Results 31 to 40 of about 662 (104)

Predicting Seismic Debris Distributions of Collapsed Unreinforced Masonry Structures Through Physics Engines and Machine Learning Algorithms

Earthquake Spectra, Volume 42, Issue 1, February 2026.
Unreinforced masonry (URM) structures are widespread worldwide, particularly in older urban districts. However, URM buildings—particularly those constructed before the introduction of modern building codes—are highly vulnerable to seismic hazard, and prone to experiencing local and/or global failures when subjected to significant horizontal shaking ...
Jiadaren Liu, Ersilia Giordano, Daniele Malomo   +2 more
wiley   +1 more source

Operationalizing Equity: How Disaggregated Risk Metrics Support Equity in Emergency Management

Earthquake Spectra, Volume 42, Issue 1, February 2026.
While numerous studies show that natural hazards disproportionately impact socially vulnerable populations, approaches to integrate social equity into emergency management to reduce such vulnerability have been ill‐defined and inconsistent. Considering earthquake disasters, emergency managers often lack information from near‐real‐time seismic loss ...
Marísa Macías, Elizabeth Reddy, Elijah Knodel, Sean McGowan, Sabine Loos   +4 more
wiley   +1 more source

Coseismic Landslide Area Prediction Using Generalised Additive Model: A Case Study of the 2013 Minxian Earthquake

Geoscience Data Journal, Volume 13, Issue 1, January 2026.
Comparison between observed and predicted landslide areas in log scale. ABSTRACT This study aims to establish a regional model for predicting seismic landslide areas. Using the 2013 Minxian earthquake‐induced landslide database as the research foundation, mathematical statistics and GIS techniques were applied to predict landslide areas through the ...
Xiaoyi Shao, Chong Xu, Siyuan Ma
wiley   +1 more source

Deep Learning–Based Automated Crack Detection for Post‐Earthquake Damage Assessment in Reinforced Concrete Structures

Advances in Civil Engineering, Volume 2026, Issue 1, 2026.
This study explores the integration of deep learning technologies, specifically U‐Net based segmentation methods, for evaluating earthquake‐induced damages. The study leverages a dataset derived from the Kahramanmaraş earthquake to train and test deep learning models capable of identifying and quantifying structural damages such as concrete cracks. The
Kemal Hacıefendioğlu, Serhat Demir, Vagelis Plevris   +2 more
wiley   +1 more source

Geological and Historical‐Based Approaches to Define Scenario Earthquake: Case‐Studies and Application at Municipality‐Scale in Italy

Earthquake Engineering &Structural Dynamics, Volume 54, Issue 15, Page 3828-3845, December 2025.
ABSTRACT This study compares two approaches for determining earthquake magnitude (M) and source‐to‐site distance (R) to assess seismic scenarios in Italy. The first method relies on geological criteria from the Italian seismogenic sources database (DISS3.3.0), while the second method uses historical earthquakes from the DBMI‐CPTI15 Italian catalogue ...
Sgobba Sara, Minotti Elisa, Freddi Marta, Luzi Lucia   +3 more
wiley   +1 more source

Observational Seismic Fragility Models for Unreinforced Masonry Buildings Based on Building‐by‐Building Damage Data From the 2012 Emilia Earthquakes

Earthquake Engineering &Structural Dynamics, Volume 54, Issue 11, Page 2927-2946, September 2025.
ABSTRACT Over recent decades, Italy has been affected by multiple earthquakes, revealing the significant vulnerability of existing structures. These seismic events have resulted in notable human and economic losses, emphasising the importance of evaluating the impact of one or more seismic events on various building types.
Chiara Monteferrante, Gianluca Salamida, Claudio Mazzotti, Nicola Buratti   +3 more
wiley   +1 more source

Finite-Fault Rupture Detector (FinDer): Going Real-Time in Californian ShakeAlert Warning System [PDF]

, 2015
Rapid detection of local and regional earthquakes and issuance of fast alerts for impending shaking is considered beneficial to save lives, reduce losses, and shorten recovery times after destructive events (Allen et al., 2009). Over the last two decades,
Böse, M., Felizardo, C., Heaton, T. H.
core   +1 more source

Did They Feel It? Legacy Macroseismic Data Illuminates an Engimatic 20th Century Earthquake

Earth and Space Science, Volume 12, Issue 9, September 2025.
Abstract The challenges and the importance of preserving legacy instrumental records of earthquakes are now well‐recognized (e.g., Richards & Hellweg, 2020, https://doi.org/10.1785/0220200053). Seismologists may not be aware of parallel challenges and opportunities with legacy macroseismic data for earthquakes in the United States. For much of the 20th
Susan E. Hough, Lori Dengler, Robert McPherson, Lijam Hagos, Margaret Hellweg   +4 more
wiley   +1 more source

Multicriteria Fuzzy Analysis for a GIS-Based Management of Earthquake Scenarios [PDF]

, 2018
Objective of this article is the formulation andthe implementation of a decision-making model for theoptimal management of emergencies. It is based on theaccurate definition of possible scenarios resulting fromprediction and prevention strategies and ...
DE GREGORIO, Daniela, Grazia D’Urso, Maria, Masi, Daniele, Zuccaro, Giulio   +3 more
core   +1 more source

An open‐access simulated earthquake ground‐motion database for an M7 Hayward Fault earthquake in the San Francisco Bay Region

Earthquake Spectra, Volume 41, Issue 3, Page 2560-2597, August 2025.
Comprehensive understanding of earthquake ground motions, particularly in the near‐fault region of large‐magnitude events, is limited by gaps in strong‐motion data. This challenge is prominent in areas with high seismic hazard but infrequent large earthquakes where data is sparse and difficult to interpret.
David McCallen, Arben Pitarka, Houjun Tang, Rie Nakata, Khalid M Mosalam, Floriana Petrone, Selim Günay, Claudio Perez   +7 more
wiley   +1 more source