The main factor which makes fires following earthquakes (FFE) different from other built environment fires is the ignition process. Ignitions following earthquake (IFE) is directly related to built environment and how buildings respond to strong ground motions and other seismic-related hazards. The ignition models used in many FFE hazard models are based on statistical correlation between extent of built environment and strong ground motion parameters. Damage pattern seen in recent FFE cases reveal that most of fires during past earthquakes started from inside buildings. Intra-structure IFE depends on many factors, the most important one is strong ground motion and how it shakes buildings and their contents. Various kinds of ignitions can be generated as the results of damage to structural elements, nonstructural elements and contents of a building. For example ignitions caused by nonstructural elements (e.g., utility networks) are more related to overall building damage state and degrees of differential displacement, while ignitions caused by contents (e.g., stove, TV) are more controlled by the maximum response acceleration felt at each storey. This paper proposes an analytical approach to model probabilistic IFE. Factors controlling IFE such as strong ground motion, building vulnerability, content overturning, utility breakage, etc., are modeled probabilistically in order to estimate the overall ignition probability inside a building. This paper tries to provide an analytical approach towards modeling sources of intra-structure ignitions and their associated uncertainties. The approach is introduced as an alternative solution to the statistical ignition model currently used in many FFE hazard models. However, detailed studies towards quantification and calibration of probability functions used in this study are beyond the scope of this paper and require further statistical data and investigation. A computer program has been also designed and developed in this research which models ignitions following earthquakes for building stock. This analytical tool is well designed inside an open-source GIS platform which provides powerful spatial analyses as well as effective visualization capabilities. This paper also presents the preliminary results for a probabilistic ignition following scenario earthquake for one of the city district in Tehran. The results are also compared to those derived using the HAZUS ignition model.

Presented herein is a new set of empirical models for prediction of horizontal peak ground velocity (PGV), peak ground acceleration (PGA), and 5% damped elastic response spectra in the 0.01–10 s vibration period range in Iran. The models are based on a carefully curated databank of Iranian strong ground motions composed of 1350 pairs of orthogonal horizontal acceleration time histories from 370 regional earthquakes with $4.5≤Mw≤7.4$ and source‐to‐site distances up to 200 km. The adaptive wavelet denoising approach used for waveform processing in this study allows extending the range of oscillator periods for which predictions can be made. We investigated the possible regional dependency of strong ground motions from three different regions (Alborz–Azerbaijan–Kopeh Dagh, Zagros, and central Iran–Makran) of the Iranian plateau through analysis of variance. Our results show that data from these regions can be combined into a single database because only a few magnitude–distance intervals exhibit significant regional differences. Local site effects are taken into account using a site classification scheme based on $VS30$⁠. The predictive models also include style‐of‐faulting (SoF) terms. Because of the paucity of data for normal‐faulting events, the applicability of the proposed attenuation models is limited to unspecified, strike‐slip, and reverse SoFs. We propose four models that employ point‐source (hypocentral and epicentral) and extended‐source (rupture and Joyner–Boore) distance metrics to enhance the flexibility of the prediction tool within the context of seismic hazard assessment studies. The results of between‐event and within‐event residuals show that the proposed new models are unbiased. We present a detailed comparison of our results with a selection of local, regional, and global predictive models developed by other authors.