Structural Safety最新文献

{"title":"Seismic fragility analysis of building clusters considering the effects of mainshock-aftershock sequences","authors":"Si-Qi Li,&nbsp;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":"2026-01-01","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}

{"title":"Machine learning-aided deterministic, partially probabilistic, and fully probabilistic seismic resilience assessment methods for highway bridges","authors":"Xiaowei Wang ,&nbsp;Dang Yang ,&nbsp;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":"2026-01-01","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}

{"title":"The cell renormalized method for the solution of KF equation and RDKF equation under additive Poisson white noise","authors":"Jinheng Song ,&nbsp;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":"2026-01-01","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}

{"title":"Branch-and-bound algorithm for efficient reliability analysis of general coherent systems","authors":"Ji-Eun Byun ,&nbsp;Hyeuk Ryu ,&nbsp;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":"2026-01-01","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}

{"title":"LSTM-augmented probability-informed neural network-driven evolution estimation for time-dependent reliability analysis","authors":"Hongyuan Guo ,&nbsp;Jiaxin Zhang ,&nbsp;You Dong ,&nbsp;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":"2026-01-01","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}

{"title":"Partial ring simulation: An efficient method identifying importance domain for structural reliability analysis","authors":"Yu Leng ,&nbsp;Chao-Huang Cai ,&nbsp;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":"2026-01-01","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}

{"title":"In Memoriam Erik VanMarcke August 6, 1941 – July 7, 2025","authors":"Ning Lin","doi":"10.1016/j.strusafe.2025.102655","DOIUrl":"10.1016/j.strusafe.2025.102655","url":null,"abstract":"","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"118 ","pages":"Article 102655"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265424","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}

{"title":"Analytical solution of the generalized density evolution equation for stochastic systems: Euler-Bernoulli beam under noisy excitations and nonlinear vibration of Kirchhoff plate","authors":"Yongfeng Zhou ,&nbsp;Jie Li","doi":"10.1016/j.strusafe.2025.102619","DOIUrl":"10.1016/j.strusafe.2025.102619","url":null,"abstract":"<div><div>The Generalized Density Evolution Equation (GDEE) describes the evolution of probability densities driven by physical processes. The numerical solution of the GDEE, implemented through a fully developed computational framework, is referred to as the Probability Density Evolution Method (PDEM). However, the absence of analytical solutions presents challenges for error calibration in numerical methods. In this study, analytical solutions of the GDEE are derived, focusing primarily on stochastic dynamic systems. The forced vibration of an Euler-Bernoulli beam subjected to random excitations is first analyzed, yielding analytical solutions for mid-span displacement response. For lower dimensional scenarios, two cases are examined: random harmonic loading and random step loading, both involving uncertainties in structural parameters. Results reveal that the corresponding displacement responses are non-Gaussian and non-stationary random processes. For higher dimensional scenarios, additional noise excitation is considered. By employing the Stochastic Harmonic Function (SHF) representation, noise excitation is effectively approximated as a superposition of finite random harmonic loads. Analytical derivations demonstrate that the SHF representation gradually converges toward the actual noise as the expansion terms increase. Furthermore, to illustrate the versatility of the developed analytical method, a nonlinear free vibration analysis of a Kirchhoff plate without external excitations is presented, showcasing its applicability to broader structural dynamic problems. These analytical solutions provide valuable benchmarks for further in-depth research into the PDEM, especially for the calibration of numerical methods.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"117 ","pages":"Article 102619"},"PeriodicalIF":5.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262721","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}

{"title":"Risk-targeted design wind speeds for multi-level aerodynamic performances of long-span bridges: A real data-informed case study","authors":"Zihang Liu ,&nbsp;Genshen Fang ,&nbsp;Nikolaos Nikitas ,&nbsp;Yizhe Lan ,&nbsp;Lin Zhao ,&nbsp;Yaojun Ge","doi":"10.1016/j.strusafe.2025.102637","DOIUrl":"10.1016/j.strusafe.2025.102637","url":null,"abstract":"<div><div>Most current bridge wind-resistant design standards adopt “uniform hazard” basis, ensuring the resistance exceeds design wind speed at a given return period. However, wind-induced failure probabilities for bridge structures, especially accounting for multi-level performances, are ambiguous, leading to significant differences in risk levels. To achieve the controllability and consistency in aerodynamic performances of long-span bridges, this study introduces the “uniform risk” into bridge wind engineering to determine the risk-targeted design wind speeds. Four performance objectives (occupant comfort, operational, continuous occupancy and instability), associated with the annual failure probability, are summarized. A case study is performed for Xihoumen Bridge by developing fragility curves corresponding to different vibration thresholds. Buffeting-related fragility curves are derived through a data-driven random model based on long-term measurements, while flutter fragility curve is obtained by Monte Carlo simulations incorporating various uncertainties. By combining with the wind hazard curves, failure probabilities for multi-level performances are estimated. Risk-targeted design wind speeds are calculated through the risk integral method, and the annual failure probabilities for code-recommended versus risk-targeted flutter design speeds are compared. Results indicate that Xihoumen Bridge has excessive wind resistance for the continuous occupancy and instability, while the annual failure risk for occupant comfort is relatively high. The code-recommended design wind speed falls short of ensuring multi-level performance objectives, whereas the risk-targeted design wind speeds effectively meet these criteria. This study provides a forward step in bridge wind engineering to develop the “uniform risk” design basis, serving the uptake of performance-based wind engineering design.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"117 ","pages":"Article 102637"},"PeriodicalIF":5.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654440","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}

{"title":"Failure probability estimate of corroded reinforced concrete structures based on sparse representation of steel weight loss distributions","authors":"Siyi Jia ,&nbsp;Mitsuyoshi Akiyama ,&nbsp;Dan M. Frangopol ,&nbsp;Zhejun Xu","doi":"10.1016/j.strusafe.2025.102622","DOIUrl":"10.1016/j.strusafe.2025.102622","url":null,"abstract":"<div><div>Uncertainties associated with the non-uniform spatial distribution of steel weight loss (SWL) should be considered appropriately when estimating the load-bearing capacity loss of corroded reinforced concrete (RC) structures. Addressing these uncertainties necessitates a probabilistic analysis using high-dimensional SWL data, which can lead to inaccurate condition assessments for corroded RC structures. This paper presents a dimension-reduction approach for SWL distribution based on the K-means singular vector decomposition (K-SVD) algorithm, which enforces a sparse representation of SWL distributions using a combination of non-standard distribution features learned from experimental SWL data. The K-SVD algorithm involves an iterative two-stage supervised learning process. In the dictionary learning stage, K-SVD identifies non-standard distribution features tailored to the localized characteristics of SWL data, based on which the orthogonal matching pursuit (OMP) algorithm is employed in the coding learning stage to derive a sparse representation of SWL distributions. The efficacy of K-SVD is evaluated using 83 experimental samples of SWL distributions. The results reveal that the K-SVD algorithm can derive a sparse representation of SWL distribution while preserving the distribution details of SWL. With just 15 learned non-standard distribution features, K-SVD achieves the same accuracy in reconstructing 168-dimensional SWL distribution data as the baseline Karhunen-Loève OMP (KL-OMP) method, which uses 75 standard features. Subsequently, the sparse representation is used to compute the flexural failure probability of corroded RC beams, for which a Kriging surrogate model is constructed. The results show that the sparse representation significantly enhances the accuracy of the Kriging surrogate model and improves the computational stability of the flexural failure probabilities, which is crucial for accurately assessing the condition of corroded RC structures.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"117 ","pages":"Article 102622"},"PeriodicalIF":5.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241153","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}