Methods for risk and resilience evaluation in interdependent infrastructure networks

Urban infrastructure plays a key role in the structure and dynamics of every city. Besides ensuring the sustainability of communities and businesses, high-quality infrastructure services are crucial for generating jobs and attracting capital investments. Modern infrastructure systems are highly interconnected to enhance efficiency and safety of operations; however, the interconnections increase the risks of cascading failures during extreme events, such as natural disasters, acts of terrorism, and pandemics. Not only are the normal operations interrupted during such events, but prolonged operational disruptions in infrastructure services also have debilitating effects on emergency response and economic recovery in affected regions. With the emergence of new threats and intensifying climate change, the resilience of infrastructure systems has become a necessity rather than a choice for our cities. As with any resource allocation problem, potential resilience investments require identifying priorities and evaluating project alternatives. Appropriate resilience indicators can be used to rank and prioritize infrastructure components and systems as well as to evaluate the efficacy of resilience interventions. The dissertation proposes five indicator-based methodological frameworks to assist decision-makers in analyzing the intrinsic risks and resilience in large-scale interdependent infrastructure networks. For generic interdependent networks, an agent-based simulation approach is adopted. In this approach, the interdependent network is modeled as a weighted bi-directed network where nodes represent infrastructure components and links denote the interconnections. For evaluating the risks of cascading failures and the network's resilience, a hybrid risk measure based on the well-known Inoperability Input-Output Model (IIM) using expert judgments is developed. In the process, to handle the issue of epistemic uncertainty associated with subjective infrastructure dependency data, a method based on possibility theory is also proposed. Later, the hybrid risk measure is extended to develop two resilience indexes for quantifying the criticality and susceptibility of infrastructure components and ranking algorithms are presented. In addition, the hybrid risk measure is combined with socio-economic characteristics obtained from census data to develop a priority index to quantify the risks of cascading failures in various urban communities. With regard to infrastructure-specific networks, the dissertation developed infrastructure ranking and prioritization methods for two distinct transportation systems, specifically road networks, and marine port systems, based on empirical disaster data. For characterizing the resilience of road networks, the dissertation proposed three indicators based on the concepts of resilience triangle and extreme travel time observations. The dissertation combined time series decomposition techniques with anomaly detection algorithms to segregate disaster effects from normal traffic patterns. For characterizing the risks of natural hazards to port systems, the dissertation employed disaster impact data along with international trade data and identified the ports with the highest risks.Civil, Architectural, and Environmental Engineerin

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

Integrated simulation models are emerging as an alternative for analyzing large-scale interdependent infrastructure networks due to their modeling advantages over traditional interdependency models. This paper presents an open-source integrated simulation package for the component-level analysis of interdependent power-, water-, transport networks. The simulation platform, named ’InfraRisk’ and developed in Python, can simulate network-wide effects of disaster-induced infrastructure failures and subsequent post-disaster restoration. InfraRisk consists of an infrastructure module, a hazard module, a recovery module, a simulation module, and a resilience quantification module. The infrastructure module integrates existing infrastructure network packages (wntr for water distribution systems, pandapower for power systems, and a static traffic assignment model for road transport systems) through an interface that facilitates the network-level simulation of infrastructure failures. The hazard module generates infrastructure component failures based on various disaster characteristics. The recovery module determines repair sequences and assigns repair crews based on predefined heuristics-based recovery strategies or model predictive control (MPC) based optimization. Based on the schedule, the simulation module simulates the consequences of the disaster impacts and the recovery actions on the performance of the interdependent network. The resilience quantification module offers system-level and consumer-level metrics to quantify both the risks and resilience of the integrated infrastructure networks against disaster events. InfraRisk provides a virtual platform for decision-makers to experiment and develop region-specific pre-disaster and post-disaster policies to enhance the overall resilience of interdependent urban infrastructure networks.ISSN:2210-670

AvaliaÃÃo do uso de membrana de polipropileno na neoformaÃÃo Ãssea de alveolo pÃs-exodontia: um estudo clÃnico e tomogrÃfico

nÃo hÃThe preservation of post-extraction alveolar ridge is one of the challenges of dentistry, especially when rehabilitation claim with endosseous implants. As a result of tooth loss, the residual alveoli tends to reabsorb, creating occasions where there is need for grafting surgery to rehabilitation through supported implant prosthesis. This condition can be prevented, among other techniques, by Guided Bone Regeneration. This study aimed to evaluate the effectiveness of a non-absorbable membrane on bone healing after tooth extraction alveoli sites. A study was conducted with 18 patients requiring extraction, a total of 20 sites, prior to the installation of the implant, which sought care in Face Defects Nucleos the Federal University of CearÃ. Was performed prior clinical and radiographic evaluation to surgical procedures, these were divided into two groups: the test group (n = 10) there was installation of non-absorbable membrane, and the control group (n = 10) the membrane was not used. All patients underwent computed tomography cone beam at fifteen and ninety days postoperatively. In both exams vertical and horizontal linear measurements were made through the ImageJ software, post-extraction alveolar center of fifteen and ninety days. It was observed that the retention time in the test group (0.45  0.78) showed if the distance significantly greater than the height of the control group (-2.25  0.97), in which can be seen a significant reduction in bone height (p <0.001). It was noted significant difference in the pattern of variation of the horizontal measured in treated groups with the membrane (0.45  1.92) and control (-1.22  0.49) (p=0,015). The use of non-absorbable membrane did not cause infection, swelling or allergic reaction immunoinflammatory site. The conditions evaluated, clinical and tomographic, noticed a bone maintenance height and widht of fresh alveoli sites can benefit from the installation of endosseous implants.A preservaÃÃo do rebordo alveolar pÃs-exodontia à um dos desafios da Odontologia, principalmente quando hà pretensÃo de reabilitaÃÃo com implantes endÃsseos. Em consequÃncia da perda dentÃria, o alvÃolo residual tende a reabsorver, criando ocasiÃes em que hà necessidade de cirurgias de enxertia para reabilitaÃÃo atravÃs de prÃtese implanto suportada. Essa condiÃÃo pode ser prevenida, entre outras tÃcnicas, atravÃs da RegeneraÃÃo Ãssea Guiada. O presente estudo objetivou avaliar a eficÃcia de uma membrana nÃo absorvÃvel no reparo Ãsseo de sÃtios de alvÃolos pÃs exodontia. Foi realizado um estudo com 18 pacientes necessitando de exodontia (20 sÃtios cirÃrgicos) prÃvia à instalaÃÃo de implante, que procuraram atendimento no NÃcleo de Defeitos da Face da Universidade Federal do CearÃ. Foi realizada avaliaÃÃo clÃnica e radiogrÃfica prÃvia aos procedimentos cirÃrgicos, estes foram divididos em dois grupos: no grupo teste (n=10) houve instalaÃÃo da membrana nÃo absorvÃvel, e no grupo controle (n=10) a membrana nÃo foi usada. Todos os pacientes realizaram tomografia computadorizada de feixe cÃnico aos quinze e noventa dias de pÃs-operatÃrio. Em ambos os exames foram realizadas mensuraÃÃes lineares verticais e horizontais, atravÃs do software ImageJ, do centro do alvÃolo pÃs-exodontia de quinze e noventa dias. Observou-se que a manutenÃÃo em altura do grupo teste (0,68Â0,57) mostrou-se superior à da distÃncia em altura do grupo controle (-2,25Â0,97), na qual se pÃde perceber reduÃÃo significativa de perda Ãssea (p<0.001). Houve diferenÃa significante no padrÃo de variaÃÃo da medida horizontal nos grupos tratado com a membrana (0,06Â1,20) e controle (-1,22Â0,49) (p=0,015). O uso da membrana nÃo absorvÃvel nÃo gerou infecÃÃo, inchaÃo ou reaÃÃo alÃrgica imunoinflamatÃria local. Nas condiÃÃes avaliadas notou-se de forma clÃnica e tomogrÃfica uma manutenÃÃo Ãssea em altura de sÃtios de alvÃolos frescos, podendo beneficiar a instalaÃÃo de implantes endÃsseos

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

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 are used to derive the cluster-level features by grouping component-level features based on their topological and functional characteristics. Finally, ML algorithms are used to develop models that predict the network-wide impacts of disruptive events using the cluster-level features. The applicability of the method is demonstrated using an interdependent power-water-transport testbed. The proposed method can be used to develop decision-support tools for post-disaster recovery of infrastructure networks

Designing resilient and economically viable water distribution systems: A Multi-dimensional approach

Enhancing the resilience of critical infrastructure systems requires substantial investment and entails trade-offs between environmental and economic benefits. To this aim, we propose a methodological framework that combines resilience and economic analyses and assesses the economic viability of alternative resilience designs for a Water Distribution System (WDS) and its interdependent power and transportation systems. Flow-based network models simulate the interdependent infrastructure systems and Global Resilience Analysis (GRA) quantifies three resilience metrics under various disruption scenarios. The economic analysis monetizes the three metrics and compares two resilience strategies involving the installation of remotely controlled shutoff valves. Using the Micropolis synthetic interdependent water-transportation network as an example, we demonstrate how our framework can guide infrastructure stakeholders and utility operators in measuring the value of resilience investments. Overall, our approach highlights the importance of economic analysis in designing resilient infrastructure systems

Designing resilient and economically viable water distribution systems: A Multi-dimensional approach

Enhancing the resilience of critical infrastructure systems requires substantial investment and entails trade-offs between environmental and economic benefits. To this aim, we propose a methodological framework that combines resilience and economic analyses and assesses the economic viability of alternative resilience designs for a Water Distribution System (WDS) and its interdependent power and transportation systems. Flow-based network models simulate the interdependent infrastructure systems and Global Resilience Analysis (GRA) quantifies three resilience metrics under various disruption scenarios. The economic analysis monetizes the three metrics and compares two resilience strategies involving the installation of remotely controlled shutoff valves. Using the Micropolis synthetic interdependent water-transportation network as an example, we demonstrate how our framework can guide infrastructure stakeholders and utility operators in measuring the value of resilience investments. Overall, our approach highlights the importance of economic analysis in designing resilient infrastructure systems

Predicting Resilience of Interdependent Urban Infrastructure Systems

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.ISSN:2169-353