Structural Safety最新文献

{"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}

{"title":"Probabilistic analysis of operational ice damage for Polar class vessels using full-scale data","authors":"Mikko Suominen ,&nbsp;Mihkel Kõrgesaar ,&nbsp;Rocky Taylor ,&nbsp;Martin Bergström","doi":"10.1016/j.strusafe.2023.102423","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102423","url":null,"abstract":"<div><p>To ensure the safety of maritime operations in polar waters, the IMO enforced the International Code for Ships Operating in Polar Waters (Polar Code) in 2017. To address ice navigation related risks, the Polar Code refers to a set of guidelines known as the Polar Operational Limit Assessment Risk Indexing System (POLARIS). Following POLARIS, operational limits for ice navigation are defined based on the Risk Index Outcome (RIO) value, which takes into account the prevailing ice conditions and the ice class of a ship.</p><p>Recent studies indicate that the POLARIS guidelines are well-founded. However, no direct relationship between RIO values and the probability of an ice-induced hull structural damage has been established. To enable a more accurate analysis of ice navigation risks, this article addresses this issue by (i) relating measured ice-induced loads to RIO values corresponding to the ice conditions in which the loads were measured, (ii) calculating the load limits for plastic deformation and rupture of the ice belt of hull structures representing different ice classes, and (iii) defining the probability of structural damage for different load limits. The study utilizes long-term full-scale ice load measurements carried out onboard Polar Supply and Research Vessel (PSRV) S.A. Agulhas II in the Antarctic Ocean. The load limits were calculated for ice class standards, PC3, PC4, PC5, PC6, and PC7 in accordance with the Unified Requirements of the International Association of Classification Societies (IACS).</p><p>On a general level, the results are consistent with earlier findings indicating that the POLARIS guidelines are well-founded. If a ship operates mainly at ‘normal operation’ level, the probability for fracture at hull are at probability levels of 10^-3 and 10^-4 for ice classes PC3 to PC5. The probability levels for PC6 and PC7 are higher that is possibly a result from conservative load probability distributions. When the portion of operations at ‘special consideration’ level becomes significant, the probability of fracture at the hull increases significantly. However, large ice thicknesses and the largest load magnitudes may be associated with positive RIO values. Some inconsistencies are recognized, and the uncertainty and limitations of the analysis are discussed.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"107 ","pages":"Article 102423"},"PeriodicalIF":5.8,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473023001108/pdfft?md5=46a6f41b2608d4c2632fbd50ca45bbd2&pid=1-s2.0-S0167473023001108-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558917","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":"Determination of load combination factors for code calibration","authors":"Colin C. Caprani,&nbsp;M. Shihabuddin Khan","doi":"10.1016/j.strusafe.2023.102415","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102415","url":null,"abstract":"<div><p>Semi-probabilistic methods are widely used for structural engineering design and assessment. In such methods, the safety and performance adequacy of structures are typically tied to partial safety factors for load and resistance, and load combination factors. There exist some heuristic strategies for estimating these partial factors, such as the design value method and first order reliability coefficient method. However, when adopted for the estimation of load combination factors, these strategies are either inaccurate or have non-unique estimates. Excessive conservatism in factor estimates is not desirable, particularly for the performance assessment of existing structures. In this study we propose a method for estimating load combination factors using a matrix linear algebra approach. Specifically, we develop a closed-form analytical expression to estimate unique load combination factors for typical code calibration problems. A comparative study of the existing heuristic approaches with the new approach is presented and demonstrated on two case studies. It is shown that the proposed method offers a valuable means of deriving load combination factors: one that avoids hueristics, and conservatism.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"107 ","pages":"Article 102415"},"PeriodicalIF":5.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473023001029/pdfft?md5=6ccb794cec4f3095886c015680a7338e&pid=1-s2.0-S0167473023001029-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138468077","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":"Post-disaster restoration planning of interdependent infrastructure Systems: A framework to balance social and economic impacts","authors":"Xiubing Huang,&nbsp;Naiyu Wang","doi":"10.1016/j.strusafe.2023.102408","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102408","url":null,"abstract":"<div><p>Hazard-induced service interruption of interdependent infrastructure systems (IISs) (e.g., electricity, water, gas, etc.) can lead to significant disruptions of social and economic functions of a modern society. An effective post-event restoration of the IISs is therefore of paramount importance to the overall recovery of a hazard-stricken community as a whole. As opposed to approaches with pure engineering perspectives, this study proposes an IISs restoration planning methodology aimed at balancing tradeoffs between the loss of social services (e.g., health care, food supply, etc.) and that of economic productions (e.g., construction, manufacturing, trade, etc.) throughout the IISs restoration process. The methodology is distinguished from previous researches with the following contributions: i) quantitatively relates the losses of various social services and economic productions to the service disruptions of IISs through the functionality loss of buildings; ii) the IISs disruption-induced overall losses of social services and economic productions accumulated throughout the whole recovery process is set as the bi-objective in formulating IISs restoration plans, and the Pareto optimal solutions are given to satisfy different decision preferences; iii) physics-based models capturing operational mechanisms of the IISs are embedded to provide realistic estimations of commodity supplies at each time step of the restoration optimization. The optimization is coupled with Monte Carlo simulation to uncover the impact of decision preference on community recovery from a statistical point of view. Testbed illustration shows that the decision preference makes significant impact on the recovery of the community as a whole and of different areas in the community with different socioeconomic characteristics.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"107 ","pages":"Article 102408"},"PeriodicalIF":5.8,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138468002","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":"Human and organizational factors influencing structural safety: A review","authors":"Xin Ren ,&nbsp;Karel C. Terwel ,&nbsp;Pieter H.A.J.M. van Gelder","doi":"10.1016/j.strusafe.2023.102407","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102407","url":null,"abstract":"<div><p>A broad review of the existing literature concerning Human and Organizational Factors (HOFs) and human errors influencing structural safety is presented in this study. Publications on this research topic were collected from the Scopus database. Two research focal points of this topic, namely modelling and evaluating the human error effects on structural reliability, and identifying causal factors for structural defects and failures, have been recognized and discussed with an in-depth literature review. The review of studies with a model focus summarizes the models and methods that have been developed to evaluate structural reliability considering human error effects. Besides, the review of publications on the factor subject outlines the most acknowledged HOFs that influence structural safety. Moreover, an additional spotlight was given to the studies from the offshore industry for the advanced development in HOFs and contributing the first complete Human Reliability Analysis (HRA) method for structural reliability analysis. In conclusion, this study provides a holistic overview of the knowledge developed in existing research on the topic of HOFs and human error influencing structural safety. Furthermore, current developments and challenges are reflected, and future research directions are explored for academics entering and working in this field. Additionally, the insights into HOFs generated from this review can assist engineers with better hazard identification and quality assurance in practice.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"107 ","pages":"Article 102407"},"PeriodicalIF":5.8,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473023000942/pdfft?md5=05957ee498a97b2be3ad0b96b2c7651e&pid=1-s2.0-S0167473023000942-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138468076","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 safety of RC piers with parameter uncertainties: Assessing dimensionless response using Bayesian linear regression","authors":"Rana Roy,&nbsp;Atanu Santra","doi":"10.1016/j.strusafe.2023.102414","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102414","url":null,"abstract":"<div><p>Both maximum and residual deformations are essential for seismic safety during and following strong earthquakes, especially in the near field. These parameters are often calculated per unidirectional analysis, albeit bidirectional analysis may be carried out for important systems with the availability of growing computational power. However, a pressing challenge in earthquake engineering that continues to exist in the face of several uncertainties is to represent these response statistics – essentially with wide dispersion - in an expressive and effective format. The present paper aims to represent the maximum and residual deformation to unidirectional and bidirectional shaking in a rational format, even when uncertainties in ground motion and structural characteristics are prevalent. To this end, a bridge pier with uncertain material parameters is subjected to a wide range of stochastically simulated near-field motions with forward-directive signature, and the responses are computed to unidirectional and bidirectional shaking. In an attempt to develop predictive models, the responses are regressed first in terms of<!--> <!-->primary parameters<!--> <!-->characterizing structural material and ground motions. Next, by standard principles of mechanics, the response is recast in a dimensionless and orientationally consistent format in terms of<!--> <!-->derived parameters. This reduces the number of independent variables yet connotes a sound basis of mechanics. The predictive model in terms of derived dimensionless parameters is further extended in the Bayesian framework to improve the predictive model in a probabilistic sense.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"107 ","pages":"Article 102414"},"PeriodicalIF":5.8,"publicationDate":"2023-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138439511","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":"Stochastic analysis and reliability assessment of critical RC structural components considering material properties uncertainty","authors":"A.R. Ibrahim ,&nbsp;D.A. Makhloof","doi":"10.1016/j.strusafe.2023.102412","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102412","url":null,"abstract":"<div><p>The unavoidable heterogeneity in the mechanical characteristics of concrete is widely acknowledged. Although it is widely considered as either perfectly correlated or entirely independent random variables in engineering practice; however, such treatment is illogical, and the outcomes may be deceptive. In high-rise buildings comprised of multiple structural components, it is crucial to consider the material properties’ spatial variability (MPSV) to obtain a reliable structural response and avoid damage to these structures. To this end, three main components, including column, rectangular shear wall, and U-shaped shear wall, are considered herein to investigate their stochastic response. The MPSV is represented by a covariance matrix decomposition-based random field generator combined with a GF-discrepancy-based point selection strategy to generate samples efficiently. A simplified strategy is developed to represent the random field for the U-shaped wall. Moreover, the probability density evolution method combined with the extreme value event is employed to obtain the failure probability of the studied components, where failure probabilities of 18%, 23%, and 32% are recorded for the studied RC column, rectangular shear wall, and U-shaped shear wall, respectively. Furthermore, different failure modes were identified and could not be determined through the deterministic analysis, highlighting the importance of accounting for material uncertainty. The proposed framework proved that the stochastic response and non-linear behavior of the considered components could be well captured and provide full perspective about the uncertainty quantification and reliability assessment and can be further implemented to capture the stochastic response and safety assessment of high-rise buildings.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"107 ","pages":"Article 102412"},"PeriodicalIF":5.8,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138423618","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}