Geotechnical Resilience through Intelligent Design

Información del proyecto

GRID

Identificador del acuerdo de subvención: 101182689

DOI

10.3030/101182689

Fecha de la firma de la CE 4 Octubre 2024

Fecha de inicio 1 Noviembre 2024

Fecha de finalización 31 Octubre 2028

Financiado con arreglo a

• Marie Skłodowska-Curie Actions (MSCA)

Coste total

Sin datos

Aportación de la UE

€ 1 242 000,00

Coordinado por

UNIVERSITAET FUER BODENKULTUR WIEN
Austria

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

Data sets, microdata, etc (1)

Repository of bechmark monitoring and testing data sets

Digital repository of standardised benchmark geotechnical monitoring and testing data sets for ML model development and calibration with description of the development and application of innovative geotechnical monitoring technologies for big data acquisition

Documents, reports (1)

Guideline for integration of ML in geotechnical engineering

Guideline for the application and integration of ML in geotechnical engineering projects

Data Management Plan (1)

Data Management Plan

Publicaciones

Conference proceedings (4)

Machine learning predictions on an extensive geotechnical dataset of laboratory tests in Austria

Autores: Enrico Soranzo
Editor: Research Publishing, Singapore
DOI: 10.3850/GRF-25280825_ISGSR-166-P377-CD

MACHINE LEARNING PREDICTION OF SOIL PARTICLE SIZE DISTRIBUTION FROM SMARTPHONE IMAGES

Autores: Enrico Soranzo
Editor: FOMLIG2025

AI-assisted student grading system in geotechnics

Autores: Soranzo, E.
Editor: ÖGG, Austrian Society for Geomechanics
DOI: 10.53243/ICSMGE2026-809

An Adaptive Physics-informed Deep Operator Neural Network for 1-D Unsaturated Infiltration Model Coupled with Soil Deformation

Autores: Biao Yuan, Xiaohui Chen
Editor: Research Publishing, Singapore
DOI: 10.3850/GRF-25280825_ISGSR-081-P054-CD

Other (1)

Predicting Soil Grain Size Distributions from Images

Autores: Lukas Leibold, Enrico Soranzo
Publicado en: Kaggle
Editor: Kaggle

Peer reviewed articles (7)

Revisiting strain localization from the view of structured inhomogeneity

Autores: Zhihui Wang, Roberto Cudmani
Publicado en: Computers and Geotechnics, Edición 190, 2025, ISSN 0266-352X
Editor: Elsevier BV
DOI: 10.1016/J.COMPGEO.2025.107694

Predictive modeling of pore pressure build-up in vibratory pile driving through machine learning

Autores: Sepideh Fadaei, Amir Hamidi, Enrico Soranzo
Publicado en: Geoscience Frontiers, 2025, ISSN 1674-9871
Editor: Elsevier BV
DOI: 10.1016/J.GSF.2025.102209

Large language models for automated grading in geotechnics

Autores: Enrico Soranzo
Publicado en: Machine Learning and Data Science in Geotechnics, 2025, ISSN 3029-0414
Editor: Emerald
DOI: 10.1108/MLAG-01-2025-0001

Comprehensive Dataset of Dynamic Probing Heavy Test Results for Subsurface Characterization

Autores: E. Soranzo
Publicado en: Data in Brief, 2025, ISSN 2352-3409
Editor: Elsevier BV
DOI: 10.1016/j.dib.2025.111391

Convolutional neural network prediction of the particle size distribution of soil from close-range images

Autores: Enrico Soranzo, Carlotta Guardiani, Wei Wu
Publicado en: Soils and Foundations, Edición 65, 2025, ISSN 0038-0806
Editor: Elsevier BV
DOI: 10.1016/j.sandf.2025.101575

Soil improvement with laponite: effects on soil-structure interaction in liquefiable sands

Autores: Jinghua Zhang, Zhihua Yang, Emilio Bilotta, Lucia Mele, Qing Sun, Haitao Yu, Yong Yuan
Publicado en: Computers and Geotechnics, Edición 185, 2025, ISSN 0266-352X
Editor: Elsevier BV
DOI: 10.1016/J.COMPGEO.2025.107360

Dataset of close-range soil images of soils and corresponding particle size distributions

Autores: E. Soranzo
Publicado en: Data in Brief, 2025, ISSN 2352-3409
Editor: Elsevier BV
DOI: 10.1016/J.DIB.2025.111631