Bridge life-cycle performance and cost: analysis, prediction, optimisation and decision-making

Dan M. Frangopol, You Dong, Samantha Sabatino

Structure and Infrastructure Engineering · 2017 · 257 citations · 185 references

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Concepts

TL;DR

Bridge life‑cycle engineering requires a rational basis for decisions on design, construction, inspection, maintenance, and replacement under uncertainty, and recent advances in reliability, risk, resilience, sustainability, network interdependence, and climate‑change impacts inform multi‑objective optimisation that balances performance and cost. This study proposes a generalized framework for assessing bridge life‑cycle performance and cost, focusing on analysis, prediction, optimisation, and decision‑making under uncertainty. The framework integrates structural health monitoring and probabilistic life‑cycle optimisation using multi‑attribute utility and risk‑attitude models to update bridge management decisions.

Abstract

The development of a generalised framework for assessing bridge life-cycle performance and cost, with emphasis on analysis, prediction, optimisation and decision-making under uncertainty, is briefly addressed. The central issue underlying the importance of the life-cycle approach to bridge engineering is the need for a rational basis for making informed decisions regarding design, construction, inspection, monitoring, maintenance, repair, rehabilitation, replacement and management of bridges under uncertainty which is carried out by using multi-objective optimisation procedures that balance conflicting criteria such as performance and cost. A number of significant developments are summarised, including time-variant reliability, risk, resilience, and sustainability of bridges, bridge transportation networks and interdependent infrastructure systems. Furthermore, the effects of climate change on the probabilistic life-cycle performance assessment of highway bridges are addressed. Moreover, integration of SHM and updating in bridge management and probabilistic life-cycle optimisation considering multi-attribute utility and risk attitudes are presented.

References

185