Structural reliability prediction of a steel bridge element using Dynamic Object Oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method.

Markov chain splitting methods in structural reliability integral estimation

Monte Carlo simulation methods involving splitting of Markov chains have been used in evaluation of multi-fold integrals in different application areas. We examine in this paper the performance of these methods in the context of evaluation of reliability integrals from the point of view of characterizing the sampling fluctuations. The methods discussed include the Au-Beck subset simulation, Holmes-Diaconis-Ross method, and generalized splitting algorithm. A few improvisations based on first order reliability method are suggested to select algorithmic parameters of the latter two methods. The bias and sampling variance of the alternative estimators are discussed. Also, an approximation to the sampling distribution of some of these estimators is obtained. Illustrative examples involving component and series system reliability analyses are presented with a view to bring out the relative merits of alternative methods. (C) 2015 Elsevier Ltd. All rights reserved.

Potential applications of discrete-event simulation and fuzzy rule-based systems to structural reliability and availability

This chapter discusses and illustrates some potential applications of discrete-event simulation (DES) techniques in structural reliability and availability analysis, emphasizing the convenience of using probabilistic approaches in modern building and civil engineering practices. After reviewing existing literature on the topic, some advantages of probabilistic techniques over analytical ones are highlighted. Then, we introduce a general framework for performing structural reliability and availability analysis through DES. Our methodology proposes the use of statistical distributions and techniques – such as survival analysis – to model component-level reliability. Then, using failure- and repair-time distributions and information about the structural logical topology (which allows determination of the structural state from their components’ state), structural reliability, and availability information can be inferred. Two numerical examples illustrate some potential applications of the proposed methodology to achieving more reliable and structural designs. Finally, an alternative approach to model uncertainty at component level is also introduced as ongoing work. This new approach is based on the use of fuzzy rule-based systems and it allows the introduction of experts’ opinions and evaluations in our methodology.

New Optimization Algorithms for Structural Reliability Analysis

Solution of structural reliability problems by the First Order method require optimization algorithms to find the smallest distance between a limit state function and the origin of standard Gaussian space. The Hassofer-Lind-Rackwitz-Fiessler (HLRF) algorithm, developed specifically for this purpose, has been shown to be efficient but not robust, as it fails to converge for a significant number of problems. On the other hand, recent developments in general (augmented Lagrangian) optimization techniques have not been tested in aplication to structural reliability problems. In the present article, three new optimization algorithms for structural reliability analysis are presented. One algorithm is based on the HLRF, but uses a new differentiable merit function with Wolfe conditions to select step length in linear search. It is shown in the article that, under certain assumptions, the proposed algorithm generates a sequence that converges to the local minimizer of the problem. Two new augmented Lagrangian methods are also presented, which use quadratic penalties to solve nonlinear problems with equality constraints. Performance and robustness of the new algorithms is compared to the classic augmented Lagrangian method, to HLRF and to the improved HLRF (iHLRF) algorithms, in the solution of 25 benchmark problems from the literature. The new proposed HLRF algorithm is shown to be more robust than HLRF or iHLRF, and as efficient as the iHLRF algorithm. The two augmented Lagrangian methods proposed herein are shown to be more robust and more efficient than the classical augmented Lagrangian method.

Application of level II reliability theory

Level II reliability theory provides an approximate method whereby the reliability of a complex engineering structure which has multiple strength and loading variables may be estimated. This technique has been applied previously to both civil and offshore structures with considerable success. The aim of the present work is to assess the applicability of the method for aircraft structures, and to this end landing gear design is considered in detail. It is found that the technique yields useful information regarding the structural reliability, and further it enables the critical design parameters to be identified.

Uncertainty propagation in inverse reliability-based design of composite structures

An approach for the analysis of uncertainty propagation in reliability-based design optimization of composite laminate structures is presented. Using the Uniform Design Method (UDM), a set of design points is generated over a domain centered on the mean reference values of the random variables. A methodology based on inverse optimal design of composite structures to achieve a specified reliability level is proposed, and the corresponding maximum load is outlined as a function of ply angle. Using the generated UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on an evolutionary learning process. Then, a Monte Carlo simulation using ANN development is performed to simulate the behavior of the critical Tsai number, structural reliability index, and their relative sensitivities as a function of the ply angle of laminates. The results are generated for uniformly distributed random variables on a domain centered on mean values. The statistical analysis of the results enables the study of the variability of the reliability index and its sensitivity relative to the ply angle. Numerical examples showing the utility of the approach for robust design of angle-ply laminates are presented.

Reliability-based design optimization strategies based on FORM: a review

In deterministic optimization, the uncertainties of the structural system (i.e. dimension, model, material, loads, etc) are not explicitly taken into account. Hence, resulting optimal solutions may lead to reduced reliability levels. The objective of reliability based design optimization (RBDO) is to optimize structures guaranteeing that a minimum level of reliability, chosen a priori by the designer, is maintained. Since reliability analysis using the First Order Reliability Method (FORM) is an optimization procedure itself, RBDO (in its classical version) is a double-loop strategy: the reliability analysis (inner loop) and the structural optimization (outer loop). The coupling of these two loops leads to very high computational costs. To reduce the computational burden of RBDO based on FORM, several authors propose decoupling the structural optimization and the reliability analysis. These procedures may be divided in two groups: (i) serial single loop methods and (ii) unilevel methods. The basic idea of serial single loop methods is to decouple the two loops and solve them sequentially, until some convergence criterion is achieved. On the other hand, uni-level methods employ different strategies to obtain a single loop of optimization to solve the RBDO problem. This paper presents a review of such RBDO strategies. A comparison of the performance (computational cost) of the main strategies is presented for several variants of two benchmark problems from the literature and for a structure modeled using the finite element method.

Reliability of buildings in service limit state for maximum horizontal displacements

Brazilian design code ABNT NBR6118:2003 - Design of Concrete Structures - Procedures - [1] proposes the use of simplified models for the consideration of non-linear material behavior in the evaluation of horizontal displacements in buildings. These models penalize stiffness of columns and beams, representing the effects of concrete cracking and avoiding costly physical non-linear analyses. The objectives of the present paper are to investigate the accuracy and uncertainty of these simplified models, as well as to evaluate the reliabilities of structures designed following ABNT NBR6118:2003[1&] in the service limit state for horizontal displacements. Model error statistics are obtained from 42 representative plane frames. The reliabilities of three typical (4, 8 and 12 floor) buildings are evaluated, using the simplified models and a rigorous, physical and geometrical non-linear analysis. Results show that the 70/70 (column/beam stiffness reduction) model is more accurate and less conservative than the 80/40 model. Results also show that ABNT NBR6118:2003 [1] design criteria for horizontal displacement limit states (masonry damage according to ACI 435.3R-68(1984) [10]) are conservative, and result in reliability indexes which are larger than those recommended in EUROCODE [2] for irreversible service limit states.

Reliability of tunnel liners

The possibility of application of structural reliability theory to the computation of the safety margins of excavated tunnels is presented. After a brief description of the existing procedures the limitations of the safety coefficients such as they usually defined, the proposed limit states are precised as well as the random variables and the applied methodology. Also presented are simple examples, some of them based in actual cases, and to end, some conclusions are established the most important one being the probability of using the method to solve the inverse problem of identification.

Reliability of tunnel liners

The possibility of application of structural reliability theory to the computation of the safety margins of excavated tunnels is presented. After a brief description of the existing procedures and the limitations of the safety coefficients such as they are usually defined, the proposed limit states are precised as well as the random variables and the applied methodology. Also presented are simple examples, some of them based in actual cases, and to end, some conclusions are established the most important one being the probability of using the method to solve the inverse problem of identification.

A framework for reliability assessment of ship hull damage under ship bow impact

Peer reviewed

A framework for reliability assessment of ship hull damage under ship bow impact

Peer reviewed

A framework for reliability assessment of ship hull damage under ship bow impact

Peer reviewed

Preliminary study on bridge health prediction using Dynamic Objective Oriented Bayesian Network (DOOBN)

The availability of bridges is crucial to people’s daily life and national economy. Bridge health prediction plays an important role in bridge management because maintenance optimization is implemented based on prediction results of bridge deterioration. Conventional bridge deterioration models can be categorised into two groups, namely condition states models and structural reliability models. Optimal maintenance strategy should be carried out based on both condition states and structural reliability of a bridge. However, none of existing deterioration models considers both condition states and structural reliability. This study thus proposes a Dynamic Objective Oriented Bayesian Network (DOOBN) based method to overcome the limitations of the existing methods. This methodology has the ability to act upon as a flexible unifying tool, which can integrate a variety of approaches and information for better bridge deterioration prediction. Two demonstrative case studies are conducted to preliminarily justify the feasibility of the methodology