桥梁结构时变可靠度分析计算过程研究

现今桥梁结构可靠度的计算的方法有很多,但是大多数都只是关于桥梁结构时不变可靠度的,所谓的时变可靠度的计算,是针对结构抗力在基准期内是否发生变化的一种称谓。一旦涉及抗力的衰减,桥梁结构的可靠度的计算分析过程就更加复杂起来了,令很多初学者难以理清思路,因此本文介绍了桥梁结构时变可靠度的计算分析过程,以供参考。     

抗风结构随机时变动力可靠度分析

本文研究了结构的随机时变特性及其随机时变强度的衰减规律。分析了随机时变对构在随机动力茶载作用下的动力反应，基于时变界限模式及首次超越破准则，提出了抗风结构的随机时变动力可靠度分析方法。     

结构时变动力可靠度的实用计算方法

本文在结构可抗力的时变性进一步明确分类的基础上，从最一般的情况出发，推导了考虑结构抗力时变性，随机性和模糊性的结构动力可靠性的公式，通过对交差事件机理的讨论，提出了时变可靠度的简化计算，并借助泊松过程法的特点，采用新的思路处理界限的模糊性，所得到的公式便于实际应用，同时还给出了地模糊可靠度取值范围的估计。     

钢闸门结构时变抗力模型及其可靠度分析

根据现有文献资料,对影响钢闸门结构构件抗力衰减的主要因素钢材锈蚀进行了统计分析,得到了锈蚀规律。在此基础上,进一步分析得到了闸门构件抗力的衰减模型。并以受弯构件为例,对φ(t)是确定性函数和随机变量、荷载是时不变和时变情况的可靠度进行了分析计算,从而为现役闸门结构使用年限的预测和维修加固奠定了基础。这些工作目前在国内还是首次。     

抗风结构时变动力可靠性理论

本文将可靠性界限分为四类：确定性，随机性，模糊与模糊随机界限，而将结构抗力的时变性质分为两类：在一次强风过程中的时变性与整个使用时期限中的时变性，与上述分类相对应，建立了一整套动力可靠性分析公式。     

考虑非平稳车载过程的在役桥梁时变可靠度分析

近年来,我国有大批桥梁得以新建。与此同时,日益增加的车辆荷载使得在役桥梁面临着更大的服役风险。在已有研究中,桥梁结构的可靠度评估将车载随机过程看作平稳泊松过程,且假定荷载效应概率模型在评估期内独立同分布。历史数据表明,我国的汽车荷载存在明显的逐年增加的趋势,随时间不再服从同一分布,已有方法也不再适用。为此,该文改进了已有方法,提出了考虑非平稳车载过程的在役桥梁时变可靠度评估新方法。算例表明,改进的方法简便易行,结果准确。已有方法可看作是该文改进方法的特例。随后,利用这一方法研究了结构可靠度对车载随机过程参数的敏感性。结果表明,车载随机过程参数对结构可靠度的影响显著。在20年的评估期内,均值每年增加1.1%或标准差每年增加1.3%,都会造成结构的失效概率增大两倍。     

桥梁结构时变可靠度计算的新方法

为提高桥梁结构时变可靠度的计算效率,该文提出了新的计算方法。该方法基于结构时变可靠度分析的基本理论,利用Taylor级数展开,将可靠度计算从传统的积分运算形式转换为代数运算的形式,从而简化计算过程,提高计算效率。为了考虑桥梁初始承载力和衰减过程的不确定性,该文基于全概率方法,给出了计算劣化结构时变可靠度的显式公式。通过将该文方法应用于某钢筋混凝土桥梁进行时变可靠度分析,并与Monte Carlo模拟方法进行对比,表明了该文方法的准确性和高效性。     

结构灰色可靠度计算方法研究

本文提出了结构的灰色抗力，灰色荷载和灰色可靠度的概念，并对这些灰概念的白色过程及灰色可靠度的计算分析进行了阐述，示你计算了结果与实际情况十分吻合，说明文中提出了计算结果灰色可靠度的方法是正确的。     

随机参数结构的地震可靠度分析

本文利用随机有限元法对具有随机参数的结构进行地震可靠度分析。     

既有钢筋混凝土桥梁构件抗力模糊随机时变概率模型研究

构件的抗力概率模型是进行桥梁结构时变可靠性研究的基础之一。既有钢筋混凝土桥梁材料的老化与损伤情况复杂使其抗力同时具有随机性、模糊性和时变性是一个模糊随机过程。在分析影响既有钢筋混凝土桥梁构件抗力不确定性因素的基础上,考虑桥梁在服役过程中的耐久性损伤对构件抗力的影响,在常规方法只能考虑抗力随机时变性基础上,进一步考虑模糊性,结合实测数据和现有资料建立了既有钢筋混凝土桥梁中混凝土强度、钢筋截面积和钢筋强度模糊随机时变模型,进而研究了在不修复情况下桥梁构件抗力模糊时变概率模型,分析了抗力平均值和标准差随时间和阈值变化的规律,并以受弯构件为例给出了具体分析过程和结果。     

Reliability-based design optimization under stationary stochastic process loads

Time-dependent reliability-based design ensures the satisfaction of reliability requirements for a given period of time, but with a high computational cost. This work improves the computational efficiency by extending the sequential optimization and reliability analysis (SORA) method to time-dependent problems with both stationary stochastic process loads and random variables. The challenge of the extension is the identification of the most probable point (MPP) associated with time-dependent reliability targets. Since a direct relationship between the MPP and reliability target does not exist, this work defines the concept of equivalent MPP, which is identified by the extreme value analysis and the inverse saddlepoint approximation. With the equivalent MPP, the time-dependent reliability-based design optimization is decomposed into two decoupled loops: deterministic design optimization and reliability analysis, and both are performed sequentially. Two numerical examples are used to show the efficiency of the proposed method.     

基于随机无网格伽辽金法和蚂蚁算法的结构可靠性分析

 用摄动随机无网格伽辽金法(PSEFGM)求解随机结构的响应,然后采用蚂蚁算法对结构可靠性进行了分析。摄动随机无网格伽辽金法具有不需要划分单元和精度高等特点。蚂蚁算法是一种智能型随机搜素优化算法,对目标函数没有任何可微甚至连续的要求,可有效克服经典算法易于陷入局部最优解的常见弊病。数值实例表明,在随机结构可靠性分析方面,随机无网格迦辽金法与蚂蚁算法比经典算法具有明显的优势。     

A response surface method based on weighted regression for structural reliability analysis

Approximation methods such as the response surface method (RSM) are widely used to alleviate the computational burden of engineering analyses. For reliability analysis, the common approach in the RSM is to use regression methods based on least square methods. However, for structural reliability problems, RSMs should approximate the performance function around the design point where its value is close to zero. Therefore, in this study, a new response surface called ADAPRES is proposed, in which a weighted regression method is applied in place of normal regression. The experimental points are also selected from the region where the design point is most likely to exist. Examples are given to demonstrate the benefit of the proposed method for both numerical and implicit performance functions.     

多种随机载荷下的结构动态可靠性计算

考虑随机载荷的加载形式分为动载荷累加,等幅且服从参数为 的泊松分布,和载荷随时间变化且不服从任何分布的三种情况,利用应力-强度干涉理论和随机过程理论分别建立了结构强度退化情况下的结构动态可靠性预测的三种计算公式。该公式形式简单、便于计算,且可获得结构的可靠度随时间变化的规律。最后通过算例说明文中计算公式的合理性,可行性,实用性。     

Efficient structural reliability analysis via a weak-intrusive stochastic finite element method

This paper presents a novel methodology for structural reliability analysis by means of the stochastic finite element method (SFEM). The key issue of structural reliability analysis is to determine the limit state function and corresponding multidimensional integral that are usually related to the structural stochastic displacement and/or its derivative, e.g., the stress and strain. In this paper, a novel weak-intrusive SFEM is first used to calculate structural stochastic displacements of all spatial positions. In this method, the stochastic displacement is decoupled into a combination of a series of deterministic displacements with random variable coefficients. An iterative algorithm is then given to solve the deterministic displacements and the corresponding random variables. Based on the stochastic displacement obtained by the SFEM, the limit state function described by the stochastic displacement (and/or its derivative) and the corresponding multidimensional integral encountered in reliability analysis can be calculated in a straightforward way. Failure probabilities of all spatial positions can be obtained at once since the stochastic displacements of all spatial points have been known by using the proposed SFEM. Furthermore, the proposed method can be applied to high-dimensional stochastic problems without any modification. One of the most challenging problems encountered in high-dimensional reliability analysis, known as the curse of dimensionality, can be circumvented with great success. Three numerical examples, including low- and high-dimensional reliability analysis, are given to demonstrate the good accuracy and the high efficiency of the proposed method.     

A time-variant reliability analysis method for structural systems based on stochastic process discretization

In this paper, we propose a new method for analyzing time-variant system reliability based on stochastic process discretization, which provides an effective tool for reliability design of many relatively complex structures considering the whole lifecycle. Within a design lifetime, the stochastic process is discretized into a series of random variables, and meanwhile, we can derive a time-invariant limit-state function in each time interval; the discretized random variables from the stochastic processes and the original random variables are transformed to the independent normal space, and a conventional time-invariant system reliability problem is derived through the linearization to each discretized limit-state functions; by solving this time-invariant system reliability problem, we can obtain the structural reliability or failure probability within the design lifetime. Finally, in this paper, we provide four numerical examples to verify the effectiveness of the method.     

梁结构刚度动态非概率可靠性分析

 为了定量分析在疲劳载荷作用下梁在不同寿命期内刚度的可靠性,建立梁结构物理性能退化的精确公式就十分重要.依据疲劳载荷造成的累积损伤对材料极限应力的影响,基于材料剩余强度模型,利用材料强度与弹性模量之间的关系,推导出结构弹性模量的退化表达式,并在此基础上,提出梁弹性模量退化系数的递推表达式,推导出圆截面梁剩余抗弯刚度的表达式.在对结构可靠性分析时,概率可靠性模型和模糊可靠性模型对于原始数据信息要求较高.为了充分利用结构的不确定性信息弥补原始数据的不足,将梁的初始弹性模量及所受的疲劳载荷等看作区间变量,利用区间模型建立基于刚度退化的梁刚度动态非概率可靠性模型.最后,结合工程实例的计算表明了该方法对梁的刚度退化分析及其刚度动态可靠性分析是可行、有效和合理的.     

An efficient and unbiased method for sensitivity analysis of stochastic reaction networks

We consider the problem of estimating parameter sensitivity for Markovian models of reaction networks. Sensitivity values measure the responsiveness of an output with respect to the model parameters. They help in analysing the network, understanding its robustness properties and identifying the important reactions for a specific output. Sensitivity values are commonly estimated using methods that perform finite-difference computations along with Monte Carlo simulations of the reaction dynamics. These methods are computationally efficient and easy to implement, but they produce a biased estimate which can be unreliable for certain applications. Moreover, the size of the bias is generally unknown and hence the accuracy of these methods cannot be easily determined. There also exist unbiased schemes for sensitivity estimation but these schemes can be computationally infeasible, even for very simple networks. Our goal in this paper is to present a new method for sensitivity estimation, which combines the computational efficiency of finite-difference methods with the accuracy of unbiased schemes. Our method is easy to implement and it relies on an exact representation of parameter sensitivity that we recently proved elsewhere. Through examples, we demonstrate that the proposed method can outperform the existing methods, both biased and unbiased, in many situations.