Future of machine learning in geotechnics
KK Phoon, W Zhang - … : Assessment and Management of Risk for …, 2023 - Taylor & Francis
Machine learning (ML) is widely used in many industries, resulting in recent interests to
explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms …
explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms …
Transfer learning in environmental remote sensing
Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
Vulnerability of buildings to landslides: The state of the art and future needs
Landslides are one of the most destructive hazard processes that cause tremendous loss of
lives and damage to the built environment. As a crucial part of disaster risk management …
lives and damage to the built environment. As a crucial part of disaster risk management …
Cross-project prediction for rock mass using shuffled TBM big dataset and knowledge-based machine learning methods
YP Zhang, ZY Chen, F Jin, LJ Jing, H Xing… - Science China …, 2023 - Springer
Extensive research has confirmed the successful prediction of rock mass quality in tunnel
boring machine (TBM) construction using machine learning methods based on big data …
boring machine (TBM) construction using machine learning methods based on big data …
[HTML][HTML] A domain adaptation approach to damage classification with an application to bridge monitoring
Data-driven machine-learning algorithms generally suffer from a lack of labelled health-state
data, mainly those referring to damage conditions. To address such an issue, population …
data, mainly those referring to damage conditions. To address such an issue, population …
Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities
J Jia, W Ye - Remote Sensing, 2023 - mdpi.com
Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster
prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in …
prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in …
A rapid self-supervised deep-learning-based method for post-earthquake damage detection using UAV data (case study: Sarpol-e Zahab, Iran)
Immediately after an earthquake, rapid disaster management is the main challenge for
relevant organizations. While satellite images have been used in the past two decades for …
relevant organizations. While satellite images have been used in the past two decades for …
Future of Machine Learning in Geotechnics (FOMLIG), 5–6 Dec 2023, Okayama, Japan
This report presents the key talking points in the First Workshop on the Future of Machine
Learning in Geotechnics (FOMLIG), that include data infrastructure, geotechnical context …
Learning in Geotechnics (FOMLIG), that include data infrastructure, geotechnical context …
Projected land use changes in the Qinghai-Tibet Plateau at the carbon peak and carbon neutrality targets
R Xu, P Shi, M Gao, Y Wang, G Wang, B Su… - Science China Earth …, 2023 - Springer
Based on historical land use for eight periods from 1980 to 2020 and the projected land use
under seven Shared Socioeconomic Pathways (SSPs: SSP1-1.9, SSP1-2.6, SSP2-4.5 …
under seven Shared Socioeconomic Pathways (SSPs: SSP1-1.9, SSP1-2.6, SSP2-4.5 …
A novel weighted ensemble transferred U-net based model (WETUM) for post-earthquake building damage assessment from UAV data: A comparison of deep …
E Khankeshizadeh, A Mohammadzadeh… - … on Geoscience and …, 2024 - ieeexplore.ieee.org
Nowadays, unmanned aerial vehicle (UAV) remote sensing (RS) data are key operational
sources used to produce a reliable building damage map (BDM), which is of great …
sources used to produce a reliable building damage map (BDM), which is of great …