Carlos Zavala

Early damage estimation in the immediate aftermath of a catastrophic earthquake is critical for effective disaster response and informed decision-making, especially in densely populated urban areas with high seismic activity, such as the Lima Metropolitan Area, Peru. This research focuses on designing and developing a web-based geographic information system for early earthquake damage estimation, featuring a four-stage workflow. Stage 0 generates pre-event damage estimations using earthquake scenarios, infrastructure datasets, and vulnerability models. Stage 1, initiated immediately after an earthquake, selects the closest pre-calculated scenario by comparing the peak ground acceleration values recorded by accelerometers with pre-estimated intensity measures. Stage 2 creates ShakeMaps by interpolating observed ground motion indices to produce the updated damage estimates based on real-time intensity distributions. A future Stage 3 will refine these estimates using post-event observed data. Data acquisition relies on open geospatial services and Peruvian governmental platforms, integrating datasets related to general buildings, essential buildings (schools and hospitals), transportation networks (bridges and roads), and lifeline systems (water supply and sewage). The system was implemented using open-source geospatial tools, providing a cost-effective, scalable solution for early-time disaster management in earthquake regions.

Seismic risk assessment is essential for reducing earthquake-related damage in urban areas. Accurate recognition of building features is a key factor in evaluating seismic vulnerability, yet traditional manual inspection methods are inefficient and prone to error. This study proposes a deep learning-based framework using convolutional neural networks for automated instance segmentation of building features to support seismic risk estimation in Lima, Peru, a seismically active region with diverse architectural styles. Leveraging state-of-the-art models like Mask R-CNN, the framework identifies and segments structural components such as walls, windows, and columns from building imagery. By integrating geospatial data and remote sensing technologies, the proposed approach enhances seismic risk evaluations through automated, scalable, and precise feature recognition. Despite challenges such as occlusions, varying lighting conditions, and architectural diversity, the model aims to adapt through specialized training tailored to Lima’s urban landscape, contributing to more efficient disaster preparedness and response in earthquake-prone regions.

Effective post-earthquake decision-making requires reliable information regarding the structural damage to infrastructure. Service stations—commonly known as gas stations—represent critical infrastructure whose failure can have severe consequences in urban areas, including fires, casualties, property damage, and health risks. This study investigates the seismic behavior of such structures using a 1:10 scaled model tested on the shaking table at Peruvian-Japanese Center for Seismic Research and Disaster Mitigation. The experimental response was analyzed using signal processing techniques and modal identification within a bandpass frequency range of 1 to 5 Hz. Based on similitude laws, an equivalent full-scale dynamic model was calibrated to represent the real structure’s response. Subsequently, following SEAOC recommendations and the Peruvian Seismic Design Standard (NTE E.030), 150 capacity analyses were performed on service station models, considering structural height as the primary variable. For each height, force–drift relationships were derived, and corresponding peak ground accelerations were calculated for deterministic seismic events. The resulting damage intervals provide quantitative thresholds for evaluating the seismic performance of service stations. These outcomes can be incorporated into Sistema Experto Integrado para la Evaluación de Daños por Sismo, an expert system for seismic damage assessment in urban environments.

This paper presents a method for identifying the damping coefficient during earthquakes in quasi-real time using the information from sensors installed in real structures. The method uses a given number of points to perform a quadratic regression in such a way as to reduce the error in identification. A coefficient of determination is also used to discard values that have not been correctly identified. The validity of the method is assessed by dynamic analysis in single-degree-of-freedom systems, using the initial stiffness proportional damping model and the tangent stiffness proportional damping model. Good identification results are obtained using a data acquisition sampling frequency of 200 Hz. The same is true for using a sampling frequency of 100 Hz as long as the system period is greater than 0.40 s. Additionally, it is observed that a 20% identification error does not substantially affect the seismic response of the system. The method is also applied to results from shake table tests, and their identified values are analyzed. A comparison is made between the behavior of the damping coefficient observed in the experimental tests with the behavior assumed in the dynamic analysis. In general terms, it is observed that the damping coefficient does not present abrupt changes in its values when changing the instantaneous stiffness as occurs in the tangent stiffness proportional damping model. Additional conclusions are presented that allow us to continue expanding the knowledge on the damping coefficient behavior during earthquakes.

Between 1997 and 2013, low-ductility reinforced concrete (RC) wall systems were widely used in Peru’s real estate sector due to their economic advantages. Despite good engineering practice recommending a five-story height limit, this restriction was not explicitly included in the Peruvian standards of that period. Regulatory provisions were often supported by numerical models calibrated using one-story wall specimens, which neglect the flexural behavior that dominates in medium-rise structures. This underscores the need to evaluate the inelastic flexural response of such buildings under seismic loading. Cyclic loading tests were conducted on a half-scale, five-story RC wall to calibrate numerical models using a nonlinear multi-spring model of concrete and steel reinforcement behavior. The calibrated model enables accurate assessment of damage progression and performance under varying seismic intensities. Findings reveal that buildings constructed with low-ductility RC walls may have overestimated seismic capacity in their original design. Under severe seismic events, these buildings are likely to exceed reparability thresholds, while under rare seismic events, they approach structural collapse.

El Perú es un país con una alta actividad sísmica, lo que conlleva a un riesgo potencial de tsunamis que podrían afectar la costa peruana. En la ciudad de Lima, el sistema estructural más comúnmente utilizado es la albañilería confinada, principalmente debido a su bajo costo económico. El presente estudio experimental tiene por objetivo estudiar el comportamiento de los muros de albañilería confinada bajo acción de cargas perpendiculares al muro producidas por el impacto de las olas de un Tsunami. El estudio experimental se realizó en el Laboratorio de Estructuras del Centro Peruano Japonés de Investigaciones Sísmicas y Mitigación de Desastres - CISMID, donde se construyeron 2 muros de albañilería confinada, los cuales se ensayaron bajó acción de cargas perpendiculares aplicadas por medio de gatas hidráulicas con un control por fuerza simulando las fuerzas inducidas por un tsunami. Del ensayo experimental se obtuvo una falla por flexión perpendicular alcanzando una carga total equivalente a una ola de 1.85 metros de altura para una distorsión de 1/75 al nivel de la losa aligerada. Además, se alcanzó la deformación límite para estructuras de albañilería confinada indicada en la Norma Peruana E030 para una carga equivalente a una altura de ola de 1.64 metros. En última instancia, se compararon los resultados obtenidos experimentalmente con los obtenidos a través de un modelo de elementos finitos, logrando obtener buena correspondencia entre ambos.

Since the decade of the 1990 the neural networks algorithms have been used for compute approximate solutions for different problems in engineering. In the building behavior against loads is important to know its response. The behavior during the earthquakes and the estimation of the response is quite difficult to compute due to the nonlinearity of geometry and material properties. Neural networks approach is a powerful tool for computing the response of structures with an appropriate learning process from big data of structural components. Even if some material parameters are unknown, the learning on a neural network will be possible and will provide an estimation using collect information from experience and learning. To make a learning process in this paper, we present a simple algorithm of back propagation implemented in python programming language where the output shows the decrease of the error and how the response start to learn from the beginning until the end of the process. The results show good agreement between the learning data set and predicted response after the neural network learning.

Non-engineered confined masonry dwellings are characterized by having different masonry units. One of the most common cases in the Lima Metropolitan Area is that this type of dwelling has its first floors with handmade solid units and upper floors are composed of industrial horizontal hollowed (tubular) bricks. Nonlinear static analysis by the incrementally increasing loading pattern is inadequate to estimating earthquake response of structures with height irregularity, such as non-engineered confined masonry dwellings, because the loading pattern changes during the history of reversal loads due to damaging earthquakes. This paper presents the effect of nonlinear static analysis using adaptive loading patterns in confined masonry dwellings when the type of masonry units changes drastically. Nonlinear static analyses are applied using non-adaptive and adaptive displacement-based pushover procedures. A series of nonlinear time history analyses are also conducted to measure the error in the estimation of the seismic response. The adaptive loading pattern is based on displacement approximation by the modal analysis at each step of incremental-increasing loads. The load pattern is updated considering the modal analysis of an equivalent linear model using the equivalent stiffness of the structure at the secant of the maximum previous response. The application of the adaptive loading pattern in nonlinear static analysis significantly approximates the earthquake response of confined masonry dwellings with height irregularity provided by the change of masonry bricks.

Title: Earthquake Damage Assessment of Buried Pipeline Networks in the Lima Metropolitan Area | Keywords: earthquake damage assessment, buried pipeline, fragility function, drinking water system, sewage system | Author: Italo Inocente, Miguel Diaz, Jorge Gallardo, Yoshihisa Maruyama, Luis Quiroz, and Carlos Zavala

The conjunction of seismic records and site effect parameters can lead to the adequate estimation of seismic indexes in urban areas. In this regard, this study uses the map of soil amplification factors obtained in previous studies and the availability of time history waveforms at different locations throughout Metropolitan Lima and Callao to estimate the geospatial distribution of maximum values of horizontal acceleration after the occurrence of earthquakes. Results for three earthquakes of intermediate magnitude and distinct epicenter locations are publicly available in an online system created within the framework of this study (Amaru Peru) and showed that amplified motions could be mainly observed in the lowlands of populated slopes as well as in the eolian sandy deposits.