Structural Safety最新文献

{"title":"Seismic risk and failure modes assessment of steel BRB frames under earthquake sequences","authors":"Fernando Gutiérrez-Urzúa ,&nbsp;Fabio Freddi ,&nbsp;Enrico Tubaldi","doi":"10.1016/j.strusafe.2025.102598","DOIUrl":"10.1016/j.strusafe.2025.102598","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102598"},"PeriodicalIF":5.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A phase-control-based method for the simulation of homogeneous random fields of fluctuating wind speed","authors":"Jia-Hang Lyu ,&nbsp;Jian-Bing Chen ,&nbsp;Pol D. Spanos ,&nbsp;Jie Li","doi":"10.1016/j.strusafe.2025.102593","DOIUrl":"10.1016/j.strusafe.2025.102593","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102593"},"PeriodicalIF":5.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682072","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":"Reliability-based quantification of the benefits of machine learning predictive models in seismic structural design and performance assessment","authors":"Muneera A. Aladsani,&nbsp;Henry V. Burton","doi":"10.1016/j.strusafe.2025.102596","DOIUrl":"10.1016/j.strusafe.2025.102596","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102596"},"PeriodicalIF":5.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705452","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":"A refined TMCMC algorithm for adaptive model updating for the probabilistic analysis of complex engineering structures","authors":"Yu-Xiao Wu ,&nbsp;De-Cheng Feng ,&nbsp;Shi-Zhi Chen","doi":"10.1016/j.strusafe.2025.102582","DOIUrl":"10.1016/j.strusafe.2025.102582","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102582"},"PeriodicalIF":5.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682071","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":"Physical-informed random field technique for virtual modelling based building probabilistic vulnerability assessment","authors":"Zhiyi Shi ,&nbsp;Yuan Feng ,&nbsp;Mark G. Stewart ,&nbsp;Wei Gao","doi":"10.1016/j.strusafe.2025.102595","DOIUrl":"10.1016/j.strusafe.2025.102595","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102595"},"PeriodicalIF":5.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic nonlinear dynamic analysis and system reliability evaluation of RC structures involving spatial variation under stochastic ground motions","authors":"Xin Chen ,&nbsp;Jie Li","doi":"10.1016/j.strusafe.2025.102581","DOIUrl":"10.1016/j.strusafe.2025.102581","url":null,"abstract":"<div><div>Dynamic analysis and system reliability evaluation are crucial in the design of seismic-resilient reinforced concrete (RC) structures. Uncertainties in earthquake ground motions (EGM) and the spatial variation of heterogeneous concrete must be thoroughly considered. However, implementing these analyses poses significant challenges due to the inherent complexity and high computational costs associated with stochastic nonlinear dynamic analysis and the quantification of concrete’s spatial variation through random field theory. To address these issues, we propose a novel methodology for the stochastic dynamic analysis and system reliability evaluation of RC structures involving spatial variation under stochastic ground motions. In the methodology, a two-scale random field model developed within the framework of stochastic damage mechanics is adopted to capture the coupling effects of the nonlinearity and the spatial variation of concrete. Additionally, a physical-based stochastic ground motion model is utilized to represent the randomness of EGM. Furthermore, the probability density evolution method is employed to derive probabilistic information (statistical moments, and probability density function (PDF), etc.) of dynamic responses, and the system reliability is evaluated by the physical synthesis method. A well-designed five-story RC frame structure is analyzed to demonstrate the efficacy of the proposed methodology and to investigate the influence of concrete’s spatial variation and randomness of EGM on structural responses. The results indicate that the proposed methodology can effectively obtain the probabilistic information of stochastic responses and system reliability, and the concrete’s spatial variation has a non-negligible impact on the structural responses and system reliability.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102581"},"PeriodicalIF":5.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535099","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":"A practical framework for determining target reliability indices for the assessment of existing structures based on risk-informed decision-making","authors":"Jianxu Su ,&nbsp;Junping Zhang ,&nbsp;Colin C. Caprani ,&nbsp;Junyong Zhou","doi":"10.1016/j.strusafe.2025.102583","DOIUrl":"10.1016/j.strusafe.2025.102583","url":null,"abstract":"<div><div>Target reliability levels define structural safety requirements. Most current studies on target reliability indices (<span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span>) have focused on reliability-based design for new structures. However, existing structures face significant safety challenges due to ongoing aging and financial constraints that limit maintenance and reinforcement efforts. Therefore, determining appropriate <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for the assessment of existing structures is crucial to balance the tradeoff between safety and economy. This study develops a practical, risk-informed framework to streamline the determination of <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for the reliability assessment of existing structures. It involves six critical steps including context definition, structural system modeling, failure statistics analysis, risk criteria establishment, and <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> selection. The framework’s practical application is carefully demonstrated through a case study centered on the reliability assessment of existing medium- and small-span (MS) bridges in China. A database was compiled for failure statistics of MS bridges, documenting 241 bridge collapse incidents in China spanning from 1983 to 2024. The statistical analysis of lethality ratios and fatalities from these failure events is incorporated into individual risk criteria, group risk criteria, cost optimization, and the marginal lifesaving cost principle. Using these criteria, alongside a refined as low as reasonably practicable (ALARP) principle, informed decisions are made on selecting <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for reliability differentiation. Finally, three safety levels of <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> are recommended for the bridge system as well as individual components. The proposed methodology framework, as demonstrated in the case study on MS bridges in China, can be readily applicable to the determination of <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for various other existing civil structures.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102583"},"PeriodicalIF":5.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527432","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":"Integrating risk perceptions in a value of information framework using cumulative prospect theory","authors":"Zaid Y Mir Rangrez ,&nbsp;Jayadipta Ghosh ,&nbsp;Colin Caprani ,&nbsp;Siddhartha Ghosh","doi":"10.1016/j.strusafe.2025.102573","DOIUrl":"10.1016/j.strusafe.2025.102573","url":null,"abstract":"<div><div>Value of information (VoI) analysis provides a framework that can be used to decide on an optimal monitoring strategy, to carry out an efficient maintenance of civil infrastructure. Existing VoI frameworks adopt utility functions to characterize the risk appetite of an asset manager based on expected utility theory (EUT). However, these utility functions cannot predict the decision choices under uncertainty resulting from failure risk perceptions. Cumulative prospect theory (CPT) is a comprehensive model for characterizing an asset manager’s risk appetite and perception. CPT captures both, the preference for different action outcomes using a value function and corresponding risk perceptions exhibited by an asset manager using a probability weight function. The present study proposes a CPT-based VoI framework which integrates risk perceptions and appetite within the VoI analysis. The proposed framework is implemented to investigate the sensitivity of the resulting expected VoI and the monitoring decisions to risk perception profiles. It is observed that the VoI is sensitive to the risk perception profile of an asset manager. An in-depth analysis of the decision patterns reveal that the risk profile affects the choice of prior optimal action that in turn dictates which type of posterior actions contribute positively or negatively towards the cost savings when referenced to the cost of prior optimal action. Based on these finding, the paper recommends to calibrate an asset manager’s risk perception profile to predict the decisions that an asset manager perceives as optimal for a given failure risk, and to evaluate the expected VoI resulting from such decisions.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102573"},"PeriodicalIF":5.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576880","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 of Ove Dalager Ditlevsen","authors":"Armen Der Kiureghian","doi":"10.1016/j.strusafe.2025.102585","DOIUrl":"10.1016/j.strusafe.2025.102585","url":null,"abstract":"","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102585"},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508593","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":"Response probability distribution estimation of expensive computer simulators: A Bayesian active learning perspective using Gaussian process regression","authors":"Chao Dang ,&nbsp;Marcos A. Valdebenito ,&nbsp;Nataly A. Manque ,&nbsp;Jun Xu ,&nbsp;Matthias G.R. Faes","doi":"10.1016/j.strusafe.2025.102579","DOIUrl":"10.1016/j.strusafe.2025.102579","url":null,"abstract":"<div><div>Estimation of the response probability distributions of computer simulators subject to input random variables is a crucial task in many fields. However, achieving this task with guaranteed accuracy remains an open computational challenge, especially for expensive-to-evaluate computer simulators. In this work, a Bayesian active learning perspective is presented to address the challenge, which is based on the use of the Gaussian process (GP) regression. First, estimation of the response probability distributions is conceptually interpreted as a Bayesian inference problem, as opposed to frequentist inference. This interpretation provides several important benefits: (1) it quantifies and propagates discretization error probabilistically; (2) it incorporates prior knowledge of the computer simulator, and (3) it enables the effective reduction of numerical uncertainty in the solution to a prescribed level. The conceptual Bayesian idea is then realized by using the GP regression, where we derive the posterior statistics of the response probability distributions in semi-analytical form and also provide a numerical solution scheme. Based on the practical Bayesian approach, a Bayesian active learning (BAL) method is further proposed for estimating the response probability distributions. In this context, the key contribution lies in the development of two crucial components for active learning, i.e., stopping criterion and learning function, by taking advantage of the posterior statistics. It is empirically demonstrated by five numerical examples that the proposed BAL method can efficiently estimate the response probability distributions with desired accuracy.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102579"},"PeriodicalIF":5.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}