Reliability Evaluation Methods of Large-Scale Water Supply Networks During Seismic Disaster

Isoyama, Ryoji; Katayama, Tsuneo

doi:10.2208/jscej1969.1982.321_37

Cited by 13 publications

(8 citation statements)

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“…Every simulation-based paper reviewed used lifeline infrastructure data in some capacity. Lifeline systems are commonly represented as connected graphs [26,28,34,35]. Component failure rates are frequently obtained from other works [26,32,36,37].…”

Section: Data Set Featuresmentioning

confidence: 99%

“…Lifeline systems are commonly represented as connected graphs [26,28,34,35]. Component failure rates are frequently obtained from other works [26,32,36,37]. Another common data set feature is repair crew information such as repair rate/efficiency and number of crews [27,28,29,30,31,32,38].…”

Section: Data Set Featuresmentioning

confidence: 99%

“…Luna et al [38] researches water supply system restoration from earthquakes using discrete event simulation and a colored Petri nets approach. They use the data set of Isoyama and Katayama [26]. The data set includes the network representation of the trunk water supply system for Tokyo, damage probabilities for system components, repair crews, trucks, replacement pipes and excavators.…”

Section: Notable Data Setsmentioning

confidence: 99%

“…The authors compare their model against [26]; however, they do not use baseline restoration data to test the model.…”

Section: Notable Data Setsmentioning

confidence: 99%

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Modeling of Lifeline Infrastructure Restoration Using Empirical Quantitative Data

Martell,

Miles,

Choe

2020

Preprint

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Disaster recovery is widely regarded as the least understood phase of the disaster cycle. In particular, the literature around lifeline infrastructure restoration modeling frequently mentions the lack of empirical quantitative data available.Despite limitations, there is a growing body of research on modeling lifeline infrastructure restoration, often developed using empirical quantitative data.This study reviews this body of literature and identifies the data collection and usage patterns present across modeling approaches to inform future efforts using empirical quantitative data. We classify the modeling approaches into simulation, optimization, and statistical modeling. The number of publications in this domain has increased over time with the most rapid growth of statistical modeling. Electricity infrastructure restoration is most frequently modeled, followed by the restoration of multiple infrastructures, water infrastructure, and transportation infrastructure. Interdependency between multiple infrastructures is increasingly considered in recent literature. Researchers gather the data from a variety of sources, including collaborations with utility companies, national databases, and post-event damage and restoration reports. This study provides discussion and recommendations around data usage practices within the lifeline restoration modeling field. Following the recommendations would facilitate the development of a community of practice around restoration modeling and

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Section: Data Set Featuresmentioning

confidence: 99%

Section: Data Set Featuresmentioning

confidence: 99%

Section: Notable Data Setsmentioning

confidence: 99%

“…The authors compare their model against [26]; however, they do not use baseline restoration data to test the model.…”

Section: Notable Data Setsmentioning

confidence: 99%

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Modeling of Lifeline Infrastructure Restoration Using Empirical Quantitative Data

Martell,

Miles,

Choe

2020

Preprint

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“…Ανάπτυξη ολοκληρωμένης μεθοδολογίας διαχείρισης της σεισμικής διακινδύνευσης και ο υπολογισμός των παροχών και των πιέσεων για κάθε κατάσταση αστοχίας πραγματοποιήθηκε με τη μέθοδο Newton-Raphson. Ανάλογη προσέγγιση ακολούθησαν και οι Isoyama και Katayama (1981) που υπολόγισαν την πιθανότητα αστοχίας των αγωγών συναρτήσει του εδάφους, του υλικού κατασκευής τους, του βάθους τοποθέτησης και του μήκους τους, βασιζόμενοι σε καταγραφές προηγούμενων σεισμών. Με τη βοήθεια της μεθόδου Monte Carlo, οι Isoyama και Katayama εκτίμησαν τις καταστάσεις αστοχίας ενώ το δίκτυο επιλύθηκε υδραυλικά με τη μέθοδο Πιθανότητα Ροής και τον τύπο Hazen-Williams.…”

Section: -5unclassified

Συμβολή Στην Ανάλυση Της Σεισμικής Τρωτότητας Δικτύων Κοινής Ωφέλειας Σε Αστικό Περιβάλλον. Ανάπτυξη Ολοκληρωμένης Μεθοδολογίας Διαχείρισης Της Σεισμικής Διακινδύνευσης

Alexoudi¹,

Αλεξούδη²

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Γενικές μελέτες τρωτότητας _____________________________________________ 2-2 2.2.2 Μελέτες με την βοήθεια σεναρίων χωρίς αναλύσεις εξυπηρετικότητας _____________ 2-2 2.2.3 Μελέτες με την βοήθεια σεναρίων με αναλύσεις εξυπηρετικότητας________________ 2-3 2.2.4 Μελέτες με στόχο την δημιουργία στρατηγικών αποκατάστασης __________________ 2-4 2.2.5 Άλλες σχετικές μελέτες _________________________________________________ 2-5 2.3 ΙΣΤΟΡΙΚΗ ΕΞΕΛΙΞΗ ΤΩΝ ΜΕΘΟΔΟΛΟΓΙΩΝ ΣΕΙΣΜΙΚΗΣ ΔΙΑΚΙΝΔΥΝΕΥΣΗΣ___ 2-6 2.4 ΑΠΟΤΙΜΗΣΗ ΤΗΣ ΠΡΟΟΔΟΥ ΤΩΝ ΜΕΘΟΔΟΛΟΓΙΩΝ ΣΕΙΣΜΙΚΗΣ ΔΙΑΚΙΝΔΥΝΕΥΣΗΣ ______________________________________________________ 2-8 2.5 ΑΒΕΒΑΙΟΤΗΤΕΣ ΣΤΙΣ ΜΕΘΟΔΟΛΟΓΙΕΣ ΣΕΙΣΜΙΚΗΣ ΔΙΑΚΙΝΔΥΝΕΥΣΗΣ _____ 2-10 2.5.1 Αβεβαιότητες στη συλλογή στοιχείων _____________________________________ 2-10 2.5.2 Αβεβαιότητες στην αποτίμηση της σεισμικής δράσης. _________________________ 2-11 2.5.2.1 Αιτιοκρατική και πιθανοτική προσέγγιση του σεισμικού φορτίου__________________ 2-11 2.5.3 Αβεβαιότητες στις σχέσεις τρωτότητας ____________________________________ 2-13 2.5.4 Αβεβαιότητες στον ορισμό του επιπέδου αστοχίας ___________________________ 2-13 2.5.5 Αβεβαιότητες στην εκτίμηση του οικονομικού κόστους σε ένα πολυδιάστατο και πολύπλοκο αστικό περιβάλλον __________________________________________ 2-13 ΒΙΒΛΙΟΓΡΑΦΙΑ _____________________________________________________________ 2-14 3 ΔΙΚΤΥΑ ΚΟΙΝΗΣ ΩΦΕΛΕΙΑΣ: περιγραφή, απογραφή τυπολογία, αστοχία, αλληλεπιδράσεις. Εφαρμογή στα Δ.Κ.Ω της Θεσ/νίκης και στα δίκτυα ύδρευσης της Λευκάδας και της Duzce______________________________________________________ 3-1 3.1 ΕΙΣΑΓΩΓΗ _____________________________________________________________ 3-1 3.2 ΙΣΤΟΡΙΚΗ ΕΞΕΛΙΞΗ ΤΩΝ ΚΑΤΑΛΟΓΩΝ ΑΠΟΓΡΑΦΗΣ ΠΛΗΡΟΦΟΡΙΩΝ ΚΑΙ ΤΗΣ ΤΥΠΟΛΟΓΙΑΣ ΤΩΝ Δ.Κ.Ω ________________________________________________ 3-2 3.3 ΔΙΚΤΥΟ ΥΔΡΕΥΣΗΣ _____________________________________________________ 3-4 II Περιεχόμενα Συμβολή στην ανάλυση της σεισμικής τρωτότητας Δικτύων Κοινής Ωφέλειας σε αστικό περιβάλλον. Περιεχόμενα III Ανάπτυξη ολοκληρωμένης μεθοδολογίας διαχείρισης της σεισμικής διακινδύνευσης

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Earthquake physical damage and functional serviceability of lifeline network models

Kawakami¹

1990

Earthq Engng Struct Dyn

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SUMMARYThe connectivity of lifeline systems is studied in this paper in terms of two ratios: 'damage ratio', which is the degree of physical damage to the system, and is defined as the expected number of failures per unit length or per link; and 'service ratio', which is a measurement of system operation. First, the relationship between the two ratios in a historic event is examined. Second, a method is developed for determining this relationship in simple network systems immediately following an earthquake. Density function, expected value and covariance of service ratio are calculated as functions of the damage ratio. Even in a simple checkerboard-type network, the density function of the service ratio has two peaks. Third, the variation in reliability of the network is examined with respect to difference in scale and configuration of the network, which may accompany growth of the community. When the damage ratio is small, the service ratio of a complex network is as large as, or larger, than that of a simple network. This pattern is, however, reversed for larger damage ratios. Finally, the relationship prior to operation restoration is examined. During the initial stages of restoration, the service ratio increases steeply, almost linearly, with decrease in the damage ratio. The increase then slows in the last stages of restoration.

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Reliability Evaluation Methods of Large-Scale Water Supply Networks During Seismic Disaster

Cited by 13 publications

References 0 publications

Modeling of Lifeline Infrastructure Restoration Using Empirical Quantitative Data

Modeling of Lifeline Infrastructure Restoration Using Empirical Quantitative Data

Earthquake physical damage and functional serviceability of lifeline network models

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