High spatial-resolution loss estimation using dense array strong-motion near-fault records. Case study for Hveragerði and the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mrow><mml:msub><mml:mi mathvariant="bold-italic">M</mml:mi><mml:mi mathvariant="bold">w</mml:mi></mml:msub></mml:mrow></mml:math>6.3 Ölfus earthquake, South Iceland

The most recent and costliest damaging earthquake in Iceland is the Mw6.3 29-May-2008 Ölfus to date. In particular, Hveragerði town located extreme near-fault region, suffered intense horizontal peak ground accelerations (PGA) of ∼ 40–90%g large amplitude long-period pulses, recorded on a dense urban strong-motion array town. this study we collated high-spatial resolution exposure database (building-by-building) complete with actual reported losses classified buildings by building materials construction year according code design requirements place at time. We took advantage data evaluated set well-known motion intensity measures (IM), including PGA, pseudo-acceleration response spectra short-to-long periods, Arias Intensity Cumulative Absolute Velocity. applied empirical Bayesian kriging geostatistical analyses generate high-resolution shakemaps provide IM estimates for each building. showed significant systematic variation IMs across small area, lowest motions observed centrally highest values outskirts. Furthermore, correlation analysis was carried out damage ratio IMs, but only low-to-moderate correlations were observed. A key reason incurred primarily due non-structural components, which do not apply. seismic loss assessment scenario both building-by-building, municipality levels spatial resolution. local global fragility models associated detail typologies identified based SERA taxonomy scheme. results show that functions severely underestimate performance stock, except one-story reinforced concrete buildings, while overall masonry predicted losses. On other hand, conformed well damages timber buildings. high-spatial-resolution predictions gave better correlated typologies.