Major faults of the Tien Shan, Central Asia, have long repeat times, but fail in large (MwE 7+) earthquakes. In addition, there may be smaller, buried faults off the major faults which are not properly characterized or even recognized as active. These all pose hazard to cities along the mountain range front such as Almaty, Kazakhstan. Here, we explore the seismic hazard and risk for Almaty from specific earthquake scenarios. We run three historical-based earthquake scenarios (1887 Verny MwE 7.3, 1889 Chilik MwE 8.0 and 1911 Chon-Kemin MwE 8.0) on the current population and four hypothetical scenarios for near-field faulting. By making high-resolution Digital Elevation Models (DEMs) from SPOT and Pleiades stereo optical satellite imagery, we identify fault splays near and under Almaty. We assess the feasibility of using DEMs to estimate city building heights, aiming to better constrain future exposure datasets. Both Pleiades and SPOT-derived DEMs find accurate building heights of the majority of sampled buildings within error; Pleiades tri-stereo estimates 80% of 15 building heights within one sigma and has the smallest average percentage difference to field-measured heights (14%). A moderately sized MwE 6.5 earthquake rupture occurring on a blind thrust fault, under folding north of Almaty is the most damaging scenario explored here due to the modeled fault stretching under Almaty, with estimated 12,300E5,000 completely damaged buildings, 4,100 E 3,500 fatalities and an economic cost of 4,700 E 2,700 Million US dollars (one sigma uncertainty). This highlights the importance of characterizing location, extent, geometry, and activity of small faults beneath cities.

The incorporation of machine learning (ML) algorithms in earthquake engineering can improve existing methodologies and enable new frameworks to solve complex problems. In the present study, the use of artificial neural networks (ANNs) for the derivation of seismic vulnerability models for building portfolios is explored. Large sets of ground motion records (GMRs) and structural models representing the building stock in the Balkan region were used to train ANNs for the prediction of structural response, damage and economic loss conditioned on a vector of ground shaking intensity measures. The structural responses and loss ratios (LRs) generated using the neural networks were compared with results based on traditional regression models using scalar intensity measures in terms of efficiency, sufficiency, bias and variability. The results indicate a superior performance of the ANN models over traditional approaches, potentially allowing a greater reliability and accuracy in scenario and probabilistic seismic risk assessment.

Seismic fragility and vulnerability assessment is an essential step in the evaluation of probabilistic seismic risk. Ideally, models developed and calibrated for the building portfolio of interest would be readily available. However, the lack of damage data and insufficient analytical studies lead to a paucity of fragility and vulnerability models, in particular in the developing world. This study describes the development of an analytical fragility and vulnerability model covering the most common building classes at the global scale. Nearly five hundred functions were developed to cover the majority of combinations of construction material, height, lateral load resisting system and seismic design level. The fragility and vulnerability were derived using nonlinear time-history analyses on equivalent single-degree-of-freedom oscillators and a large set of ground motion records representing several tectonic environments. The resulting fragility and vulnerability functions were validated through a series of tests which include the calculation of the average annual loss ratio for a number of locations, the comparison of probabilities of collapse across all building classes, and the repetition of past seismic events. The set of vulnerability functions was used for the assessment of economic losses due to earthquakes as part of the global seismic risk model supported by the Global Earthquake Model Foundation.

A uniform and comprehensive classification system, often referred to as taxonomy, is fundamental for the characterization of building portfolios for natural hazard risk assessment. A building taxonomy characterizes assets according to attributes that can influence the likelihood of damage due to the effects of natural hazards. Within the scope of the Global Earthquake Model (GEM) initiative, a building taxonomy (GEM Building Taxonomy V2.0) was developed with the goal of classifying buildings according to their seismic vulnerability. This taxonomy contained 13 building attributes, including the main material of construction, lateral load-resisting system, date of construction and number of stories. Since its release in 2012, the taxonomy has been used by hundreds of experts working on exposure and risk modeling efforts. These applications allowed the identification of several limitations, which led to the improvement and expansion of this taxonomy into a new classification system compatible with multi-hazard risk assessment. This expanded taxonomy (named GED4ALL) includes more attributes and several details relevant for buildings exposed to natural hazards beyond earthquakes. GED4ALL has been applied in several international initiatives, enabling the identification of the most common building classes in the world, and facilitating compatibility between exposure models and databases of vulnerability and damage databases.

Several destructive natural hazards have occurred throughout Africa over the past century, yet few comprehensive exposure models exist for the continent. The high population growth and rapid pace of urbanization within many African countries entail a significant potential for increased economic and human losses, particularly where substantial urban growth encroaches upon hazard-prone regions with inadequate land management and building design regulations. This study introduces a new exposure model for all African countries using national and global datasets with a uniform approach across the continent, developed for a baseline year (2020) and six future years (2025, 2030, 2035, 2040, 2045, and 2050). The exposure model was originally derived with subnational statistics, and then further spatially disaggregated using Earth Observation (EO) data. This refined spatial resolution allows the model to reflect a realistic population distribution within each country and thereby better characterizes the potential risk to natural hazards and allows identification of disaster risk hotspots. The results indicate the current concentrations of building stock in Africa, in addition to regions where the urban building stock is expected to nearly triple (such as in Central and East Africa) or at least double (such as in West and South Africa).

Reporte sobre la guía para profesores para el desarrollo de un curso introductorio de riesgo sísmico.

El presente documento es el resultado del esfuerzo colaborativo entre la Fundación GEM, la administración del Área Metropolitana del Valle de Aburrá (AMVA) y su proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá - SIATA y la Universidad EAFIT. El objetivo de este reporte es generar material didáctico para sensibilizar a la comunidad sobre el riesgo sísmico que pueda servir de apoyo a las personas encargadas de comunicar este tema a diferentes grupos de la sociedad. El público objetivo de la sesión va desde estudiantes en diferentes grados de escolaridad a la comunidad general, incluyendo al personal que participa en las actividades de planeación, reducción y mitigación del riesgo de desastres.

An example-based guide to building PSHA models using open-source data and tools.

El presente documento es el resultado del esfuerzo colaborativo entre la Fundación GEM, el Servicio Geológico de los Estados Unidos, la oficina del Plan de Ordenamiento Territorial del Ayuntamiento de Santiago de los Caballeros y el Servicio Geológico Nacional. El objetivo de este reporte es presentar los resultados de la evaluación de riesgo urbano para el municipio de Santiago de los Caballeros, obtenidos dentro del contexto del Proyecto para la Comunicación y Formación en la Evaluación de Riesgos por Terremotos (TREQ), financiado por la Oficina de Ayuda Humanitaria de los Estados Unidos (BHA, por sus siglas en inglés).

El presente documento es el resultado del esfuerzo colaborativo entre la Fundación GEM, el Servicio Geológico de los Estados Unidos, la Secretaría de Gestión del Riesgo de Cali, el Servicio Geológico Colombiano y la Universidad EAFIT. El objetivo de este reporte es presentar los resultados de la evaluación de riesgo urbano para la ciudad de Santiago de Cali, obtenidos dentro del contexto del Proyecto para la Comunicación y Formación en la Evaluación de Riesgos por Terremotos (TREQ), financiado por la Oficina de Ayuda Humanitaria de los Estados Unidos (BHA, por sus siglas en inglés).