
Markov models are often used in bridge management systems to evaluate intervention strategies (ISs) for bridges affected by manifest deterioration processes (MnDPs). These models do not directly take into consideration the effect of latent deterioration processes (LtDPs) on the object, i.e. the deterioration that might occur due to natural hazards (e.g. earthquakes and floods). In cases where there is a negligible probability of the occurrence of natural hazards, this is justified, otherwise it is not. In this paper, a model is proposed that can be used to evaluate ISs for bridge elements and bridges considering both MnDPs and LtDPs. The model is an extension of the Markov models, and includes condition states (CSs) that can occur due to both MnDPs and LtDPs, as well as the probabilities of transition (p.o.ts) between them. The contributions to the p.o.ts due to MnDPs are initially estimated using well-established methods and adjusted for the contributions to the p.o.ts due to LtDPs, which are estimated using fragility curves and adjusted considering element dependencies, i.e. how the elements of a bridge work together. The use of the model is demonstrated by predicting the future CSs of a bridge affected by both MnDPs and LtDPs.
Fragility curves, Risk, 2205 Civil and Structural Engineering, Markov models, 2210 Mechanical Engineering, 1909 Geotechnical Engineering and Engineering Geology, Infrastructure management, 2215 Building and Construction, Reliability and Quality, Optimal intervention strategy, Hazard risks, 2213 Safety, Bridge management, 2212 Ocean Engineering
Fragility curves, Risk, 2205 Civil and Structural Engineering, Markov models, 2210 Mechanical Engineering, 1909 Geotechnical Engineering and Engineering Geology, Infrastructure management, 2215 Building and Construction, Reliability and Quality, Optimal intervention strategy, Hazard risks, 2213 Safety, Bridge management, 2212 Ocean Engineering
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 12 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
