pages":"Article 102656"},"PeriodicalIF":6.3,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The cell renormalized method for the solution of KF equation and RDKF equation under additive Poisson white noise 加性泊松白噪声下KF方程和RDKF方程的单元重归一化解

IF 6.3 1区工程技术

Structural Safety Pub Date : 2025-09-16 DOI: 10.1016/j.strusafe.2025.102649

Jinheng Song , Jie Li

{"title":"The cell renormalized method for the solution of KF equation and RDKF equation under additive Poisson white noise","authors":"Jinheng Song , Jie Li","doi":"10.1016/j.strusafe.2025.102649","DOIUrl":"10.1016/j.strusafe.2025.102649","url":null,"abstract":"<div><div>Poisson white noise is frequently occurring in various engineering applications. Consequently, solving the stochastic dynamic response of structures subjected to Poisson white noise excitation constitutes a crucial research challenge. In this paper, using the Kolmogorov–Feller (KF) and reduced-dimensional KF (RDKF) equations, a highly efficient numerical approach is proposed for determining the probability distribution of the response. The process begins by generating Poisson white noise using stochastic harmonic function and subsequently computing the dynamic response of the structure. The cell renormalized method is then employed to compute the derivate moments at the centers of each cell. Following this, Gaussian Process Regression (GPR) is utilized to model the continuous derivate moments curve or surface within the state space. Finally, the path integral solution is applied to solve the KF and RDKF equations, ultimately yielding the desired probability distribution of the structural response. To highlight the advantages of the proposed methodology, a series of numerical examples, including one and two dimensional scenarios, linear and nonlinear systems, are all employed to substantiate the applicability of proposed method.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"118 ","pages":"Article 102649"},"PeriodicalIF":6.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

In Memoriam Erik VanMarcke August 6, 1941 – July 7, 2025 纪念埃里克·范马克1941年8月6日- 2025年7月7日

IF 6.3 1区工程技术

Structural Safety Pub Date : 2025-09-11 DOI: 10.1016/j.strusafe.2025.102655

Ning Lin

引用次数: 0

MetaIMNet: A physics-informed neural network architecture for surrogate response and fragility modeling of structures subjected to time-varying hazard loads MetaIMNet：一个基于物理信息的神经网络架构，用于在时变危险载荷下的替代响应和易损性建模

IF 6.3 1区工程技术

Structural Safety Pub Date : 2025-09-08 DOI: 10.1016/j.strusafe.2025.102650

Sushreyo Misra, Paolo Bocchini

{"title":"MetaIMNet: A physics-informed neural network architecture for surrogate response and fragility modeling of structures subjected to time-varying hazard loads","authors":"Sushreyo Misra, Paolo Bocchini","doi":"10.1016/j.strusafe.2025.102650","DOIUrl":"10.1016/j.strusafe.2025.102650","url":null,"abstract":"<div><div>Extreme events such as earthquakes and hurricanes cause widespread damage and disruption to infrastructure assets such as buildings and bridges. Catastrophe modeling and accurate extreme event risk and resilience assessment require portfolio-level fragility functions of these assets, which involve the establishment of functional relationships between a relevant peak response quantity, also known as the engineering demand parameter (EDP), and select features characterizing the hazard. Given the computational demands of analyzing several statistical combinations of hazard and structural features, while running nonlinear time history analyses for each combination, surrogate demand models relating peak EDP to relevant intensity measures (IMs) of the input time history are popular. Although traditional IMs such as peak accelerations and velocities, average velocities, and peak spectral accelerations determined <em>a priori</em> have been traditionally found to be effective predictors of response and damage, their use in surrogate models in fragility model development introduces additional model uncertainties. In a bid to enable more robust and accurate surrogate modeling, we propose MetaIMNet; a physics-informed framework based on a neural network that simultaneously extracts key features from the time history of the load and leverages these features for structure specific response prediction. The framework is illustrated through a case study which shows that it outperforms traditional surrogate modeling strategies at a nominal added computational cost associated with model training, and can be used as an effective surrogate model for developing fragility functions for a wide range of hazards and structures.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"118 ","pages":"Article 102650"},"PeriodicalIF":6.3,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning-aided deterministic, partially probabilistic, and fully probabilistic seismic resilience assessment methods for highway bridges 基于机器学习的公路桥梁确定性、部分概率和全概率地震弹性评估方法

IF 6.3 1区工程技术

Structural Safety Pub Date : 2025-09-08 DOI: 10.1016/j.strusafe.2025.102651

Xiaowei Wang , Dang Yang , Aijun Ye

{"title":"Machine learning-aided deterministic, partially probabilistic, and fully probabilistic seismic resilience assessment methods for highway bridges","authors":"Xiaowei Wang , Dang Yang , Aijun Ye","doi":"10.1016/j.strusafe.2025.102651","DOIUrl":"10.1016/j.strusafe.2025.102651","url":null,"abstract":"<div><div>Highway bridges are critical lifelines vulnerable to seismic hazards, yet balancing computational efficiency and modeling fidelity in their resilience assessment remains a persistent challenge. This study develops a machine learning (ML)-aided framework integrating three multi-fidelity methods—deterministic (DT), partially probabilistic (PP), and fully probabilistic (FP)—to enable rapid seismic resilience quantification for highway bridges. Four ML algorithms are rigorously optimized and compared, with Random Forests emerging as the most effective for predicting engineering demand parameters (EDPs) such as column drift ratios, bearing displacements, and joint movements. The Random Forests-based surrogate models, publicly shared via Zenodo, significantly reduce computational costs while maintaining accuracy. A case study reveals that DT methods, while computationally lean, underestimate restoration time particularly under strong excitations due to the neglection of uncertainties in structural damage evaluation and restoration model parameters. The FP method integrates uncertainties in damage and restoration, achieving the highest fidelity but with computational costs and technical requirements. The PP method balances accuracy and efficiency by probabilistically evaluating damage while using deterministic restoration models. The hierarchical DT-PP-FP approach provides practitioners with adaptable tools for diverse precision, data availability, and resource constraints, advancing seismic resilience assessment of bridges through ML-driven efficiency and probabilistic rigor.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"118 ","pages":"Article 102651"},"PeriodicalIF":6.3,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Partial ring simulation: An efficient method identifying importance domain for structural reliability analysis 部分环模拟：结构可靠性分析重要域识别的有效方法

IF 6.3 1区工程技术

Structural Safety Pub Date : 2025-08-28 DOI: 10.1016/j.strusafe.2025.102648

Yu Leng , Chao-Huang Cai , Zhao-Hui Lu

{"title":"Partial ring simulation: An efficient method identifying importance domain for structural reliability analysis","authors":"Yu Leng , Chao-Huang Cai , Zhao-Hui Lu","doi":"10.1016/j.strusafe.2025.102648","DOIUrl":"10.1016/j.strusafe.2025.102648","url":null,"abstract":"<div><div>Failure probability of a structure is dominated by the importance domain whose extent is much smaller than the whole random variable space. Once the importance domain is identified, the failure probability can be evaluated efficiently through compressing the sampling space into the importance domain. Recently, ring simulation has attempted to identify the importance interval in one dimension (i.e., the radius). To obtain a complete importance domain in all dimensions, a new simulation method, called “partial ring simulation”, is proposed for the efficient estimation of the failure probability. In the proposed method, the importance domain, consisting of importance radius and importance direction, is adaptively identified by a stepwise strategy utilizing the information from prior steps. For generating samples located in the importance domain, a Markov chain Monte Carlo sampling is then constructed. The effectiveness of the proposed method is validated by four examples involving parallel, series, and nonlinear limit state functions, small failure probabilities, and high-dimensional problems. The results indicate that the proposed method greatly improves the computational efficiency of ring simulation.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"118 ","pages":"Article 102648"},"PeriodicalIF":6.3,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seismic fragility analysis of building clusters considering the effects of mainshock-aftershock sequences 考虑主余震序列影响的建筑群地震易损性分析

IF 6.3 1区工程技术

Structural Safety Pub Date : 2025-08-19 DOI: 10.1016/j.strusafe.2025.102647

Si-Qi Li, Lin-Lin Zheng

{"title":"Seismic fragility analysis of building clusters considering the effects of mainshock-aftershock sequences","authors":"Si-Qi Li, Lin-Lin Zheng","doi":"10.1016/j.strusafe.2025.102647","DOIUrl":"10.1016/j.strusafe.2025.102647","url":null,"abstract":"<div><div>The impact of the mainshock earthquake on the regional building cluster is significant, directly causing varying degrees of damage to many houses. A large amount of onsite seismic loss observation data indicate that aftershocks after the main earthquake also impact the damage to and vulnerability of regional buildings. To study the seismic fragility and risk of typical building clusters under mainshock-aftershock sequences, this paper innovatively proposes a structural seismic fragility model that considers the intensity measures of the mainshock-aftershock by combining total probability and Bayesian theory. A computational intensity model has been developed that considers the directionality of ground motion under mainshock-aftershock sequences. The established model was verified and analyzed on the basis of 384,882 accelerations monitored by nine strong motion stations during the Jiuzhaigou earthquake on August 8, 2017, in China. The calculated intensity point cloud and stripe models were generated on the basis of the directional effect of ground motion. Using the Chinese earthquake intensity scale and the proposed computational intensity model, the fragility of three types of building clusters (1212 buildings) affected by the Jiuzhaigou earthquake was estimated, and a structural failure probability model considering mainshock-aftershock sequences was established. A seismic fragility curve of a building cluster considering the influence of mainshock-aftershock sequences was plotted via the Gaussian process, least squares regression algorithm, and data-driven techniques. An innovative structural fragility correlation surface was generated to analyze the correlation characteristics between different fragility levels under the influence of mainshock-aftershock sequences. The traditional earthquake damage index method has been improved, and a structural fragility index function considering the impact of mainshock-aftershocks has been proposed.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"118 ","pages":"Article 102647"},"PeriodicalIF":6.3,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0