- journal 37
- ml 18
- conference 14
- mpm 12
- lbm 8
- fem 7
- gns 6
- dem 5
- ai 4
- cnn 4
- hpc 4
- porescale 4
- fwi 3
- heat-transfer 3
- swcc 3
- biology 2
- granular-flow 2
- insitu 2
- llm 2
- nhe 2
- nsf 2
- operator-learning 2
- traffic-flow 2
- viz 2
- xai 2
- boem 1
- concrete-flow 1
- diff-programming 1
- diffsim 1
- experiments 1
- gpu 1
- kan 1
- liquefaction 1
- microsim 1
- multiphase 1
- offshore 1
- open-source 1
- robotics 1
- sciml 1
- shm 1
- teaching 1
- unsaturated 1
- wind 1
- wireless-charging 1
journal
SetONet: A Deep Set-based Operator Network for Solving PDEs with permutation invariant variable input sampling
Neural operators, particularly the Deep Operator Network (DeepONet), have shown promise in learning mappings between function spaces for solving differential equations. However, standard DeepONet requires...
MLPs and KANs for data-driven learning in physical problems: A performance comparison
There is increasing interest in solving partial differential equations (PDEs) by casting them as machine learning problems. Recently, there has been a spike in exploring...
Towards scientific machine learning for granular material simulations – challenges and opportunities
Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational...
Runout of liquefaction-induced tailings dam failure: Influence of earthquake motions and residual strength
This study utilizes a hybrid Finite Element Method (FEM) and Material Point Method (MPM) to investigate the runout of liquefaction-induced flow slide failures. The key...
Measuring the effective stress parameter using the multiphase lattice Boltzmann method and investigating the source of its hysteresis
The effective stress parameter, χ, is essential for calculating the effective stress in unsaturated soils. Experimental measurements have captured different relationships between χ and the...
A hybrid Finite Element and Material Point Method for modeling liquefaction-induced tailings dam failures
This paper presents a hybrid Finite Element Method (FEM) and Material Point Method (MPM) approach for modeling liquefaction-induced tailings dam failures from initiation through runout....
Machine Learning Aided Modeling of Granular Materials: A Review
Artificial intelligence (AI) has become a buzz word since Google’s AlphaGo beat a world champion in 2017. In the past five years, machine learning as...
Basis-to-Basis Operator Learning using Function Encoders
We present Basis-to-Basis (B2B) operator learning, a novel approach for learning operators on Hilbert spaces of functions based on the foundational ideas of function encoders....
Sequential Hybrid Finite Element and Material Point Method to Simulate Slope Failures
Numerical modeling of slope failures seeks to predict two key phenomena: the initiation of failure and the post-failure runout. Currently, most modeling methods for slope...
Explainable AI models for predicting liquefaction-induced lateral spreading
Introduction: Earthquake-induced liquefaction can cause substantial lateral spreading, posing threats to infrastructure. Machine learning (ML) can improve lateral spreading prediction models by capturing complex soil...
Inverse analysis of granular flows using differentiable graph neural network simulator
Inverse problems in granular flows, such as landslides and debris flows, involve estimating material parameters or boundary conditions based on target runout profile. Traditional high-fidelity...
An inverse analysis of fluid flow through granular media using differentiable lattice Boltzmann method
In this study, we introduce an effective method for the inverse analysis of fluid flow problems, focusing on accurately determining boundary conditions and characterizing the...
Reflections on teaching engineering through murder mysteries
This paper presents a reflective analysis of a novel approach to Problem-Based Learning (PBL) to teach abstract concepts in a large-class setting, specifically tailored for...
Graph Neural Network-based surrogate model for granular flows
Accurate simulation of granular flow dynamics is crucial for assessing various geotechnical risks, including landslides and debris flows. Granular flows involve a dynamic rearrangement of...
Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in geotechnical engineering
The widespread adoption of large language models (LLMs), such as OpenAI’s ChatGPT, could revolutionize various industries, including geotechnical engineering. However, GPT models can sometimes generate...
GNS: A generalizable Graph Neural Network-based simulator for particulate and fluid modeling
We develop a PyTorch-based Graph Network Simulator (GNS) that learns physics and predicts the flow behavior of particulate and fluid systems. GNS discretizes the domain...
Using explainability to design physics-aware CNNs for solving subsurface inverse problems
We present a novel method of using explainability techniques to design physics-aware neural networks. We demonstrate our approach by developing a convolutional neural network (CNN)...
Evaluation of particle motions in stabilized specimens of transparent sand using deep learning segmentation
Individual particle rotation and displacement were measured in triaxial tests on transparent sand stabilized with geogrid simulants. The Cellpose U-Net model, originally developed to segment...
Conductive and convective heat transfer in inductive heating of subsea buried pipelines
Inductive heating with high-voltage cables reduces the risk of hydrate formation by raising the temperature of the production fluid in pipelines. Heating the pipeline results...
In-situ visualization of natural hazards with Galaxy and Material Point Method
Visualizing regional-scale landslides is the key to conveying the threat of natural hazards to stakeholders and policymakers. Traditional visualization techniques are restricted to post-processing a...
Biomechanical trade‑offs in the pelvic floor constrain the evolution of the human birth canal
Compared to most other primates, humans are characterized by a tight fit between the maternal birth canal and the fetal head, leading to a relatively...
Using Convolutional Neural Networks (CNN) to develop starting models for 2D full waveform inversion
Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamen-tally change engineering site characterization by enabling the recovery of high resolution 2D/3D...
Investigating the thixotropic behaviour of tremie concrete using the slump‑flow test and the Material Point Method
A new thixotropic model is developed integrating the Papanastasiou-Bingham model with thixotropy equations to simulate the flow behaviour of Tremie Concrete in the Material Point...
Microsimulation Analysis for Network Traffic Assignment (MANTA) at Metropolitan‑Scale for Agile Transportation Planning
Abstract:
Context‑specific volume‑delay curves by combining crowdsourced traffic data with Automated Traffic Counters (ATC): a case study for London
Large Deformation Modelling in Geomechanics
Mechanics of granular column collapse in fluid at varying slope angles
Numerical study of a sphere descending along an inclined slope in a liquid
Network analysis of the hominin origin of Herpes Simplex virus 2 from fossil data
High Performance Computing for City‑Scale Modelling and Simulations
Trends in large‑deformation analysis of landslide mass movements with particular emphasis on the material point method
Post‑failure behavior of tunnel heading collapse by MPM simulation
Lateral Dynamic Response and Effect of Weakzone on the Stiffness of Full Scale Single Piles
Transient dynamics of a 2D granular pile
Lateral vibration response of full scale single piles: Case studies in India
A site‑specific study on evaluation of design ground motion parameters
Site‑specific seismic analysis of deep stiff soil sites
ml
SetONet: A Deep Set-based Operator Network for Solving PDEs with permutation invariant variable input sampling
Neural operators, particularly the Deep Operator Network (DeepONet), have shown promise in learning mappings between function spaces for solving differential equations. However, standard DeepONet requires...
Towards scientific machine learning for granular material simulations – challenges and opportunities
Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational...
Machine Learning Aided Modeling of Granular Materials: A Review
Artificial intelligence (AI) has become a buzz word since Google’s AlphaGo beat a world champion in 2017. In the past five years, machine learning as...
Inverse analysis of granular flows using differentiable graph neural network simulator
Inverse problems in granular flows, such as landslides and debris flows, involve estimating material parameters or boundary conditions based on target runout profile. Traditional high-fidelity...
Three-dimensional granular flow simulation using graph neural network-based learned simulator
Reliable evaluations of geotechnical hazards like landslides and debris flow require accurate simulation of granular flow dynamics. Traditional numerical methods can simulate the complex behaviors...
Differentiable programming for inverse estimation of soil permeability and design of duct banks
Underground duct banks carrying power cables dissipate heat to the surrounding soil. The amount of heat dissipated determines the current rating of cables, which in...
An inverse analysis of fluid flow through granular media using differentiable lattice Boltzmann method
In this study, we introduce an effective method for the inverse analysis of fluid flow problems, focusing on accurately determining boundary conditions and characterizing the...
Accelerating particulate and fluid simulations with graph neural networks for solving forward and inverse problems
We leverage physics-embedded differentiable graph network simulators (GNS) to accelerate particulate and fluid simulations to solve forward and inverse problems. GNS represents the domain as...
Graph Neural Network-based surrogate model for granular flows
Accurate simulation of granular flow dynamics is crucial for assessing various geotechnical risks, including landslides and debris flows. Granular flows involve a dynamic rearrangement of...
Enabling knowledge discovery in natural hazard engineering datasets on DesignSafe
Data-driven discoveries require identifying relevant data relationships from a sea of complex, unstructured, and heterogeneous scientific data. We propose a hybrid methodology that extracts metadata...
Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in geotechnical engineering
The widespread adoption of large language models (LLMs), such as OpenAI’s ChatGPT, could revolutionize various industries, including geotechnical engineering. However, GPT models can sometimes generate...
GNS: A generalizable Graph Neural Network-based simulator for particulate and fluid modeling
We develop a PyTorch-based Graph Network Simulator (GNS) that learns physics and predicts the flow behavior of particulate and fluid systems. GNS discretizes the domain...
Using explainability to design physics-aware CNNs for solving subsurface inverse problems
We present a novel method of using explainability techniques to design physics-aware neural networks. We demonstrate our approach by developing a convolutional neural network (CNN)...
Evaluation of particle motions in stabilized specimens of transparent sand using deep learning segmentation
Individual particle rotation and displacement were measured in triaxial tests on transparent sand stabilized with geogrid simulants. The Cellpose U-Net model, originally developed to segment...
A frequency-velocity CNN for developing near-surface 2D Vs images from linear-array, active-source wavefield measurements
This paper presents a frequency-velocity convolutional neural network (CNN) for rapid, non-invasive 2D shear wave velocity (Vs) imaging of near-surface geo-materials. Operating in the frequency-velocity...
Minority Report: A graph network oracle for in situ visualization
In situ visualization techniques are hampered by a lack of foresight: crucial simulation phenomena can be missed due to a poor sampling rate or insufficient...
A machine learning approach to predicting pore pressure response in liquefiable sands under cyclic loading
Shear stress history controls the pore pressure response in liquefiable soils. The excess pore pressure does not increase under cyclic loading when shear stress amplitude...
Using Convolutional Neural Networks (CNN) to develop starting models for 2D full waveform inversion
Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamen-tally change engineering site characterization by enabling the recovery of high resolution 2D/3D...
conference
Investigating the Effect of CPT in Lateral Spreading Prediction Using Explainable AI
This study proposes an autoencoder approach to extract latent features from cone penetration test profiles to evaluate the potential of incorporating CPT data in an...
Modeling liquefaction-induced runout of a tailings dam using a hybrid finite element and material point method approach
Tailings dams impound large amounts of saturated soil which can be highly susceptible to liquefaction. Liquefaction results in a severe loss of strength in the...
Minority Report: A graph network oracle for in situ visualization
In situ visualization techniques are hampered by a lack of foresight: crucial simulation phenomena can be missed due to a poor sampling rate or insufficient...
A machine learning approach to predicting pore pressure response in liquefiable sands under cyclic loading
Shear stress history controls the pore pressure response in liquefiable soils. The excess pore pressure does not increase under cyclic loading when shear stress amplitude...
Hybrid Finite Element and Material Point Method to simulate granular column collapse from failure initiation to runout
The performance evaluation of a potentially unstable slope involves two key components: the initiation of the slope failure and the post-failure runout. The Finite Element...
Effect Of Slope Angle On The Runout Evolution of Granular Column Collapse for Varying Initial Volumes
In nature, submarine slope failures usually carry thousands of cubic-meters of sediments across extremely long distances and cause tsunamis and damages to offshore structures. This...
Power electronics packaging for in-road wireless charging installations
When power electronics are deployed under the road surface as part of a wireless system it is important to know that their packaging provides adequate...
Effect of Initial Volume on the Run-Out Behavior of Submerged Granular Columns
Submarine landslides transport thousands of cubic meters of sediment across continental shelves even at slopes as low as 1° and can cause significant casualty and...
Investigating the effect of porosity on the soil water retention curve using the multiphase Lattice Boltzmann Method
The soil water retention curve (SWRC) is the most commonly used relationship in the study of unsaturated soil. In this paper, the effect of porosity...
A site‑specific study on evaluation of design ground motion parameters
Site‑specific seismic analysis of deep stiff soil sites
Numerical prediction of ground vibration caused by a subway
Endochronic modeling of static triaxial response of sand
Seismic response of shallow and deep stiff soil sites
mpm
Runout of liquefaction-induced tailings dam failure: Influence of earthquake motions and residual strength
This study utilizes a hybrid Finite Element Method (FEM) and Material Point Method (MPM) to investigate the runout of liquefaction-induced flow slide failures. The key...
A hybrid Finite Element and Material Point Method for modeling liquefaction-induced tailings dam failures
This paper presents a hybrid Finite Element Method (FEM) and Material Point Method (MPM) approach for modeling liquefaction-induced tailings dam failures from initiation through runout....
Sequential Hybrid Finite Element and Material Point Method to Simulate Slope Failures
Numerical modeling of slope failures seeks to predict two key phenomena: the initiation of failure and the post-failure runout. Currently, most modeling methods for slope...
Modeling liquefaction-induced runout of a tailings dam using a hybrid finite element and material point method approach
Tailings dams impound large amounts of saturated soil which can be highly susceptible to liquefaction. Liquefaction results in a severe loss of strength in the...
GNS: A generalizable Graph Neural Network-based simulator for particulate and fluid modeling
We develop a PyTorch-based Graph Network Simulator (GNS) that learns physics and predicts the flow behavior of particulate and fluid systems. GNS discretizes the domain...
Hybrid Finite Element and Material Point Method to simulate granular column collapse from failure initiation to runout
The performance evaluation of a potentially unstable slope involves two key components: the initiation of the slope failure and the post-failure runout. The Finite Element...
In-situ visualization of natural hazards with Galaxy and Material Point Method
Visualizing regional-scale landslides is the key to conveying the threat of natural hazards to stakeholders and policymakers. Traditional visualization techniques are restricted to post-processing a...
Investigating the thixotropic behaviour of tremie concrete using the slump‑flow test and the Material Point Method
A new thixotropic model is developed integrating the Papanastasiou-Bingham model with thixotropy equations to simulate the flow behaviour of Tremie Concrete in the Material Point...
TACC Frontera Pathways
Geoelements group wins TACC pathways proposal to simulate the Oso landslide.
Large Deformation Modelling in Geomechanics
Trends in large‑deformation analysis of landslide mass movements with particular emphasis on the material point method
Post‑failure behavior of tunnel heading collapse by MPM simulation
lbm
Measuring the effective stress parameter using the multiphase lattice Boltzmann method and investigating the source of its hysteresis
The effective stress parameter, χ, is essential for calculating the effective stress in unsaturated soils. Experimental measurements have captured different relationships between χ and the...
Multiphase lattice Boltzmann modeling of cyclic water retention behavior in unsaturated sand based on X-ray Computed Tomography
The water retention curve (WRC) defines the relationship between matric suction and saturation and is a key function for determining the hydro-mechanical behavior of unsaturated...
Investigating the source of hysteresis in the Soil-Water Characteristic Curve using the multiphase lattice Boltzmann method
The soil-water characteristic curve (SWCC) is the most fundamental relationship in unsaturated soil mechanics, relating the amount of water in the soil to the corresponding...
Effect Of Slope Angle On The Runout Evolution of Granular Column Collapse for Varying Initial Volumes
In nature, submarine slope failures usually carry thousands of cubic-meters of sediments across extremely long distances and cause tsunamis and damages to offshore structures. This...
Effect of Initial Volume on the Run-Out Behavior of Submerged Granular Columns
Submarine landslides transport thousands of cubic meters of sediment across continental shelves even at slopes as low as 1° and can cause significant casualty and...
Investigating the effect of porosity on the soil water retention curve using the multiphase Lattice Boltzmann Method
The soil water retention curve (SWRC) is the most commonly used relationship in the study of unsaturated soil. In this paper, the effect of porosity...
Mechanics of granular column collapse in fluid at varying slope angles
Numerical study of a sphere descending along an inclined slope in a liquid
fem
Runout of liquefaction-induced tailings dam failure: Influence of earthquake motions and residual strength
This study utilizes a hybrid Finite Element Method (FEM) and Material Point Method (MPM) to investigate the runout of liquefaction-induced flow slide failures. The key...
A hybrid Finite Element and Material Point Method for modeling liquefaction-induced tailings dam failures
This paper presents a hybrid Finite Element Method (FEM) and Material Point Method (MPM) approach for modeling liquefaction-induced tailings dam failures from initiation through runout....
Sequential Hybrid Finite Element and Material Point Method to Simulate Slope Failures
Numerical modeling of slope failures seeks to predict two key phenomena: the initiation of failure and the post-failure runout. Currently, most modeling methods for slope...
Modeling liquefaction-induced runout of a tailings dam using a hybrid finite element and material point method approach
Tailings dams impound large amounts of saturated soil which can be highly susceptible to liquefaction. Liquefaction results in a severe loss of strength in the...
Hybrid Finite Element and Material Point Method to simulate granular column collapse from failure initiation to runout
The performance evaluation of a potentially unstable slope involves two key components: the initiation of the slope failure and the post-failure runout. The Finite Element...
Finite Element Analysis of Pelvic Floor
Civil Engineering analysis technique of finite elements is used for the first time to answer an evolutionary question
Biomechanical trade‑offs in the pelvic floor constrain the evolution of the human birth canal
Compared to most other primates, humans are characterized by a tight fit between the maternal birth canal and the fetal head, leading to a relatively...
gns
Inverse analysis of granular flows using differentiable graph neural network simulator
Inverse problems in granular flows, such as landslides and debris flows, involve estimating material parameters or boundary conditions based on target runout profile. Traditional high-fidelity...
Three-dimensional granular flow simulation using graph neural network-based learned simulator
Reliable evaluations of geotechnical hazards like landslides and debris flow require accurate simulation of granular flow dynamics. Traditional numerical methods can simulate the complex behaviors...
Accelerating particulate and fluid simulations with graph neural networks for solving forward and inverse problems
We leverage physics-embedded differentiable graph network simulators (GNS) to accelerate particulate and fluid simulations to solve forward and inverse problems. GNS represents the domain as...
Graph Neural Network-based surrogate model for granular flows
Accurate simulation of granular flow dynamics is crucial for assessing various geotechnical risks, including landslides and debris flows. Granular flows involve a dynamic rearrangement of...
GNS: A generalizable Graph Neural Network-based simulator for particulate and fluid modeling
We develop a PyTorch-based Graph Network Simulator (GNS) that learns physics and predicts the flow behavior of particulate and fluid systems. GNS discretizes the domain...
Minority Report: A graph network oracle for in situ visualization
In situ visualization techniques are hampered by a lack of foresight: crucial simulation phenomena can be missed due to a poor sampling rate or insufficient...
dem
Effect Of Slope Angle On The Runout Evolution of Granular Column Collapse for Varying Initial Volumes
In nature, submarine slope failures usually carry thousands of cubic-meters of sediments across extremely long distances and cause tsunamis and damages to offshore structures. This...
Effect of Initial Volume on the Run-Out Behavior of Submerged Granular Columns
Submarine landslides transport thousands of cubic meters of sediment across continental shelves even at slopes as low as 1° and can cause significant casualty and...
Mechanics of granular column collapse in fluid at varying slope angles
Numerical study of a sphere descending along an inclined slope in a liquid
Transient dynamics of a 2D granular pile
ai
NSF NAIRR Pilot grant
NSF Awards NAIRR Pilot grant to accelerated exascale AI-integrated simulations
NSF CAREER Award
NSF OAC Awards CAREER Award to support AI-accelerated simulations
Chishiki.AI: NSF awards $7M to pioneering AI-Accelerated Civil Engineering
Chishiki.AI is a groundbreaking initiative for AI-powered Civil Engineering.
UT Austin Researchers Receive Major FHWA Grant for Pioneering Highway Transportation Research
FHWA funds a pioneering initiative for AI-powered Structural Health Monitoring of bridges.
cnn
Using explainability to design physics-aware CNNs for solving subsurface inverse problems
We present a novel method of using explainability techniques to design physics-aware neural networks. We demonstrate our approach by developing a convolutional neural network (CNN)...
Evaluation of particle motions in stabilized specimens of transparent sand using deep learning segmentation
Individual particle rotation and displacement were measured in triaxial tests on transparent sand stabilized with geogrid simulants. The Cellpose U-Net model, originally developed to segment...
A frequency-velocity CNN for developing near-surface 2D Vs images from linear-array, active-source wavefield measurements
This paper presents a frequency-velocity convolutional neural network (CNN) for rapid, non-invasive 2D shear wave velocity (Vs) imaging of near-surface geo-materials. Operating in the frequency-velocity...
Using Convolutional Neural Networks (CNN) to develop starting models for 2D full waveform inversion
Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamen-tally change engineering site characterization by enabling the recovery of high resolution 2D/3D...
hpc
Investigating the effect of porosity on the soil water retention curve using the multiphase Lattice Boltzmann Method
The soil water retention curve (SWRC) is the most commonly used relationship in the study of unsaturated soil. In this paper, the effect of porosity...
TACC Frontera Pathways
Geoelements group wins TACC pathways proposal to simulate the Oso landslide.
Large Deformation Modelling in Geomechanics
Trends in large‑deformation analysis of landslide mass movements with particular emphasis on the material point method
porescale
Evaluation of particle motions in stabilized specimens of transparent sand using deep learning segmentation
Individual particle rotation and displacement were measured in triaxial tests on transparent sand stabilized with geogrid simulants. The Cellpose U-Net model, originally developed to segment...
Multiphase lattice Boltzmann modeling of cyclic water retention behavior in unsaturated sand based on X-ray Computed Tomography
The water retention curve (WRC) defines the relationship between matric suction and saturation and is a key function for determining the hydro-mechanical behavior of unsaturated...
Investigating the source of hysteresis in the Soil-Water Characteristic Curve using the multiphase lattice Boltzmann method
The soil-water characteristic curve (SWCC) is the most fundamental relationship in unsaturated soil mechanics, relating the amount of water in the soil to the corresponding...
Investigating the effect of porosity on the soil water retention curve using the multiphase Lattice Boltzmann Method
The soil water retention curve (SWRC) is the most commonly used relationship in the study of unsaturated soil. In this paper, the effect of porosity...
fwi
Using explainability to design physics-aware CNNs for solving subsurface inverse problems
We present a novel method of using explainability techniques to design physics-aware neural networks. We demonstrate our approach by developing a convolutional neural network (CNN)...
A frequency-velocity CNN for developing near-surface 2D Vs images from linear-array, active-source wavefield measurements
This paper presents a frequency-velocity convolutional neural network (CNN) for rapid, non-invasive 2D shear wave velocity (Vs) imaging of near-surface geo-materials. Operating in the frequency-velocity...
Using Convolutional Neural Networks (CNN) to develop starting models for 2D full waveform inversion
Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamen-tally change engineering site characterization by enabling the recovery of high resolution 2D/3D...
heat-transfer
Conductive and convective heat transfer in inductive heating of subsea buried pipelines
Inductive heating with high-voltage cables reduces the risk of hydrate formation by raising the temperature of the production fluid in pipelines. Heating the pipeline results...
Facebook Industry Project: Rethinking the Thermal Design of Duct Banks
Facebook awards Dr Chadi El Mohtar and Dr Krishna Kumar an industry researh project to improve the heat dissipation in duct banks.
Power electronics packaging for in-road wireless charging installations
When power electronics are deployed under the road surface as part of a wireless system it is important to know that their packaging provides adequate...
swcc
Multiphase lattice Boltzmann modeling of cyclic water retention behavior in unsaturated sand based on X-ray Computed Tomography
The water retention curve (WRC) defines the relationship between matric suction and saturation and is a key function for determining the hydro-mechanical behavior of unsaturated...
Investigating the source of hysteresis in the Soil-Water Characteristic Curve using the multiphase lattice Boltzmann method
The soil-water characteristic curve (SWCC) is the most fundamental relationship in unsaturated soil mechanics, relating the amount of water in the soil to the corresponding...
Investigating the effect of porosity on the soil water retention curve using the multiphase Lattice Boltzmann Method
The soil water retention curve (SWRC) is the most commonly used relationship in the study of unsaturated soil. In this paper, the effect of porosity...
biology
Finite Element Analysis of Pelvic Floor
Civil Engineering analysis technique of finite elements is used for the first time to answer an evolutionary question
Biomechanical trade‑offs in the pelvic floor constrain the evolution of the human birth canal
Compared to most other primates, humans are characterized by a tight fit between the maternal birth canal and the fetal head, leading to a relatively...
granular-flow
Effect Of Slope Angle On The Runout Evolution of Granular Column Collapse for Varying Initial Volumes
In nature, submarine slope failures usually carry thousands of cubic-meters of sediments across extremely long distances and cause tsunamis and damages to offshore structures. This...
Effect of Initial Volume on the Run-Out Behavior of Submerged Granular Columns
Submarine landslides transport thousands of cubic meters of sediment across continental shelves even at slopes as low as 1° and can cause significant casualty and...
insitu
Minority Report: A graph network oracle for in situ visualization
In situ visualization techniques are hampered by a lack of foresight: crucial simulation phenomena can be missed due to a poor sampling rate or insufficient...
In-situ visualization of natural hazards with Galaxy and Material Point Method
Visualizing regional-scale landslides is the key to conveying the threat of natural hazards to stakeholders and policymakers. Traditional visualization techniques are restricted to post-processing a...
llm
Enabling knowledge discovery in natural hazard engineering datasets on DesignSafe
Data-driven discoveries require identifying relevant data relationships from a sea of complex, unstructured, and heterogeneous scientific data. We propose a hybrid methodology that extracts metadata...
Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in geotechnical engineering
The widespread adoption of large language models (LLMs), such as OpenAI’s ChatGPT, could revolutionize various industries, including geotechnical engineering. However, GPT models can sometimes generate...
nhe
NSF Award: POSE Phase 1: Tuitus
NSF OAC Awards Tuitus project to support open-source ecosystem in natural hazards engineering
NSF Award: Cognitasium - Enabling Data-Driven Discoveries in Natural Hazards Engineering
NSF OAC Awards Cognitasium project to develop new data-driven discovery workflows in natural hazards
nsf
NSF Award: POSE Phase 1: Tuitus
NSF OAC Awards Tuitus project to support open-source ecosystem in natural hazards engineering
NSF Award: Cognitasium - Enabling Data-Driven Discoveries in Natural Hazards Engineering
NSF OAC Awards Cognitasium project to develop new data-driven discovery workflows in natural hazards
operator-learning
SetONet: A Deep Set-based Operator Network for Solving PDEs with permutation invariant variable input sampling
Neural operators, particularly the Deep Operator Network (DeepONet), have shown promise in learning mappings between function spaces for solving differential equations. However, standard DeepONet requires...
Basis-to-Basis Operator Learning using Function Encoders
We present Basis-to-Basis (B2B) operator learning, a novel approach for learning operators on Hilbert spaces of functions based on the foundational ideas of function encoders....
traffic-flow
Microsimulation Analysis for Network Traffic Assignment (MANTA) at Metropolitan‑Scale for Agile Transportation Planning
Abstract:
Context‑specific volume‑delay curves by combining crowdsourced traffic data with Automated Traffic Counters (ATC): a case study for London
viz
Minority Report: A graph network oracle for in situ visualization
In situ visualization techniques are hampered by a lack of foresight: crucial simulation phenomena can be missed due to a poor sampling rate or insufficient...
In-situ visualization of natural hazards with Galaxy and Material Point Method
Visualizing regional-scale landslides is the key to conveying the threat of natural hazards to stakeholders and policymakers. Traditional visualization techniques are restricted to post-processing a...
xai
Investigating the Effect of CPT in Lateral Spreading Prediction Using Explainable AI
This study proposes an autoencoder approach to extract latent features from cone penetration test profiles to evaluate the potential of incorporating CPT data in an...
Explainable AI models for predicting liquefaction-induced lateral spreading
Introduction: Earthquake-induced liquefaction can cause substantial lateral spreading, posing threats to infrastructure. Machine learning (ML) can improve lateral spreading prediction models by capturing complex soil...
boem
Dept of Interior (BOEM) Awards Offshore Wind Power Cable Burial Research Grant
BOEM Awards Offshore Wind Power Cable Research Grant
concrete-flow
Investigating the thixotropic behaviour of tremie concrete using the slump‑flow test and the Material Point Method
A new thixotropic model is developed integrating the Papanastasiou-Bingham model with thixotropy equations to simulate the flow behaviour of Tremie Concrete in the Material Point...
diff-programming
An inverse analysis of fluid flow through granular media using differentiable lattice Boltzmann method
In this study, we introduce an effective method for the inverse analysis of fluid flow problems, focusing on accurately determining boundary conditions and characterizing the...
diffsim
Differentiable programming for inverse estimation of soil permeability and design of duct banks
Underground duct banks carrying power cables dissipate heat to the surrounding soil. The amount of heat dissipated determines the current rating of cables, which in...
experiments
Evaluation of particle motions in stabilized specimens of transparent sand using deep learning segmentation
Individual particle rotation and displacement were measured in triaxial tests on transparent sand stabilized with geogrid simulants. The Cellpose U-Net model, originally developed to segment...
gpu
Back to Top ↑kan
MLPs and KANs for data-driven learning in physical problems: A performance comparison
There is increasing interest in solving partial differential equations (PDEs) by casting them as machine learning problems. Recently, there has been a spike in exploring...
liquefaction
A machine learning approach to predicting pore pressure response in liquefiable sands under cyclic loading
Shear stress history controls the pore pressure response in liquefiable soils. The excess pore pressure does not increase under cyclic loading when shear stress amplitude...
microsim
Back to Top ↑multiphase
Investigating the source of hysteresis in the Soil-Water Characteristic Curve using the multiphase lattice Boltzmann method
The soil-water characteristic curve (SWCC) is the most fundamental relationship in unsaturated soil mechanics, relating the amount of water in the soil to the corresponding...
offshore
Dept of Interior (BOEM) Awards Offshore Wind Power Cable Burial Research Grant
BOEM Awards Offshore Wind Power Cable Research Grant
open-source
NSF Award: POSE Phase 1: Tuitus
NSF OAC Awards Tuitus project to support open-source ecosystem in natural hazards engineering
robotics
NSF CAREER Award
NSF OAC Awards CAREER Award to support AI-accelerated simulations
sciml
An inverse analysis of fluid flow through granular media using differentiable lattice Boltzmann method
In this study, we introduce an effective method for the inverse analysis of fluid flow problems, focusing on accurately determining boundary conditions and characterizing the...
shm
UT Austin Researchers Receive Major FHWA Grant for Pioneering Highway Transportation Research
FHWA funds a pioneering initiative for AI-powered Structural Health Monitoring of bridges.
teaching
Reflections on teaching engineering through murder mysteries
This paper presents a reflective analysis of a novel approach to Problem-Based Learning (PBL) to teach abstract concepts in a large-class setting, specifically tailored for...
unsaturated
Measuring the effective stress parameter using the multiphase lattice Boltzmann method and investigating the source of its hysteresis
The effective stress parameter, χ, is essential for calculating the effective stress in unsaturated soils. Experimental measurements have captured different relationships between χ and the...
wind
Dept of Interior (BOEM) Awards Offshore Wind Power Cable Burial Research Grant
BOEM Awards Offshore Wind Power Cable Research Grant
wireless-charging
Power electronics packaging for in-road wireless charging installations
When power electronics are deployed under the road surface as part of a wireless system it is important to know that their packaging provides adequate...