Structural Safety最新文献

{"title":"Numerical algorithm for determining serviceability live loads and its applications","authors":"Chi Xu ,&nbsp;Jun Chen ,&nbsp;Jie Li","doi":"10.1016/j.strusafe.2023.102383","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102383","url":null,"abstract":"<div><p>The live load duration refers to the period when the live load is larger than a given threshold in the reference period. The smallest threshold that allows the duration to be shorter than the required length is employed as the design live load for serviceability<span> limit states. However, the traditional method only considers the mean duration and the probability that the duration exceeds the required length is unknown. This study proposes a new algorithm to determine the probability distributions of the live load duration. A sustained or extraordinary load process is transformed into a random variable set based on the stochastic harmonic functions. Subsequently, the duration distributions can be derived by employing the load coincidence principle and probability density evolution method. Three numerical examples including one sustained load and multiple extraordinary loads are provided and the results of the proposed algorithm are compared with those of Monte Carlo simulation. The proposed algorithm allows the exact determination of design live loads based on a predefined exceeding probability. As an application, the quasi-permanent and frequent values of seven user categories are calculated when the exceeding probabilities are taken as 10%, 5% and 2%, respectively. It is found that the quasi-permanent values can increase with increasing area and the differences between the frequent and quasi-permanent values can be more than 20 times.</span></p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102383"},"PeriodicalIF":5.8,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49698977","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":"Mixed Bayesian Network for reliability assessment of RC structures subjected to environmental actions","authors":"Hongyuan Guo ,&nbsp;You Dong ,&nbsp;Emilio Bastidas-Arteaga","doi":"10.1016/j.strusafe.2023.102392","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102392","url":null,"abstract":"<div><p>Under environmental action, reinforced concrete (RC) structures might suffer from reinforcement corrosion caused by the surrounding environment, dramatically reducing structural reliability and threatening social development. However, most of the existing reliability assessment methods for RC structures only focused on the structural performance at the design stage given the original unchanged environment, ignoring the effects of realistic exposure conditions and inspection results on reliability evaluation. Thus, this paper develops a general reliability assessment framework based on a Mixed Bayesian network (MBN), incorporating three modules, i.e., durability assessment, load-bearing capacity analysis, and time-dependent reliability analysis. In MBN, separate sub-BNs are built based on different modules and connected by pinch point variables where probabilistic information is transmitted via soft evidence. Besides, this framework considers time-dependent environmental parameters and two-dimensional chloride transport and their effects on reliability. Meanwhile, adjustment coefficients are applied to improve the results of the analytical mechanical model with respect to different limit states through the finite element model (FEM). The proposed MBN framework is illustrated for a corroded RC beam under a marine atmospheric environment to investigate the effects of environmental modeling, chloride transport patterns, and concrete crack inspection on reliability assessment. The results indicate that under the assumed conditions in the case study, early inspection of large cracks may significantly overestimate the failure probability by about 500%. Besides, failure probability might be underestimated by about 95%, ignoring the time-variant environment and two-dimensional chloride transport.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102392"},"PeriodicalIF":5.8,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49698986","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":"AK-SEUR: An adaptive Kriging-based learning function for structural reliability analysis through sample-based expected uncertainty reduction","authors":"Changle Peng ,&nbsp;Cheng Chen ,&nbsp;Tong Guo ,&nbsp;Weijie Xu","doi":"10.1016/j.strusafe.2023.102384","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102384","url":null,"abstract":"<div><p>Reliability Analysis (RA) is a critical aspect of structural design and performance evaluation aiming to determine the probability of structural failure under given random input parameters. With modern development of modeling techniques, computational models have achieved higher fidelity but at the increased cost of computational time, which poses a significant challenge for RA. Consequently, surrogate model-assisted RA has been explored as a means of improved efficiency and accuracy. This study proposes a novel learning function, Sample-based Expected Uncertainty Reduction (SEUR), for surrogate model-assisted RA. The SEUR function uses statistical information from the metamodeling with fixed hyper-parameters to construct expected failure probability bounds to sequentially update the design of experiment (DoE). The joint probability densities of input variables are accounted for through simulation methods, including Monte Carlo (MC) and subset simulation (SS). Furthermore, the discrete simulated annealing algorithm is used to search for the optimal design point. The performance of proposed AK-SEUR function is systematically evaluated using six examples of different dimensions, failure probability levels and nonlinearities. The AK-SEUR function is demonstrated to be more effective and efficient than other popular active learning methods in dealing with nonlinear performance functions, small probabilities, and complex limit states. The proposed SEUR function has the potential to improve the efficiency and accuracy of RA, particularly in situations where computational models are time-consuming and the search for the optimal solution is challenging.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102384"},"PeriodicalIF":5.8,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49698974","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":"Importance ranking of correlated variables in one analysis","authors":"Terje Haukaas","doi":"10.1016/j.strusafe.2023.102363","DOIUrl":"10.1016/j.strusafe.2023.102363","url":null,"abstract":"<div><p><span>This paper addresses the problem of ranking correlated random variables according to relative importance. The importance of a variable derives from its influence on the variability of the response from a model. Applications include any input–output model for which response derivatives are available from each response analysis. Structural analysis models, i.e., </span>finite element models<span>, represent the specific motivation for this paper. The response derivatives are collected in a vector and transformed into a standardized parameter space. Points along that vector are transformed back to the original parameter space and utilized for the purpose of model insights and parameter ranking. Comparisons are made with the first-order Sobol sensitivity index, which requires sampling instead of the proposed single-analysis approach. Results suggest that the proposed importance measure matches the first-order Sobol index in many situations. However, for pure multiplicative “interaction” models, the first-order Sobol index tends to be anchored at the zero-correlation case. In contrast, the proposed measures are sensitive to correlation and the effect of correlation can be significant.</span></p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"104 ","pages":"Article 102363"},"PeriodicalIF":5.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49496077","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":"Selection of hazard-consistent ground motions for risk-based analyses of structures","authors":"Bo Li ,&nbsp;Zhen Cai ,&nbsp;Zhongdong Duan","doi":"10.1016/j.strusafe.2023.102365","DOIUrl":"10.1016/j.strusafe.2023.102365","url":null,"abstract":"<div><p><span>In seismic risk analysis, probabilistic seismic demand analysis (PSDA) is required to determine the probability distribution of structural seismic responses. One of the key steps in PSDA is to select a suite of ground motions that are consistent with </span>seismic hazards at a target site. Current methods for selecting hazard-consistent ground motions only achieve consistency at one or several predefined periods and choose several discrete intensity levels to consider uncertainty of ground motions. To avoid drawbacks of the current methods, this study proposes a novel method for selecting hazard-consistent ground motions. In this method, a scenario earthquake set for ground motion selection is firstly obtained from seismic hazard disaggregation to a hazard level corresponding to a very small value of spectral acceleration at a specific period. Then, a suite of random target response spectra that are consistent with target site seismic hazards are simulated based on the scenario earthquake set. Finally, one suite of hazard-consistent ground motions are selected from a ground motion database. Through a numerical example, this study concludes, a suite of selected ground motions using the proposed method presents very good consistency with the target site seismic hazards. Since hazard-consistent ground motions selected using the proposed method do not depend on the building information, they can be used to accurately perform PSDA of any building and accurately predict structural seismic responses.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"104 ","pages":"Article 102365"},"PeriodicalIF":5.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43001324","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 – Christian G. Bucher","authors":"Dan M. Frangopol","doi":"10.1016/j.strusafe.2023.102367","DOIUrl":"10.1016/j.strusafe.2023.102367","url":null,"abstract":"","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"104 ","pages":"Article 102367"},"PeriodicalIF":5.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44853048","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 estimation and detection of failure surfaces via adaptive sequential decomposition of the design domain","authors":"Aleksei Gerasimov,&nbsp;Miroslav Vořechovský","doi":"10.1016/j.strusafe.2023.102364","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102364","url":null,"abstract":"<div><p><span>We propose an algorithm for selection of points from the design domain of small to moderate dimension and for failure probability estimation. The proposed active learning detects failure events and progressively refines the boundary between safe and failure domains thereby improving the failure probability estimation. The method is particularly useful when each evaluation of the performance function </span><span><math><mrow><mi>g</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span> is very expensive and the function can be characterized as either highly nonlinear, noisy, or even discrete-state (e.g., binary). In such cases, only a limited number of calls is feasible, and gradients of <span><math><mrow><mi>g</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span> cannot be used. The input design domain is progressively segmented by expanding and adaptively refining a mesh-like lock-free geometrical structure. The proposed triangulation-based approach effectively combines the features of simulation and approximation methods. The algorithm performs two independent tasks: (i) the <em>estimation</em> of probabilities through an ingenious combination of deterministic cubature rules and the application of the divergence theorem and (ii) the sequential <em>extension</em><span> of the experimental design with new points. The sequential selection of points from the design domain for future evaluation of </span><span><math><mrow><mi>g</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span><span> is carried out through a new decision approach, which maximizes instantaneous information gain in terms of the probability classification that corresponds to the local region. The extension may be halted at any time, e.g., when sufficiently accurate estimations are obtained. Due to the use of the exact geometric representation in the input domain, the algorithm is most effective for problems of a low dimension, not exceeding eight. The method can handle random vectors with correlated non-Gaussian marginals. When the values of the performance function are valid and credible, the estimation accuracy can be improved by employing a smooth surrogate model based on the evaluated set of points. Finally, we define new factors of global sensitivity to failure based on the entire failure surface weighted by the density of the input random vector.</span></p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"104 ","pages":"Article 102364"},"PeriodicalIF":5.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50177940","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 updating methods based on Bayesian inference and sequential learning Kriging","authors":"Kaixuan Feng ,&nbsp;Zhenzhou Lu ,&nbsp;Jiaqi Wang ,&nbsp;Pengfei He ,&nbsp;Ying Dai","doi":"10.1016/j.strusafe.2023.102366","DOIUrl":"10.1016/j.strusafe.2023.102366","url":null,"abstract":"<div><p><span>Reliability updating is an effective tool for reappraising reliability level of system when new observation information is obtained. The adaptive Kriging based reliability updating method (RUAK) inserts the adaptive Kriging into traditional simulation method to improve the efficiency of reliability updating. However, an identical candidate sampling pool is used to simultaneously estimate the prior failure probability and the posterior one in RUAK, which leads to a waste of computational resources in case of significant difference between the importance regions in estimation of prior and posterior failure probabilities. To overcome this disadvantage, an efficient reliability updating framework based on Bayesian inference and sequential learning Kriging is proposed in this paper. In the proposed method, two candidate sampling pools respectively for estimating the prior and posterior failure probabilities are separately constructed by prior probability density function (PDF) and posterior PDF obtained by Bayesian inference. Then, the </span>Kriging model is established and sequentially refined in these two candidate sampling pools to accurately estimate the corresponding failure probabilities. Through combining different simulation methods with the proposed framework, the Monte Carlo simulation based and importance sampling based sequential learning Kriging methods are respectively developed for reliability updating.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"104 ","pages":"Article 102366"},"PeriodicalIF":5.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45869575","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":"Assessment of risk reduction strategies for terrorist attacks on structures","authors":"","doi":"10.1016/j.strusafe.2023.102353","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102353","url":null,"abstract":"","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"104 ","pages":"Article 102353"},"PeriodicalIF":5.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50177939","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":"Adaptive Kriging-based Bayesian updating of model and reliability","authors":"Xia Jiang,&nbsp;Zhenzhou Lu","doi":"10.1016/j.strusafe.2023.102362","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102362","url":null,"abstract":"<div><p>Bayesian updating is a powerful tool to reassess and calibrate models and their reliability as new observations emerge, and the Bayesian updating with structural reliability method (BUS) is an efficient approach that reformulates it as a structural reliability problem. However, the efficiency and accuracy of BUS depend on a constant <span><math><mrow><mi>c</mi></mrow></math></span> determined by the maximum of likelihood function. To efficiently complete Bayesian updating with new observations related to implicit performance function, a method that combines adaptive Kriging with Bayesian updating is proposed. The proposed method involves three stages. Firstly, an innovatively <strong>a</strong>dvanced <strong>e</strong>xpected <strong>i</strong>mprovement (AEI) learning function is proposed to train the Kriging model of the likelihood function for estimating <em>c</em>, in which the convergence criterion and the strategy of selecting new training point guarantee the accuracy and efficiency of estimating <em>c</em>. Secondly, a new learning function based on <strong>e</strong>xpectation and <strong>v</strong>ariance of <strong>c</strong>ontribution <strong>u</strong>ncertainty <strong>f</strong>unction (EVCUF) is proposed to adaptively train the Kriging model of the performance function constructed in BUS to extract posterior samples and complete Bayesian updating of model. By simultaneously taking the expectation and variance of the contribution of the candidate sample to improving accuracy of the Kriging model into consideration, the EVCUF learning function ensures the robust and efficient convergence of the Kriging model. Finally, based on the training points of the previous two stages, the traditional U learning function is employed to subsequentially update Kriging model of the performance function for classifying posterior samples and completing Bayesian updating of reliability. Additionally, a reduction strategy of the candidate sample pool is proposed to improve the efficiency of the proposed method. After demonstrating the basic principle and advantage of the proposed method, three examples are introduced to verify the efficiency and accuracy of the proposed method.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"104 ","pages":"Article 102362"},"PeriodicalIF":5.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50177941","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}