A Benchmarking Method to Rank the Performance of Physics-Based Earthquake Simulations DOI
Octavi Gómez-Novell, Francesco Visini, Bruno Pace

и другие.

Seismological Research Letters, Год журнала: 2024, Номер unknown

Опубликована: Авг. 14, 2024

Abstract Physics-based earthquake simulators are an increasingly popular modeling tool in forecasting for seismic hazard as well fault rupture behavior studies. Their popularity comes from their ability to overcome completeness limitations of real catalogs, and also because they allow reproducing complex interaction patterns via the physical processes involved nucleation propagation. One important challenge these models revolves around selecting input parameters that will yield better similarity relationships observed nature, instance, frictional rate-and-state law—a b—or initial normal shear stresses. Because scarcity empirical data, such often selected by trial–error exploration predominantly manual model performance analyses, which can overall be time consuming. We present a new benchmarking approach analyze rank relative simultaneous simulation catalogs quantitatively scoring combined fit three reference function types: (1) earthquake-scaling relationships, (2) shape magnitude–frequency distributions, (3) rates surface ruptures paleoseismology or paleoearthquake occurrences. The provides effective potentially more efficient approximation easily identify parameter combinations closely relations behavior. facilitates exhaustive analysis many combinations, identifying systematic correlations between outputs improve understanding physics-based models. Finally, we demonstrate how method results agree with published findings other evaluations, fact reinforces its usefulness. ranking useful subsequent particularly applications, selection appropriate occurrence rate weighting logic tree.

Язык: Английский

AI-Driven Innovations in Earthquake Risk Mitigation: A Future-Focused Perspective DOI Creative Commons
Vagelis Plevris

Geosciences, Год журнала: 2024, Номер 14(9), С. 244 - 244

Опубликована: Сен. 15, 2024

This study explores the transformative potential of artificial intelligence (AI) in revolutionizing earthquake risk mitigation across six key areas. Unlike traditional approaches, this paper examines how AI-driven innovations can uniquely enhance early warning systems, enabling real-time structural health monitoring, and providing dynamic, multi-hazard assessments that seamlessly integrate seismic data with other natural hazards such as tsunamis landslides. It introduces groundbreaking applications AI earthquake-resilient design, where generative design algorithms predictive analytics create structures optimally balance safety, cost, sustainability. The also presents a novel discussion on ethical implications domain, stressing critical need for transparency, accountability, bias mitigation. Looking forward, manuscript envisions development advanced platforms capable delivering real-time, personalized assessments, immersive public training programs, collaborative tools adapt to evolving data. These promise not only significantly current preparedness but pave way toward future societal impact earthquakes is drastically reduced. work underscores AI’s role shaping safer, more resilient future, emphasizing importance continued innovation, governance, efforts.

Язык: Английский

Процитировано

7
Analysis of TEC variations and prediction of TEC by RNN during Indonesian earthquakes occurred from 2004 to 2024 and comparison with IRI-2020 model DOI
R. Mukesh, Sarat C. Dass,

M. Vijay

и другие.

Advances in Space Research, Год журнала: 2024, Номер 74(10), С. 4865 - 4905

Опубликована: Июль 24, 2024

Язык: Английский

Процитировано

3
Comparative review of intelligent structural safety in building seismic risk mitigation utilizing an integrated artificial intelligence controller DOI Creative Commons
Normaisharah Mamat, Rawad Abdulghafor, Sherzod Turaev

и другие.

Deleted Journal, Год журнала: 2025, Номер 7(5)

Опубликована: Апрель 24, 2025

Язык: Английский

Процитировано

0
Tropical Cyclone Track Prediction Harnessing Deep Learning Algorithms: A Comparative Study on the Northern Indian Ocean DOI Creative Commons
Sabbir Rahman,

M. Fahim Faisal,

Pronoy Kumar Mondal

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105009 - 105009

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
AI-Driven Risk Assessments: Advancing Cybersecurity and Sustainability DOI

Onyebuchi Sunday Onwuajuese,

Husnain Rafiq, Sarah McHale

и другие.

Advanced sciences and technologies for security applications, Год журнала: 2025, Номер unknown, С. 327 - 337

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
A Systematic Review About the Use of Machine Learning Related to Earthquake Studies DOI Creative Commons
Jimmy Aurelio Rosales-Huamaní, José Antonio Ogosi Auqui, Manuel Ego Aguirre Madrid

и другие.

Advances in Civil Engineering, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

This systematic review explores the application of machine learning (ML) techniques in earthquake prediction, analyzing studies published between 2018 and 2022. The research focuses on identifying models, methods, tools used this field, as well evaluating their effectiveness. A methodology based Kitchenham’s framework was employed, including three main phases: planning, conducting, reporting review. process involved formulating questions (RQs), rigorously searching 11 academic databases, applying inclusion exclusion criteria to refine 56,240 initial records into 126 relevant studies. Key methods identified include supervised, unsupervised, reinforcement with supervised being most utilized approach. Prominent Naive Bayes (NB), K ‐means, lasso regression, ridge random forest (RF). Variables frequently associated prediction seismic precursors, neural networks, accuracy metrics. Python TensorFlow were commonly for implementing these methods. findings reveal that while ML holds significant potential improving current is predominantly focused learning, limited exploration other methodologies. highlights need diverse approaches further evaluation underutilized techniques, emphasizing importance advancing predictive models. work contributes a comprehensive analysis state studies, gaps opportunities future research.

Язык: Английский

Процитировано

0
Impact of irregularities on seismic fragility of reinforced concrete structures DOI Creative Commons

Kshithij G. Raj,

S. Roopanjali,

P. Akshatha

и другие.

Deleted Journal, Год журнала: 2025, Номер 2(1)

Опубликована: Июнь 2, 2025

Язык: Английский

Процитировано

0
Assessing Seismic Vulnerability Methods for RC-Frame Buildings Pre- and Post-Earthquake DOI Open Access
M. Samuel, Ergang Xiong, Mahmood Haris

и другие.

Sustainability, Год журнала: 2024, Номер 16(23), С. 10392 - 10392

Опубликована: Ноя. 27, 2024

The seismic vulnerability of reinforced concrete (RC) buildings has been an important issue, especially in earthquake-prone regions with limited design codes such as South Sudan. Improving the performance is critical for maintaining structural functionality under normal service loads and rapid recovery after natural disasters earthquakes. This research aims to thoroughly assess methods used evaluate RC frame structures pre- post-earthquake scenarios. primary objective provide a comprehensive framework that integrates empirical, analytical, experimental methods, categorizing existing assessment proposing improvements resource-constrained environments. However, empirical have always historical earthquake data estimate potential damage. In contrast, analytical computational tools fragility curves probability damage at different intensities. Additionally, shaking table tests pseudo-dynamic analyses, validated theoretical predictions provided insights into behavior simulated conditions. Furthermore, key findings highlight vulnerabilities buildings, quantify probabilities, compare strengths limitations methods. challenges availability, limitations, difficulties replicating actual conditions test setups areas improvement. By addressing these challenges, review provides recommendations future studies, including integrating advanced regional hazard characterization improving enhance accuracy assessments, ultimately supporting more resilient increasing disaster preparedness.

Язык: Английский

Процитировано

2
Sosialisasi Mitigasi Bencana Gempa Bumi dan Simulasi Teknologi Internet of Things (IoT) di Sekolah Madrasah Aliyah Negeri 1 Maluku Tengah DOI Creative Commons

Gazali Rachman,

Fredrik Manuhutu,

Jamaludin Jamaludin

и другие.

AJAD Jurnal Pengabdian kepada Masyarakat, Год журнала: 2024, Номер 4(1)

Опубликована: Апрель 30, 2024

The problem in Maluku is about earthquake mitigation and the lack of our young generation or students who master Internet Things (IoT) technology. reason why this issue should be a major concern because an earthquake-prone area. Providing understanding earthquakes carried out through outreach to schools so that it hoped teachers will become pioneers can provide knowledge for each family their respective environments. Apart from that, mastery IoT technology very important. This basis creating industrial era 4.0. among increasing students' To overcome problem, socialization simulation activities were which partners Community Service Program providing training application world. It mastering basic support innovative IoT-based learning processes. provides stimulus carry various experiments are more innovative, creative independent. Earthquake workshop at MAN 1 School, Central Maluku.

Язык: Английский

Процитировано

1
Radar Wing Defender (RWD): Sistem untuk membantu para petani dalam mendeteksi dan pengusir hama burung DOI Creative Commons

Fawwaz Mulya Rabbani Fawwaz,

Rafi Fadhlurrahman,

Nadira Eka Putri

и другие.

Jurnal Komputer dan Elektro Sains, Год журнала: 2024, Номер 3(1), С. 16 - 22

Опубликована: Июнь 19, 2024

Around 90% of Indonesia's population uses rice as food. However, the production process has not been able to fully fulfil needs community, due several obstacles that affect productivity. Bird pests are one main causes disruption. Group birds known few tend cause problems. The purpose this research is get design an IoT-based bird repellent. This repellent consists two parts: hardware, or hard system, and software, soft system. An ESP32 microcontroller ultrasonic sensors used in prototype. Pest Prototype can be hammered automatically. As a result, existence prototype makes it easier for caregivers treat patient wounds case pest injuries; however, done more effectively efficiently because do need visit fields various injuries. Based on prototype, if sensor detects with range 0 4 metres, buzzer will sound, servo motor move lever bring radar-detected closer.

Язык: Английский

Процитировано

0