Toward automated formulation, calibration, and implementation of soil models: A generative computational AI framework for SANISAND plasticity DOI Creative Commons
Javad Ghorbani, Majidreza Nazem

Computers and Geotechnics, Journal Year: 2025, Volume and Issue: 185, P. 107330 - 107330

Published: May 14, 2025

Language: Английский

Enhancing accuracy and efficiency in cyclic liquefaction modeling: An automatic calibration framework for advanced constitutive models DOI Creative Commons
Jan Macháček, Sheng Zeng, Mahdi Taiebat

et al.

Computers and Geotechnics, Journal Year: 2025, Volume and Issue: 183, P. 107208 - 107208

Published: March 27, 2025

Language: Английский

Citations

1
Augmented intelligence framework for real-time ground assessment under significant uncertainty DOI Creative Commons
Javad Ghorbani,

Sougol Aghdasi,

Majidreza Nazem

et al.

Engineering With Computers, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Abstract Real-time assessment of unsaturated soils through deflection tests is challenging due to the complex effects water and air in soil pores, which significantly impact test outcomes but are difficult quantify, especially when key data like gravimetric content suction incomplete or missing. While human expertise intuition valuable high-pressure scenarios ground during compaction, they prone biases. AI-driven solutions excel at processing datasets often require highly specialised inputs, may not always be readily available. This paper aims develop a robust pragmatic approach decision-support by combining insight with AI’s computational power principles from mechanics. outlines limitations current practices discusses challenges developing reliable using on soils. To address these challenges, an augmented intelligence framework introduced that leverages fuzzy inputs for missing information incorporates sophisticated self-improving mechanism estimate data, based insights gained calibration. enhances after validation recent field trial particularly uncertain subsurface conditions. The study also demonstrates framework’s resilience qualitative assessments, maintaining accuracy across range assumptions about content.

Language: Английский

Citations

0
Coupled thermo-hydro-mechanical analysis of multi-stage artificial ground freezing for tunneling. A case study for sequential versus simultaneous freezing DOI
Yingxiao Liu, WaiChing Sun

Computers and Geotechnics, Journal Year: 2025, Volume and Issue: 185, P. 107291 - 107291

Published: May 5, 2025

Language: Английский

Citations

0
Toward automated formulation, calibration, and implementation of soil models: A generative computational AI framework for SANISAND plasticity DOI Creative Commons
Javad Ghorbani, Majidreza Nazem

Computers and Geotechnics, Journal Year: 2025, Volume and Issue: 185, P. 107330 - 107330

Published: May 14, 2025

Language: Английский

Citations

0