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Abstract. Currently, some catastrophe models used by the insurance industry account for liquefaction by applying a simple factor to shaking-induced losses. The factor is based only on local liquefaction susceptibility and this highlights the need for a more sophisticated approach to incorporating the effects of liquefaction in loss models. This study compares 11 unique models, each based on one of three principal simplified liquefaction assessment methods: liquefaction potential index (LPI) calculated from shear-wave velocity, the HAZUS software method and a method created specifically to make use of USGS remote sensing data. Data from the September 2010 Darfield and February 2011 Christchurch earthquakes in New Zealand are used to compare observed liquefaction occurrences to forecasts from these models using binary classification performance measures. The analysis shows that the best-performing model is the LPI calculated using known shear-wave velocity profiles, which correctly forecasts 78 % of sites where liquefaction occurred and 80 % of sites where liquefaction did not occur, when the threshold is set at 7. However, these data may not always be available to insurers. The next best model is also based on LPI but uses shear-wave velocity profiles simulated from the combination of USGS VS30 data and empirical functions that relate VS30 to average shear-wave velocities at shallower depths. This model correctly forecasts 58 % of sites where liquefaction occurred and 84 % of sites where liquefaction did not occur, when the threshold is set at 4. These scores increase to 78 and 86 %, respectively, when forecasts are based on liquefaction probabilities that are empirically related to the same values of LPI. This model is potentially more useful for insurance since the input data are publicly available. HAZUS models, which are commonly used in studies where no local model is available, perform poorly and incorrectly forecast 87 % of sites where liquefaction occurred, even at optimal thresholds. This paper also considers two models (HAZUS and EPOLLS) for estimation of the scale of liquefaction in terms of permanent ground deformation but finds that both models perform poorly, with correlations between observations and forecasts lower than 0.4 in all cases. Therefore these models potentially provide negligible additional value to loss estimation analysis outside of the regions for which they have been developed.

Purpose
The purpose of this paper is to synthesise critical success factors (CSFs) for advancing post-disaster infrastructure recovery and underpinning recovery authorities in decision making when facing future disasters.


Design/methodology/approach
The seismic recovery after the Canterbury (NZ) earthquake sequence in 2010-2011 was selected as a case study for identifying CSFs for an efficient recovery of infrastructure post-disaster. A combination of research approaches, including archival study, observations and semi-structured interviews were conducted for collecting data and evidences by engaging with participants involved at various tiers in the post-disaster recovery and reconstruction. The CSFs are evaluated and analysed by tracking the decision-making process, examining resultant consequences and foreseeing onwards challenges.


Findings
Six salient CSFs for strengthening infrastructure recovery management after disasters are identified. Furthermore, the study shows how each of these CSFs have been incorporated into the decision-making process in support of the post-disaster recovery and what difficulties encountered in the recovery process when implementing.


Practical implications
The proposed CSFs provide a future reference and guidance to be drawn on by decision makers when project-managing post-disaster recovery operations.


Originality/value
The value of the paper is that it bridges the gap between managerial contexts and technical aspects of post-disaster recovery process in an effort to rapidly and efficiently rebuild municipal infrastructure.

Historic areas (HAs) are highly vulnerable to natural hazards, including earthquakes, that can cause severe damage, if not total destruction. This paper proposes methods that can be implemented through a geographical information system to assess earthquake-induced physical damages and the resulting impacts on the functions of HAs and to monitor their resilience. For the assessment of damages, making reference to the universally recognised procedure of convoluting hazard, exposure, and vulnerability, this paper proposes (a) a framework for assessing hazard maps of both real and end-user defined earthquakes; (b) a classification of the exposed elements of the built environment; and (c) an index-based seismic vulnerability assessment method for heritage buildings. Moving towards the continuous monitoring of resilience, an index-based assessment method is proposed to quantify how the functions of HAs recover over time. The implementation of the proposed methods in an ad hoc customized WebGIS Decision Support System, referred to as ARCH DSS, is demonstrated in this paper with reference to the historic area of Camerino-San Severino (Italy). Our conclusions show how ARCH DSS can inform and contribute to increasing awareness of the vulnerabilities of HAs and of the severity of the potential impacts, thus supporting effective decision making on mitigation strategies, post-disaster response, and build back better. ; The main features of the ARCH DSS come from the exploitation of a DSS realized within the EU FP7 project "CIPRNet", which has allowed the design and the realization of the core structure of the CIPCast DSS. Subsequently, the basic CIPCast DSS has been improved by using funds provided by the Italian Ministry for University and Research through PON project RAFAEL ("System for Risk Analysis and Forecast for Critical Infrastructures in the AppenninEs dorsaL Regions", grant agreement no. ARS01_00305). Previous activities made in PON Project RoMA (Resilience enhancement of Metropolitan Areas, project nr. SCN_00064) have provided the basic technologies on which the ARCH DSS has been built upon. These funding agencies are greatly acknowledged. The research activities and the results that are the subject of the present work have been funded by the European project "ARCH—Advancing Resilience of historic areas against Climate-related and other Hazards" funded by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 820999. The sole responsibility for the content of this publication lies with the authors. It does not necessarily represent the opinion of the European Union. Neither the EASME nor the European Commission are responsible for any use that may be made of the information contained therein. ; Published ; 461 ; 5T. Sismologia, geofisica e geologia per l'ingegneria sismica ; JCR Journal