Selective maintenance planning of a steel truss bridge based on the Markovian approach

Abstract

Structures of strategic importance, such as bridges, require careful planning in terms of reliability, durability and safety, qualities which must be guaranteed throughout the entire life cycle of the structure. However, due to the ageing of materials and to aggressive environmental actions which cause deterioration, the response of these structures, just like others, changes over time, resulting in a loss of performance.Yet it is important to maintain a satisfactory level of performance in a bridge throughout its service. To ensure such a performance it is important to apply properly planned maintenance strategies. Appropriate maintenance strategies require knowledge of the process of deterioration and the consequent damages to be expected in order to schedule proper maintenance procedures. It would be fundamental to define a selective maintenance plan which may involve only some parts of the structure, thus allowing bridge viability even during the maintenance activity.This paper proposes the study of strategies of selective maintenance for a steel bridge immersed in an aggressive environment, starting from the simulation of each individual member. Simulation of deterioration is obtained through the application of an appropriate damage law implemented with a Monte Carlo methodology, while the time prediction of occurrence of the deterioration is obtained through the application of a Markovian probabilistic approach. The results of the Markovian approach were the starting point for choosing strategies of selective maintenance, as the Markov process allowed the identification, in probabilistic terms, of the structure members with the highest risk of collapse and the timing for achieving levels of damage related to the possible collapse of compromised members. This timing was used to identify possible intervals of maintenance. Proposed scenarios are compared with each other both in terms of associated risk, and in terms of lifecycle cost effectiveness.

A high‐performance maintenance strategy for stochastic selective maintenance

“…A probabilistic method was proposed with appropriate cost-benefit analyses for MSS in the works of Dao et al 17, 18 Garavaglia and Sgambi 19 proposed the study of strategies of selective maintenance for a steel bridge immersed in an aggressive environment. A probabilistic method was proposed with appropriate cost-benefit analyses for MSS in the works of Dao et al 17, 18 Garavaglia and Sgambi 19 proposed the study of strategies of selective maintenance for a steel bridge immersed in an aggressive environment.…”

“…Yu and Huang 16 established an optimization model for an MSS subject to both maintenance cost and time. A probabilistic method was proposed with appropriate cost-benefit analyses for MSS in the works of Dao et al 17,18 Garavaglia and Sgambi 19 proposed the study of strategies of selective maintenance for a steel bridge immersed in an aggressive environment. In these papers, there are different maintenance actives.…”

Application of Markov renewal theory and semi‐Markov decision processes in maintenance modeling and optimization of multi‐unit systems

“…An MRP is a process that keeps a record of the number of times a particle has visited each of the possible states up to time t (Pyke, 1961). MRPs were introduced by Jewell (1963aJewell ( , 1963b and de Cani (1964), and the processes are suitable for studying and modeling repetitive phenomena which renew their features after every event (Garavaglia & Sgambi, 2016). MRP is an efficient and powerful tool for maintenance modeling which allows solving analytically and numerically complex maintenance problems (Castanier, Bérenguer, & Grall, 2003).…”

Probabilistic life-cycle assessment and rehabilitation strategies for deteriorating structures: a case study

“…The procedure here described is based on studies of ideal examples (Garavaglia, Basso, and Sgambi 2012) and new constructions (Garavaglia and Sgambi 2016). In this article, the methodology has been applied to a historically significant case study, characterized by two distinct phases in its life cycle: operation (deterioration by natural aging and usage) and obsolescence (degradation by negligence and weathering).…”

“…The lack of information about the deterioration process affecting the structural elements is bypassed using a Monte Carlo simulation implemented with a damage law. This integrated method enables to investigate different environmental conditions affecting the structure 305 during its life (Biondini, Frangopol, and Garavaglia 2008;Garavaglia, Basso, and Sgambi 2012;Garavaglia and Sgambi 2016). Garavaglia and Sgambi developed the methodology in a previous work (Garavaglia and Sgambi 2016) where it was used to study the service life of a new steel bridge.…”