A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Dynamic EM Ray Tracing for Complex Outdoor and Indoor Environments with Multiple Receivers(2024) Wang, Ruichen; Manocha, Dinesh; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Ray tracing models for visual, aural, and EM simulations have advanced, gaining traction in dynamic applications such as 5G, autonomous vehicles, and traffic systems. Dynamic ray tracing, modeling EM wave paths and their interactions with moving objects, leads to many challenges in complex urban areas due to environmental variability, data scarcity, and computational needs. In response to these challenges, we've developed new methods that use a dynamic coherence-based approach for ray tracing simulations across EM bands. Our approach is designed to enhance efficiency by improving the recomputation of bounding volume hierarchy (BVH) and by caching propagation paths. With our formulation, we've observed a reduction in computation time by about 30%, all while maintaining a level of accuracy comparable to that of other simulators. Building on our dynamic approach, we've made further refinements to our algorithm to better model channel coherence, spatial consistency, and the Doppler effect. Our EM ray tracing algorithm can incrementally improve the accuracy of predictions relating to the movement and positioning of dynamic objects in the simulation. We've also integrated the Uniform Geometrical Theory of Diffraction (UTD) with our ray tracing algorithm. Our enhancement is designed to allow for more accurate simulations of diffraction around smooth surfaces, especially in complex indoor settings, where accurate prediction is important. Taking another step forward, we've combined machine learning (ML) techniques with our dynamic ray tracing framework. Leveraging a modified conditional Generative Adversarial Network (cGAN) that incorporates encoded geometry and transmitter location, we demonstrate better efficiency and accuracy of simulations in various indoor environments with 5X speedup. Our method aims to not only improve the prediction of received power in complex layouts and reduce simulation times but also to lay a groundwork for future developments in EM simulation technologies, potentially including real-time applications in 6G networks. We evaluate the performance of our methods in various environments to highlight the advantages. In dynamic urban scenes, we demonstrate our algorithm’s scalability to vast areas and multiple receivers with maintained accuracy and efficiency compared to prior methods; for complex geometries and indoor environments, we compare the accuracy with analytical solutions as well as existing EM ray tracing systems.Item Prediction of Marine Timber Pile Damage Ratings Using a Gradient Boosted Regression Model(2023) Willmott, Carly; Attoh-Okine, Nii O.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Marine pilings are critical structural elements exposed to harsh environmental conditions. Specialized routine inspection and regular maintenance are essential to keep marine facilities in good working condition. These activities generate data that can be exploited for knowledge gain with machine learning tools. A gradient boosted random forest regressor machine learning algorithm, XGBoost, was applied to datasets that contain timber pile underwater inspection and repair data over a period of 23 years. First, the data was visualized to show the longevity of different timber pile repair types. An XGBoost model was then tuned and trained on a dataset for timber piles at one pier. Variables in the dataset were evaluated for feature importance in predicting damage ratings assigned during routine underwater inspections. Next, an ensemble of XGBoost models was trained and applied to a second dataset containing the same features for an adjacent pier. This dataset was reserved for testing to demonstrate whether the ensemble trained on one pier’s data could be generalized to predict timber pile damage ratings at a nearby but separate pier. Finally, the ensemble was used to predict damage ratings on piles that had earlier data but were not rated in the two most recent inspection events. Results suggest that the ensemble is capable of predicting timber pile damage ratings to approximately +/- one damage rating on both the training and test datasets. Feature importances revealed that half of the variables (time since the first event, repair type, exposed pile length, and time since the last repair) contributed to two thirds of the relative importance in predicting damage ratings. Data visualization showed that a few repair types, such as pile replacements and encapsulations, appeared to be most successful over the long term compared with shorter-lived repairs like wraps and encasements. These results are promising indications of the advantages machine learning algorithms can offer in processing and gleaning new insights from structural repair and inspection data. Economic benefits to marine facility owners can potentially be realized through earlier anticipation of repairs and more targeted inspection and rehabilitation efforts. There are also opportunities for better understanding of deterioration rates if more data is gathered over the lifespans of structures, as well as more detailed data that can be introduced as new features.Item SUSTAINABILITY, ACCEPTANCE RISK ANALYSIS AND MACHINE LEARNING IN ASSESSING MECHANICAL PROPERTIES AND THE IMPACT OF HIGHWAY MATERIALS IN TRANSPORTATION INFRASTRUCTURE(2023) Zhao, Yunpeng; Goulias, Dimitrios G; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Improving the performance and extending the service life of transportation infrastructure is a long standing goal of Federal Highway Administration (FHWA) and the transportation community. Accurate prediction of the mechanical properties of highway materials are indispensable for enhancing the sustainability and resilience of transportation infrastructure since it provides accurate inputs for pavement mechanistic-empirical (ME) design and prediction of pavement distresses, helping to optimally allocate the maintenance needs and reduce testing frequencies which account for costly expenditures. Accurate prediction of materials properties can also reduce the acceptance risks during quality assurance (QA) without conducting extensive testing. Concrete plays an important role in the construction of transportation infrastructure. Developing an empirical and/or statistical model for accurately predicting compressive strength remains challenging and requires extensive experimental work. Thus, the objective of the study was to improve the prediction of concrete compressive strength using ML algorithms. A ML pipeline was proposed in which a two-layer stacked model was developed by combining seven individual ML models. Feature engineering was implemented, and feature importance was evaluated to provide better interpretability of the data and the model. This study promotes a more thorough assessment of alternative ML algorithms for predicting material properties. In addition, the quality of highway materials and construction translate directly to performance. To develop a statistically sound QA specification, the risks to the agency and contractor must be well understood. In this study, a Monte Carlo simulation model was developed to systematically assess the acceptance risks and the implications on pay factors (PF). The simulation was conducted using typical acceptance quality characteristics (AQCs), such as strength, for Portland cement (PCC) pavements. The analysis indicated that specific combinations of contractor and agency sample sizes and population characteristics have a greater impact on acceptance risks and may provide inconsistent PF. The proposed methodology aids both agencies and producers to better understand and evaluate the impact of sample sizes and population characteristics on the acceptance risks and PF. Finally, the use of recycled materials is a key element in generating sustainable pavement designs to save natural resources, reduce energy, greenhouse gas (GHG) emissions and costs. This study proposed a methodological life cycle assessment (LCA) framework to quantify the environmental and economic impacts of using recycled materials in pavement construction and rehabilitation. The LCA was conducted on two roadway projects with innovative recycled materials, such as construction and demolition waste (CDW) and rock dust. The proposed LCA framework can be used elsewhere to quantify the environmental and economic benefits of using recycled materials in pavements.Item Exploiting Causal Reasoning to Improve the Quantitative Risk Assessment of Engineering Systems Through Interventions and Counterfactuals(2023) Ruiz-Tagle, Andres; Groth, Katrina; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The main strength of quantitative risk assessment (QRA) is to enable risk management by providing causal insights into the risk of an engineering system or process. Bayesian Networks (BNs) have become popular in QRA because they offer an improved causal structure that represents analysts’ knowledge of a system and enable reasoning under uncertainty. Currently, the use of BNs for risk-informed decisions is based solely on associative reasoning, answering questions of the form "If we observe X=x, how likely is it to observe Y=y?” However, risk management in the industry relies on understanding how a system could change in response to external influences (e.g., interventions and decisions) and identifying the causes and mechanisms that could explain the outcome of past events (e.g., accident investigations and lessons learned). This dissertation shows that associative reasoning alone is insufficient to provide these insights, and it provides a framework for obtaining more complex causal insight using BNs with intervention and counterfactual reasoning. Intervention and counterfactual reasoning must be implemented along with BNs to provide more complex insights about the risk of a system. Intervention reasoning answers queries of the form "How does doing X=x change the likelihood of observing Y=y?” and can be used to inform the causal effect of interventions and decisions on the risk and reliability of a system. Counterfactual reasoning answers queries of the form "Had X been X=x' in an event, instead of the observed X=x, could Y have been Y=y', instead of the observed Y=y?” and can be used to learn from past events and improve safety management activities. BNs present a unique opportunity as a risk modeling approach that incorporates the complex causal dependencies present in a system’s variables and allows reasoning under uncertainty. Therefore, exploiting the causal reasoning capabilities of BNs in QRAs can be highly beneficial to improve modern risk analysis. The goal of this work is to define how to exploit the causal reasoning capabilities of BNs to support intervention and counterfactual reasoning in the QRA of complex systems and processes. To achieve this goal, this research first establishes the mathematical background and methods required to model interventions and counterfactuals within a BN approach. Then, we demonstrate the proposed methods with two case studies concerning the risk of third-party excavation damage to natural gas pipelines in the U.S. The first case study showed that the intervention reasoning methods developed in this work produce unbiased causal insights into the effectiveness of implementing new excavation practices. The second case study showed how the counterfactual reasoning methods developed in this work can expand on the lessons learned from an accident investigation on the Sun Prairie 2018 gas explosion by providing new insights into the effectiveness of current damage prevention practices. Finally, associative, intervention, and counterfactual reasoning methods with BNs were integrated into a single model and used to assess the risk of a highly complex challenge for the future of clean energy: excavation damages to natural gas pipelines transporting hydrogen. The impact of this research is a first-of-its-kind approach and a novel set of QRA methods that provide expanded causal insights for understanding failures and accidents in complex engineering systems and processes.Item Quantitative Motion Analysis of the Upper Limb: Establishment of Normative Kinematic Datasets and Systematic Comparison of Motion Analysis Systems(2022) Wang, Sophie Linyi; Kontson, Kimberly L; White, Ian; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Upper limb prosthetic devices with advanced capabilities are currently in development. With these advancements brings to light the importance of objectively and quantitatively measuring effectiveness and benefit of these devices. Recently, the application of motion capture (i.e., digital tracking of upper body movements in space) to performance-based outcome measures has gained traction as a possible tool for human movement assessment that could facilitate optimal device selection, track rehabilitative progress, and inform device regulation and review. While motion capture shows promise, the clinical, regulatory, and industry communities would benefit from access to large clinical and normative datasets from different motion capture systems and a better understanding of advantages and limitations of different motion capture approaches. The first objective of this dissertation is to establish kinematic datasets of normative and upper-limb prosthesis user motion. The normative kinematic distributions of many performance-based outcome measures are not established, and it is difficult to determine departures from normative patterns without relevant clinical expertise. In Specific Aim 1, normative and clinically relevant datasets were created using a gold standard motion capture system to record participants performing standardized tasks from outcome measures. Without kinematic data, it is also difficult to identify informative kinematic features and tasks that exhibit characteristic differences from normative motion. The second objective is to identify salient kinematic characteristics associated with departures from normative motion. In Specific Aim 2, an unsupervised K-means machine learning algorithm was applied to the previously collected data to determine motions and tasks that distinguish between normative and prosthesis user movement. The third objective is to compare three commonly used motion capture systems that vary in motion tracking mechanisms. The most informative tasks and kinematic characteristics previously identified will be used to evaluate the detection of these differences for several motion capture systems with varying tracking methods in Specific Aim 3.Item STRUCTURAL PERFORMANCE ASSESSMENT ON PREVENTIVE MAINTENANCE/REHABILITATION OF STEEL GIRDER BRIDGE SYSTEMS(2022) Zhu, Yifan; Fu, Chung C.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bridge maintenance, including preventive maintenance, rehabilitation, and replacement keeps the structure safe in its service life. Bridge maintenance methods have developed and expanded to bridge inspection, bridge condition assessments, structural health monitoring technologies, service life prediction, and maintenance with new materials or technologies. This dissertation proposes two structural performance assessments, (1) a rapid machine learning assessment classifying whether the design is under an acceptable range; and (2) comparing the structural health monitoring data with engineers’ predictions to evaluate the current structural performance.The first part of this dissertation focuses on preventive maintenance evaluation. This part discusses and plans a specific topic to instrument a newly constructed link slab system on a multiple simply-supported bridge. Before any simulation and field test of the general steel I-girder bridge model was conducted, literature regarding bridge maintenance, performance assessment, the durability of bridges, structural health monitoring method, current condition assessment methods, and research on the material and structural behavior of link slabs were reviewed and investigated. Then comprehensive experimental programs on the new materials HPFRC and ECC were conducted, and the extensive hands-on laboratory results update the nonlinearity and accuracy of the structure model. Moreover, a series of data analyses of the current steel bridge in the United States was conducted for further database establishment. Two sets of simulation-based finite element parametric analyses of the bridge with protective maintenance or structural repairing were introduced to generate preferred designs and further be used for rapid performance assessment. The inputs are the configuration of the bridge and the proposed work or deteriorated location, which generate the dataset for the training, validating, and testing of the evaluation model. The resulting regression model allows for quick assessment of the function developed by taking advantage of machine learning. This research verifies the assessment’s results using the Maryland Transportation Authority (MDTA) pilot HPFRC link slab system on the bridge overpass I-95 as a case study by comparing the prediction and actual structure health monitoring data after the assessment’s results were verified, and its performance was evaluated. This dissertation also examines one case from the Maryland State Highway Administration (MDSHA) bridge over the Patapsco River, which has several frozen expansion rocker bearings. This restrained the longitudinal movement of the superstructures due to thermal expansion and contraction. It has partially recovered to its normal condition after repairing and strengthening the pier under this bearing. In this study, we combined the finite element analysis and monitoring data to simulate structural behavior, and evaluate repair work using the proposed methods. Finally, This research evaluates the repaired bridge with partially strengthened structural components (i.e., deck, girders, or piers) and forecasts its wear and tear. In this study, the original and deteriorated bridges were numerically modeled first and simplified to 3D grid models. Then, the current condition rating process was used to determine the structural performance at the element level. After establishing the evaluation criteria and investigating the corresponding condition ratings, the rapid assessment models for the repaired bridges were carried out. The condition prediction relied on historical ratings and (if any) monitoring data.Item Molecular dynamics simulation and machine learning study of biological processes(2022) Ghorbani, Mahdi; Klauda, Jeffery B; Brooks, Bernard R; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, I use computational techniques especially molecular dynamics (MD) and machine learning to study important biological processes. MD simulations can effectively be used to understand and investigate biologically relevant systems with lengths and timescales that are otherwise inaccessible to experimental techniques. These include but are not limited to thermodynamics and kinetics of protein folding, protein-ligand binding free energies, interaction of proteins with membranes, and designing new therapeutics for diseases with rational design strategies. The first chapter includes a detailed description of the computational methods including MD, Markov state modeling and deep learning. In the second chapter, we studied membrane active peptides using MD simulation and machine learning. Two cell penetrating peptides MPG and Hst5 were simulated in the presence of membrane. We showed that MPG enters the model membrane through its N-terminal hydrophobic residues while Hst5 remains attached to the phosphate layer. Formation of helical conformation for MPG helps its deeper insertion into membrane. Natural language processing (NLP) and deep generative modeling using a variational attention based variational autoencoder (VAE) was used to generate novel antimicrobial peptides. These in silico generated peptides have a high quality with similar physicochemical properties to real antimicrobial peptides. In the third chapter, we studied kinetics of protein folding using Markov state models and machine learning. We studied the kinetics of misfolding in β2-microglobulin using MSM analysis which gave us insights about the metastable states of β2m where the outer strands are unfolded and the hydrophobic core gets exposed to solvent and is highly amyloidogenic. In the next part of this chapter, we propose a machine learning model Gaussian mixture variational autoencoder (GMVAE) for simultaneous dimensionality reduction and clustering of MD simulations. The last part of this chapter is about a novel machine learning model GraphVAMPNet which uses graph neural networks and variational approach to markov processes for kinetic modeling of protein folding. In the last chapter, we study two membrane proteins, spike protein of SARS-COV-2 and EAG potassium channel using MD simulations. Binding free energy calculations using MMPBSA showed a higher binding affinity of receptor binding domain in SARS-COV-2 to its receptor ACE2 than SARS-COV which is one of the major reason for its higher infection rate. Hotspots of interaction were also identified at the interface. Glycans on the spike protein shield the spike from antibodies. Our MD simulation on the full length spike showed that glycan dynamics gives the spike protein an effective shield. However, breaches were found in the RBD at the open state for therapeutics using network analysis. In the last section, we study ligand binding to the PAS domain of EAG potassium channel and show that a residue Tyr71 blocks the binding pocket. Ligand binding inhibits the current through EAG channel.Item Deep-Learning Based Image Analysis on Resource-Constrained Systems(2021) Lee, Eung Joo; Bhattacharyya, Shuvra S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In recent years, deep learning has led to high-end performance on a very wide variety of computer vision tasks. Among different types of deep neural networks, convolutional neural networks (CNNs) are extensively studied and utilized for image analysis purposes, as CNNs have the capability to effectively capture spatial and temporal dependencies in images. The growth in the amount of annotated image data and improvements in graphics processing units are factors in the rapid gain in popularity of CNN-based image analysis systems. This growth in turn motivates investigation into the application of CNN-based deep learning to increasingly complex tasks, including an increasing variety applications at the network edge. The application of deep CNNs to novel edge applications involves two major challenges. First, in many of the emerging edge-based application areas, there is a lack of sufficient training data or an uneven class balance within the datasets. Second, stringent implementation constraints --- including constraints on real-time performance, memory requirements, and energy consumption --- must be satisfied to enable practical deployment. In this thesis, we address these challenges in developing deep-CNN-based image analysis systems for deployment on resource-constrained devices at the network edge. To tackle the challenges for medical image analysis, we first propose a methodology and tool for semi-automated training dataset generation in support of robust segmentation. The framework is developed to provide robust segmentation of surgical instruments using deep learning. We then address the problem of training dataset generation for real-time object tracking using a weakly supervised learning method. In particular, we present a weakly supervised method for surgical tool tracking based on a class of hybrid sensor systems. The targeted class of systems combines electromagnetic (EM) and vision-based modalities. Furthermore, we present a new framework for assessing the quality of nonrigid multimodality image registration in real-time. With the augmented dataset, we construct a solution using various registration quality metrics that are integrated to form a single binary assessment of image registration effectiveness as either high quality or low quality. To address challenges in practical deployment, we present a deep-learning-based hyperspectral image (HSI) classification method that is designed for deployment on resource-constrained devices at the network edge. Due to the large volumes of data produced by HSI sensors, and the complexity of deep neural network (DNN) architectures, developing DNN solutions for HSI classification on resource-constrained platforms is a challenging problem. In this part of the thesis, we introduce a novel approach that integrates DNN-based image analysis with discrete cosine transform (DCT) analysis for HSI classification. In addition to medical image processing and HSI classification, a third application area that we investigate in this thesis is on-board object detection from Unmanned Aerial Vehicles (UAVs), which represents another important domain of interest for the edge-based deployment of CNN methods. In this part of the thesis, we present a novel framework for object detection using images captured from UAVs. The framework is optimized using synthetic datasets that are generated from a game engine to capture imaging scenarios that are specific to the UAV-based operating environment. Using the generated synthetic dataset, we develop new insight on the impact of different UAV-based imaging conditions on object detection performance.Item Effects of Slope Ratio, Straw Mulching, and Compost Amendment on Vegetation Establishment and Runoff Generation(2020) Owen, Dylan; Davis, Allen P; Aydilek, Ahmet; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Soil erosion management is a major environmental challenge facing highway construction. This study was undertaken to evaluate the effectiveness of compost use in lieu of topsoil for final grade turfgrass establishment on highway slopes. Two compost types, biosolids and greenwaste, and four compost/topsoil blends were compared with a topsoil standard (TS; with straw and fertilizer application) in their ability to reduce soil and nutrient loss and improve vegetation establishment. A series of greenhouse studies and field tests were conducted to analyze the effects of slope ratio, straw mulching, and compost mixing ratio on runoff by observing green vegetation (GV) establishment, runoff volume generation, and nutrient and sediment export. GV was measured using an innovative image segmentation and classification algorithm coupled with machine learning approaches with varying block size and classification acceptance thresholds. Algorithm classifications were compared to manual coverage classifications with R-squared values of 0.86 for GV, 0.87 for straw/dormant vegetation, and 0.96 for exposed soil, respectively. Straw mulching (≥95% straw cover) reduced evaporation rates and soil sealing and increased soil roughness and field capacity (FC), which significantly reduced volume runoff (34-99%) and mass export of sediment and nutrients (81-91%). With mulching, no statistical differences were found in GV establishment among the compost and TS treatments (≥95% cover in 60 days) while non-mulched media cover reached a maximum of 35%, due to limited moisture availability. Composted material (excluding 2:1 compost: topsoil mixtures) had higher hydraulic conductivity, FC, and shear strength than TS which, combined with straw mulching, reduced total runoff volume by 33-72%. This led to sediment and nutrient mass reductions of 57-97% and 6-82%, respectively, from standard TS. A general increase in runoff generation and decrease in GV was seen with slope ratio increase (41-96% more nutrient and sediment export and 81-97% lower GV from 20:1 to 2:1 slopes). However, benefits displayed at 25% slope were reduced at shallower slopes and enhanced at greater slopes. The use of compost as an additive or replacement to TS, with straw mulching, was seen to reduce runoff generation and improve runoff quality from the TS standard and is suggested as possible alternatives.Item NATIONWIDE ANNUAL AVERAGE DAILY TRAFFIC (AADT) ESTIMATION ON NON-FEDERAL AID SYSTEM (NFAS) ROADS BY MACHINE LEARNING WITH DATA MINING OF BUILT-IN ENVIRONMENT(2020) Sun, Qianqian; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study aims to address the nationwide gap in AADT data on NFAS roads in U.S. With a Spatial Autoregressive Model as a benchmark, two machine-learning approaches, i.e. Artificial Neural Network and Random Forest, show notable improvement in the accuracy of estimating AADT according to five measures, i.e. MSE, RSQ, RMSE, MAE, and MAPE. A data-mining of the built-in environment from three perspectives, i.e. on-road and off-road features, network centralities, and neighboring influences, paves the way for AADT estimation, which covers 87 variables in centrality, neighboring traffic, demographics, employment, land-use diversity, road network density, urban design, destination accessibility, etc. Data integration using different buffering sizes and statistical analysis of linearity and monotonicity promote the variable selection for estimation. When implementing the machine-learning approaches, not only the estimation performance is analyzed, but also the relationship between each variable and AADT, the interplays among variables, variable importance measures are thoroughly discussed.