Structural Safety最新文献_第10页

A conditional extreme value distribution method for dynamic reliability analysis of stochastic structures 随机结构动力可靠度分析的条件极值分布法

IF 5.8 1区工程技术

Structural Safety Pub Date : 2023-10-28 DOI: 10.1016/j.strusafe.2023.102398

Ye-Yao Weng , Xuan-Yi Zhang , Zhao-Hui Lu , Yan-Gang Zhao

{"title":"A conditional extreme value distribution method for dynamic reliability analysis of stochastic structures","authors":"Ye-Yao Weng , Xuan-Yi Zhang , Zhao-Hui Lu , Yan-Gang Zhao","doi":"10.1016/j.strusafe.2023.102398","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102398","url":null,"abstract":"<div><p>An efficient post-processing simulation method is proposed to estimate small failure probabilities of stochastic dynamic structures involving the inherent randomness of structural physical-geometrical parameters and external excitations. To extract a small failure probability, the proposed method introduces an intermediate event to represent the realizations of extreme structural response in the tail of distribution. With the aid of this intermediate event, structural failure probability is reformulated as a product of the event’s occurrence probability and the conditional exceedance probability of extreme response when the event occurs. The latter corresponds to a distribution of extreme response under the condition of the intermediate event, referred to as the conditional extreme value distribution (CEVD). Accordingly, the proposed method is termed the CEVD method. To reconstruct the CEVD, a truncated shifted generalized lognormal distribution model is employed. Bayesian estimation method is utilized to determine the two shape parameters of this model based on the samples of both original extreme value distribution and CEVD, where the CEVD samples are generated by Markov chain Monte Carlo sampling. The efficiency and accuracy of the proposed method are demonstrated through two numerical examples considering seismic reliability analyses of a 10-story nonlinear frame and a soil-foundation-structure interaction system.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102398"},"PeriodicalIF":5.8,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91686565","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

A multi-fidelity stochastic simulation scheme for estimation of small failure probabilities 小故障概率估计的多保真度随机模拟方案

IF 5.8 1区工程技术

Structural Safety Pub Date : 2023-10-25 DOI: 10.1016/j.strusafe.2023.102397

Min Li , Srinivasan Arunachalam , Seymour M.J. Spence

{"title":"A multi-fidelity stochastic simulation scheme for estimation of small failure probabilities","authors":"Min Li , Srinivasan Arunachalam , Seymour M.J. Spence","doi":"10.1016/j.strusafe.2023.102397","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102397","url":null,"abstract":"<div><p>Computing small failure probabilities is often of interest in the reliability analysis of engineering systems. However, this task can be computationally demanding since many evaluations of expensive high-fidelity models are often required. To address this, a multi-fidelity approach is proposed in this work within the setting of stratified sampling. The overall idea is to reduce the required number of high-fidelity model runs by integrating the information provided by different levels of model fidelity while maintaining accuracy in estimating the failure probabilities. More specifically, strata-wise multi-fidelity models are established based on Gaussian process models to efficiently predict the high-fidelity response and the system collapse from the low-fidelity response. Due to the reduced computational cost of the low-fidelity models, the multi-fidelity approach can achieve a significant speedup in estimating small failure probabilities associated with high-fidelity models. The effectiveness and efficiency of the proposed multi-fidelity stochastic simulation scheme are validated through an application to a two-story two-bay steel building under extreme winds.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102397"},"PeriodicalIF":5.8,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91686949","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

Relaxation-based importance sampling for structural reliability analysis 基于松弛的结构可靠性分析重要性抽样

IF 5.8 1区工程技术

Structural Safety Pub Date : 2023-10-03 DOI: 10.1016/j.strusafe.2023.102393

Jianhua Xian, Ziqi Wang

{"title":"Relaxation-based importance sampling for structural reliability analysis","authors":"Jianhua Xian, Ziqi Wang","doi":"10.1016/j.strusafe.2023.102393","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102393","url":null,"abstract":"<div><p><span>This study presents an importance sampling formulation based on adaptively relaxing parameters from the indicator function and/or the probability density function<span>. The formulation embodies the prevalent mathematical concept of relaxing a complex problem into a sequence of progressively easier sub-problems. Due to the flexibility in constructing relaxation parameters, relaxation-based importance sampling provides a unified framework for various existing variance reduction techniques, such as subset simulation, sequential importance sampling, and annealed importance sampling. More crucially, the framework lays the foundation for creating new importance sampling strategies, tailoring to specific applications. To demonstrate this potential, two importance sampling strategies are proposed. The first strategy couples annealed importance sampling with subset simulation, focusing on low-dimensional problems. The second strategy aims to solve high-dimensional problems by leveraging spherical sampling and scaling techniques. Both methods are desirable for fragility analysis in performance-based engineering, as they can produce the entire fragility surface in a single run of the sampling algorithm. Three numerical examples, including a 1000-dimensional </span></span>stochastic dynamic problem, are studied to demonstrate the proposed methods.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102393"},"PeriodicalIF":5.8,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49699006","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: 3

Probabilistic model of traffic scenarios for extreme load effects in long-span bridges 大跨度桥梁极端荷载作用下交通情景的概率模型

IF 5.8 1区工程技术

Structural Safety Pub Date : 2023-09-29 DOI: 10.1016/j.strusafe.2023.102382

Xuejing Wang , Xin Ruan , Joan R. Casas , Mingyang Zhang

{"title":"Probabilistic model of traffic scenarios for extreme load effects in long-span bridges","authors":"Xuejing Wang , Xin Ruan , Joan R. Casas , Mingyang Zhang","doi":"10.1016/j.strusafe.2023.102382","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102382","url":null,"abstract":"<div><p>The traffic scenarios that may cause extreme load effects are of great importance to the safety assessment of bridge structures. The traditional simulation method of traffic flow cannot depict the distribution pattern of vehicles on the bridge deck when the maximum effect is induced. In this paper, a probabilistic Gaussian mixture model (GMM) for heavy vehicle scenarios on the bridge deck under free-flow condition is proposed for long-span bridges based on collected Weigh in Motion (WIM) data. The scenarios of extreme response under free-flow occur more frequently than congestion scenarios and are of similar value and relevance in the daily management and safety assessment of long-span bridges.</p><p>A non-stationary Poisson process is utilized to simulate the uneven occurrence of heavy vehicles in different lanes, and it is assumed that they are located within the artificially defined cells on the bridge deck. Then, Nataf transformation is employed to consider the correlation of gross vehicle weights (GVWs) within close range in the same lane. The numerical study is carried out on a long-span cable-stayed bridge to investigate the effects of correlation in GVWs and stationarity of vehicle distribution location on the structural responses. The load responses calculated by the proposed model and Monte Carlo method for different effects are compared with the values derived from code model. The results show that with the increase of the correlation level of the neighboring GVWs, the simulated responses are more prone to get extreme values, which means an increasing probability of the most unfavorable spatial distribution of on-bridge vehicles. The same results are also found under the non-stationary simulation state for vehicle location. The non-stationary Poisson process provides an efficient, highly feasible method, which is also in the safe side, for simulating the vehicle spatial distribution for specific effects.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102382"},"PeriodicalIF":5.8,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49877424","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

Soft Monte Carlo Simulation for imprecise probability estimation: A dimension reduction-based approach 不精确概率估计的软蒙特卡罗模拟：一种基于降维的方法

IF 5.8 1区工程技术

Structural Safety Pub Date : 2023-09-27 DOI: 10.1016/j.strusafe.2023.102391

Azam Abdollahi , Hossein Shahraki , Matthias G.R. Faes , Mohsen Rashki

{"title":"Soft Monte Carlo Simulation for imprecise probability estimation: A dimension reduction-based approach","authors":"Azam Abdollahi , Hossein Shahraki , Matthias G.R. Faes , Mohsen Rashki","doi":"10.1016/j.strusafe.2023.102391","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102391","url":null,"abstract":"<div><p><span>This paper proposes an efficient solution for solving hybrid reliability problems involving random and interval variables. To meet this aim, using the soft Monte Carlo (SMC) method, a solution is proposed that breaks the random variables space into local 1-D coordinates and then, considers 1-D coordinate as an additional dimension of interval variables. Accordingly, using an optimization in increased interval variables space, the upper and lower bounds of failure probability for each 1-D problem are estimated. In addition, the total failure probabilities are presented as the </span>mathematical expectation<span> of the obtained probability bounds for 1-D coordinates. Then, it is shown that this approach is fit for application of univariate dimension reduction method to reduce the function calls of analysis in the optimization phase. This approach is validated by solving benchmark reliability problems as well as the application of the proposed method for solving real world engineering problems investigated by solving hybrid reliability analysis of reinforced concrete columns. It is shown that the proposed approach efficiently approximates the failure probability bound of problems with moderate nonlinear limit state functions with high accuracy.</span></p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102391"},"PeriodicalIF":5.8,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49698997","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

A novel Bayesian-inference-based method for global sensitivity analysis of system reliability with multiple failure modes 一种新的基于贝叶斯推理的多失效模式系统可靠性全局灵敏度分析方法

IF 5.8 1区工程技术

Structural Safety Pub Date : 2023-09-23 DOI: 10.1016/j.strusafe.2023.102394

Qiangqiang Zhao, Tengfei Wu, Jinyan Duan, Jun Hong

{"title":"A novel Bayesian-inference-based method for global sensitivity analysis of system reliability with multiple failure modes","authors":"Qiangqiang Zhao, Tengfei Wu, Jinyan Duan, Jun Hong","doi":"10.1016/j.strusafe.2023.102394","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102394","url":null,"abstract":"<div><p><span>Global reliability sensitivity analysis aims at quantifying the effects of each random source on failure probability or reliability over their whole distribution range and is highly concerned in reliability design and uncertainty control. And in practice, a structure or product usually has more than one component impacting their performance safety, which is essentially a system reliability problem. Therefore, this paper proposes a novel Bayesian-inference-based method for moment-based global sensitivity analysis of system reliability with multiple failure modes. First, the limit-state function of each component involved in the system is linearly approximated based on the reliability index. Then, the global reliability sensitivity is transformed into a problem of multivariable Gaussian probability within a given safe region where the dimension number is double of the failure modes. In this case, the Bayesian-inference-driven expectation propagation technique is introduced to solve this intractable problem in an analytical manner, based on which the closed-form solution to the global reliability sensitivity for system with multiple components is accordingly derived. Finally, a numerical case, a vehicle subjected to impact, a cantilever beam and a practical </span>engineering application to a four-panel spaceborne deployable plane antenna are studied to demonstrate the effectiveness of the proposed method by comparison with Monte Carlo simulation.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102394"},"PeriodicalIF":5.8,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49698994","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

Numerical algorithm for determining serviceability live loads and its applications 确定活荷载可使用性的数值算法及其应用

IF 5.8 1区工程技术

Structural Safety Pub Date : 2023-09-20 DOI: 10.1016/j.strusafe.2023.102383

Chi Xu , Jun Chen , Jie Li

{"title":"Numerical algorithm for determining serviceability live loads and its applications","authors":"Chi Xu , Jun Chen , Jie Li","doi":"10.1016/j.strusafe.2023.102383","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102383","url":null,"abstract":"<div><p>The live load duration refers to the period when the live load is larger than a given threshold in the reference period. The smallest threshold that allows the duration to be shorter than the required length is employed as the design live load for serviceability<span> limit states. However, the traditional method only considers the mean duration and the probability that the duration exceeds the required length is unknown. This study proposes a new algorithm to determine the probability distributions of the live load duration. A sustained or extraordinary load process is transformed into a random variable set based on the stochastic harmonic functions. Subsequently, the duration distributions can be derived by employing the load coincidence principle and probability density evolution method. Three numerical examples including one sustained load and multiple extraordinary loads are provided and the results of the proposed algorithm are compared with those of Monte Carlo simulation. The proposed algorithm allows the exact determination of design live loads based on a predefined exceeding probability. As an application, the quasi-permanent and frequent values of seven user categories are calculated when the exceeding probabilities are taken as 10%, 5% and 2%, respectively. It is found that the quasi-permanent values can increase with increasing area and the differences between the frequent and quasi-permanent values can be more than 20 times.</span></p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102383"},"PeriodicalIF":5.8,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49698977","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: 1

Mixed Bayesian Network for reliability assessment of RC structures subjected to environmental actions 环境作用下RC结构可靠性评估的混合贝叶斯网络

IF 5.8 1区工程技术

Structural Safety Pub Date : 2023-09-16 DOI: 10.1016/j.strusafe.2023.102392

Hongyuan Guo , You Dong , Emilio Bastidas-Arteaga

{"title":"Mixed Bayesian Network for reliability assessment of RC structures subjected to environmental actions","authors":"Hongyuan Guo , You Dong , Emilio Bastidas-Arteaga","doi":"10.1016/j.strusafe.2023.102392","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102392","url":null,"abstract":"<div><p>Under environmental action, reinforced concrete (RC) structures might suffer from reinforcement corrosion caused by the surrounding environment, dramatically reducing structural reliability and threatening social development. However, most of the existing reliability assessment methods for RC structures only focused on the structural performance at the design stage given the original unchanged environment, ignoring the effects of realistic exposure conditions and inspection results on reliability evaluation. Thus, this paper develops a general reliability assessment framework based on a Mixed Bayesian network (MBN), incorporating three modules, i.e., durability assessment, load-bearing capacity analysis, and time-dependent reliability analysis. In MBN, separate sub-BNs are built based on different modules and connected by pinch point variables where probabilistic information is transmitted via soft evidence. Besides, this framework considers time-dependent environmental parameters and two-dimensional chloride transport and their effects on reliability. Meanwhile, adjustment coefficients are applied to improve the results of the analytical mechanical model with respect to different limit states through the finite element model (FEM). The proposed MBN framework is illustrated for a corroded RC beam under a marine atmospheric environment to investigate the effects of environmental modeling, chloride transport patterns, and concrete crack inspection on reliability assessment. The results indicate that under the assumed conditions in the case study, early inspection of large cracks may significantly overestimate the failure probability by about 500%. Besides, failure probability might be underestimated by about 95%, ignoring the time-variant environment and two-dimensional chloride transport.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102392"},"PeriodicalIF":5.8,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49698986","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

AK-SEUR: An adaptive Kriging-based learning function for structural reliability analysis through sample-based expected uncertainty reduction AK-SEUR:一种基于kriging的基于样本的期望不确定性缩减的结构可靠性分析自适应学习函数

IF 5.8 1区工程技术

Structural Safety Pub Date : 2023-09-09 DOI: 10.1016/j.strusafe.2023.102384

Changle Peng , Cheng Chen , Tong Guo , Weijie Xu

{"title":"AK-SEUR: An adaptive Kriging-based learning function for structural reliability analysis through sample-based expected uncertainty reduction","authors":"Changle Peng , Cheng Chen , Tong Guo , Weijie Xu","doi":"10.1016/j.strusafe.2023.102384","DOIUrl":"https://doi.org/10.1016/j.strusafe.2023.102384","url":null,"abstract":"<div><p>Reliability Analysis (RA) is a critical aspect of structural design and performance evaluation aiming to determine the probability of structural failure under given random input parameters. With modern development of modeling techniques, computational models have achieved higher fidelity but at the increased cost of computational time, which poses a significant challenge for RA. Consequently, surrogate model-assisted RA has been explored as a means of improved efficiency and accuracy. This study proposes a novel learning function, Sample-based Expected Uncertainty Reduction (SEUR), for surrogate model-assisted RA. The SEUR function uses statistical information from the metamodeling with fixed hyper-parameters to construct expected failure probability bounds to sequentially update the design of experiment (DoE). The joint probability densities of input variables are accounted for through simulation methods, including Monte Carlo (MC) and subset simulation (SS). Furthermore, the discrete simulated annealing algorithm is used to search for the optimal design point. The performance of proposed AK-SEUR function is systematically evaluated using six examples of different dimensions, failure probability levels and nonlinearities. The AK-SEUR function is demonstrated to be more effective and efficient than other popular active learning methods in dealing with nonlinear performance functions, small probabilities, and complex limit states. The proposed SEUR function has the potential to improve the efficiency and accuracy of RA, particularly in situations where computational models are time-consuming and the search for the optimal solution is challenging.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"106 ","pages":"Article 102384"},"PeriodicalIF":5.8,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49698974","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: 1

Importance ranking of correlated variables in one analysis 一次分析中相关变量的重要性排序

IF 5.8 1区工程技术

Structural Safety Pub Date : 2023-09-01 DOI: 10.1016/j.strusafe.2023.102363

Terje Haukaas

{"title":"Importance ranking of correlated variables in one analysis","authors":"Terje Haukaas","doi":"10.1016/j.strusafe.2023.102363","DOIUrl":"10.1016/j.strusafe.2023.102363","url":null,"abstract":"<div><p><span>This paper addresses the problem of ranking correlated random variables according to relative importance. The importance of a variable derives from its influence on the variability of the response from a model. Applications include any input–output model for which response derivatives are available from each response analysis. Structural analysis models, i.e., </span>finite element models<span>, represent the specific motivation for this paper. The response derivatives are collected in a vector and transformed into a standardized parameter space. Points along that vector are transformed back to the original parameter space and utilized for the purpose of model insights and parameter ranking. Comparisons are made with the first-order Sobol sensitivity index, which requires sampling instead of the proposed single-analysis approach. Results suggest that the proposed importance measure matches the first-order Sobol index in many situations. However, for pure multiplicative “interaction” models, the first-order Sobol index tends to be anchored at the zero-correlation case. In contrast, the proposed measures are sensitive to correlation and the effect of correlation can be significant.</span></p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"104 ","pages":"Article 102363"},"PeriodicalIF":5.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49496077","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