Engineering Commons^™

Soil Mixing And Slurry Trench Cutoff Walls For Coal Combustion Residue Sites, Jeffrey C. Evans, Daniel Ruffing

World of Coal Ash Proceedings

No abstract provided.

Cost-Effective, Environmentally-Friendly Ash Beneficiation Technology, Scott Ziegler

World of Coal Ash Proceedings

No abstract provided.

The Utilization Of Dry Scrubber Material For The Production Of Calcium Sulfoaluminate Clinkers And Cements, Tristana Y. Duvallet, Robert B. Jewell, Anne E. Oberlink, Thomas L. Robl

World of Coal Ash Proceedings

No abstract provided.

Net Environmental Benefit Analysis (Neba) For Ccr Pond Closure Decisions -- A Pathway To Meaningful Citizen Engagement And Reducing The Potential For Unnecessary Litigation, Chris Hardin, Ric Traver

World of Coal Ash Proceedings

No abstract provided.

Challenges Met In Using Bottom Ash As A Landfill Drainage Media, William M. Strachan

World of Coal Ash Proceedings

No abstract provided.

Two Bottom Ash Management/Handling System Enhancement Case Studies – From Technology Evaluation And Design Through Construction And Performance Verification, George Mckenzie, Aaron Davis, Chris Pickelmann, Jason Obermeyer

World of Coal Ash Proceedings

No abstract provided.

Slurry-Trench Cutoff Walls For The Containment Of The Coal Combustion Residuals (Ccr) Impacted Groundwater: Evaluation Of Hydraulic Conductivity And Consolidation Properties Of Soil-Bentonite Backfills, Y.L. Yang, Krishna Reddy, Y.J. Du, R.D. Fan

World of Coal Ash Proceedings

No abstract provided.

Ccp Beneficial Use Risk Assessment: Case Studies For Three Different Applications, Andrew Bittner, Ari Lewis

World of Coal Ash Proceedings

No abstract provided.

Corrective Measures/Corrective Action (Cm/Ca) Strategies That Effectively Integrate Engineered Pond Closures And Short/Long-Term Groundwater Management With Case Study Examples, Steve Putrich

World of Coal Ash Proceedings

No abstract provided.

Managing Landfill Leachate In A World Without Ash Basins: Lessons Learned And Practical Considerations, Evan Andrews, Michael Lazar

World of Coal Ash Proceedings

No abstract provided.

Developing Enhanced Monitored Natural Attenuation Strategies Using Reactive Transport Models, Pj Nolan, Hugh Davies, Greg Hebeler, Rens Verburg

World of Coal Ash Proceedings

No abstract provided.

Experimental Test Method To Determine The Reactivity Of Fly Ash For Use In Concrete Mixture Proportioning, Antara Choudry, Deborah Glosser, O. Burkan Isgor, W. Jason Weiss

World of Coal Ash Proceedings

No abstract provided.

Overview Of Epri Research On Environmental Issues And Beneficial Use Of Coal Combustion Products, Bruce Hensel, Ken Ladwig

World of Coal Ash Proceedings

No abstract provided.

Is My Site A Candidate For A Groundwater Natural Attenuation Remedy? A Geochemist’S Perspective, Bob Glazier

World of Coal Ash Proceedings

No abstract provided.

Impoundment Closure Enhancement Measures To Support Groundwater Compliance, John Hesemann, Wayne Weber, Eric Dulle

World of Coal Ash Proceedings

No abstract provided.

Enhancing Coal Ash Impoundment Closure Using Value Engineering, Holt Wheeler, Trey Mangers, Paul Lear

World of Coal Ash Proceedings

No abstract provided.

Panel On Liquefaction Of Ponded Fly Ash: From Laboratory To Practice, Tarunjit S. Butalia

World of Coal Ash Proceedings

No abstract provided.

Development Of A Design Guideline For Pile Foundations Subjected To Liquefaction-Induced Lateral Spreading, Milad Souri, Arash Khosravifar

Student Research Symposium

Past earthquakes confirmed that seismically induced kinematic loads from soil lateral spreading and inertial loads from structure can cause severe damages to pile foundations. The research questions are:

• How to combine inertial and kinematic loads in design of pile foundations in liquefied soil?
• How the combination of inertia and kinematics changes with depth?
• How this combination is affected by long-duration earthquakes?
• How this combination affects inelastic demands in piles?

Explanation Methods For Neural Networks, Jack H. Chen, Christof Teuscher

Student Research Symposium

Neural Networks (NNs) have become a basis of almost all state-of-the-art machine learning algorithms and classifiers. While NNs have been shown to generalize well to real-world examples, researchers have struggled to show why they work on an intuitive level. We designed several methods to explain the decisions of two state-of-the-art NN classifiers, ResNet and an All-CNN, in the context of the Japanese Society of Radiological Technology (JSRT) lung nodule dataset and the CIFAR-10 image dataset. Leading explanation methods LIME and Grad-CAM generate variations of heat maps which represent the regions of the input determined salient by the NN. We analyze …

Diagnostic Imaging Of Structural Concrete Using Ground Penetrating Radar And Ultrasonic Array, Sina Mehdinia, Thomas Schumacher, Eric Wan, Xubo Song

Student Research Symposium

Structural concrete is the most widely used construction material in the world. Many structures critical to a society such as bridges, hospitals, and airports are built with concrete. While this material is well understood from a mechanical design point of view, still no accurate quantitative tools exist to assess it for damage and deterioration. This is of particular concern for an urban area like Portland with a mega-thrust earthquake waiting to occur. Non-destructive evaluation tools that can quickly and accurately give a full picture of the integrity of structural concrete elements will be key to help plan effective and safe …

An Assessment Of The Decision Making Units’ Efficiency In Service Systems (The Case Of Cellular Telecom), Maoloud Dabab, Timothy R. Anderson

Student Research Symposium

Most tools and models on performance and quality of service management are generic and do not solve the complex technical systems, which the most critical component on the network and where these tools should be applied. The objective of this research is to assess the cellular performance and Base Transceiver Station (BTS) efficiency by proposing a robust model that is derived from multiple Key Performance Indicators (KPIs) based on technical and financial aspects. The novelty of this research provides a comprehensive multidimensional model for tuning the BTS parameters, which can lead to developing a standard global mobile network KPI. The …

Reliable Explanations Via Adversarial Examples On Robust Networks, Walt Woods, Jack H. Chen, Christof Teuscher

Student Research Symposium

Neural Networks (NNs) are increasingly used as the basis of advanced machine learning techniques in sensitive fields such as autonomous vehicles and medical imaging. However, NNs have been found vulnerable to a class of imperceptible attacks, called adversarial examples, which arbitrarily alter the output of the network. To close the schism between needing reliability in real-world applications and the fragility of NNs, we propose a new method for stabilizing networks, and show that as an added bonus, our technique results in reliable, high-fidelity explanations for the NN's decision. Compared to the state-of-the-art, this technique increased the area under the curve …

Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods, Christof Teuscher

Student Research Symposium

In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …

An Unsupervised Channel Selection Method For Ssvep-Based Brain Computer Interfaces, Ethan Webster, Hadi Habibzadeh, Tolga Soyata

CURCE Annual Undergraduate Conference

Brain-computer interfaces (BCIs) provide an alternative communication channel for people with motor deficits that prevent normal communication. The underlying premise of a BCI is that a neuroimaging process such as electroencephalography (EEG) can be used to measure the user’s brain activity as signals. The obtained signals are analyzed to determine the user’s intended actions and a computer system can be used to replace voluntary muscle activity as a means of communication. The information transfer rate (ITR) of an algorithm used for determining the user’s intentions greatly affects the perceived practicality of the BCI system. Such algorithms are divided into two …

Understanding The Privacy Methods, Implications, And Challenges Of Educational Data Mining., Tyler Blanco, Liam Smith

CURCE Annual Undergraduate Conference

Studies on educational data mining have massively advanced the analytical understanding of student learning, outcome predictions, student modeling, and better evaluation. While the information retrieved can improve educational outcomes, such studies must not break the wall of trust or compromise student privacy. To this end, it’s imperative to understand the methods of how privacy is ensured in the field of educational data mining and learning analytics, the limitations of these strategies, the complications that may follow, and the possible resolutions. In this project, we take a systematic approach to review studies in the educational data mining field. Initially, we examine …

Geospatial Energy Potential And Life Cycle Assessment Of Nearshore Oscillating Water Column Systems, Aleks Siemenn, Marie-Odile Fortier

CURCE Annual Undergraduate Conference

Oscillating Water Column (OWC) systems are an iteration of terminator ocean energy technology which generate electrical energy from turbine torque induced by the compression of air in a chamber from changing water level height. OWCs are a well-established technology, however, there have been no studies to date which quantify the life cycle environmental impacts of these systems in a geographic context. The goal of this study is to optimally size an OWC system for selected New England coastline sites and then assess the environmental impacts of the varying system sizes.

An OWC system is optimally sized when the volume of …

2019 Student Steel Bridge Team, Kourtney Arena, Andrew Biglin, Brett Palmer, Nathaniel Maute, Kenneth Reiser, Jacob Dicks, Paul Kowaleski

STEM Student Research Symposium Posters

Every year AISC runs a regional and national steel bridge competition. The competition offers a chance for civil engineering students to gain valuable experience designing a bridge. In addition to this, mechanical engineering students are provided the opportunity to practice machining steel parts. For the competition, teams from participating schools are given a scenario with certain constraints to design a bridge for. At the competition, the bridges are ranked based on weight, structural efficiency, construction economy, assembly speed, deflection, aesthetics, and overall performance.

The program Risa 3D was used in order to design and perform analysis on the bridge. Using …

Forecasting Model For Disease Propensity Using Ehr Data, Soodabeh Sarafrazi, Omar Sharif, Matthew Domingo, Jie Han, Michael Chang, Omid Khazaie, Anil Kemisetti

Creative Activity and Research Day - CARD

Many diseases such as diabetes and cardiovascular diseases are actionable, i.e. they are preventable by early intervention. One to two years of early warning would represent a huge advance in dealing with these conditions and could help prevent further complications such as heart disease, stroke, kidney failure, blindness, and amputation. In this project, we are developing an extensible condition forecasting model to assess the risk of diabetes and heart problems in patients in advance. Using TensorFlow, Elastic MapReduce (EMR), and AWS Sagemaker, we are training a Wide and Deep Neural Network on a dataset of more than 170 million electronic …

Predicting Unethical Physician Behavior At Scale: A Distributed Computing Framework, Quinn Keck, Robert Sandor, Miguel Romero, Diane Woodbridge, Paul Intrevado

Creative Activity and Research Day - CARD

As the amount of publicly shared data increases,

developing a robust pipeline to stream, store and process data is

critical, as the casual user often lacks the technology, hardware

and/or skills needed to work with such voluminous data. In

this research, the authors employ Amazon EC2 and EMR,

MongoDB, and Spark MLlib to explore 28.5 gigabytes of CMS

Open Payments data in an attempt to identify physicians who

may have a high propensity to act unethically, owing to significant

transfers of wealth from medical companies. A Random Forest

Classifier is employed to predict the top decile of physicians who

have …

Quantum Criticism, Ashwini Badgujar, Paul Intrevado, Andrew Wang, Kai Yu, David Guy Brizan

Creative Activity and Research Day - CARD

Quantum Criticism scrapes data from the News Articles and performs Sentiment Analysis.