In historic city centers the mitigation of seismic risk is dependent on the possibility of implementing strengthening programs. Given the cultural and economic value attached to the historic structures, however, interventions should be tailored to suit aesthetic and structural requirements of each building type, and provide sufficient reliability of performance in future earthquakes. A simple analytical model is developed to calculate load factors associated with various collapse mechanisms of wall assemblies, and vulnerability functions are derived. An application shows the capability of the procedure to quantify reduction in vulnerability associated with strengthening implementations for different typologies.

Building collapse is the dominant cause of casualties during earthquakes. In order to better predict human fatalities, the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) program requires collapse fragility functions for global building types. The collapse fragility is expressed as the probability of collapse at discrete levels of the input hazard defined in terms of macroseismic intensity. This article provides a simple procedure for quantifying collapse fragility using vulnerability criteria based on the European Macroseismic Scale (1998) for selected European building types. In addition, the collapse fragility functions are developed for global building types by fitting the beta distribution to the multiple experts’ estimates for the same building type (obtained from EERI's World Housing Encyclopedia (WHE)‐PAGER survey). Finally, using the collapse probability distributions at each shaking intensity level as a prior and field‐based collapse‐rate observations as likelihood, it is possible to update the collapse fragility functions for global building types using the Bayesian procedure.

Multi-hazard risk assessment of building portfolios is of primary importance in natural-hazard-prone regions, particularly for the prioritisation of disaster risk reduction and resilience-enhancing strategies. In this context, cultural heritage assets require special consideration because of their high vulnerability to natural hazards – due to ageing and types of construction – and their strong links with communities from both an economic and a historical–sociocultural perspective. This paper introduces a multi-hazard risk prioritisation framework specifically developed for cultural heritage assets. The proposed framework relies on a multilevel rapid-visual-survey (RVS) form for the multi-hazard exposure data collection and risk prioritisation of case-study assets. Because of the multilevel architecture of the proposed RVS form, based on three levels of refinement and information, an increasing degree of accuracy can be achieved in the estimation of structural vulnerability and, ultimately, structural risk of the considered assets. At the lowest level of refinement, the collected data are used for the computation of seismic-risk and wind-risk prioritisation indices, specifically calibrated in this study for cultural heritage assets with various structural and non-structural features. The resulting indices are then combined into a unique multi-hazard risk prioritisation index in which the intangible value of cultural heritage assets is also considered. This is achieved by defining a score expressing the cultural significance of the asset. The analytic hierarchy process is extensively used throughout the study to reduce the subjectivity involved in the framework, thus obtaining a simplified yet robust approach which can be adapted to different building typologies. The proposed framework is applied to 25 heritage buildings in Iloilo City, Philippines, for which innovative, non-invasive techniques and tools for improved surveying have also been tested. Thermal and omnidirectional cameras have helped in the collection of structural data, together with drones for the inspection of roofs. Results of the study are presented and critically discussed, highlighting advantages and drawbacks of the use of new technologies in this field.