Structural Safety最新文献

{"title":"A stratified beta-sphere sampling method combined with important sampling and active learning for rare event analysis","authors":"Fangqi Hong ,&nbsp;Jingwen Song ,&nbsp;Pengfei Wei ,&nbsp;Ziteng Huang ,&nbsp;Michael Beer","doi":"10.1016/j.strusafe.2024.102546","DOIUrl":"10.1016/j.strusafe.2024.102546","url":null,"abstract":"<div><div>Accurate and efficient estimation of small failure probability subjected to high-dimensional and multiple failure domains is still a challenging task in structural reliability engineering. In this paper, we propose a stratified beta-spheres sampling method (SBSS) to tackle this task. Initially, the whole support space of random input variables is divided into a series of subdomains by using multiple specified beta-spheres, which is a hypersphere centered in the origin in standard normal space, then, the corresponding samples truncated by beta-spheres are generated explicitly and efficiently. Based on the truncated samples, the real failure probability can be estimated by the sum of failure probabilities of these subdomains. Next, we discuss and demonstrate the unbiasedness of the estimation of failure probability. The proposed method stands out for inheriting the advantages of Monte Carlo simulation (MCS) for highly nonlinear, high-dimensional problems, and problems with multiple failure domains, while overcoming the disadvantages of MCS for rare event. Furthermore, the SBSS method equipped with importance sampling technique (SBSS-IS) is also proposed to improve the robustness of estimation. Additionally, we combine the proposed SBSS and SBSS-IS methods with GPR model and active learning strategy so as to further substantially reduce the computational cost under the desired requirement of estimated accuracy. Finally, the superiorities of the proposed methods are demonstrated by six examples with different problem settings.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102546"},"PeriodicalIF":5.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700579","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":"An augmented integral method for probability distribution evaluation of performance functions","authors":"Yan-Gang Zhao,&nbsp;Chang-Xing Zou,&nbsp;Xuan-Yi Zhang,&nbsp;Ye-Yao Weng","doi":"10.1016/j.strusafe.2024.102544","DOIUrl":"10.1016/j.strusafe.2024.102544","url":null,"abstract":"<div><div>The paper proposes an efficient augmented integral method for probability distribution evaluation of performance functions. In the proposed method, the performance function is augmented by adding an auxiliary random variable, whose probability density function (PDF) and cumulative distribution function (CDF) are formulated as the integrations of the original performance function with respect to basic random variables. The optimal auxiliary random variable is determined to provide an accurate estimation of the integrations by investigating the geometric properties of integrands and a distribution parameter optimization approach based on moment analysis. According to the convolution formula, the relationship between the PDFs of the augmented performance function and the original performance function is clarified. Then, the PDF of the original performance function is calculated by solving an unconstrained optimization problem that is established using the convolution formula. Finally, four numerical examples are investigated to demonstrate the efficiency and accuracy of the proposed method for structural reliability analysis. The results indicate that the proposed method can evaluate the probability distribution of performance functions accurately and efficiently, even when the performance functions are strongly nonlinear and implicit.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102544"},"PeriodicalIF":5.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700578","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":"Preface of the special issue: The Joint Committee of Structural Safety: past, present and a perspective on the future","authors":"Jochen Köhler,&nbsp;Ton Vrouwenvelder,&nbsp;Michael Havbro Faber (President, and Past Presidents of the Joint Committee on Structural Safety),&nbsp;Maria Pina Limongelli (JCSS Reporter and Executive Guest Editor of the Special Issue)","doi":"10.1016/j.strusafe.2024.102542","DOIUrl":"10.1016/j.strusafe.2024.102542","url":null,"abstract":"","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"113 ","pages":"Article 102542"},"PeriodicalIF":5.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139059","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":"Bivariate cubic normal distribution for non-Gaussian problems","authors":"Xiang-Wei Li,&nbsp;Xuan-Yi Zhang,&nbsp;Yan-Gang Zhao","doi":"10.1016/j.strusafe.2024.102541","DOIUrl":"10.1016/j.strusafe.2024.102541","url":null,"abstract":"<div><div>Probabilistic models play critical role in various engineering fields. Numerous critical issues exist in probabilistic modeling, especially for non-Gaussian correlated random variables. Traditional parameter-based bivariate distribution models are typically developed for specific types of random variables, which limits their flexibility and applicability. In this study, a flexible bivariate distribution model is proposed, in which the joint cumulative distribution function (JCDF) is derived by expressing the probability as the summation of three basic probabilities corresponding to simple functions. These probabilities are computed using a univariate cubic normal distribution, and thus the proposed model is named as bivariate cubic normal (BCN) distribution. The proposed BCN distribution has been applied in modeling several common bivariate distributions and actual engineering datasets. Results show that the BCN distribution accurately fits the JCDFs of both theoretical distributions and practical datasets, offering significant improvement over existing models. Furthermore, the proposed BCN distribution is utilized in seismic reliability assessment and the calculation of the mean recurrence interval and hazard curve of hurricane wind speed and storm size. Results demonstrate that the BCN distribution excels in modeling and matching capabilities, proving its accuracy and effectiveness in civil engineering applications.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102541"},"PeriodicalIF":5.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533296","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 JCSS probabilistic model Code, future developments","authors":"R.D.J.M. Steenbergen ,&nbsp;A.C.W.M. Vrouwenvelder","doi":"10.1016/j.strusafe.2024.102540","DOIUrl":"10.1016/j.strusafe.2024.102540","url":null,"abstract":"<div><div>To assess and verify the reliability of structures, reliability based building codes allow for the application of full-probabilistic methods and semi-probabilistic methods (i.e. the partial factor method). In principle, both methods should be equivalent and lead to (approximately) the same reliability level. Therefore partial factors should be as much as possible determined based on a probabilistic background and calibration exercises. On the other hand, as the probabilistic design method may be considered as more rational and consistent than the partial factor design, there is a tendency to use probabilistic methods directly in the assessment of special of important new structures and also in the assessment of existing structures. In both the calibration exercise and in the full probabilistic assessment of structures, we face the problem that many assumptions have to be made. In particular in regard to the statistical modelling of random variables and in regard to accepted approximative methods of calculation. This often brings the engineer to a challenging position. In the past years the JCSS probabilistic model code (PMC) has served as an often-consulted operational code for this purpose. In the present paper, the JCSS PMC and its future developments are presented and discussed.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"113 ","pages":"Article 102540"},"PeriodicalIF":5.7,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138981","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":"Yet another Bayesian active learning reliability analysis method","authors":"Chao Dang ,&nbsp;Tong Zhou ,&nbsp;Marcos A. Valdebenito ,&nbsp;Matthias G.R. Faes","doi":"10.1016/j.strusafe.2024.102539","DOIUrl":"10.1016/j.strusafe.2024.102539","url":null,"abstract":"<div><div>The well-established Bayesian failure probability inference (BFPI) framework offers a solid foundation for developing new Bayesian active learning reliability analysis methods. However, there remains an open question regarding how to effectively leverage the posterior statistics of the failure probability to design the two key components for Bayesian active learning: the stopping criterion and learning function. In this study, we present another innovative Bayesian active learning reliability analysis method, called ‘Weakly Bayesian Active Learning Quadrature’ (WBALQ), which builds upon the BFPI framework to evaluate extremely small failure probabilities. Instead of relying on the posterior variance, we propose a more computationally feasible measure of the epistemic uncertainty in the failure probability by examining its posterior first absolute central moment. Based on this measure and the posterior mean of the failure probability, a new stopping criterion is devised. A recently developed numerical integrator is then employed to approximate the two analytically intractable terms inherent in the stopping criterion. Furthermore, a new learning function is proposed, which is partly derived from the epistemic uncertainty measure. The performance of the proposed method is demonstrated by five numerical examples. It is found that our method is able to assess extremely small failure probabilities with satisfactory accuracy and efficiency.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102539"},"PeriodicalIF":5.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322476","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":"Improved Bayesian model updating of geomaterial parameters for slope reliability assessment considering spatial variability","authors":"Shui-Hua Jiang ,&nbsp;Hong-Peng Hu ,&nbsp;Ze Zhou Wang","doi":"10.1016/j.strusafe.2024.102536","DOIUrl":"10.1016/j.strusafe.2024.102536","url":null,"abstract":"<div><p>In engineering practice, Bayesian model updating using field data is often conducted to reduce the substantial inherent epistemic uncertainties in geomaterial properties resulting from complex geological processes. The Bayesian Updating with Subset simulation (BUS) method is commonly employed for this purpose. However, the wealth of field data available for engineers to interpret can lead to challenges associated with the “curse of dimensionality”. Specifically, the value of the likelihood function in the BUS method can become extremely small as the volume of field data increases, potentially falling below the accuracy threshold of computer floating-point operations. This undermines both the computational efficiency and accuracy of Bayesian model updating. To effectively address this technical challenge, this paper proposes an improved BUS method developed based on parallel system reliability analysis. Leveraging the Cholesky decomposition-based midpoint method, the total failure domain in the original BUS method, which involves a low acceptance rate, is subdivided into several sub-failure domains with a high acceptance rate. Facilitated with an improved Metropolis-Hastings algorithm, the improved BUS method enables the consideration of a large volume of field data and spatial variability of geomaterial properties in the probabilistic back analysis. The results of an illustrative soil slope, involving spatially variable undrained shear strength, demonstrate that the improved BUS method is effective in simultaneously incorporating a substantial volume of field measurements and observations in the model updating process. Through a comparison with the original BUS method, the improved BUS method is shown to be useful for Bayesian model updating of high-dimensional spatially variable geomaterial properties and slope reliability assessment.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102536"},"PeriodicalIF":5.7,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024001073/pdfft?md5=03862f608e5112a4db4d8519e06c7cf1&pid=1-s2.0-S0167473024001073-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271458","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":"Data-enhanced design charts for efficient reliability-based design of geotechnical systems","authors":"M.K. Lo,&nbsp;Y.F. Leung,&nbsp;M.X. Wang","doi":"10.1016/j.strusafe.2024.102527","DOIUrl":"10.1016/j.strusafe.2024.102527","url":null,"abstract":"<div><div>This paper proposes a new design chart approach for reliability assessment, which enables clear visualization of the representative soil shear strength parameters under various reliability levels and effective stress levels. Utilizing the design charts, reliability assessment or reliability-based design can be performed with significantly reduced numbers of evaluations of the geotechnical system response. The design charts are established solely based on the probability distributions of soil parameters, and are applicable to a variety of geotechnical problems involving the same soil type. For practical illustration of the proposed approach, design charts are produced from the shear strength databases of saprolitic soils and colluvial soils in Hong Kong, and then applied to the reliability-based design of a slope with soil nail reinforcements. The ensuing design solutions require much fewer soil nails compared to the conventional design practice, while also achieving a better system reliability. The same charts are then applied to the reliability-based design of a retaining wall, where a series of design options are identified with equivalent reliability index against overturning failure and pullout failure. Through the proposed approach, the use of design charts promotes efficient reliability-based design of geotechnical systems with rational incorporation of reliability concepts.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102527"},"PeriodicalIF":5.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419735","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":"Recent developments in mechanical and uncertainty modelling of concrete","authors":"Jianbing Chen ,&nbsp;Xiaodan Ren ,&nbsp;De-Cheng Feng ,&nbsp;Jochen Kohler ,&nbsp;John Dalsgaard Sørensen ,&nbsp;Jian-Ying Wu ,&nbsp;Jia-Liang Le ,&nbsp;Robby Caspeele","doi":"10.1016/j.strusafe.2024.102526","DOIUrl":"10.1016/j.strusafe.2024.102526","url":null,"abstract":"<div><div>Concrete is one of the most widely used materials in civil and infrastructure engineering in the world, just following water. Therefore, the serviceability and safety of concrete structures are of paramount importance. The modeling of mechanical properties of concrete and the uncertainty quantification are the two cornerstones for reliability evaluation and rational design decision of concrete structures. In the past 50 years, extensive endeavors have been devoted to these two aspects and great progresses have been made. In the present paper, investigations of and advances in mechanical and probabilistic modeling of concrete are reviewed, including the constitutive law of concrete material, the uncertainty quantification of parameters and constitutive laws of concrete, the nonlinear analysis of concrete structures, and the modeling of concrete properties in the design codes including the JCSS Probabilistic Model Code, fib Model Code, Chinese standard and Eurocodes. In particular, the transitions from uni-axial to multi-axial constitutive law, from probability distribution of major parameters and empirical relationship between parameters to full probabilistic quantification of the constitutive law of concrete, and from structural nonlinear analysis based on component internal force vs. deformation restoring force relationship to the framework based on continuum mechanics involving constitutive law are stressed.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"113 ","pages":"Article 102526"},"PeriodicalIF":5.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138980","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":"System reliability analysis of building clusters considering inter-structural seismic demand correlation","authors":"Mengjie Xiang ,&nbsp;Mengze Lyu ,&nbsp;Jiaxu Shen ,&nbsp;Zekun Xu ,&nbsp;Jun Chen","doi":"10.1016/j.strusafe.2024.102528","DOIUrl":"10.1016/j.strusafe.2024.102528","url":null,"abstract":"<div><p>The seismic engineering demand parameters (EDPs) of building clusters exhibit significant spatial correlations and need full consideration in regional risk and reliability assessments. This study presents an efficient scheme to determine the joint distribution of multi-structure EDPs, which captures all EDP correlations and enables direct calculation of system reliability for building clusters. This scheme generates spatially correlated random ground motion fields through ground motion cross power spectrum density (PSD) models with stochastic harmonic function simulations. Subsequently, the decoupled multi-probability density evolution method (M−PDEM) is integrated to conduct seismic analysis of building clusters under random ground motion fields to determine their EDP joint distribution. An example of three linear single-degree-of-freedom (SDOF) models shows that the proposed scheme requires only hundreds of analyses to achieve the same accuracy as 10⁵ Monte Carlo Simulation (MCS) analyses, while also capturing the nonlinear correlations among EDPs. Finally, an engineering application of three reinforced concrete (RC) frame shear-wall buildings under a rare earthquake scenario is investigated, and the joint collapse probability by the scheme is compared with that by commonly-adopted assumptions of mutual independence and linear correlation. The results reveal that relative errors by the two assumptions can reach up to 39 % and 22 %, respectively.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102528"},"PeriodicalIF":5.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142172563","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}