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 restoration period of the water supply system in Lima, Peru, after a scenario earthquake was estimated in this study. To achieve the objective, the probabilistic assessment model for post-earthquake residual capacity of the utility lifeline system initially proposed by Nojima and Sugito (2005) and revised by following related studies was employed. The dataset of water distribution pipelines was provided by Potable Water and Sewer System Service in Lima, Peru (SEDAPAL), and the spatial distribution of ground motion with a moment magnitude of 8.6 was considered as a scenario earthquake in this study. The water disruption was anticipated to continue for approximately one month in certain districts of Lima, Peru. The estimated smallest water supplying ratio was 21.1% in Villa El Salvador after the scenario earthquake.

Damage assessment methods fall into contact and non-contact approaches. Contact methods, like physical measurements, material sampling, and ultrasonic testing, provide detailed data but are time-consuming and require specialized equipment. In contrast, non-contact methods assess damage remotely, allowing for faster, safer, and large-scale evaluations, especially useful in post-disaster scenarios. However, there are currently no standardized non-contact methods for assessing damage levels in confined masonry walls after damaging seismic events in Peru. On the other hand, an experimental database of cyclic loading tests on confined masonry walls is available, supporting numerical simulations with calibrated mathematical models to estimate damage levels. This research extends the application of this database by analyzing the crack pattern imagery from the tested walls and correlating it with the lateral deformation (drift) to identify the damage levels. A high-accuracy crack measurement technique was developed, combining a convolutional neural network to generate a binary crack mask and a binary search algorithm to extract polylines and convert them into length measurements, achieving a detection accuracy of 78%. The measured crack patterns were normalized into an index, which was then correlated with the amplitude of the lateral deformation in each hysteretic loop. Finally, a relationship was established between drift and the damage level index. These findings contribute to the development of a rapid, non-contact damage assessment method for confined masonry walls in seismic-prone regions.

Title: Wavelet Transform Method for the Evaluation of the Seismic Response of One Isolated Building in the Lima Metropolitan Area | Keywords: structural health monitoring, wavelet transform, earthquake, seismic response, monitoring system | Author: Joseph Jaramillo, Miguel Diaz, Carlos Zavala, Koichi Kusunoki, Italo Inocente, and Danty Otero

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.