InfraRisk: An open-source simulation platform for resilience analysis in interconnected power–water–transport networks

Balakrishnan, Srijith; Cassottana, Beatrice

doi:10.1016/j.scs.2022.103963

Cited by 14 publications

(10 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we employed InfraRisk, an open-source Python-based integrated simulation platform, for simulating infrastructure disruptions and subsequent recovery in interdependent power-water-transport networks [25]. InfraRisk integrates existing domain-specific infrastructure simulators (pandapower for power systems, wntr for water distribution systems, and a static traffic assignment package for transport network) via an object-oriented interface to perform the interdependent simulations.…”

Section: Interdependent Infrastructure Simulationmentioning

confidence: 99%

“…Since the objective of the ML model is to predict the network-level resilience, we converted the consumer-level resource supply values generated by the simulation model to network-level resilience metrics. In this study, we used Prioritized Consumer Serviceability (PCS) as the measure of performance (MOP) for tracking the performance of the networks [25], defined as…”

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

Balakrishnan¹,

Cassottana²,

Verma³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Recent studies increasingly adopt simulation-based machine learning (ML) models to analyze critical infrastructure system resilience. For realistic applications, these ML models consider the component-level characteristics that influence the network response during emergencies. However, such an approach could result in a large number of features and cause ML models to suffer from the 'curse of dimensionality'. We present a clustering-based method that simultaneously minimizes the problem of high-dimensionality and improves the prediction accuracy of ML models developed for resilience analysis in large-scale interdependent infrastructure networks. The methodology has three parts: (a) generation of simulation dataset, (b) network component clustering, and (c) dimensionality reduction and development of prediction models. First, an interdependent infrastructure simulation model simulates the network-wide consequences of various disruptive events. The component-level features are extracted from the simulated data. Next, clustering algorithms

show abstract

Section: Interdependent Infrastructure Simulationmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

Balakrishnan¹,

Cassottana²,

Verma³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The simulation model InfraRisk [24] has been deployed to model the interdependent power-water-transportation network. InfraRisk calls three Python packages to run simulation for the individual infrastructure systems.…”

Section: A Simulation Platform Overviewmentioning

confidence: 99%

“…This study focuses on disruptions to normal functioning and potential damage of infrastructure system components due to weather-related events, which are replicated via the hazard module in the simulation platform [24]. The failure probability of components is computed as:…”

Section: B Hazard Scenario Generationmentioning

confidence: 99%

“…The recovery actions are deterministically sequenced and implemented based on the availability of repair crews and the repair sequence, which is obtained by prioritizing failed components for repair based on a pre-determined recovery strategy and accounting for road-accessibility constraints as shown in Fig. 2 [24]. According to the algorithm in Fig.…”

Section: Network Recoverymentioning

confidence: 99%

See 1 more Smart Citation

Predicting Resilience of Interdependent Urban Infrastructure Systems

Cassottana¹,

Biswas

Balakrishnan³

et al. 2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

Climate change is increasing the frequency and the intensity of weather events, leading to large-scale disruptions to critical infrastructure systems. The high level of interdependence among these systems further aggravates the extent of disruptions. To mitigate these impacts, models and methods are needed to support rapid decision-making for optimal resource allocation in the aftermath of a disruption and to substantiate investment decisions for the structural reconfiguration of these systems. In this paper, we leverage infrastructure simulation models and Machine Learning (ML) algorithms to develop resilience prediction models. First, we employ an interdependent infrastructure simulation model to generate infrastructure disruption and recovery scenarios and compute the resilience value for each scenario. The infrastructure-, disruption-, and recovery-related attributes are recorded for each scenario and ML algorithms are employed on the synthetic dataset to develop accurate resilience prediction models. The results of the prediction models are analyzed and possible design strategies suggested based on the resilience enhancement attributes. The proposed methodology can support infrastructure agencies in the resource-allocation process for pre-and post-disaster interventions.

show abstract

A stochastic model of future extreme temperature events for infrastructure analysis

Villa

Tyler

Govertsen

et al. 2023

Environmental Modelling & Software

View full text Add to dashboard Cite

InfraRisk: An open-source simulation platform for resilience analysis in interconnected power–water–transport networks

Cited by 14 publications

References 42 publications

Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

Predicting Resilience of Interdependent Urban Infrastructure Systems

A stochastic model of future extreme temperature events for infrastructure analysis

Contact Info

Product

Resources

About