Structural Safety最新文献

{"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":"2025-06-01","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}

{"title":"Modeling probabilistic micro-scale wind field for risk forecasts of power transmission systems during tropical cyclones","authors":"Xiubing Huang,&nbsp;Naiyu Wang","doi":"10.1016/j.strusafe.2025.102620","DOIUrl":"10.1016/j.strusafe.2025.102620","url":null,"abstract":"<div><div>Tropical cyclones (TCs) pose significant risks to power transmission systems, causing extensive damage, widespread outages and severe socio-economic impacts. While reliable risk forecasting of these systems during TCs hinges on accurate wind predictions, operational numerical weather prediction (NWP) models struggle to deliver unbiased, high-resolution probabilistic wind-field forecasts necessary for infrastructure risk projections. This study introduces the Probabilistic Micro-Scale Wind-Field model (ProbMicro-WF) designed to enhance real-time hazard modeling for power system risk forecasts during TC evolution. This model improves NWP wind forecast by achieving the following: 1) probabilistic calibration and bias correction for NWP wind forecasts, leveraging historical TC observational data to improve prediction accuracy at high wind speeds; 2) terrain-modified statistical downscaling that translates mesoscale forecasts to micro-scale wind fields, capturing localized wind dynamics critical for tower- and transmission line-specific risk evaluation; and 3) a spatiotemporal stochastic model that preserves wind-field correlation structures, mitigating systemic underestimation of risk variance across geographically dispersed infrastructure during TC evolution. Finally, the ProbMicro-WF model is applied to the power transmission system in Zhejiang Province, China (105,500 km²) during Super Typhoon Lekima in 2019, highlighting its capability to simulate spatially coherent, high-resolution wind fields, enabling robust pre-event mitigation and real-time grid management in TC-prone regions.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"116 ","pages":"Article 102620"},"PeriodicalIF":5.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195770","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":"Optimizing uncertainty estimation in Enhanced Monte Carlo methods","authors":"Konstantinos N. Anyfantis","doi":"10.1016/j.strusafe.2025.102617","DOIUrl":"10.1016/j.strusafe.2025.102617","url":null,"abstract":"<div><div>The probability of failure serves as a key metric in a structural reliability analysis, but its accurate estimation remains computationally demanding, particularly for low-probability failure events. The Enhanced Monte Carlo (EMC) method has been developed in order to alleviate from inefficiencies due to the high number of required simulations. Recent advancements integrate Machine Learning techniques with the EMC to further accelerate the estimation process. However, a critical limitation of EMC lies in its fitted confidence interval (CI) estimation, which tends to overestimate uncertainty, leading to unnecessary computational overhead. This study proposes a new prescriptive CI formulation constructed from the method’s hyperparameters, offering a more accurate and computationally efficient approach to uncertainty quantification. The method is general and can be applied to any reliability problem that can be described by a probability curve. The effectiveness of the proposed method is demonstrated through a benchmark reliability problem and a real-world marine structural application. The results indicate significant improvements in efficiency without compromising accuracy, paving the way for enhanced structural reliability assessments.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"116 ","pages":"Article 102617"},"PeriodicalIF":5.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134106","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":"Improved variance estimation for subset simulation by accounting for the correlation between Markov chains","authors":"Qingqing Miao,&nbsp;Ying Min Low","doi":"10.1016/j.strusafe.2025.102606","DOIUrl":"10.1016/j.strusafe.2025.102606","url":null,"abstract":"<div><div>Subset simulation (SS) is a popular structural reliability analysis method, especially for problems characterized by low failure probabilities and high-dimensional complexities. Unlike most variance reduction methods, SS obviates the need for prior domain information, making it versatile across diverse applications. Markov chain Monte Carlo (MCMC) algorithms are required for sampling from an unknown conditional distribution, resulting in correlated samples. There is plenty of literature on SS in several aspects, such as the improvement of MCMC algorithms, and combining SS with other techniques. However, one aspect that appears to be neglected concerns the variance estimation crucial for assessing the accuracy of the probability estimate. To date, most studies on SS still rely on the conventional variance estimation method, which only considers the correlation within a Markov chain (intrachain) but neglects the correlation across separate chains (interchain) and different subset levels (interlevel). This study aims to improve understanding of this topic and develop a more accurate variance estimation method for SS. An investigation based on multiple independent SS runs reveal that the intrachain, interchain and interlevel correlations are all important. Subsequently, a new variance estimation method is proposed to account for the intrachain and interchain correlations. The proposed method is easy to apply, has small sampling uncertainty and only utilizes samples from a single SS run. Results indicate a notable improvement in accuracy compared to the conventional method.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"116 ","pages":"Article 102606"},"PeriodicalIF":5.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928318","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":"Optimal redundancy allocation and quality control in structural systems","authors":"André T. Beck,&nbsp;Lucas A. Rodrigues da Silva,&nbsp;Luis G.L. Costa,&nbsp;Jochen Köhler","doi":"10.1016/j.strusafe.2025.102603","DOIUrl":"10.1016/j.strusafe.2025.102603","url":null,"abstract":"<div><div>Reliability-Based and Risk-Based design optimization are popular research topics nowadays. Yet, not many studies have addressed the progressive collapse, the optimal robustness nor the optimal redundancy of structural systems. By way of fundamental examples, it is shown herein that redundancy is of little benefit, unless the structural system is exposed to external ‘shocks’. These ‘shocks’ are abnormal loading events; unanticipated failure modes; gross errors in design, construction or operation; operational abuse; and other factors that have historically contributed to observed structural collapses. Shocks may lead to structural damage or complete loss of structural members. The effect of such shocks on system reliability is generically represented by a member damage probability. This is a hazard-imposed damage probability, which is shown to be the key factor justifying the additional spending on structural redundancy. In structural reliability theory, it is understood that quality control should handle gross errors and their impacts; yet, it is shown herein that optimal redundancy is related to the frequency of inspections. The study reveals an intricate interaction between optimal redundancy and optimal quality control by way of inspections, challenging the separation between structural reliability theory and quality control in safety management.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"116 ","pages":"Article 102603"},"PeriodicalIF":5.7,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115715","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":"Structural reliability analysis using gradient-enhanced physics-informed neural network and probability density evolution method","authors":"Zidong Xu,&nbsp;Hao Wang,&nbsp;Kaiyong Zhao,&nbsp;Han Zhang","doi":"10.1016/j.strusafe.2025.102604","DOIUrl":"10.1016/j.strusafe.2025.102604","url":null,"abstract":"<div><div>In past decade, probability density evolution method (PDEM) has become one of the most popular approaches to conduct overall structural reliability analysis (SRA). The main procedure of the PDEM-based SRA lies in solving the generalized probability density evolution equation (GDEE) related to virtual stochastic process (VSP). Common methods for GDEE solving are highly sensitive to the choice of solving parameters, which may affect the accuracy, efficiency and stability of the solution. Recently, physics-informed neural network (PINN) and its extended form have successfully utilized to solve differential equations in different fields. With this in view, the gradient-enhanced PINN (gPINN) are utilized to solve the GDEE of the VSP for SRA, which leads to an improved approach, termed as evolutionary probability density (EPD)-gPINN model. Specifically, the normalized GDEE and the additional gradient residual equations are derived as the physical loss. Meanwhile, to offer sufficient supervised training data, an easy-to-operate data augmentation procedure is established. Numerical examples are posed for validating the validity of the proposed framework. Parametric analysis is conducted to investigate the influence of the network parameters to the predictive performance. Results indicate that using proper weight of the gradient loss, the proposed framework can efficiently conduct the SRA, whose predictive performance outperforms PINN.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"116 ","pages":"Article 102604"},"PeriodicalIF":5.7,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902246","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":"Modeling the spatial corrosion of strand and FE-based Monte Carlo simulation for structural performance assessment of deteriorated PC beams","authors":"Taotao Wu ,&nbsp;Mitsuyoshi Akiyama ,&nbsp;De-Cheng Feng ,&nbsp;Sopokhem Lim ,&nbsp;Dan M. Frangopol ,&nbsp;Zhejun Xu","doi":"10.1016/j.strusafe.2025.102605","DOIUrl":"10.1016/j.strusafe.2025.102605","url":null,"abstract":"<div><div>Structural performance assessments of corroded prestressed concrete (PC) beams using numerical models that account for spatial corrosion distribution and are validated against experimental results remain limited compared to those of reinforced concrete (RC) beams. This study proposes a probabilistic analysis method to evaluate the structural performance of corroded PC beams, incorporating the spatial corrosion distribution of strands and wires. The method is further applied to compare the structural performance of corroded PC and RC beams. Three finite element (FE) models are developed and compared for their accuracy in predicting the structural behavior of PC beams: (a) using the mean steel weight loss of the strand, (b) incorporating the spatial corrosion distribution of the strand, and (c) considering the spatial corrosion distribution of the six outer wires. The model incorporating the spatial corrosion distribution of the six outer wires achieves the highest accuracy, as it effectively simulates the first wire breakage that governs the flexural load-bearing and deflection ductility capacities of PC beams. The probabilistic distribution parameters representing the spatial variability of corrosion are derived from experimental data. Using this distribution, Monte Carlo simulation-based spatial corrosion samples are integrated into the most accurate FE model to obtain the probability density functions (PDFs) of corroded PC beams. The results indicate that PC beams with the same total steel weight loss can exhibit significantly different flexural load-bearing and deflection ductility capacities due to spatial variability, underscoring the importance of a probabilistic assessment. Furthermore, the PDFs of PC members are shifted to the left compared to those of RC members with the same degree of corrosion. Notably, early wire breakage results in lower mean values and standard deviations for the deflection ductility of corroded PC beams compared to RC beams.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"116 ","pages":"Article 102605"},"PeriodicalIF":5.7,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924023","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":"Impact of structural information fidelity on reduced-order model development for regional risk assessment","authors":"Sang-ri Yi ,&nbsp;Alexandros A. Taflanidis ,&nbsp;Parisa Toofani Movaghar ,&nbsp;Carmine Galasso","doi":"10.1016/j.strusafe.2025.102602","DOIUrl":"10.1016/j.strusafe.2025.102602","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"116 ","pages":"Article 102602"},"PeriodicalIF":5.7,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084524","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":"Efficient reliability analysis for offshore wind turbines: Leveraging SVM and augmented oversampling technique","authors":"Xukai Zhang,&nbsp;Arash Noshadravan","doi":"10.1016/j.strusafe.2025.102597","DOIUrl":"10.1016/j.strusafe.2025.102597","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102597"},"PeriodicalIF":5.7,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877330","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 probabilistic inverse problem and its solving method based on probability density evolution theory and convex optimization algorithms","authors":"Yuhan Zhu,&nbsp;Jie Li","doi":"10.1016/j.strusafe.2025.102600","DOIUrl":"10.1016/j.strusafe.2025.102600","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102600"},"PeriodicalIF":5.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877329","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}