Integrated system identification and reliability evaluation of stochastic building structures

System identification and reliability evaluation play a significant role in structural health monitoring to ensure the serviceability and safety of existing structures. Although the development of system identification methods has attained much attention and some degree of maturity, reliability evaluation of existing structures still remains a challenging problem especially when uncertainties in measurement data and inherent randomness, which are inevitably involved in civil structures, are considered. In this regard, this paper presents a framework for integrated system identification and reliability evaluation of stochastic building structures. Two algorithms are proposed to respectively evaluate component reliability and system reliability of stochastic building structures by combining a statistical moment-based system identification method and a probability density evolution equation-based reliability evaluation method. System identification is embedded in the procedure of reliability evaluation of a stochastic building structure. The uncertainties in both the structure and the external excitation are considered. Numerical examples show that the structural component and system reliabilities of a three-story shear building structure with three damage scenarios can be effectively evaluated by the proposed methods.     

考虑场地效应的建筑群动力可靠度PDEM评估

城市建筑群的动力可靠度评估对于区域防灾减灾具有重要意义。场地效应会导致地震动的空间变异性和土-结构相互作用效应,进而显著影响区域建筑群的非线性地震响应,而确定性区域震害模拟方法仍不足以准确反映随机建筑群动力系统的整体性态。该研究引入实测地震动场和土-结构相互作用效应,发展了建筑群系统非线性地震响应时域求解方法,并遵循概率守恒假定,建立基于概率密度演化方法(PDEM)的建筑群系统动力可靠度评估框架。以某高层框架结构建筑群为例,进行了场地效应下确定性建筑群非线性地震响应分析,进而完成了随机建筑群动力可靠度评估,并评估了场地效应对建筑群系统动力可靠度影响,得到了有益结论。     

可靠度对随机变量及失效模式相关系数的敏感度分析及其工程应用

客观存在的诸多不确定性因素使得工程不可避免地存在着风险。随着重大工程的不断涌现,人们越来越关心如何提高工程系统的安全度,降低工程结构的风险。遗憾的是如何降低工程系统的风险直至目前还没有固定的科学方法,大多是基于工程师的经验。以可靠度理论为基础,从可靠度对工程结构的某些参数的敏感性及可靠度对失效模式的相关系数入手,探讨如何从加强或削弱结构参数及失效模式间的相关性的角度出发,来进行降低结构风险的研究。给出了两个算例来说明方法在工程中的应用。     

A generic method for estimating system reliability using Bayesian networks

This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples.     

Energy-based collapse assessment of concrete structures subjected to random damage evolutions

The assessment of structural capacity against collapse is conducive to the optimal design of new structures as well as checking the safety of existing structures. However, the evaluation cannot be typically carried out by means of destructive tests on prototype or reduced scale structures. In this regard, the numerical models that adequately represent the prototype structures can be alternatively used. Specifically, both the nonlinearities and randomness as well as their coupling effect of materials need to be represented in a unified manner in structural analysis. The present paper aims at providing an effective approach to incorporate the stochastic nature of damage constitutive relationships in collapse analysis and assessment of concrete structures subjected to earthquake ground motions. Within the framework of stochastic damage mechanics, the spatial variability of concrete is represented by a two-scale stationary random fields. The concept of covariance constraint is introduced to bridge the two-scale random fields such that the scale-of-fluctuation of the random material property is satisfied at both scales. Random damage evolution induced structural collapse analysis is achieved via the nonlinear stochastic finite element method. To address the randomness propagation across scales, the probability density evolution method is employed. By exerting the absorbing boundary condition associated with an energy-based collapse criterion on the generalized probability density evolution equation, the anti-collapse reliability of concrete structures can be evaluated with fair accuracy and efficiency. Numerical investigation regarding an actual high-rise reinforced concrete frame-shear wall structure indicates that the random damage evolution of concrete dramatically affects the structural nonlinear behaviors and even leads to entirely different collapse modes. The proposed method provides a systematic treatment of both uncertainties and nonlinearities in collapse assessment of complex concrete structures.     

随机有限元-最大熵法

本文提出一种用于结构可靠性分析的随机有限元-最大熵法。它是利用随机有限元法计算结构响应量的前几阶矩,然后利用最大熵法拟会响应量的概率分布,据此算出结构的失效概率。此法具有精度较高、计算量较小的优点。     

Reliability analysis of structures based on a probability‐uncertainty hybrid model

The traditional reliability analysis method based on probabilistic method requires probability distributions of all the uncertain parameters. However, in practical applications, the distributions of some parameters may not be precisely known due to the lack of sufficient sample data. The probabilistic theory cannot directly measure the reliability of structures with epistemic uncertainty, ie, subjective randomness and fuzziness. Hence, a hybrid reliability analysis (HRA) problem will be caused when the aleatory and epistemic uncertainties coexist in a structure. In this paper, by combining the probability theory and the uncertainty theory into a chance theory, a probability‐uncertainty hybrid model is established, and a new quantification method based on the uncertain random variables for the structural reliability is presented in order to simultaneously satisfy the duality of random variables and the subadditivity of uncertain variables; then, a reliability index is explored based on the chance expected value and variance. Besides, the formulas of the chance theory‐based reliability and reliability index are derived to uniformly assess the reliability of structures under the hybrid aleatory and epistemic uncertainties. The numerical experiments illustrate the validity of the proposed method, and the results of the proposed method can provide a more accurate assessment of the structural system under the mixed uncertainties than the ones obtained separately from the probability theory and the uncertainty theory.     

Developments in structural system reliability assessments of fixed steel offshore platforms

In recent years there have been significant developments in the area of system reliability assessments which are becoming increasingly important given the framework of the ‘goal setting’ regime and other changes that are taking place within the offshore industry. The objective of this paper is to review and critically examine recent developments in system reliability methods for fixed steel offshore platforms specifically and identify areas that need to be examined further to maximise the benefits from the use of these techniques.The paper examines the range of proposed methods for system reliability assessment of fixed steel offshore structures under extreme environmental loading. The associated characteristics of the various methods are examined and the paper concentrates in particular on the treatment of the resistance. The various system effects including both deterministic and probabilistic effects and their relative contribution to the overall system reliability are addressed. Key issues such as the modelling uncertainties and sensitivities, validation and benchmarking of the proposed methods are also examined. The study also highlights a number of technical and philosophical issues which need to be addressed to increase the benefits from system reliability applications in design and re-assessment of fixed platforms.     

Fatigue reliability study on T‐welded component considering load shedding

In this paper, a coupled reliability method for structural fatigue evaluation considering load shedding is first proposed based on probabilistic fracture mechanics in which the uncertainties of the structural parameters are taken into account. Then, the method is applied to predict the fatigue reliability of the T‐welded structure to the case of considering load shedding or not. The compared results show that by considering the load shedding, the structural fatigue reliability might be improved with less conservativeness. The influence rules of the load‐shedding coefficient on the fatigue failure probability of the T‐welded component are investigated, and some interesting results are obtained. That is, the influences of load‐shedding coefficient on the fatigue failure probability can be divided into three regions, namely the high, medium and low fatigue failure areas. The last area is the most intriguing when we try to design a T‐welded structure. The thickness of T‐welded structure along the crack propagation direction is found to be one of the important design variables for the design of fatigue reliability, in which the low‐fatigue failure zone is used as one of the reliability constraints. The basic design frame of T‐welded structure is established to constrain the fatigue failure probability within the low‐fatigue failure area.     

System reliability analysis of spatial variance frames based on random field and stochastic elastic modulus reduction method

This paper presents the stochastic elastic modulus reduction method for system reliability analysis of spatial variance frames based on the perturbation stochastic finite element method (PSFEM) and the local average of a random field. The stochastic responses and reliability index of each element of a structural frame are characterized by the PSFEM and the first-order second-moment method, to properly handle the correlation structures and scale of fluctuation of random fields. A strategy of elastic modulus adjustment for the estimation of system reliability is developed to determine the range and magnitude of elastic modulus reduction, by taking the element reliability index as a governing parameter. The collapse mechanism and system reliability index of a stochastic framed structure are determined through iterative computations of the PSFEM. Compared with the failure mode approaches in traditional system reliability analysis, the proposed method avoids two major difficulties, namely the identification of significant failure modes and estimation of the joint probability of failure modes. The influences of the correlation structure and scale of fluctuation of the random field upon system reliability are investigated to demonstrate the accuracy and computational efficiency of the proposed methodology in system reliability analysis of spatial variance frames.     

On fast integration for time variant structural reliability

In evaluating structural reliability under stochastic loadings, the system parameters such as stiffness, damping, strength, excitation frequency content and duration, etc., are usually assumed given. In reality, however, these quantities are seldom perfectly known. Their uncertainties may play a major role as far as the overall structural reliability is concerned. This paper reviews currently available methods to include this parameter uncertainties and a new method is also proposed. The accuracy and analytical and numerical efforts required are compared. Through numerical examples on systems under dynamic excitation, collapse of ductile or brittle redundant systems, the advantages of the proposed method are demonstrated.     

Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

The problem of time variant reliability analysis of existing structures subjected to stationary random dynamic excitations is considered. The study assumes that samples of dynamic response of the structure, under the action of external excitations, have been measured at a set of sparse points on the structure. The utilization of these measurements in updating reliability models, postulated prior to making any measurements, is considered. This is achieved by using dynamic state estimation methods which combine results from Markov process theory and Bayes’ theorem. The uncertainties present in measurements as well as in the postulated model for the structural behaviour are accounted for. The samples of external excitations are taken to emanate from known stochastic models and allowance is made for ability (or lack of it) to measure the applied excitations. The future reliability of the structure is modeled using expected structural response conditioned on all the measurements made. This expected response is shown to have a time varying mean and a random component that can be treated as being weakly stationary. For linear systems, an approximate analytical solution for the problem of reliability model updating is obtained by combining theories of discrete Kalman filter and level crossing statistics. For the case of nonlinear systems, the problem is tackled by combining particle filtering strategies with data based extreme value analysis. In all these studies, the governing stochastic differential equations are discretized using the strong forms of Ito–Taylor’s discretization schemes. The possibility of using conditional simulation strategies, when applied external actions are measured, is also considered. The proposed procedures are exemplified by considering the reliability analysis of a few low-dimensional dynamical systems based on synthetically generated measurement data. The performance of the procedures developed is also assessed based on a limited amount of pertinent Monte Carlo simulations.     

改进响应面法计算钢筋混凝土框架体系可靠度

钢筋混凝土框架结构体系可靠度是评定结构抗震性能的重要指标,也是对既有结构进行加固的评估基础。以框架底层柱端极限弯矩承载力作为参考变量可有效评估框架结构体系可靠度,该方法采用Pushover静力推覆分析,通过研究框架结构底层柱端极限弯矩承载能力对Pushover曲线的影响规律,由Pushover曲线上的基准点推广得到结构失效响应面,从而计算出结构体系可靠度。应用该方法对一榀钢筋混凝土框架进行了体系可靠度计算,计算结果与直接响应面法和蒙特卡洛法分析结果进行对比,结果表明,该方法计算快捷,且能够很好地评估出框架体系抗震可靠度。     

结构时变可靠度计算的全随机过程模型

为了对既有结构的可靠性进行正确评估,必须考虑时间变化的影响,对目前可靠度分析中采用的只考虑荷载随时间变化的半随机过程模型进行改进。基于结构抗力和作用效应相互独立的基本假设,充分依据作用效应和抗力的时变特性,考虑结构抗力为独立增量过程,并计算了抗力的自相关系数,得到了计算结构失效概率的近似算法,建立了结构时变可靠度计算的全随机过程模型。通过实例验证,该方法简单易行,便于工程应用,为基于时变可靠度理论的既有结构评估和寿命预测提供理论依据。     

Updating robust reliability using structural test data

The concept of robust reliability is defined to take into account uncertainties from structural modeling in addition to the uncertain excitation that a structure will experience during its lifetime. A Bayesian probabilistic methodology for system identification is integrated with probabilistic structural analysis tools for the purpose of updating the assessment of the robust reliability based on dynamic test data. Methods for updating the structural reliability for both identifiable and unidentifiable models are presented. Application of the methodology to a simple beam model of a single-span bridge with soil-structure interaction at the abutments, including a case with a tuned-mass damper attached to the deck, shows that the robust reliabilities computed before and after updating with “measured” dynamic data can differ significantly.     

基于概率密度演化理论的结构抗震可靠性分析

运用随机过程的正交展开方法,将地震动加速度过程表示为由10个左右的独立随机变量所调制的确定性函数的线性组合形式。结合概率密度演化方法和等价极值事件的基本思想,研究了非线性结构的抗震可靠度分析问题。以具有滞回特性的非线性结构为例,对某一多自由度的剪切型框架结构进行了抗震可靠性分析。结果表明：按照复杂失效准则计算的结构抗震可靠度较之结构各层抗震可靠度均低。这一研究为基于概率密度函数的、精细化的抗震可靠度计算提供了新的途径。     

A semi-analytical Monte Carlo simulation method for system's reliability with load sharing and damage accumulation

A novel approach for assessing a systems' reliability with dependency structures, load sharing, and damage accumulation and reversal is proposed in this paper. It is a blend of analytical reliability analysis performed at the component level, and is based on understanding the failure mechanism of the components, and a Monte Carlo simulation for the entire system to assess the reliability at the system level incorporating the dynamics of the system behavior as the components interact, share loads, and age over time. Model reduction is deployed to reduce the complexity and accelerate the simulation and convergence of the analytical methods such as FORM and SORM performed at the component level. Numerical examples are provided to illustrate the usability and performance of the method.     

动力可靠度约束下基于概率测度变换-全局收敛移动渐近线法的结构优化设计EI北大核心CSCD

基于动力可靠度的结构优化是实现随机动力系统优化设计的重要途径。针对设计变量为系统中部分随机变量分布均值的情形,提出了一种基于动力可靠度的结构优化设计方法。在该方法中,通过概率密度演化理论实现了结构动力可靠度的高效分析。在此基础上,结合概率测度变换,可以在不增加任何确定性结构分析的前提下,实现动力可靠度对设计变量的灵敏度分析。进而,通过将上述概率密度演化-测度变换方法嵌入全局收敛移动渐近线法,实现了基于动力可靠度的结构优化设计问题的高效求解。数值算例的结果表明,所提方法可以显著降低结构分析次数,具有较高的效率与稳健性。     

Defect identification under uncertain blast loading

A novel finite element-based system identification procedure is proposed to detect defects in existing frame structures when excited by blast loadings. The procedure is a linear time-domain system identification technique where the structure is represented by finite elements and the input excitation is not required to identify the structure. It identifies stiffness parameter (EI/L) of all the elements and tracks the changes in them to locate the defect spots. The similar procedure can also be used to monitor health of structures just after natural events like strong earthquakes and high winds. With the help of several numerical examples, it is shown that the algorithm can identify defect-free and defective structures even in the presence of noise in the output response information. The accuracy of the method is much better than other methods currently available even when input excitation information was used for identification purpose. The method not only detects defective elements but also locate the defect spot more accurately within the defective element. The structures can be excited by single or multiple blast loadings and the defect can be relatively small and large. With the help of several examples, it is established that the proposed method can be used as a nondestructive defect evaluation procedure for the health assessment of existing structures.     

An efficient analytical Bayesian method for reliability and system response updating based on Laplace and inverse first-order reliability computations

This paper presents an efficient analytical Bayesian method for reliability and system response updating without using simulations. The method includes additional information such as measurement data via Bayesian modeling to reduce estimation uncertainties. Laplace approximation method is used to evaluate Bayesian posterior distributions analytically. An efficient algorithm based on inverse first-order reliability method is developed to evaluate system responses given a reliability index or confidence interval. Since the proposed method involves no simulations such as Monte Carlo or Markov chain Monte Carlo simulations, the overall computational efficiency improves significantly, particularly for problems with complicated performance functions. A practical fatigue crack propagation problem with experimental data, and a structural scale example are presented for methodology demonstration. The accuracy and computational efficiency of the proposed method are compared with traditional simulation-based methods.