A Methodology to Account for One‐Way Infrastructure Interdependency in Preservation Activity Scheduling

Alinizzi, Majed; Chen, Sikai; Labi, Samuel; Kandil, Amr

doi:10.1111/mice.12380

Cited by 16 publications

(13 citation statements)

References 71 publications

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“…Several studies have proposed approaches to assess the reliability, resilience, vulnerability, and failure process of power, transportation, and water supply networks individually (Barabási & Albert, 1999; Buldyrev, Parshani, Paul, Stanley, & Havlin, 2010; Jenelius & Mattsson, 2012; Ouyang & Fang, 2017; Pietrucha‐Urbanik & Tchórzewska‐Cieślak, 2018; Sumalee & Kurauchi, 2006; Ulak, Kocatepe, Sriram, Ozguven, & Arghandeh, 2018). However, these critical infrastructures are interconnected and interdependent (Alinizzi, Chen, Labi, & Kandil, 2018; Homeland Security, 2019; Martani, Jin, Soga, & Scholtes, 2016). Considering the increased connectedness and interdependencies among infrastructure systems, studies have proposed a holistic approach to assess the resilience to disruptions (Hasan & Foliente, 2015; Lu et al., 2018; Pant, Thacker, Hall, Alderson, & Barr, 2018; Rinaldi, Peerenboom, & Kelly, 2001; Sriram et al., 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Roy

Hasan

Mozumder

2020

Computer aided Civil Eng

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Rapid identification of infrastructure disruptions during a disaster plays an important role in restoration and recovery operations. Due to the limitations of using physical sensing technologies, such as the requirement to cover a large area in a short period of time, studies have investigated the potential of social sensing for damage/disruption assessment following a disaster. However, previous studies focused on identifying whether a social media post is damage related or not. Hence, advanced methods are needed to infer actual infrastructure disruptions and their locations from such data. In this paper, we present a multilabel classification approach to identify the cooccurrence of multiple types of infrastructure disruptions considering the sentiment toward a disruption-whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). In addition, we propose a dynamic mapping framework for visualizing infrastructure disruptions. We use a geo-parsing method that extracts location from the texts of a social media post. We test the proposed approach using Twitter data collected during hurricanes Irma and Michael. The proposed multilabel classification approach performs better than a baseline method (using simple keyword search and sentiment analysis). We also find that disruption-related tweets, based on specific keywords, do not necessarily indicate an actual disruption. Many tweets represent general conversations, concerns about a potential disruption, and positive emotion for not being affected by any disruption. In addition, a dynamic disruption map has potential in showing county and point/coordinate level disruptions. Identifying disruption types and their locations is vital for disaster recovery, response, and relief actions. By inferring the co-occurrence of multiple disruptions, the proposed approach may help coordinate among infrastructure service providers and disaster management organizations. 1 INTRODUCTION Cities and communities all over the world largely depend on critical infrastructure systems/services such as electrical power, water distribution, communication services, and transportation networks. The growing interconnectedness and © 2020 Computer-Aided Civil and Infrastructure Engineering interdependency among these systems have changed the organizational and operational factors and increased the vulnerability in the face of unwanted disruptions. These systems provide critical services to a large population, and thus when disrupted they affect our quality of life, local and regional economy, and the overall community wellbeing. The need to

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Section: Literature Reviewmentioning

confidence: 99%

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Roy

Hasan

Mozumder

2020

Computer aided Civil Eng

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“…The aforementioned interconnection of systems is often referred to as interdependency, which, in the case of infrastructure systems, may occur between different types of systems, between different stages of system development, and between the different operational and maintenance phases of systems [37]. Interdependencies have a significant impact on the disruption dynamics of critical infrastructure.…”

Section: Interdependenciesmentioning

confidence: 99%

“…Interdependencies can act as an intensifying structure as they transport effects from different levels and places to others [38]. It is also worth noting that the diversity of systems, together with the interdependencies between them, make urban areas vulnerable to cascading effects [37].…”

Section: Interdependenciesmentioning

confidence: 99%

Critical Infrastructures: The Operational Environment in Cases of Severe Disruption

et al. 2019

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The functioning and resilience of modern societies have become more and more dependent on critical infrastructures. Severe disturbance to critical infrastructure is likely to reveal chaotic operational conditions, in which infrastructure service providers, emergency services, police, municipalities, and other key stakeholders must act effectively to minimize damages and restore normal operations. This paper aims to better understand this kind of operational environment resulting from, for example, a terrorist attack. It emphasizes mutual interdependencies among key stakeholders in such situations. The empirical contribution is based on observations from a workshop, in which participants representing the critical services and infrastructures in Finland discussed in thematic groups. Two scenarios guided the workshop discussions; nationwide electricity grid disruption and presumably intentionally contaminated water supply in a city. The results indicate that more attention should be paid to the interdependencies between critical infrastructures, as well as to the latent vulnerabilities hidden inside the systems. Furthermore, producing security seems to require continuous interaction and creation of meanings between extremely different actors and logics. This implies a need for changes in thinking, particularly concerning the ability to define problems across conventional administrative structures, geographical boundaries and conferred powers.

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“…Various maintenance, repair, and rehabilitation (MRR) activities, whether reactive or preventive, are usually implemented to restore the structural integrity and operational functionality to a superior level so as to keep the asset system working properly. As an MRR activity does not necessarily recover an asset to an as‐good‐as‐new state, it is imperative to consider both asset deterioration and maintenance effectiveness in long‐term performance prediction and capital planning (Alinizzi, Chen, Labi, & Kandil, ; El Hajj, Schoefs, Castanier, & Yeung, ; Labi & Sinha, ; Memarzadeh & Pozzi, ; Smith Freeman, & Pendleton, ; Sousa, Alçada‐Almeida, & Coutinho‐Rodrigues, ). While the significance has been long recognized (e.g., Lytton, ; Markow, ; Rajagopal & George, ), the modeling of maintenance effectiveness has been until now mainly limited in pavement research (e.g., Ouyang & Madanat ; Pantelias & Zhang, ).…”

Section: Introductionmentioning

confidence: 99%

“…While the significance has been long recognized (e.g., Lytton, 1987;Markow, 1991;Rajagopal & George, 1991), the modeling of maintenance effectiveness has been until now mainly limited in pavement research (e.g., Ouyang & Madanat 2004;Pantelias & Zhang, 2009). Outside of pavement management, maintenance effectiveness was often integrated into the partially observable Markov decision process (POMDP) models (Alinizzi et al, 2018;Memarzadeh & Pozzi, 2016;Osman, Atef, & Moselhi, 2012;Papakonstantinou, Andriotis, & Shinozuka, 2018;Pozzi, Memarzadeh, & Klima, 2017;Srinivasan & Parlikad, 2013;Thöns, 2018;Van Erp & Orcesi, 2018). However, these POMDP studies take an assumed maintenance effectiveness model with little empirical support, if any at all.…”

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confidence: 99%

Integrative modeling of performance deterioration and maintenance effectiveness for infrastructure assets with missing condition data

Lin

Yuan

Tovilla

2019

Computer aided Civil Eng

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Deterioration modeling is an important analytical component in infrastructure asset management. It concerns the prediction of performance and remaining service life of assets of different designs under omnifarious working environments. For long‐term prediction, it also requires to characterize maintenance effectiveness because maintenance activities do not necessarily bring an asset to a completely renewed status. Deterioration modeling research has for decades been largely focusing on the modeling of the natural deterioration process per se, whereas the modeling of maintenance effectiveness is only a recent focus of investigation, mainly in pavement research. In practice, the asset conditions immediately before and after a given maintenance treatment both are not often known. This has made the modeling of maintenance effectiveness and long‐term deterioration prediction a challenging task. To bridge the gap, this paper presents a novel approach that integrates the modeling of deterioration and maintenance effectiveness into one process. The natural deterioration of asset performance is modeled as a continuous‐time Markov chain, whereas the effectiveness of a maintenance measure is modeled as a discrete‐time Markov chain. To account for missing condition data before and after the maintenance event, the paper also develops a robust statistical method based on Markov chain Monte Carlo simulation. A real‐life case study on a municipal sewer pipe system is carried out for demonstration of the proposed integrated modeling approach. The functional deterioration of sewer pipes and the effectiveness of flushing operations that target to bring flow capacity to intact state are modeled. Influences of pipe length, diameter, slope, and sewershed area are examined. The present work is a valuable step toward development of evidence‐based risk‐informed asset management framework.

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A Methodology to Account for One‐Way Infrastructure Interdependency in Preservation Activity Scheduling

Cited by 16 publications

References 71 publications

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Critical Infrastructures: The Operational Environment in Cases of Severe Disruption

Integrative modeling of performance deterioration and maintenance effectiveness for infrastructure assets with missing condition data

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