Aging concrete structures: a review of mechanics and concepts

The safe and cost-efficient management of our built infrastructure is a challenging task considering the expected service life of at least 50 years. In spite of time-dependent changes in material properties, deterioration processes and changing demand by society, the structures need to satisfy many technical requirements related to serviceability, durability, sustainability and bearing capacity. This review paper summarizes the challenges associated with the safe design and maintenance of aging concrete structures and gives an overview of some concepts and approaches that are being developed to address these challenges

Meta-models for structural reliability and uncertainty quantification

A meta-model (or a surrogate model) is the modern name for what was traditionally called a response surface. It is intended to mimic the behaviour of a computational model M (e.g. a finite element model in mechanics) while being inexpensive to evaluate, in contrast to the original model which may take hours or even days of computer processing time. In this paper various types of meta-models that have been used in the last decade in the context of structural reliability are reviewed. More specifically classical polynomial response surfaces, polynomial chaos expansions and kriging are addressed. It is shown how the need for error estimates and adaptivity in their construction has brought this type of approaches to a high level of efficiency. A new technique that solves the problem of the potential biasedness in the estimation of a probability of failure through the use of meta-models is finally presented.Comment: Keynote lecture Fifth Asian-Pacific Symposium on Structural Reliability and its Applications (5th APSSRA) May 2012, Singapor

System reliability analyses and optimal maintenance planning of corroding pipelines

The failure of corroding pipeline joints may induce severe consequences. However, maintenance is expensive due to the cost of excavating and repairing a single joint and typically a significant number of joints that need repair. It is central to develop an optimal cost-effective maintenance strategy that balances cost and safety. A key component of the strategy is the reliability based condition evaluation of pipeline joints. The focus of the research reported in this thesis is therefore developing efficient reliability assessment methods for pipeline individual joints, and developing an optimal maintenance framework for the entire pipeline system. First, efficient system reliability methods relying on the first-order reliability method (FORM) and important sampling (IS) are developed for the assessment of the time-dependent probabilities of small leak and burst failure of pipeline joints containing multiple corrosion defects. In addition, a novel method is developed within the FORM to obtain the design points efficiently. An improved equivalent component approach for evaluating multi-normal integrals is also developed to improve the efficiency of the FORM for system reliability analysis. In addition, a multi-objective optimization-based maintenance framework for corroding pipeline systems is formulated optimizing three objectives, i.e. the conditioned probabilities of burst and small leak, respectively, and repair cost. An improved genetic algorithm with a pre-training population is utilized to investigate the optimal Pareto front. The benefits of this framework enable decision makers to access a series of non-dominated optimal repairing solutions with respect to multiple conflicting objectives

Probability-based uncertainty evaluation through Markov Chain Monte Carlo sampling and response surface technologies

Inspection of a pre-stressed concrete variable cross-section box girder bridge discovered the phenomenon of padding in expansion joint, corrosion of steel plate and local edge failure in pot type rubber bearing, and cracks of box girder. They are the main sources of structural uncertainty for structural performance evaluation, and how to quantificationally evaluate their influences on bridge performance is important. In this article, an approach using the Markov Chain Monte Carlo sampling technology and the Response Surface Method is proposed to deal with the uncertainty problem. First, a population of finite element (FE) models will be established by sampling the main uncertainty sources through the Markov Chain Monte Carlo technology. Then, the posterior probability of each FE model will be evaluated by using the measured static responses and identified structural dynamic characteristics. Especially, the second order response surface method will be used in this step to improve the computation efficiency. Through the above procedures, probability features of the defined key parameters representing structural uncertainty, including the stiffness of expansion joint, the stiffness of pot type rubber bearing and the elasticity modulus of the box girder will be estimated, which will provide valuable information for reliable structural performance evaluation

Efficient adaptive importance sampling for time-dependent reliability analysis of structures

Various methods have been used by researchers to evaluate the time-dependent reliability of structures. Among them, the stochastic-process-based method is theoretically the most rigorous but also computationally the most expensive. To enable the wide application of the stochastic-process-based method in the time-dependent reliability analysis of complex problems, an efficient importance sampling method is presented. This new method, extended from an existing method for time-independent reliability analysis, offers an efficient solution for time-dependent problems of structural systems with multiple important regions. Furthermore, to enhance the efficiency and robustness of the proposed method, a number of numerical measures are proposed. The capability and efficiency of the proposed method are demonstrated through two numerical examples

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

This paper presents a survey on the development and use of Artificial Neural Network (ANN) models in structural reliability analysis. The survey identifies the different types of ANNs, the methods of structural reliability assessment that are typically used, the techniques proposed for ANN training set improvement and also some applications of ANN approximations to structural design and optimization problems. ANN models are then used in the reliability analysis of a ship stiffened panel subjected to uniaxial compression loads induced by hull girder vertical bending moment, for which the collapse strength is obtained by means of nonlinear finite element analysis (FEA). The approaches adopted combine the use of adaptive ANN models to approximate directly the limit state function with Monte Carlo simulation (MCS), first order reliability methods (FORM) and MCS with importance sampling (IS), for reliability assessment. A comprehensive comparison of the predictions of the different reliability methods with ANN based LSFs and classical LSF evaluation linked to the FEA is provided

Value of information from vibration-based structural health monitoring extracted via Bayesian model updating

Quantifying the value of the information extracted from a structural health monitoring (SHM) system is an important step towards convincing decision makers to implement these systems. We quantify this value by adaptation of the Bayesian decision analysis framework. In contrast to previous works, we model in detail the entire process of data generation to processing, model updating and reliability calculation, and investigate it on a deteriorating bridge system. The framework assumes that dynamic response data are obtained in a sequential fashion from deployed accelerometers, subsequently processed by an output-only operational modal analysis scheme for identifying the system's modal characteristics. We employ a classical Bayesian model updating methodology to sequentially learn the deterioration and estimate the structural damage evolution over time. This leads to sequential updating of the structural reliability, which constitutes the basis for a preposterior Bayesian decision analysis. Alternative actions are defined and a heuristic-based approach is employed for the life-cycle optimization. By solving the preposterior Bayesian decision analysis, one is able to quantify the benefit of the availability of long-term SHM vibrational data. Numerical investigations show that this framework can provide quantitative measures on the optimality of an SHM system in a specific decision context