Modification and Validation of an Assessment Model of Post-Earthquake Lifeline Serviceability Based on the Great East Japan Earthquake Disaster

Abstract

The assessmentmodel for post-earthquake lifeline serviceability of electric power, water, and city gas supply systems has been modified and applied to the Great East Japan Earthquake Disaster with the detailed and updated data required for damage assessment. The estimated and observed population affected after the main shock and its decreasing process has been compared for validation on a prefectural basis. Reasonable agreement has been found between the estimation and observation except for discrepancies attributed to unconsidered factors, such as damage to hierarchically high facilities caused by tsunami, liquefaction, and/or ground shaking.

A stochastic model for time series prediction of the number of post-earthquake fire ignitions in buildings based on the ignition record for the 2011 Tohoku Earthquake

“…There is a possibility that such a tendency was related to the situation of electrical power. According to the analysis of the 1995 Kobe Earthquake and the 2011 Tohoku Earthquake (Nojima et al, 2003; Nojima and Kato, 2014), in the areas exposed to larger seismic intensity, the power outage rates were larger immediately after the earthquakes and the power recovered slower. The cumulative ignition rates later than 7 days after the earthquake were larger in the areas exposed to larger seismic intensity.…”

Post-disaster infrastructure restoration: A comparison of events for future planning

“…The term functionality can represent a wide range of variables such as; the population without service [53], connections not receiving service [17,38,68], or the percentage of operable nodes/ links [2] amongst others. Depending on the spatial and temporal resolution of collected data, variations in population densities can provide a range of functionality estimates.…”

“…Aside from a simple linear recovery, a range of shapes are proposed in the literature to represent restoration such as; exponential of each concavity [7,37], trigonometric [11,71], gamma distribution [44,53], and mechanically damped curves [11] amongst others. Such functions tend to suit those datasets where recovery is modeled back to the initial pre-disruption conditions due to observed datasets commonly showing restoration rates slowing over time [20,56,80].…”

“…Similarly, though calibration to the 1995 Hyogoken-Nanbu Earthquake, Nojima and Kato [53] predict outage durations and affected populations for Japanese earthquakes based on shaking intensity, lifeline service penetration, and population density predictors.…”

Optimal Restoration Strategy of a Water Pipeline Network in Surigao City, Philippines

“…al. 2002), utility lifelines (Nojima 2012), redundancy index of lifeline systems (Hoshiya and Yamamoto 2002). In 2004, Ueno et.…”