Modeling of Triaxial Pressure Tests with Uniform Granular Materials Discrete Particle Method DOI Open Access

Mehmet Uğur Yılmazoğlu

Kastamonu University Journal of Engineering and Sciences, Год журнала: 2024, Номер unknown

Опубликована: Дек. 13, 2024

Predicting the mechanical behavior of soils on which structures and facilities are built is crucial in civil engineering. Although solutions made by modeling as continuous homogeneous environments due to their ease fast solutions, soil combination particles a multiphase environment. Therefore, Discrete Element Method, offers closer approach properties, was used study. This study modeled granular materials under triaxial compression tests using Method (DEM). DEM, an ideal numerical technique for simulating particle environments, investigate responses assemblies when subjected varying confining pressures. The research focused effects shape, size distribution, contact mechanics material's stress-strain relationship deformation during test. Using DEM PFC3D, test uniform sands estimate Poisson's ratio, Young's modulus, bearing capacity.

Язык: Английский

Morphological variability and its influence on the true triaxial mechanical response of rockfill: A DEM approach DOI

Chenhui Guan,

Chunshun Zhang,

Qixin Wu

и другие.

Computers and Geotechnics, Год журнала: 2025, Номер 182, С. 107153 - 107153

Опубликована: Фев. 24, 2025

Язык: Английский

Процитировано

0
CNN-based calibration of discrete element method parameters for calcareous sand DOI

Yangpan Fu,

Huawei Tong,

Jie Yuan

и другие.

Environmental Earth Sciences, Год журнала: 2025, Номер 84(5)

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Prediction of stress-strain behavior of rock materials under biaxial compression using a deep learning approach DOI Creative Commons
Changsheng Li, Xinsong Zhang

PLoS ONE, Год журнала: 2025, Номер 20(4), С. e0321478 - e0321478

Опубликована: Апрель 29, 2025

Deep learning has significantly advanced in predicting stress-strain curves. However, due to the complex mechanical properties of rock materials, existing deep methods have problem insufficient accuracy curves materials. This paper proposes a method based on long short-term memory autoencoder (LSTM-AE) for materials discrete element numerical simulations. The LSTM-AE approach uses LSTM network construct both encoder and decoder, where extracts features from input data decoder generates target sequence prediction. mean square error ( MSE ), root RMSE absolute MAE coefficient determination R 2 ) predicted true values are used as evaluation metrics. proposed is compared with network, recurrent neural (RNN), BP (BPNN), XGBoost model. results indicate that outperforms LSTM, RNN, BPNN, XGBoost. Furthermore, robustness confirmed by 10 sets special samples. scalability handling large datasets its applicability laboratory need further verification. Nevertheless, this study provides valuable reference solving prediction

Язык: Английский

Процитировано

0
Artificial intelligence for computational granular media DOI
Tongming Qu, Jidong Zhao, Y.T. Feng

и другие.

Computers and Geotechnics, Год журнала: 2025, Номер 185, С. 107310 - 107310

Опубликована: Май 7, 2025

Процитировано

0
3D-measurement of particles and particulate assemblies - A review of the paradigm shift in describing anisotropic particles DOI Creative Commons
Xiaodong Jia, R.A. Williams

Powder Technology, Год журнала: 2024, Номер 447, С. 120109 - 120109

Опубликована: Авг. 3, 2024

The goal of seeking advanced solutions to the descriptions particle shape, packing and tomographic measurement were key areas promoted by Professor Reg Davies. In this paper we review reflect on revolution that has taken place over last 30 years in our ability describe measure shape going beyond simple factors their real morphologies complex particles particulate assemblies. presents a comprehensive how been described some critical analyses form extended tabulations. We show digital approaches can be used predict properties assemblies use simulations processing. note current status prospects for continued development microtomographic systems enable 3D also imaging structures property processing predictions. Examples these developments appraisal utility will given.

Язык: Английский

Процитировано

2
A discrete element solution method embedded within a Neural Network DOI Creative Commons
Sadjad Naderi, Boyang Chen,

Tongan Yang

и другие.

Powder Technology, Год журнала: 2024, Номер unknown, С. 120258 - 120258

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

1
Modeling of Triaxial Pressure Tests with Uniform Granular Materials Discrete Particle Method DOI Open Access

Mehmet Uğur Yılmazoğlu

Kastamonu University Journal of Engineering and Sciences, Год журнала: 2024, Номер unknown

Опубликована: Дек. 13, 2024

Predicting the mechanical behavior of soils on which structures and facilities are built is crucial in civil engineering. Although solutions made by modeling as continuous homogeneous environments due to their ease fast solutions, soil combination particles a multiphase environment. Therefore, Discrete Element Method, offers closer approach properties, was used study. This study modeled granular materials under triaxial compression tests using Method (DEM). DEM, an ideal numerical technique for simulating particle environments, investigate responses assemblies when subjected varying confining pressures. The research focused effects shape, size distribution, contact mechanics material's stress-strain relationship deformation during test. Using DEM PFC3D, test uniform sands estimate Poisson's ratio, Young's modulus, bearing capacity.

Язык: Английский

Процитировано

0