Structures and Infrastructure Systems

Life?Cycle Performance, Management, and Optimization

Dan M. Frangopol

• Publisher: CRC Press
• Year: 2019
• eBook ISBN: 9781351182782
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Civil Engineering
• Index: Technology & Engineering

Our knowledge to model, design, analyse, maintain, manage and predict the life-cycle performance of infrastructure systems is continually growing. However, the complexity of these systems continues to increase and an integrated approach is necessary to understand the effect of technological, environmental, economic, social, and political interactions on the life-cycle performance of engineering infrastructure. In order to accomplish this, methods have to be developed to systematically analyse structure and infrastructure systems, and models have to be formulated for evaluating and comparing the risks and benefits associated with various alternatives. Civil engineers must maximize the life-cycle benefits of these systems to serve the needs of our society by selecting the best balance of the safety, economy, resilience and sustainability requirements despite imperfect information and knowledge. Within the context of this book, the necessary concepts are introduced and illustrated with applications to civil and marine structures. This book is intended for an audience of researchers and practitioners world?wide with a background in civil and marine engineering, as well as people working in infrastructure maintenance, management, cost and optimization analysis. The chapters originally published as articles in Structure and Infrastructure Engineering.

Part I

State-of-the-art

Life-cycle performance, management, and optimisation of structural systems under uncertainty: accomplishments and challenges¹

Dan M. Frangopol

Our knowledge to model, analyse, design, maintain, monitor, manage, predict and optimise the life-cycle performance of structures and infrastructures under uncertainty is continually growing. However, in many countries, including the United States, the civil infrastructure is no longer within desired levels of performance and safety. Decisions regarding civil infrastructure systems should be supported by an integrated reliability-based life-cycle multi-objective optimisation framework by considering, among other factors, the likelihood of successful performance and the total expected cost accrued over the entire life-cycle. The primary objective of this paper is to highlight recent accomplishments in the life-cycle performance assessment, maintenance, monitoring, management and optimisation of structural systems under uncertainty. Challenges are also identified.

1. Introduction

Stable economic growth and social development of most countries are intimately dependent upon the reliable and durable performance of their structures and infrastructures. Natural hazards, ageing, and functionality fluctuations can inflict detrimental effects on the performance of structural systems during their life-cycles. Even the inherently conservative initial design of structural systems may not protect a structure from these threats. Natural phenomena such as earthquakes, hurricanes and floods can create structural disasters. Ageing and/or increased structural performance demand may significantly affect the vulnerability of constructed facilities (Tsompanakis 2010, Esteva et al. 2010, Casciati and Faravelli 2010). Environmental stressors are the primary factors that drive the ageing process. The effect of structural ageing is perhaps most widely apparent in bridge deterioration, exacerbated by increase in traffic over time, but also impacts other civil infrastructure systems such as buildings and nuclear power plants (Ellingwood and Mori 1993, Ellingwood 1998, 2005).

The accurate modelling of structures and the loading conditions to which they are expected to be exposed during their life-cycle as well as their possible deterioration mechanisms are major issues of structural and engineering mechanics, respectively (Schuëller 1998). Uncertainty in the modelling of structures and randomness in loading phenomena require the use of probabilistic methods in life-cycle analysis. Explicitly distinguishing the two types of uncertainty, namely the aleatory and epistemic, is crucial for the proper handling of a probabilistic analysis approach (Ang and Tang 2007). Whereas randomness (or aleatory uncertainty) cannot be reduced, improvement in knowledge or in the accuracy of predictive models will reduce the epistemic uncertainty (Ang and De Leon 2005).

Ultimately, optimal decisions are to be made that ensure maintaining or improving reliability of structural systems under multiple objectives and various constraints. This can only be achieved through proper integrated risk management planning in a life-cycle comprehensive framework. Figure 1 shows a schematic representation of a life-cycle integrated management framework example. In this framework, tools for structural performance assessment and prediction, structural health monitoring (SHM), integration of new information (from SHM and/or inspection), and optimisation of strategies (inspection, maintenance, monitoring, repair, and replacement) are required. Life-cycle performance assessment is the backbone of the process which requires current evaluation and future prediction. Uncertainty is an integral component in all aspects of this or any life-cycle management (LCM) framework (Frangopol and Liu 2007a, Frangopol and Okasha 2009).

Figure 1. Schematic representation of life-cycle integrated management framework.

This paper aims to highlight recent accomplishments in the life-cycle performance assessment, maintenance, monitoring, management, and optimisation of structural systems under uncertainty. Challenges are also identified.

2. System performance assessment and prediction

It is generally recognised that probability-based concepts and methods provide a rational and more scientific basis for treating uncertainties (Ang and Tang 1984, 2007). Uncertainty associated with natural randomness and uncertainties arising from imperfections in modelling and prediction of reality are combined. The results derived from the effects of the combined uncertainties are useful, and indeed have led to the development of more consistent criteria for safety assessment and design of engineered structures and systems (Ang and De Leon 1997, 2005, Ang 2010).

The commonly employed methodology to design and evaluate structural systems based on component analysis reflects the current activities of both structural designers and inspectors who look at structural members to satisfy individual safety checks (Moses 1989, Galambos 1989). This methodology, which is enforced in most structural design and evaluation specifications, leads either (a) to a considerable waste of resources because of over-conservatism in the design and/or evaluation of structural systems which are able to continue to carry loads after the damage or failure of one or more of their members, or (b) to overestimation of the actual load carrying capacity and/or underdesign of structural systems which are not able to redistribute loads (Hendawi and Frangopol 1994).

Alternatively, system-based safety measures provide more rational assessment means for structures. The relationship between system safety and member safety depends on the system’s configuration and whether the members are modelled in parallel, or in series, or both, the ductility of the members and the degree of mutual dependence among failure modes (Moses 1982, Ghosn et al. 2010). System reliability, redundancy and robustness have been the most widely studied system-based performance measures in structural engineering. Such studies include time-invariant measures (Moses 1982, Frangopol and Curley 1987, Frangopol 1987, Fu 1987, De et al. 1990, Paliou et al. 1990, Frangopol and Nakib 1991, Frangopol et al. 1992, 1998, Ghosn and Moses 1998, Bertero and Bertero 1999, Gharaibeh and Frangopol 2000, Gharaibeh et al. 2000a,b, Liu et al. 2001, Ghosn and Frangopol 2007, Biondini et al. 2008, Ghosn et al. 2010) and time-variant measures (Mori and Ellingwood 1993, Enright and Frangopol 1998a,b, Estes and Frangopol 1999, 2001, 2005, Akgül and Frangopol 2004a,b, Yang et al. 2004, 2006a,b, Okasha and Frangopol 2010a). A novel methodology promoting the system design based on robustness, resistance and sustainability has been proposed by Shinozuka (2008).

The probability of failure of a system is defined as the probability of violating any of the limit state functions that define its failure modes. Limit states of structural systems, as an example, are expressed by equations relating the resistances of the structural components to the load effects acting on these components. Safety margins at a point in time t are expressed as

where M(t) = instantaneous safety margin, R(t) = instantaneous resistance, and Q(t) = instantaneous load effect. By assuming that R and Q are statistically independent random variables, the (instantaneous) probability of failure is (Ellingwood 2005)

where F_R(x,t) is the instantaneous cumulative probability distribution function of the resistance and f_Q(x, t) is the instantaneous probability density function of the load effect. For condition assessment and service life prediction, the probability of satisfactory performance over a service or inter-inspection period is a more relevant metric of performance (Ellingwood 2005). The reliability function provides the probability of survival of a structural component subjected to a sequence of discrete stochastic load events described by a Poisson point process with mean occurrence rate λ during a period of time t_L. This probability can be calculated as (Mori and Ellingwood 1993)

where R₀ is the initial strength, f_R0(r) is the probability density function of R₀, r · g(t) = strength at time t, and g(t) = degradation function. Equation (3) usually must be evaluated numerically by Monte Carlo simulation for realistic deterioration mechanisms (Ciampoli and Ellingwood 2002).

Recent catastrophic structural failures in the United States and throughout the world have reinforced the importance of providing civil structures with system redundancy and robustness to ensure the safety of their users and occupants. Although most engineers agree on the general goals of structural redundancy, issues related to defining these goals and establishing objective measures to quantify redundancy and robustness remain largely unresolved. In this context, system redundancy is defined as the ability of a structural system to redistribute the applied load after reaching the ultimate capacity of its main load-carrying members (Frangopol 1987). Robustness was defined as the ability of the system to still carry some load after the brittle fracture of one or more critical components (De et al. 1989). It is worth mentioning that research in structural robustness is lagging far behind that in structural redundancy. Even though numerous redundancy measures exist in the literature to date, no standard redundancy measure ha...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright
• Contents
• Citation Information
• Notes on Contributors
• Preface
• Part I: State-of-the-art
• 1. Life-cycle performance, management, and optimisation of structural systems under uncertainty: accomplishments and challenges
• 2. Bridge network performance, maintenance and optimisation under uncertainty: accomplishments and challenges
• 3. Life-cycle of structural systems: recent achievements and future directions
• 4. Bridge life-cycle performance and cost: analysis, prediction, optimisation and decision-making
• Part II: General methodology
• 5. Optimal bridge maintenance planning using improved multi-objective genetic algorithm
• 6. Maintenance and management of civil infrastructure based on condition, safety, optimization, and life-cycle cost
• 7. Life cycle utility-informed maintenance planning based on lifetime functions: optimum balancing of cost, failure consequences and performance benefit
• 8. Efficient multi-objective optimisation of probabilistic service life management
• Part III: Life-cycle performance under corrosion and fatigue
• 9. Probabilistic limit analysis and lifetime prediction of concrete structures
• 10. Integration of the effects of airborne chlorides into reliability-based durability design of reinforced concrete structures in a marine environment
• 11. Fatigue system reliability analysis of riveted railway bridge connections
• 12. Fatigue performance assessment and service life prediction of high-speed ship structures based on probabilistic lifetime sea loads
• 13. Experimental investigation of the spatial variability of the steel weight loss and corrosion cracking of reinforced concrete members: novel X-ray and digital image processing techniques
• 14. Reliability-based durability design and service life assessment of reinforced concrete deck slab of jetty structures
• Part IV: Life-cycle performance under earthquakes
• 15. Life-cycle cost of civil infrastructure with emphasis on balancing structural performance and seismic risk of road network
• 16. Long-term seismic performance of RC structures in an aggressive environment: emphasis on bridge piers
• 17. Performance analysis of Tohoku-Shinkansen viaducts affected by the 2011 Great East Japan earthquake
• 18. Probabilistic assessment of an interdependent healthcare–bridge network system under seismic hazard
• Part V: Inspection and monitoring
• 19. Application of the statistics of extremes to the reliability assessment and performance prediction of monitored highway bridges
• 20. Probabilistic bicriterion optimum inspection/monitoring planning: applications to naval ships and bridges under fatigue
• 21. Integration of structural health monitoring in a system performance based life-cycle bridge management framework
• 22. Critical issues, condition assessment and monitoring of heavy movable structures: emphasis on movable bridges
• Part VI: Redundancy as life-cycle performance indicator
• 23. Time-variant redundancy of structural systems
• 24. Redundancy and robustness of highway bridge superstructures and substructures
• 25. Effects of post-failure material behaviour on redundancy factors for design of structural components in nondeterministic systems
• 26. Time-variant redundancy and failure times of deteriorating concrete structures considering multiple limit states
• Index