Structural Safety最新文献

{"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}

{"title":"Estimating intraevent and interevent variability and spatial correlation of tropical cyclone wind fields and their use for the risk assessment of a portfolio of structures","authors":"C. Sheng ,&nbsp;Q.Y. Fan ,&nbsp;H.P. Hong","doi":"10.1016/j.strusafe.2024.102440","DOIUrl":"10.1016/j.strusafe.2024.102440","url":null,"abstract":"<div><p>Often the tropical cyclone (TC) wind hazard assessment requires the use of the TC wind field model. While theoretical models typically predict the observed wind field well, there can be a spatially varying residual correlation that impacts the damage assessment of communities or groups of structures. In this study, we focus on developing spatial correlation models and assessing the intraevent and interevent variability of the TC wind field using the H*Wind dataset and two widely used wind field models - the vertically averaged boundary layer slab model and the gradient wind field-based model. Our models and the statistics of the interevent and intraevent variability are integrated into a framework for evaluating the wind-induced damage of a portfolio of structures. The framework includes simulating TC tracks and wind fields, considering interevent and intraevent variabilities, and assessing peak linear elastic and nonlinear responses. Numerical examples illustrating the use of this framework are provided, indicating that realistic spatial correlation of the TC wind field needs to be considered to assess the correlation coefficient of the damage factor of a pair of spatially distributed structures and the probability distribution of the damage cost of a portfolio of structures.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"108 ","pages":"Article 102440"},"PeriodicalIF":5.8,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139515545","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":"Life-cycle performance of aluminium cladding panels in resisting hailstorms","authors":"Shuangmin Shi ,&nbsp;Nelson Lam ,&nbsp;Yiwen Cui ,&nbsp;Guoxing Lu ,&nbsp;Emad Gad ,&nbsp;Lihai Zhang","doi":"10.1016/j.strusafe.2024.102439","DOIUrl":"10.1016/j.strusafe.2024.102439","url":null,"abstract":"<div><p>This paper delves into cumulative damage on aluminium cladding panels attributed to hailstorms throughout the lifespan of the installations. 40 gas gun tests subjecting the cladding panel to repeated impact were undertaken for the purpose of studying cumulative damage behaviour. Insights from these tests were integrated into a hail size distribution model to characterise the probabilistic distribution of permanent indentation resulted from multiple hailstorm events. A life-cycle analysis framework was subsequently introduced, incorporating the natural variability of hailstone sizes and dynamic response of claddings to repeated ice impact. Intervention criterion can be established based on knowledge of the accumulation of permanent indentation into the cladding panels. Proactive actions are recommended should the indentations become visible to prevent worsening damage. Randomness of hailstorm occurrences was considered using hazard function which can be inferred from historical observations. Practical application of the proposed model is illustrated through case studies of two Australian states, coupled with comparative analyses highlighting key factors influencing cladding performance. The ability to account for stochasticity distinguishes the presented framework from existing deterministic approaches.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"108 ","pages":"Article 102439"},"PeriodicalIF":5.8,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000109/pdfft?md5=081f65d63c65acb90a2d20878b58b889&pid=1-s2.0-S0167473024000109-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139497960","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":"Seismic risk assessment of highway bridges in western Canada under crustal, subcrustal, and subduction earthquakes","authors":"Yihan Shao,&nbsp;Yazhou Xie","doi":"10.1016/j.strusafe.2024.102441","DOIUrl":"10.1016/j.strusafe.2024.102441","url":null,"abstract":"<div><p>This study conducts seismic risk assessment of highway bridges in western Canada. The performance-based earthquake engineering (PBEE) framework is enhanced to assess the expected annual repair cost ratio (ARCR) and annual restoration time (ART) of a benchmark bridge class under the region’s three types of earthquakes - shallow crustal earthquakes (CEs), deep subcrustal earthquakes (SCEs), and megathrust Cascadia subduction earthquakes (CSEs). First, event-specific seismic hazard models are considered, whereas event-consistent ground motions are selected for non-linear time history analyses. Compared with those from CEs and SCEs, CSE ground motions feature a much longer duration. This long-duration effect is captured by validating the numerical model of the bridge column against (1) a cyclic pushover test under standard versus long-duration loading protocols and (2) a shaking table test excited by six consecutive ground motions. Besides, the Park and Ang damage index is utilized as the column’s engineering demand parameter (EDP) and updated as a demand-capacity ratio model when reaching four different damage states. A comprehensive list of ground motion intensity measures (IMs) is considered where the spectra acceleration at one second, <em>S_a</em>(1.0), is chosen as the most suitable IM based on its performance in proficiency, efficiency, practicality, and EDP-IM correlation across all three earthquake events. Subsequently, component- and system-level fragility models are derived under each earthquake type using the <em>cloud</em> analysis that convolves the seismic demands with capacity models for multiple bridge components. To further quantify and propagate the epistemic uncertainty associated with the development of probabilistic seismic demand models (PSDMs), the bootstrap resampling technique is utilized to generate numerous seismic demand datasets and develop a stochastic set of seismic fragility curves. Finally, the bootstrapped, event-dependent fragility models are combined with the respective hazard models and probabilistic loss functions to assess the expected ARCR and ART for the benchmark bridge class. This study underscores the significantly higher seismic risk of highway bridges when facing CSEs, followed by CEs and SCEs.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"108 ","pages":"Article 102441"},"PeriodicalIF":5.8,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000122/pdfft?md5=3f866428852cd30cf9bfa39c28df1f99&pid=1-s2.0-S0167473024000122-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139497913","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":"Probabilistic models of dynamic increase factor (DIF) for reinforced concrete structures: A Bayesian approach","authors":"Dade Lai ,&nbsp;Fabrizio Nocera ,&nbsp;Cristoforo Demartino ,&nbsp;Yan Xiao ,&nbsp;Paolo Gardoni","doi":"10.1016/j.strusafe.2024.102430","DOIUrl":"https://doi.org/10.1016/j.strusafe.2024.102430","url":null,"abstract":"<div><p><span>The response of structures under rapidly varying loads can be affected by strain rate sensitivity generally expressed using Dynamic Increase Factor (</span><span><math><mrow><mi>D</mi><mi>I</mi><mi>F</mi></mrow></math></span>). Current models for estimating the <span><math><mrow><mi>D</mi><mi>I</mi><mi>F</mi></mrow></math></span> in Reinforced Concrete (RC) structures are generally deterministic and have restricted applicability due to their dependence on limited experimental data resulting in bias. This paper overcomes these limitations by proposing three probabilistic models that quantify compressive and tensile concrete and steel <span><math><mrow><mi>D</mi><mi>I</mi><mi>F</mi></mrow></math></span><span><span>, accounting for the relevant uncertainties. The proposed models are based on existing deterministic models with the addition of probabilistic correction terms. Bayesian updating<span> is employed to estimate the unknown model parameters using observational data from a large collection of experimental observations. The models incorporate model uncertainties stemming from assumed model form and (potential) missing variables through a model error term. The proposed probabilistic models are used to evaluate the reliability of RC structures under dynamic loads. As an illustration, the proposed probabilistic models are used to estimate the reliability of an example RC column under combined dynamic axial force and moment, and a RC column or beam under dynamic bending moments resulting in cracking. In the two examples, we consider the ACI 318-19 requirements for Ultimate Limit State (ULS) and </span></span>Serviceability Limit States (SLS). In comparison to deterministic </span><span><math><mrow><mi>D</mi><mi>I</mi><mi>F</mi></mrow></math></span> models, the proposed probabilistic models yield enhanced predictive accuracy, presenting a practical and robust approach to assess the structural reliability under impact and blast loads.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"108 ","pages":"Article 102430"},"PeriodicalIF":5.8,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434503","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":"Probability density function modelling and credible region construction for multivariate, asymmetric, and multimodal distributions of geotechnical data","authors":"Zi-Tong Zhao ,&nbsp;He-Qing Mu ,&nbsp;Ka-Veng Yuen","doi":"10.1016/j.strusafe.2023.102429","DOIUrl":"10.1016/j.strusafe.2023.102429","url":null,"abstract":"<div><p>Geotechnical data are typically Multivariate, Uncertain, and Irregular (MUI), so the probability distribution of geotechnical data is Multivariate, Asymmetric, and Multimodal (MAM). Probability Density Function (PDF) modelling and Credible Region (CR) construction are two key issues for a MAM distribution. There are two fundamental difficulties in characterizing a MAM distribution. The first is on joint PDF modelling as many traditional approaches collapse for a MAM distribution. Copula theory has attracted special attention for this purpose but very few works attempted to tackle the critical problem of probabilistic prediction on target variables using available information of remaining variables based on the copula-based joint PDF. The second is on CR construction of a MAM distribution as it cannot find a unique CR of a MAM distribution given an exceedance probability only. There is still a lack of a unified approach for CR construction for a MAM distribution of geotechnical data. Aiming to resolve these two fundamental difficulties, we propose the BAyeSIan Copula-based Highest density region/contour (BASIC-H) for providing a systematic framework of PDF modelling and CR construction of a MAM distribution. This framework contains Stage-PDF and Stage-CR. Stage-PDF fuses the copula theory and Bayesian inference to develop optimal, robust, and hyper-robust predictions on the posterior distribution and posterior predictive distribution. Stage-CR adopts the constraint for the CR that the probability density of every point inside the CR is at least as large as the probability density of any point outside, which is the same as the idea of the HDR (Highest Density Region). The Monte Carlo Simulation (MCS), based on the developed optimal, robust, and hyper-robust posterior distributions and posterior predictive distributions, is performed for estimation of the probability density boundary, which is a key parameter for constructing the HDR. Examples using simulated data and Quaternary clay data are presented to illustrate the capabilities of the BASIC-H in PDF modelling and CR construction of MAM distributions of geotechnical data.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"107 ","pages":"Article 102429"},"PeriodicalIF":5.8,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139031806","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 approach to the sustainability assessment of reinforced concrete structures in conditions of climate change","authors":"Salim Idris Malami ,&nbsp;Dimitri V. Val ,&nbsp;Benny Suryanto ,&nbsp;Husham A. Salman ,&nbsp;Xiao-Hui Wang","doi":"10.1016/j.strusafe.2023.102428","DOIUrl":"10.1016/j.strusafe.2023.102428","url":null,"abstract":"<div><p>The paper presents a probabilistic method based on two methodologies – Life Cycle Cost Analysis (LCCA) and Life Cycle Assessment (LCA), for evaluating the sustainability of reinforced concrete (RC) structures in terms of their costs and CO₂ emissions. The method considers the whole life of a RC structure by taking into account CO₂ initially embodied in its construction materials, the absorption of CO₂ by concrete due to carbonation during the service life of the structure, potential damage to the structure due to carbonation-induced corrosion of reinforcing steel that may require repairs, and relevant costs. Since there are numerous uncertainties associated with the calculation of CO₂ emissions and costs, a probabilistic approach is beneficial. The emphasis is made on RC structures made of the so-called “green concretes”, in which Portland cement is partially replaced with supplementary cementitious materials such as fly ash and ground granulated blast-furnace slag. The issue of a changing climate is also addressed. The method is illustrated by assessing the sustainability of a multi-story RC carpark made of different concrete types at three different locations (London, Paris and Marseille) for present and future climate conditions. This assessment's results show that using green concretes leads to a major reduction in CO₂ emissions and a small decrease in the life-cycle cost of the carpark RC elements. The relative sustainability performance of green concretes slightly improves compared to Portland cement concrete for future climate conditions.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"107 ","pages":"Article 102428"},"PeriodicalIF":5.8,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473023001157/pdfft?md5=90884fbdac61741f95a119ffcedc3a16&pid=1-s2.0-S0167473023001157-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138569559","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 improved adaptive Kriging model for importance sampling reliability and reliability global sensitivity analysis","authors":"Da-Wei Jia,&nbsp;Zi-Yan Wu","doi":"10.1016/j.strusafe.2023.102427","DOIUrl":"10.1016/j.strusafe.2023.102427","url":null,"abstract":"<div><p>An improved adaptive Kriging model for importance sampling (IS) reliability and reliability global sensitivity analysis is proposed by introducing the IS density function into learning function. Considering the variance information of Kriging prediction, the formula of traditional IS method is extended to the form considering the uncertainty of symbol function. The estimated variance of failure probability caused by the prediction uncertainty of Kriging model is obtained, and the corresponding coefficient of variation (COV) is defined. Based on the standard deviation information of failure probability, a novel learning function considering the characteristic of IS density function is proposed, which are used to minimize the prediction uncertainty of Kriging. The corresponding stopping criterion is defined based on the COV information. In order to increase the likelihood that the selected sample points fall around the limit state boundary, the penalty function method is introduced to improve the learning function. Once the failure probability is obtained, the variable global sensitivity index is calculated through the failed sample set and Bayes theorem. The results show that: By introducing IS density function and penalty function into learning function, the sample points which contribute more to the failure probability can be obtained more effectively in IS method. The proposed method has high accuracy and efficiency compared with traditional Kriging-based IS method.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"107 ","pages":"Article 102427"},"PeriodicalIF":5.8,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138569561","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 effective active learning strategy for reliability-based design optimization under multiple simulation models","authors":"Seonghyeok Yang ,&nbsp;Mingyu Lee ,&nbsp;Yongsu Jung ,&nbsp;Hyunkyoo Cho ,&nbsp;Weifei Hu ,&nbsp;Ikjin Lee","doi":"10.1016/j.strusafe.2023.102426","DOIUrl":"10.1016/j.strusafe.2023.102426","url":null,"abstract":"<div><p>This paper proposes an effective active learning strategy for reliability-based design optimization (RBDO) problems in which the constraint functions are acquired from multiple simulation models. To achieve this goal, a new active learning function (ALF) is derived by estimating the increased reliability of active constraint functions after adding one point to the train points of constraint functions in each simulation model. The proposed ALF distinguishes possibly active constraint functions that seem active near the current optimum and considers how the constraint functions are active. In the proposed RBDO method, a Kriging model is iteratively updated by adding the best point to the train points of constraint functions included in the crucial simulation model until the optimum converges and the Kriging model is sufficiently accurate. The best point and the crucial simulation model are obtained by comparing the proposed ALF. The ALF is further modified to apply to problems where the cost of each simulation model is different. To verify the effectiveness of the proposed method, two numerical and one engineering examples are analyzed. The results show that the proposed method efficiently and accurately obtains the RBDO optimum involving multiple simulation models, regardless of simulation cost.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"107 ","pages":"Article 102426"},"PeriodicalIF":5.8,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138569560","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}