Results 191 to 200 of about 64,515 (298)

Comparative evaluation of impact-based and hybrid approaches for real time earthquake shaking prediction. [PDF]

Sci Rep
Rea R, Kagawa T, Zollo A.
europepmc   +1 more source

Generalised Kinematic Single‐Impact and Multi‐Impact Models for Rocking Structures

Earthquake Engineering &Structural Dynamics, Volume 55, Issue 6, Page 1179-1202, May 2026.
ABSTRACT The rocking motion is fundamental in earthquake engineering, as it reflects the dynamic behaviour of many structural systems. However, simulating the impacts during rocking motion remains a challenging topic, as they occur in a very short time, generate high impulsive forces, cause sudden changes in velocities and result in rapid energy losses.
Georgios Vlachakis, Carla Colombo, Anastasios I. Giouvanidis, Paulo B. Lourenço   +3 more
wiley   +1 more source

The role of artificial intelligence for early warning systems: Status, applicability, guardrails, and ways forward. [PDF]

iScience
Tiggeloven T, Pfeiffer S, Matanó A, van den Homberg M, Thalheimer L, Reichstein M, Torresan S.   +6 more
europepmc   +1 more source

Analytical Floor Response Spectra for Performance‐Based Seismic Design of Non‐structural Elements in Reinforced Concrete Frame Buildings

Earthquake Engineering &Structural Dynamics, Volume 55, Issue 6, Page 1292-1312, May 2026.
ABSTRACT Damage to non‐structural elements significantly impacts the seismic performance of buildings in terms of economic and functionality losses. Consequently, performance‐based seismic design of non‐structural elements has become a key pillar of a comprehensive building‐seismic resilience strategy, for instance, through loss‐targeted earthquake ...
Roberto J. Merino, Roberto Gentile, Carmine Galasso   +2 more
wiley   +1 more source

A case study of the three-dimensional co-seismic ionospheric disturbance evolution. [PDF]

Sci Rep
Song R, Hattori K, Zhang X, Liu JY, Yoshino C.   +4 more
europepmc   +1 more source

Landslide susceptibility mapping using a machine learning approach and different environmental factors in the Western Ghats region (India)

Earth Surface Processes and Landforms, Volume 51, Issue 5, May 2026.
Landslide susceptibility mapping in India's Western Ghats using machine learning revealed high‐risk zones driven by deforestation, slope alteration and road proximity. The Random Forest model showed highest accuracy, supporting targeted mitigation, planning and early warning systems.
Manoranjan Mishra, Rajkumar Guria, Suman Paul, Richarde Marques da Silva, Celso Augusto Guimarães Santos   +4 more
wiley   +1 more source

Analysis of human flow during a natural disaster utilizing trajectory-free mobile network data: a case study of earthquake. [PDF]

Sci Rep
Huang MW, Lin CY, Ke MC, Li WS, Chang TY.   +4 more
europepmc   +1 more source

Knowledge factors and associated challenges for successful disaster knowledge sharing : Global Assessment Report input paper (GAR 2015) [PDF]

, 2015
Amaratunga, Dilanthi, Haigh, Richard, Pathirage, Chaminda, Seneviratne, Krisanthi   +3 more
core  

Opportunities for the U.S. Geological Survey's National Seismic Hazard Model to Improve Seismic Risk Assessment of Critical Infrastructure

Earthquake Spectra, Volume 42, Issue 2, May 2026.
As fragility and risk modeling techniques and computational capabilities evolve, complemented by moving toward more routine and systematic seismic risk assessment of all buildings and critical infrastructure, the authors pose a few critical questions to investigate how the U.S. Geological Survey (USGS) National Seismic Hazard Models (NSHMs) can be used
Kishor S. Jaiswal, N. Simon Kwong
wiley   +1 more source

Precursory electrical resistivity signatures of shear failure in saturated sandstone under varying shear rates. [PDF]

Sci Rep
Jia P, Zhang D, Liu C, Sun B, Zeng Z.
europepmc   +1 more source