Modeling Time-varying Reliability and Resilience of Deteriorating Infrastructure

Iannacone, Leandro; Sharma, Neetesh; Tabandeh, Armin; Gardoni, Paolo

doi:10.1016/j.ress.2021.108074

Cited by 77 publications

(18 citation statements)

References 58 publications

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“…where ẋ[𝑘] (𝜐) epresents the instantaneous rate of change due to deterioration processes at time 𝜐. Iannacone et al (2022) provided an example of the application of this formulation to the deterioration of interdependent networks.…”

Section: Modeling Deterioration and Recoverymentioning

confidence: 99%

“…The definition of 𝑄(𝑡) depends on the modeled infrastructure and type of analysis. For example, in the case of transportation infrastructure, we can estimate 𝑄(𝑡) from topology-based approaches (e.g., Guidotti et al, 2017), whereas in the case of potable water infrastructure, we can estimate 𝑄(𝑡) from flow-based approaches (e.g., Iannacone et al, 2022). If the region of interest is divided into 𝑛 𝑎 service areas, then we can map 𝐐…”

Section: Measuring the Performance Of Infrastructurementioning

confidence: 99%

“…Then, for every earthquake scenario in Figure 4, we estimate 𝑄(𝑡 0 + , 𝜀 eq ) by translating the physical damage into the changes in 𝐂 [2] (𝑡, 𝜀 eq ) and 𝐃 [2] (𝑡, 𝜀 eq ) to estimate 𝐒 [2] (𝑡, 𝜀 eq ) (details in Iannacone et al, 2022).…”

Section: Estimating the Modeling Resolution Of An Example Water Infra...mentioning

confidence: 99%

“…Accordingly, an infrastructure is represented as a collection of networks, where each network captures a specific feature/function of the infrastructure. For example, we can model the water infrastructure with two networks, a structural network that captures the structural state of the infrastructure and the water flow network that models the functionality state (Iannacone et al., 2022).…”

Section: Modeling the Time‐varying Performance Of Infrastructurementioning

confidence: 99%

“…Iannacone et al. (2022) provided an example of the application of this formulation to the deterioration of interdependent networks.…”

Section: Modeling the Time‐varying Performance Of Infrastructurementioning

confidence: 99%

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Selection of the modeling resolution of infrastructure

Nocera

Gardoni

2022

Computer aided Civil Eng

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Accurate risk analysis requires representative mathematical models of infrastructure. One of the main challenges in developing mathematical models of infrastructure is selecting the modeling resolution from multiple candidates for the required analyses, each candidate having different computational cost, accuracy, and possibly ability to provide information. The goal of selecting the model resolution is to allocate computational resources to the model that best delivers the desired information with the desired accuracy level. This paper proposes a mathematical formulation to select the appropriate resolution for the modeling of infrastructure. We formulate the model selection as an iterative process until a tradeoff is achieved among accuracy, simplicity, and computational efficiency.To select the appropriate level of resolution, we propose novel metrics that measure the level of agreement between estimates of the quantities of interest computed using different levels of resolution. Specifically, we propose global metrics of accuracy that inform if a model is sufficiently detailed or oversimplified. Likewise, we introduce local metrics of accuracy to inform which parts of the model (e.g., spatial portions of the infrastructure model) need refinements. We illustrate the proposed mathematical formulation by selecting the level of resolution of the water infrastructure of Seaside, Oregon, for the purpose of assessing its functionality after a seismic hazard.

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Section: Modeling Deterioration and Recoverymentioning

confidence: 99%

Section: Measuring the Performance Of Infrastructurementioning

confidence: 99%

Section: Estimating the Modeling Resolution Of An Example Water Infra...mentioning

confidence: 99%

Section: Modeling the Time‐varying Performance Of Infrastructurementioning

confidence: 99%

“…Iannacone et al. (2022) provided an example of the application of this formulation to the deterioration of interdependent networks.…”

Section: Modeling the Time‐varying Performance Of Infrastructurementioning

confidence: 99%

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Selection of the modeling resolution of infrastructure

Nocera

Gardoni

2022

Computer aided Civil Eng

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A method for time‐varying reliability calculation of RC bridges considering random deterioration process

Yang

Huang

2023

Quality & Reliability Eng

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This paper proposes a new method based on dual neural networks to improve the efficiency of time‐varying reliability calculation of bridge structures. Based on the basic theory of structural time‐varying reliability analysis, this method uses the dyadic neural network integration method to convert the time‐varying reliability calculation of bridges from the traditional Monte Carlo simulation to the direct integration operation, which improves operation efficiency and retains high working accuracy. In addition, considering the randomness of the decay process of different bridge resistances, the variation law of time‐varying reliability of bridges in the process of uncertain resistance reduction is studied, and an explicit formula for time‐varying reliability of bridges is given and solved by using the proposed dual neural network method. Finally, the effectiveness of the proposed method is verified by an engineering arithmetic example. The result indicates that the bridge has a greater probability of failure under random conditions.

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