Structural Safety最新文献

{"title":"TEMPORARY REMOVAL: Probabilistic modelling of deterioration of reinforced concrete structures","authors":"Dimitri V. Val, Carmen Andrade, Miroslav Sykora, Mark G. Stewart, Emilio Bastidas-Arteaga, Jan Mlcoch, Quynh Chau Truong, Charbel-Pierre El Soueidy","doi":"10.1016/j.strusafe.2024.102454","DOIUrl":"https://doi.org/10.1016/j.strusafe.2024.102454","url":null,"abstract":"Reinforced concrete (RC) structures deteriorate over time which affects their strength and serviceability. To develop measures for protecting new RC structures against deterioration and assess the condition of existing RC structures subjected to deterioration an understanding of the deterioration processes and the ability to predict their development, including structural consequences, are essential. This problem has attracted significant attention from researchers, including those working in the area of structural reliability (in particular within the JCSS) since there are major uncertainties associated with the deterioration processes and their structural effects. The paper presents an overview of the probabilistic modelling of various deterioration processes affecting RC structures such as corrosion of reinforcing steel, freezing-thawing, alkali-aggregate reaction, sulphate attack and fatigue, and their structural implications, including the historical perspective and current state-of-the-art. It also addresses the issues related to the inspection/monitoring of deteriorating RC structures and the analysis of collected data taking into account relevant uncertainties. Examples illustrating the application of the presented probabilistic models are provided. Finally, the current gaps in the knowledge related to the problem, which require further attention, are discussed.","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"36 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140017468","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":"Eurocode-compliant system-level reliability analyses of trussed portal frames under climatic loads","authors":"Lauri Jaamala ,&nbsp;Henna Hietikko-Kaukola ,&nbsp;Kristo Mela ,&nbsp;Juha Tulonen ,&nbsp;Anssi Hyvärinen","doi":"10.1016/j.strusafe.2024.102451","DOIUrl":"10.1016/j.strusafe.2024.102451","url":null,"abstract":"<div><p>Eurocode 3 provides <strong>G</strong>eometrically and <strong>M</strong>aterially <strong>N</strong>onlinear <strong>A</strong>nalysis with <strong>I</strong>mperfections-method (GMNIA) in which the entire structure can be designed in system-level. In GMNIA, reliability of structural system is verified by using a system safety factor which is obtained in Eurocode 3 based on numerical or experimental capacity test results. Unfortunately, such test results are scarcely published in the literature thus complicating the determination of the factor for a design engineer. In the so-called Direct Design Method, however, system safety factors are provided in advance for the design engineer by system-level reliability studies. This study determines the Eurocode-compliant GMNIA system safety factor for Warren truss portal frames by using the approach of the Direct Design Method. Advanced numerical models are utilized to perform Monte Carlo-simulations for entire structural systems. These simulations provide statistical distributions of system resistances which are employed in the First-Order Reliability Method to derive the system safety factors. Studied structures are made of S700 cold-formed hollow sections. Various system geometries, system configurations and load combinations consisting of snow and wind loads are investigated and a suitable system safety factor is proposed for the ultimate limit state design of Warren truss portal frames. The proposed system safety factor is applied in a practical comparison in which Warren system is designed both by the conventional Eurocode 3 method and GMNIA. This comparison reveals that GMNIA has a remarkable potential to offer reduced material consumption in design compared to the conventional method.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"108 ","pages":"Article 102451"},"PeriodicalIF":5.8,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000225/pdfft?md5=b59d0cbe447a44d4e72132efbdc11a98&pid=1-s2.0-S0167473024000225-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139923174","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":"An adaptive data-driven subspace polynomial dimensional decomposition for high-dimensional uncertainty quantification based on maximum entropy method and sparse Bayesian learning","authors":"Wanxin He ,&nbsp;Gang Li ,&nbsp;Yan Zeng ,&nbsp;Yixuan Wang ,&nbsp;Changting Zhong","doi":"10.1016/j.strusafe.2024.102450","DOIUrl":"10.1016/j.strusafe.2024.102450","url":null,"abstract":"<div><p>Polynomial dimensional decomposition (PDD) is a surrogate method originated from the ANOVA (analysis of variance) decomposition, and has shown powerful performance in uncertainty quantification (UQ) accuracy and convergence recently. However, complex high-dimensional problems result in a large number of polynomial basis functions, leading to heavy computational burden, and the probability distributions of the input random variables are indispensable for PDD modeling and UQ, which may be unavailable in practical engineering. This study establishes an adaptive data-driven subspace PDD (ADDSPDD) for high-dimensional UQ, which employs two types of data for modeling the PDD basis function and the low-dimensional subspace directly, namely, the data of input random variables and the input-response samples. Firstly, we propose a data-driven zero-entropy criterion-based maximum entropy method for reconstructing the probability density functions (PDF) of input variables. Then, with the aid of the established PDFs, a data-driven subspace PDD (DDSPDD) is proposed based on the whitening transformation. To recover the subspace of the function of interest accurately and efficiently, we put forward an approximate active subspace method (AAS) based on the Taylor expansion under some mild premises. Finally, we integrate an adaptive learning algorithm into the DDSPDD framework based on the sparse Bayesian learning theory, obtaining our ADDSPDD; thus, the real subspace and the significant PDD basis functions can be identified with limited computational budget. We validate the proposed method by using four examples, and systematically compare four existing dimension-reduction methods with the AAS. Results show that the proposed framework is effective and the AAS is a good choice when the corresponding assumptions are satisfied.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"108 ","pages":"Article 102450"},"PeriodicalIF":5.8,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139923189","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-based sizing and shape optimization of trusses considering millions of failure sequences","authors":"Lucas A. Rodrigues da Silva ,&nbsp;André J. Torii ,&nbsp;André T. Beck","doi":"10.1016/j.strusafe.2024.102448","DOIUrl":"10.1016/j.strusafe.2024.102448","url":null,"abstract":"<div><p>System-Reliability-Based Design Optimization (S-RBDO) of structures considering progressive collapse is a complex problem, as the number of potential failure sequences increases geometrically with the degree of static indeterminacy of the structure. Existing methods for identifying failure sequences in structural systems are computationally expensive and prone to missing some critical failure sequences, especially within an optimization framework. In this context, identifying the most critical failure sequences to simplify the problem is fundamental. Herein, we propose a novel system-reliability-based framework for sizing and shape optimization of trusses. The procedure identifies minimal cut sets using the recently developed null space method, which has been proven more efficient than traditional failure path-based methods. The most probable failure sequence is selected from each identified minimal cut set. System reliability is estimated using the dominant failure sequences for the whole structure, selected based on their correlations using the Probabilistic Network Evaluation Technique (PNET). Craziness-Based Particle Swarm Optimization (CRPSO) is employed as the optimization algorithm. Numerical examples involving hundreds to millions of failure sequences demonstrate applicability and efficiency of the proposed framework on truss optimization problems with different material post-failure behaviors. Results suggest that, in a system-reliability analysis considering progressive collapse, the most critical failure sequences are those obtained from minimal cut sets. Furthermore, results show that the procedure proposed herein can outperform other frameworks based on traditional failure path-based methods. Simple truss sizing and shape optimization is considered herein, but the conclusions have immediate relevance to the optimal design of realistic structures considering progressive collapse.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"108 ","pages":"Article 102448"},"PeriodicalIF":5.8,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139815009","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":"Performance evaluation of high-rise buildings using database-assisted design approach","authors":"Dikshant Saini,&nbsp;Bahareh Dokhaei,&nbsp;Behrouz Shafei,&nbsp;Alice Alipour","doi":"10.1016/j.strusafe.2024.102447","DOIUrl":"10.1016/j.strusafe.2024.102447","url":null,"abstract":"<div><p>In recent years, performance-based design (PBD) has gained attention and is sought to be the benchmark approach in the field of wind engineering. While the concept of performance-based design is well-accepted in earthquake engineering, it is yet to be embraced for the design of buildings to resist severe wind loads. This paper introduces a framework for the performance-based wind design (PBWD) of tall steel buildings using a time domain analysis that keeps the process of wind effects and the structural design process integrated, transparent, and fully auditable. From the perspective of PBWD, the main objective is to achieve a desirable performance level for a given hazard level, i.e., mean recurrence interval of extreme wind. The wind effects are directly related to the mean annual return of wind through a well-accepted simulation approach. A 180 m tall standard CAARC building is used for the case study to illustrate the proposed methodology. The wind load time histories are determined using the pressure tap data on exterior faces of the building measured in the wind tunnel. With the calculated wind loads, nonlinear dynamic analysis is conducted with various wind directions and mean wind speeds based on database-assisted design (DAD) approach. The key performance measures such as demand-to-capacity indices, inter-story drift, damage deformation index, and floor accelerations are calculated as a function of wind directions and mean wind speed. The obtained responses are used in conjunction with a local wind climatological database to determine the extreme wind effects for any specific mean recurrence interval. The performance of the steel building is evaluated for three performance criteria, including occupant comfort, operational, and continuous occupancy. The conducted performance assessment reveals that building fails to satisfy the serviceability requirement of drifts. However, the building satisfies the requirements for the occupant comfort and operational performance levels for strength design, while it also satisfies the continuous occupancy, limited interruption in Risk category II. The result reveals that the proposed framework provides realistic assessment of performance of the building incorporating the wind directionality and return period of the wind speeds.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"109 ","pages":"Article 102447"},"PeriodicalIF":5.8,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139679365","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":"Models and methods for probabilistic safety assessment of steel structures subject to fatigue","authors":"Johan Maljaars, John Leander, Alain Nussbaumer, John Daalsgaard Sørensen, Daniel Straub","doi":"10.1016/j.strusafe.2024.102446","DOIUrl":"https://doi.org/10.1016/j.strusafe.2024.102446","url":null,"abstract":"<p>We review of the state of the art in probabilistic modelling for fatigue reliability of civil engineering and offshore structures. The modeling of randomness and uncertainty in fatigue resistance and fatigue load variables are presented in some detail. This is followed by a review of the specifics of reliability analysis for fatigue limit states and a background on the semi-probabilistic treatment of fatigue safety. We discuss the different life-cycle reliability concepts and give an overview on probabilistic inspection planning. We describe the choices made in the Probabilistic Model Code of the Joint Committee of Structural Safety, present alternatives to these choices and suggest areas of future research.</p>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"15 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139664630","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":"Probabilistic model updating of steel frame structures using strain and acceleration measurements: A multitask learning framework","authors":"Taro Yaoyama,&nbsp;Tatsuya Itoi,&nbsp;Jun Iyama","doi":"10.1016/j.strusafe.2024.102442","DOIUrl":"10.1016/j.strusafe.2024.102442","url":null,"abstract":"<div><p><span>This paper proposes a multitask learning framework for probabilistic model updating by jointly using strain and acceleration measurements. This framework can enhance the structural damage assessment and response prediction of existing steel frame structures with quantified uncertainty. Multitask learning may be used to address multiple similar inference tasks simultaneously to achieve a more robust prediction performance by transferring useful knowledge from one task to another, even in situations of data scarcity. In the proposed model-updating procedure, a spatial frame is decomposed into multiple planar frames that are viewed as multiple tasks and jointly analyzed based on the hierarchical Bayesian model, leading to robust estimation results. The procedure uses a displacement–stress relationship in the modal space because it directly reflects the elemental stiffness and requires no prior knowledge concerning the mass, unlike most existing model-updating techniques. Validation of the proposed framework by using a full-scale vibration test on a one-story, one-bay by one-bay moment resisting steel frame, wherein structural damage to the column bases is simulated by loosening the </span>anchor bolts, is presented. The experimental results suggest that the displacement–stress relationship has sufficient sensitivity toward localized damage, and the Bayesian multitask learning approach may result in the efficient use of measurements such that the uncertainty involved in model parameter estimation is reduced. The proposed framework facilitates more robust and informative model updating.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"108 ","pages":"Article 102442"},"PeriodicalIF":5.8,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139555970","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":"Calibration of burst strength models of corroded pipelines using the hierarchical Bayesian method","authors":"U. Bhardwaj,&nbsp;A.P. Teixeira,&nbsp;C. Guedes Soares","doi":"10.1016/j.strusafe.2024.102444","DOIUrl":"10.1016/j.strusafe.2024.102444","url":null,"abstract":"<div><p>This paper proposes a probabilistic framework to calibrate burst strength models of intact and corroded pipelines based on the hierarchical Bayesian method. The approach uses burst test data of intact and corroded pipelines of different steel grades compiled from the literature and accounts for the variations among the data sources. First, the most appropriate burst strength models for corrosion-free and corroded pipelines are adopted. The burst pressure prediction models are categorised under low, medium and high-grade steel classes. Using the hierarchical Bayesian approach model uncertainty factors are derived to calibrate the burst strength models. The mean values and uncertainty of posterior probabilities of the model uncertainty factors are estimated for intact and corroded pipelines in three material categories. This study further investigates the uncertainty propagated by calibrated and non-calibrated models and draws important observations regarding the uncertainty associated with the calibration. The prediction uncertainties follow a non-linear increasing trend as corrosion defect increases. This study's importance is demonstrated with a case study that shows the differences in the uncertainty resulting from the use of the proposed approach compared to the conventional method. Additionally, for corroded pipes, model uncertainty factors are described as a function of defect depth with regression parameters estimated from hierarchical Bayesian-based regression analysis. Finally, a comparison between calibrated and non-calibrated models indicates that the calibrated models provide non-conservative predictions.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"108 ","pages":"Article 102444"},"PeriodicalIF":5.8,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000158/pdfft?md5=7a729f4829186e0b2872c3c8428bb482&pid=1-s2.0-S0167473024000158-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139587275","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":"Control variates with splitting for aggregating results of Monte Carlo simulation and perturbation analysis","authors":"Cristóbal H. Acevedo ,&nbsp;Marcos A. Valdebenito ,&nbsp;Iván V. González ,&nbsp;Héctor A. Jensen ,&nbsp;Matthias G.R. Faes ,&nbsp;Yong Liu","doi":"10.1016/j.strusafe.2024.102445","DOIUrl":"10.1016/j.strusafe.2024.102445","url":null,"abstract":"<div><p>Estimation of second-order statistics allows characterizing the uncertainty associated with the response of stochastic finite element models. Two common approaches for estimating these statistics are Monte Carlo simulation and perturbation. The purpose of this paper is to present a framework to aggregate the results obtained by means of these two approaches under the umbrella of Control Variates with Splitting. This allows to produce estimates of the second-order statistics of the system’s response with improved precision and accuracy. More specifically, Control Variates is implemented in such a way that the variance of the estimates of second-order statistics is minimized. In addition, the application of intervening variables for enhancing perturbation is considered as well, showing substantial advantages by increasing the accuracy of the estimates of second-order statistics. The application of the proposed framework is illustrated by means of an example involving the estimation of second-order statistics of a model involving confined seepage flow.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"108 ","pages":"Article 102445"},"PeriodicalIF":5.8,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016747302400016X/pdfft?md5=5b2e5d5de55b963ac4b0934b14678c8b&pid=1-s2.0-S016747302400016X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139515544","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 modelling of in-structure concrete strength based on stochastic damage model and Burr distribution","authors":"Jingran He ,&nbsp;Junjie Hong ,&nbsp;Ruofan Gao ,&nbsp;Jinju Tao ,&nbsp;Hongmin Yan","doi":"10.1016/j.strusafe.2024.102443","DOIUrl":"10.1016/j.strusafe.2024.102443","url":null,"abstract":"<div><p>The probability measure of low-quality concrete is essential for the reliability analysis of concrete structures. However, this problem is usually neglected, and the normal distribution or lognormal distribution is often selected as the probability distribution of concrete strength. In this study, a better solution for this problem is given by theoretically deriving of the Burr distribution based on the stochastic damage model. A large amount of in-situ test data in engineering structures is applied to perform a K-S test of different distribution types and to fit the distribution parameters. As a result, the advantage of Burr distribution in representing the tail probability is explained by both theoretical derivation and fitting results. And the Burr distribution is accepted by the K-S test in every strength grade while the other distribution types are all partly rejected.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"108 ","pages":"Article 102443"},"PeriodicalIF":5.8,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139498006","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}