Publications

@article{Alrashidi2024-np,

title = {UHPC Fresh Chloride Limit Testing},

author = {Raid S Alrashidi and Rami Zamzami and Megan S Voss and Daniel J Alabi and Christopher C Ferraro and H R Hamilton and Joel B Harley and Kyle A Riding},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=100&pagesize=100&citation_for_view=Isf8yn0AAAAJ:SGW5VrABaM0C},

doi = {10.14359/51742104},

year  = {2024},

date = {2024-07-01},

urldate = {2024-07-01},

journal = {Special Publication of the American Concrete Institute},

volume = {363},

pages = {1–20},

publisher = {American Concrete Institute},

abstract = {UHPC Fresh Chloride Limit Testing Search Subscribe to Email ​▼ 

 Membership Membership In today’s market, it is imperative to be 

 knowledgeable and have an edge over the competition. ACI members 

 have it…they are engaged, informed, and stay up to date by taking 

 advantage of benefits that ACI membership provides them. Read 

 more about membership Types of Membership Individual Student 

 Young Professional Organizational Sustaining ACI MEMBERSHIP Enjoy 

 the benefits of an ACI Membership Learn More Become an ACI Member 

 Member Directory Sustaining Members Honors and Awards Career 

 Center About ACI The American Concrete Institute Founded in 1904 

 and headquartered in Farmington Hills, Michigan, USA, the 

 American Concrete Institute is a leading authority and resource 

 worldwide for the development, dissemination, and adoption of its 

 consensus-based standards, technical resources, …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Voss2024-sc,

title = {Development of Modified Double-Punch Test for Quality-Control 

 Testing of UHPC Tensile Performance},

author = {Megan S Voss and Daniel Alabi and Raid S Alrashidi and Taylor A Rawlinson and Christopher C Ferraro and H R Hamilton and Joel B Harley and Kyle A Riding},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:eO3_k5sD8BwC},

year  = {2024},

date = {2024-07-01},

journal = {Special Publication},

volume = {363},

pages = {38–59},

abstract = {Development of Modified Double-Punch Test for Quality-Control 

 Testing of UHPC Tensile Performance Search Subscribe to Email ​▼ 

 Membership Membership In today’s market, it is imperative to be 

 knowledgeable and have an edge over the competition. ACI members 

 have it…they are engaged, informed, and stay up to date by taking 

 advantage of benefits that ACI membership provides them. Read more 

 about membership Types of Membership Individual Student Young 

 Professional Organizational Sustaining ACI MEMBERSHIP Enjoy the 

 benefits of an ACI Membership Learn More Become an ACI Member 

 Member Directory Sustaining Members Honors and Awards Career 

 Center About ACI The American Concrete Institute Founded in 1904 

 and headquartered in Farmington Hills, Michigan, USA, the American 

 Concrete Institute is a leading authority and resource worldwide 

 for the development, dissemination, …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alabi2024-cn,

title = {Electromagnetic Sensor for the Nondestructive Testing of 

 Ultra-High Performance Concrete},

author = {Daniel J Alabi and Megan S Voss and Raid S Alrashidi and Christopher C Ferraro and Kyle Riding and Joel B Harley},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:DkZNVXde3BIC},

doi = {10.14359/51742105},

year  = {2024},

date = {2024-07-01},

urldate = {2024-07-01},

journal = {Special Publication of the American Concrete Institute},

volume = {363},

pages = {21–37},

publisher = {American Concrete Institute},

abstract = {Electromagnetic Sensor for the Nondestructive Testing of 

 Ultra-High Performance Concrete Search Subscribe to Email ​▼ 

 Membership Membership In today’s market, it is imperative to be 

 knowledgeable and have an edge over the competition. ACI members 

 have it…they are engaged, informed, and stay up to date by taking 

 advantage of benefits that ACI membership provides them. Read 

 more about membership Types of Membership Individual Student 

 Young Professional Organizational Sustaining ACI MEMBERSHIP Enjoy 

 the benefits of an ACI Membership Learn More Become an ACI Member 

 Member Directory Sustaining Members Honors and Awards Career 

 Center About ACI The American Concrete Institute Founded in 1904 

 and headquartered in Farmington Hills, Michigan, USA, the 

 American Concrete Institute is a leading authority and resource 

 worldwide for the development, dissemination, and adoption …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Khurjekar2024-cj,

title = {Reliability assessment of guided wave damage localization with 

 deep learning uncertainty quantification methods},

author = {Ishan D Khurjekar and Joel B Harley},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:kzcSZmkxUKAC},

doi = {10.1016/j.ndteint.2024.103099},

year  = {2024},

date = {2024-06-01},

urldate = {2024-06-01},

journal = {NDT & E international: independent nondestructive testing and 

 evaluation},

volume = {144},

number = {103099},

pages = {103099},

publisher = {Elsevier BV},

abstract = {Guided wave-based structural health monitoring is an attractive 

 option for detecting structural defects in an automated manner. 

 In this work, we focus on the task of damage localization. Deep 

 learning methods have been shown to have superior performance for 

 damage localization. Yet, environmental variations introduce 

 uncertainty in the system and reduce its reliability. For this 

 reason, it is crucial to assess the reliability of estimates 

 taken from structural health monitoring systems. In this work, we 

 estimate the localization reliability from a single snapshot of 

 sparse array guided wave measurements instead of reporting values 

 averaged over an entire set of test measurements. The assessment 

 strategy can be added to any deep learning localization model and 

 produces both a localization and uncertainty estimate. The deep 

 learning model is trained using only guided wave simulations. We 

 assess the uncertainty using both simulated and experimental data 

 with temperature variations. Multiple deep learning-based 

 uncertainty quantification methods are analyzed. Results 

 demonstrate correlations between uncertainty, temperature 

 variations, and the presence of synthetic damage. We also compare 

 with reliability derived from delay-and-sum localization. We find 

 that a deep ensemble learning strategy provides the most reliable 

 damage localization and uncertainty quantification.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Melville2024-lu,

title = {Anisotropic physics-regularized interpretable machine learning of 

 microstructure evolution},

author = {Joseph Melville and Vishal Yadav and Lin Yang and Amanda R Krause and Michael R Tonks and Joel B Harley},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:kF1pexMAQbMC},

doi = {10.1016/j.commatsci.2024.112941},

year  = {2024},

date = {2024-04-01},

urldate = {2024-04-01},

journal = {Computational materials science},

volume = {238},

number = {112941},

pages = {112941},

publisher = {Elsevier BV},

abstract = {Anisotropic Physics-Regularized Interpretable Machine Learning 

 Microstructure Evolution (APRIMME) is a general-purpose machine 

 learning solution for grain growth simulations. In prior work, 

 PRIMME employed a deep neural network to predict site-specific 

 migration as a function of its neighboring sites to model normal, 

 isotropic, grain growth behavior. This work aims to extend this 

 method by incorporating grain boundary misorientation-based grain 

 growth behavior. APRIMME is trained on anisotropic simulations 

 created using the Monte Carlo-Potts (MCP) model. The results of 

 this work are compared statistically using grain radius, number 

 of sides per grain, mean neighborhood misorientations, and the 

 standard deviation of triple junction dihedral angles, and are 

 found to match in most cases. The exceptions are small and seem 

 to be related to two causes: (1) the deterministic model of 

 APRIMME is learning from the stochastic simulations of MCP, which 

 seems to accentuate triple junction behaviors; and, (2) a bias 

 against very small grains is made evident in a quicker decrease 

 in grains than expected at the beginning of an APRIMME 

 simulation. APRIMME is also evaluated for its general ability to 

 capture anisotropic grain growth behavior by first investigating 

 different test case initial conditions, including a circle grain, 

 three grain, and hexagonal grain microstructures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Conry2024-bh,

title = {The evolution of grain boundary energy in textured and untextured 

 Ca‐doped alumina during grain growth},

author = {Bryan Conry and Molly Kole and W Ryan Burnett and Joel B Harley and Michael R Tonks and Michael S Kesler and Amanda R Krause},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:RoXSNcbkSzsC},

doi = {10.1111/jace.19367},

year  = {2024},

date = {2024-03-01},

urldate = {2024-03-01},

journal = {Journal of the American Ceramic Society. American Ceramic Society},

volume = {107},

number = {3},

pages = {1725–1735},

publisher = {Wiley},

abstract = {AbstractThe role of anisotropic grain boundary energy in grain 

 growth is investigated using textured microstructures that 

 contain a high proportion of special grain boundaries. Textured 

 and untextured Ca‐doped alumina was prepared by slip casting 

 inside and outside a high magnetic field, respectively. At 

 1600°C, the textured microstructure exhibits faster growth than 

 the untextured microstructure and its population of low‐angle 

 boundaries increases. Atomic force microscopy (AFM) is employed 

 to measure the geometry of thermal grooves to assess the relative 

 grain boundary energy of these systems before and after growth. 

 In the textured microstructure, the grain boundary energy 

 distribution narrows and shifts to a lower average energy. 

 Conversely, the energy distribution broadens for the untextured 

 microstructure as it grows and exhibits abnormal grain growth. 

 Further analysis of the boundary networks neighboring abnormal 

 grains reveals an energy incentive that facilitates their growth. 

 These results suggest that coarsening is not the only dominant 

 grain growth mechanism and that the system can lower its energy 

 effectively by replacing high energy boundaries with those of low 

 energy. The faster growth of lower energy boundaries suggests 

 that isotropic simulations do not adequately account for 

 anisotropic grain growth mechanisms or anisotropic mobility.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tappan2024-lk,

title = {Explainable AI Elucidates Musculoskeletal Biomechanics: A Case 

 Study Using Wrist Surgeries},

author = {Isaly Tappan and Erica M Lindbeck and Jennifer A Nichols and Joel B Harley},

url = {http://dx.doi.org/10.1007/s10439-023-03394-9},

doi = {10.1007/s10439-023-03394-9},

year  = {2024},

date = {2024-03-01},

urldate = {2024-03-01},

journal = {Annals of biomedical engineering},

volume = {52},

number = {3},

pages = {498–509},

publisher = {Springer Science and Business Media LLC},

abstract = {As datasets increase in size and complexity, biomechanists have 

 turned to artificial intelligence (AI) to aid their analyses. 

 This paper explores how explainable AI (XAI) can enhance the 

 interpretability of biomechanics data derived from 

 musculoskeletal simulations. We use machine learning to classify 

 the simulated lateral pinch data as belonging to models with 

 healthy or one of two types of surgically altered wrists. This 

 simulation-based classification task is analogous to using 

 biomechanical movement and force data to clinically diagnose a 

 pathological state. The XAI describes which musculoskeletal 

 features best explain the classifications and, in turn, the 

 pathological states, at both the local (individual decision) 

 level and global (entire algorithm) level. We demonstrate that 

 these descriptions agree with assessments in the literature and 

 additionally identify the blind spots that can be missed with 

 traditional statistical techniques.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yang2024-ik,

title = {A triple junction energy study using an inclination-dependent 

 anisotropic Monte Carlo Potts grain growth model},

author = {Lin Yang and Vishal Yadav and Joseph Melville and Joel B Harley and Amanda R Krause and Michael R Tonks},

url = {https://www.sciencedirect.com/science/article/pii/S0264127524001357},

doi = {10.1016/j.matdes.2024.112763},

year  = {2024},

date = {2024-03-01},

urldate = {2024-03-01},

journal = {Materials & design},

volume = {239},

number = {112763},

pages = {112763},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Diaz2024-rq,

title = {Sensitivity Analysis of Upper Limb Musculoskeletal Models During 

 Isometric and Isokinetic Tasks},

author = {Maximillian Diaz and Joel B Harley and Jennifer Nichols},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:w0F2JDEymm0C},

doi = {10.1115/1.4064056/7060150/bio-23-1225},

year  = {2024},

date = {2024-02-01},

urldate = {2024-02-01},

journal = {Journal of Biomechanical Engineering},

pages = {1–37},

abstract = {Sensitivity coefficients are used to understand how errors in 

 subject-specific musculoskeletal model parameters influence model 

 predictions. Previous sensitivity studies in the lower limb 

 calculated sensitivity using perturbations that do not fully 

 represent the diversity of the population. Hence, the present 

 study performs sensitivity analysis in the upper limb using a 

 large synthetic dataset to capture physiological diversity 

 associated with age, sex, race, and lifestyle. The large dataset (n= 401 synthetic subjects) was created by adjusting maximum 

 isometric force, optimal fiber length, pennation angle, and mass 

 to induce atrophy, hypertrophy, osteoporosis, and osteopetrosis in 

 two upper limb musculoskeletal models. Simulations of three 

 isometric and two isokinetic upper limb tasks were performed using 

 each synthetic subject to predict muscle activations. Sensitivity 

 coefficients were calculated using three different …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gazda2024-pl,

title = {Reducing the effects of rain and moisture on spread spectrum time 

 domain reflectometry monitoring of photovoltaics},

author = {Nikodem Gazda and Brian Paulsen and Ayobami Edun and Cynthia Furse and Joel B Harley},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=D5vHehEAAAAJ&citation_for_view=D5vHehEAAAAJ:KlAtU1dfN6UC},

doi = {10.1109/jsen.2024.3423833},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {IEEE sensors journal},

pages = {1–1},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Melville2024-da,

title = {A new efficient grain growth model using a random 

 Gaussian-sampled mode filter},

author = {Joseph Melville and Vishal Yadav and Lin Yang and Amanda R Krause and Michael R Tonks and Joel B Harley},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:-95Q15plzcUC},

doi = {10.1016/j.matdes.2023.112604},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Materials & design},

volume = {237},

number = {112604},

pages = {112604},

publisher = {Elsevier BV},

abstract = {This paper presents the use of a Gaussian neighborhood mode 

 filter for predicting grain growth in a manner similar to the 

 solutions obtained by a Monte Carlo Potts model. This flexible 

 grain growth model can quickly utilize modern, computationally 

 optimized data science strategies on graphics processing units to 

 simulate grain growth up to 100 times faster than the 

 state-of-the-art, publicly available Monte Carlo Potts model. We 

 show that, given the correct neighborhood, the mode filter can 

 replicate normal grain growth in two or three dimensions. In 

 addition, the paper briefly demonstrates the ability to model 

 limited anisotropic in grain boundary energy and mobility. 

 Anisotropic grain boundary energy is modeled by defining a 

 weighted mode filter operation. Anisotropic grain boundary 

 mobility is modeled by scaling and orienting the Gaussian 

 neighborhood in a particular direction.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jackson2023-tg,

title = {The center-center image closely approximates other methods for 

 syndesmosis reduction clamp placement},

author = {Nicholas J Jackson and Koen Flores and Andrew Blake and Joel B Harley and Christopher W Reb and Jennifer A Nichols},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:-nhnvRiOwuoC},

doi = {10.1177/19386400231213741},

year  = {2023},

date = {2023-12-01},

journal = {Foot & ankle specialist},

pages = {19386400231213741},

publisher = {SAGE Publications},

abstract = {BACKGROUND: The optimal placement for a syndesmosis reduction 

 clamp remains an open question. This study compared the 

 center-center axis, which localizes clamp placement using only an 

 internally rotated lateral ankle X-ray, with other common 

 approaches, whose accuracy can only be confirmed using computed 

 tomography (CT). METHODS: Bone models of anatomically aligned (n = 6) and malreduced (n = 48) limbs were generated from CT scans 

 of cadaveric specimens. Four axes for guiding clamp placement 

 (center-center, centroid, B2, and trans-syndesmotic) were then 

 analyzed, using digitally reconstructed radiographs derived from 

 the bone models. Each axis' location was defined using 

 angle-height pairs that describe axis orientation along the full 

 anatomical region where syndesmosis fixation occurs. RESULTS: In 

 anatomically aligned limbs, the center-center axis was located on 

 average (±95% CI [confidence interval]), 0.64° (±0.50°) internal 

 rotation, 1.03° (±0.73°) internal rotation, and 2.09° (±7.29°) 

 external rotation from the centroid, B2, and trans-syndesmotic 

 axes, respectively. Fibular displacement altered the magnitude of 

 limb rotation needed to identify the center-center axis. 

 CONCLUSION: The center-center technique is a valid method that 

 closely approximates previously described methods for syndesmosis 

 clamp placement without using CT, and the magnitude of C-arm 

 rotation needed to transition from a talar dome lateral to a 

 center-center view may be a potential method for assessing 

 syndesmosis reduction. LEVELS OF EVIDENCE: Level III: 

 Retrospective comparative study.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tetali2023-dc,

title = {Wave physics-informed matrix factorizations},

author = {Harsha Vardhan Tetali and J Harley and B Haeffele},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:YsrPvlHIBpEC},

doi = {10.1109/TSP.2023.3348948},

year  = {2023},

date = {2023-12-01},

journal = {IEEE transactions on signal processing: a publication of the IEEE 

 Signal Processing Society},

volume = {72},

pages = {535–548},

publisher = {IEEE},

abstract = {With the recent success of representation learning methods, which 

 includes deep learning as a special case, there has been 

 considerable interest in developing techniques that incorporate 

 known physical constraints into the learned representation. As 

 one example, in many applications that involve a signal 

 propagating through physical media (e.g., optics, acoustics, 

 fluid dynamics, etc.), it is known that the dynamics of the 

 signal must satisfy constraints imposed by the wave equation. 

 Here we propose a matrix factorization technique that decomposes 

 such signals into a sum of components, where each component is 

 regularized to ensure that it nearly satisfies wave equation 

 constraints. Although our proposed formulation is non-convex, we 

 prove that our model can be efficiently solved to global 

 optimality. Through this line of work we establish theoretical 

 connections between wave-informed learning and filtering theory 

 in signal processing. We further demonstrate the application of 

 this work on modal analysis problems commonly arising in 

 structural diagnostics and prognostics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lindbeck2023-vo,

title = {Predictions of thumb, hand, and arm muscle parameters derived 

 using force measurements of varying complexity and neural 

 networks},

author = {Erica M Lindbeck and Maximillian T Diaz and Jennifer A Nichols and Joel B Harley},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:KNjnJ3z-R6IC},

doi = {10.1016/j.jbiomech.2023.111834},

year  = {2023},

date = {2023-12-01},

journal = {Journal of biomechanics},

volume = {161},

number = {111834},

pages = {111834},

publisher = {Elsevier BV},

abstract = {Subject-specific musculoskeletal models are a promising avenue 

 for personalized healthcare. However, current methods for 

 producing personalized models require dense, biomechanical 

 datasets that include expensive and time-consuming physiological 

 measurements. For personalized models to be clinically useful, we 

 must be able to rapidly generate models from simple, easy to 

 collect data. In this context, the objective of this paper is to 

 evaluate if and how simple data, namely height/weight and pinch 

 force data, can be used to achieve model personalization via 

 machine learning. Using simulated lateral pinch force 

 measurements from a synthetic population of 40,000 randomly 

 generated subjects, we train neural networks to estimate four 

 Hill-type muscle model parameters and bone density. We compare 

 parameter estimates to the true parameters of 10,000 additional 

 synthetic subjects. We also generate new personalized models 

 using the parameter estimates and perform new lateral pinch 

 simulations to compare predicted forces using these personalized 

 models to those generated using a baseline model. We demonstrate 

 that increasing force measurement complexity reduces the 

 root-mean-square error in the majority of parameter estimates. 

 Additionally, musculoskeletal models using neural network-based 

 parameter estimates provide up to an 80% reduction in absolute 

 error in simulated forces when compared to a generic model. Thus, 

 easily obtained force measurements may be suitable for 

 personalizing models of the thumb, although extending the method 

 to more tasks and models involving other joints likely requires 

 additional measurements.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Toma2023-bl,

title = {Modeling fission gas release at the mesoscale using 

 multiscale DenseNet regression with attention mechanism and 

 inception blocks},

author = {Peter Toma and Md Ali Muntaha and Joel B Harley and Michael R Tonks},

url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:pAkWuXOU-OoC},

year  = {2023},

date = {2023-10-01},

journal = {arXiv [cond-mat.mes-hall]},

abstract = {Mesoscale simulations of fission gas release (FGR) in nuclear 

 fuel provide a powerful tool for understanding how 

 microstructure evolution impacts FGR, but they are 

 computationally intensive. In this study, we present an 

 alternate, data-driven approach, using deep learning to 

 predict instantaneous FGR flux from 2D nuclear fuel 

 microstructure images. Four convolutional neural network 

 (CNN) architectures with multiscale regression are trained 

 and evaluated on simulated FGR data generated using a hybrid 

 phase field/cluster dynamics model. All four networks show 

 high predictive power, with $R^2$ values above 98%. The 

 best performing network combine a Convolutional Block 

 Attention Module (CBAM) and InceptionNet mechanisms to 

 provide superior accuracy (mean absolute percentage error of 

 4.4%), training stability, and robustness on very low 

 instantaneous FGR flux values.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}