Structural Safety最新文献_第2页

Bayesian inference of the spatial distribution of steel corrosion in reinforced concrete structures using corrosion-induced crack width 利用腐蚀引起的裂缝宽度对钢筋混凝土结构中钢筋腐蚀的空间分布进行贝叶斯推断

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-08-15 DOI: 10.1016/j.strusafe.2024.102518

{"title":"Bayesian inference of the spatial distribution of steel corrosion in reinforced concrete structures using corrosion-induced crack width","authors":"","doi":"10.1016/j.strusafe.2024.102518","DOIUrl":"10.1016/j.strusafe.2024.102518","url":null,"abstract":"<div><p>Observations of corrosion-induced crack widths offer crucial information about the corrosion states of steel reinforcements in reinforced concrete (RC) structures, enabling a cost-effective method for inferring corrosion states through inverse analysis. However, the uncertainty associated with the relationship between corrosion-induced cracking and steel weight loss necessitates a probabilistic inference method, especially when considering the spatial distributions of steel weight loss, which provides important information to estimate the load-bearing capacity loss of corroded RC structures. This paper proposes a Bayesian framework to infer the steel weight loss distribution in RC structures based on the observed corrosion-induced crack width. To reduce the dimensions of the Bayesian inference, a Karhunen-Loève transform is applied to extract the principal distribution features of the steel weight loss. The forward model of the Bayesian inference adopts a data-driven sequence-to-sequence transduction approach to predict corrosion-induced crack width from steel weight loss. This model incorporates a novel nonlinear convolution kernel for input encoding and a sparse polynomial chaos expansion for decoding, which proves more accurate and efficient than finite element simulations. The Hamiltonian Markov chain Monte Carlo (HMCMC) sampler is used to efficiently sample from the posterior distribution of the Bayesian inference. The case study of the proposed method demonstrated that Bayesian inference provides robust range estimation for the steel weight loss distribution, with its 95% confidence interval encompassing most observations. Additionally, the method efficiently inferred high-dimensional steel weight loss sequences up to 61 dimensions, taking advantage of the dimension reduction technique and the gradient-informed HMCMC sampler.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000894/pdfft?md5=0f5d711e21a26a3b04be8fcede689bc6&pid=1-s2.0-S0167473024000894-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049092","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}

引用次数: 0

A novel efficient RFEM for reliability analysis and design of multi-line dynamically installed anchor for floating offshore wind turbines 用于浮式海上风力涡轮机多线动态安装锚可靠性分析和设计的新型高效 RFEM

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-08-12 DOI: 10.1016/j.strusafe.2024.102520

{"title":"A novel efficient RFEM for reliability analysis and design of multi-line dynamically installed anchor for floating offshore wind turbines","authors":"","doi":"10.1016/j.strusafe.2024.102520","DOIUrl":"10.1016/j.strusafe.2024.102520","url":null,"abstract":"<div><p>A novel multi-line dynamically installed anchor was previously proposed by the authors to allow for the mooring of multiple floating offshore wind turbine, resulting in a significant reduction in the total number and cost of anchors required for floating wind farms. Considering the spatial variability of soil properties and the uncertainty of environmental loads, the present study performs reliability analysis and design of the multi-line dynamically installed anchor. Firstly, a strategy to repeatedly use fundamental random variables is proposed and validated for reducing the number of random variables used in Karhunen-Loève expansion in the simulation of random field of soil properties when the ratio of the soil domain dimension to the scale of fluctuation is large. Then, the efficiency, accuracy, and robustness of the RFEM (random finite element method) combined with K-MCS (Kriging model and Monte Carlo simulation) based on the proposed strategy are validated through examples of random capacity of foundations. Next, the random capacities and probabilistic <em>VHMT</em> failure envelopes of the multi-line dynamically installed anchor in spatially variable soil are investigated. Finally, the reliability design of multi-line dynamically installed anchors is conducted and compared with that of multi-line pile anchors, in which both the spatial variability of soil and the uncertainty of loads are condidered. The results show that the costs for multi-line dynamically installed anchors are obviously less than those of multi-line pile anchors.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998467","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}

引用次数: 0

Probabilistic analysis of near-field blast loads considering fireball surface instabilities and stochastic detonator location 考虑火球表面不稳定性和随机雷管位置的近场爆炸载荷概率分析

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-08-12 DOI: 10.1016/j.strusafe.2024.102522

{"title":"Probabilistic analysis of near-field blast loads considering fireball surface instabilities and stochastic detonator location","authors":"","doi":"10.1016/j.strusafe.2024.102522","DOIUrl":"10.1016/j.strusafe.2024.102522","url":null,"abstract":"<div><p>High speed video analysis of near-field explosive detonations displays distinct stages of emergent hydrodynamic instabilities in the fireball/shock-air interface. Typically, beyond 10 charge radii, the instabilities experienced large growths giving rise to more chaotic behaviour of the interface and thus an increasing uncertainty in surface velocity. These surface instabilities are suggested as the primary cause of blast parameter variability in the near-field. However, as a deterministic tool, numerical simulation of the detonation process and subsequent blast wave propagation is not able to replicate the stochastic nature of fireball surface instabilities and hence near-field blast parameter variability. Therefore, it is necessary to develop new methods to simulate and characterise the stochastic features of the fireball/shock-air interface. This paper proposes an algorithm to generate an explosive charge element with random shape in finite element model in order to simulate irregularities in the fireball/shock-air interface, and therefore produce variabilities comparable to those from direct observation. The effect of chaotic fireball/shock-air interface on near-field loading is explored through a large number of numerical simulations in order to investigate the statistical distribution of parameters including peak overpressure and impulse. Subsequently, the effect of stochastic detonator location is explored in a similar manner. A computational procedure based on the Monte Carlo Method is proposed to establish a probabilistic model of near-field blast loads, termed <em>PSL-Blast</em>. The reliability of design blast loads calculated using the UFC 3-340-02 design manual is then estimated using <em>PSL-Blast</em>, which suggests that reliability decreases with decreasing scaled distance<em>.</em> Finally, reliability-based safety factors of blast loads are calculated based on different blast settings.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049076","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}

引用次数: 0

Calibrating resistance factors of pile groups based on individual pile proof load tests 根据单根桩的验证荷载试验校准桩群的阻力系数

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-07-31 DOI: 10.1016/j.strusafe.2024.102517

{"title":"Calibrating resistance factors of pile groups based on individual pile proof load tests","authors":"","doi":"10.1016/j.strusafe.2024.102517","DOIUrl":"10.1016/j.strusafe.2024.102517","url":null,"abstract":"<div><p>Pile load tests have been utilized to reduce the uncertainty of pile resistance, thus leading to a higher resistance factor used in the Load and Resistance Factor Design (LRFD). Previous studies have primarily focused on calibrating resistance factors for single piles based on load tests. This calibration hinges upon the resistance bias factor of single piles, defined as the ratio of measured resistance to predicted resistance. Due to the redundancy in the pile group system, it is conventionally assumed that if the individual piles within the group achieve a lower reliability index (e.g., 2.0–2.5), the pile group as a whole attains the target reliability index of 3. However, the approach is empirical as it does not consider system redundancy directly. Moreover, this empirical approach disregards the correlation between resistance bias factors of individual piles, which is inherently influenced by the spatial variability of soils. In this study, the random finite difference method (RFDM) is employed to evaluate the correlation between resistance bias factors of individual piles in spatially variable soils. The resultant correlation matrix is subsequentially employed in Bayes’ theorem to update resistance bias factors using individual pile load test results and their corresponding test locations. The updated resistance bias factors are then used for the direct calibration of resistance factors for pile groups within the framework of LRFD. A pile group subject to vertical loading in undrained clays is adopted for illustration. Comparative analyses between the proposed approach and the empirical approach demonstrate that the latter tends to overestimate the resistance factor. Furthermore, the proposed approach enables the determination of optimal locations for conducting subsequent load tests based on previous test results.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000882/pdfft?md5=c13c52be39ce8682f3bc3102422121c5&pid=1-s2.0-S0167473024000882-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935414","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}

引用次数: 0

A partial decomposition cutting method with CF-discrepancy for points selection in stochastic seismic analysis of structures 用于结构随机地震分析中点选择的 CF 差分部分分解切割法

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-07-24 DOI: 10.1016/j.strusafe.2024.102513

{"title":"A partial decomposition cutting method with CF-discrepancy for points selection in stochastic seismic analysis of structures","authors":"","doi":"10.1016/j.strusafe.2024.102513","DOIUrl":"10.1016/j.strusafe.2024.102513","url":null,"abstract":"<div><p>The probability density evolution method is renowned for its effectiveness in conducting stochastic seismic response analyses of structures with uncertain parameters. Within this method, the points selection strategy, particularly in high-dimensional problems, is of paramount importance to achieving a balance between accuracy and efficiency. This paper proposes a novel point selection method designed to capture the probabilistic response of structural dynamic systems. The method starts by generating an initial uniform point set within a unit cube, using an improved number-theoretical method with a large number size. It then employs a partial decomposition cutting method to select a small number of samples from this initial uniform point set, which are subsequently scaled to the unit cube to serve as the representative points. These representative points are then transformed into the original random-variate space, and the corresponding assigned probabilities are computed accordingly. To enhance accuracy, a characteristic function-based discrepancy is proposed and applied to rearrange the representative points in the original random-variate space. The effectiveness of this method is demonstrated through two numerical examples, along with comparisons to results obtained using Monte Carlo Simulation and other comparable point sets.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784096","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}

引用次数: 0

Aggregation of data from multiple recording stations for extreme wind analysis and prediction 汇总多个记录站的数据，进行极端风力分析和预测

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-07-20 DOI: 10.1016/j.strusafe.2024.102516

{"title":"Aggregation of data from multiple recording stations for extreme wind analysis and prediction","authors":"","doi":"10.1016/j.strusafe.2024.102516","DOIUrl":"10.1016/j.strusafe.2024.102516","url":null,"abstract":"<div><p>Accurate, reliable and robust techniques for the probabilistic estimation of extreme wind speeds are essential for the design of structures for wind loading. Aggregating gust wind data from various stations with similar, homogeneous wind climates into a ‘superstation’ for hazard analysis has been employed since the 1980′s to reduce the effects of sampling errors. A concern that has been raised recently is that prediction biases may arise from such aggregation, when the data exhibit non-homogeneity due to inevitable short data lengths or imperfect homogeneity of the wind climates. By Monte Carlo simulation, we show that superstation aggregation is an unbiased technique for high recurrence level estimations when an appropriate fitting method is used, and the apparent biases are dependent on the method used for fitting the hazard model. To ensure homogeneity, we introduce a de–trending technique for minimizing any biases in the aggregated wind data. Four model-fitting methods for superstation analysis are compared, and shown that the introduced de-trending method is effective for eliminating the biases due to sampling errors and non-homogeneity.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000870/pdfft?md5=0ec97a36caed11ae8db808b1f81b6ecb&pid=1-s2.0-S0167473024000870-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784125","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}

引用次数: 0

Multi-hazard life-cycle consequence analysis of deteriorating engineering systems 老化工程系统的多危害生命周期后果分析

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-07-20 DOI: 10.1016/j.strusafe.2024.102515

{"title":"Multi-hazard life-cycle consequence analysis of deteriorating engineering systems","authors":"","doi":"10.1016/j.strusafe.2024.102515","DOIUrl":"10.1016/j.strusafe.2024.102515","url":null,"abstract":"<div><p>Probabilistic life-cycle consequence (LCCon) analysis (e.g., assessment of repair costs, downtime, or casualties over an asset’s service life) can enable optimal life-cycle management of critical assets under uncertainties. This can lead to effective risk-informed decision-making for future disaster management (i.e., risk mitigation and/or resilience-enhancing strategies/policies) implementation. Nevertheless, despite recent advances in understanding, modeling, and quantifying multiple-hazard (or multi-hazard) interactions, most available LCCon analytical formulations fail to accurately compute the exacerbated consequences which may stem from incomplete or absent repair actions between different interacting hazard events. This paper introduces a discrete-time, discrete-state Markovian framework for efficient multi-hazard LCCon analysis of deteriorating engineering systems (e.g., buildings, infrastructure components) that appropriately accounts for complex interactions between natural hazard events and their effects on a system’s performance. The Markovian assumption is used to model the probability of a system being in any performance level (i.e., limit state) after multiple hazard events inducing either instantaneous and/or gradual deterioration and after potential repair actions through implementing stochastic (transition) matrices. LCCon estimates are then obtained by combining limit state probabilties with suitable system-level consequence models in a computationally efficient manner. The proposed framework is illustrated for two case studies subject to earthquake and flood events as well as environment-induced corrosion during their service life. The first is a reinforced concrete building and the second is a simple transportation road network with a reinforced concrete bridge.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000869/pdfft?md5=4e87be0fce91bbe78884df44f5ad9163&pid=1-s2.0-S0167473024000869-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848965","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}

引用次数: 0

A distributionally robust data-driven framework to reliability analysis 分布稳健的数据驱动可靠性分析框架

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-07-17 DOI: 10.1016/j.strusafe.2024.102501

{"title":"A distributionally robust data-driven framework to reliability analysis","authors":"","doi":"10.1016/j.strusafe.2024.102501","DOIUrl":"10.1016/j.strusafe.2024.102501","url":null,"abstract":"<div><p>This paper proposes a reliability analysis framework that accounts for the error caused by characterizing a data set as a probabilistic model. To this end we model the uncertain parameters as a probability box (p-box) of Sliced-Normal (SN) distributions. This class of distributions enables the analyst to characterize complex parameter dependencies with minimal modeling effort. The p-box, which spans the maximum likelihood and the moment-bounded maximum entropy estimates, yields a range of failure probability values. This range shrinks as the amount of data available increases. In addition, we leverage the semi-algebraic nature of the SNs to identify the most likely points of failure (MLPs). Such points allow the efficient estimation of failure probabilities using importance sampling. When the limit state functions are also semi-algebraic, semidefinite programming is used to guarantee that the computed MLPs are correct and complete, therefore ensuring that the resulting reliability analysis is accurate. This framework is applied to the reliability analysis of a truss structure subject to deflection and weight requirements.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000729/pdfft?md5=22e47572f7a330944052d4ec2aa801cb&pid=1-s2.0-S0167473024000729-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783997","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}

引用次数: 0

IM-based seismic reliability assessment of the pre-code masonry building stock in metropolitan area of Lisbon 基于 IM 的里斯本大都会区规范前砌体建筑群抗震可靠性评估

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-07-14 DOI: 10.1016/j.strusafe.2024.102514

{"title":"IM-based seismic reliability assessment of the pre-code masonry building stock in metropolitan area of Lisbon","authors":"","doi":"10.1016/j.strusafe.2024.102514","DOIUrl":"10.1016/j.strusafe.2024.102514","url":null,"abstract":"<div><p>Earthquakes have a long history of causing catastrophic damage to communities, resulting in structural collapses, loss of life, and economic turmoil. To enable informed decision-making and reduce the impact of these events in earthquake-prone regions, seismic risk studies provide relevant information to support stakeholders in the implementation of effective risk-based policies. The present work addresses the seismic reliability of the pre-code masonry building stock in the Metropolitan Area of Lisbon, which is the region of Portugal that faces the highest seismic risk due to the coexistence of moderate to high seismic hazard and highest demographic-economic exposure. The adopted general framework combines several hazard studies developed for the region under investigation and a synthetic database of masonry buildings representative of the pre-code building stock in Lisbon. Through analytical–numerical probabilistic approaches, new second-order hazard solutions with structural dependency are derived for the mean annual frequency of limit-state exceedance, which can be integrated into national application documents for Eurocode 8. In light of these results, the reliability assessment of the building stock is conducted in several Local Administrative Units by means of an improved SAC/FEMA formulation. The study represents the first comprehensive investigation of its kind in this region, providing essential information to define appropriate target safety level for code calibration and support future risk studies.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000857/pdfft?md5=604c8770fad125992c0d247e9943cfda&pid=1-s2.0-S0167473024000857-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141637897","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}

引用次数: 0

Hierarchical Bayesian modeling of highway bridge network extreme traffic loading 公路桥梁网络极端交通负荷的分层贝叶斯建模

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-07-14 DOI: 10.1016/j.strusafe.2024.102503

{"title":"Hierarchical Bayesian modeling of highway bridge network extreme traffic loading","authors":"","doi":"10.1016/j.strusafe.2024.102503","DOIUrl":"10.1016/j.strusafe.2024.102503","url":null,"abstract":"<div><p>The road network consists of bridges of various lengths and configurations, all of which require accurate prediction of traffic load within their lifetime. However, current prediction methods are limited to modeling and predicting traffic load for a handful of individual bridges only; no method can simultaneously model and predict the traffic load of all bridges within an entire road network. Further, conventional models neglect the information that exists in the traffic load effect data established for different bridges, leading to large estimation uncertainties for each bridge and load effect examined. This study proposes a hierarchical Bayesian model that can estimate the traffic load effect of multiple bridges simultaneously, and subsequently create predictions for the remaining (unexamined) bridges within the road network. The proposed model is demonstrated using the traffic load data and influence lines used in the background study for the Eurocode 1 Load Model 1. The results show significant reductions in prediction uncertainties, better fits as measured by leave-one-out statistics, more robust fits against extremes, and the emergence of intuitive correlation structures between different bridges’ traffic loads that are absent in conventional models. This paper also presents a potential new strategy to reduce estimation uncertainty, and a method to predict parameters and return levels for bridges across an entire network made possible by the proposed hierarchical Bayesian model.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000742/pdfft?md5=f14dc58ccd8879f52aeaf0ae6661d703&pid=1-s2.0-S0167473024000742-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141716116","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}

引用次数: 0