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Spring 2023

Kullar, Jagbir S.

With rising demands in industry for reliable electrical cable distribution networks comes an inherent need for utility providers to know well the condition of the assets in their network. Heightened expectations from regulators and consumers require methods of reliability assessment to improve...

Spring 2023

Shojaee, Seyedamir

In this dissertation, a machine-learning method is utilized to enhance the accuracy of wake parameters calculated by Reynolds Averaged Navier Stokes (RANS) k-ω SST model of flow on and around wall-mounted rectangular cylinders. Using high-quality results from Large Eddy Simulation (LES), this...

Fall 2021

Mohammadhosseinzadeh Golabchi, Hamidreza

Considering the high rates of labor resources in construction projects clearly indicates the importance of appropriate labor resource management methods. Accurate labor resource allocation is a substantial step towards successful labor resource management. With the recent developments in the area...

Fall 2020

Zhanbyrtayev, Madi

The ubiquity of the Internet-of-Things (IoT) devices in everyday life allows various sensors to be utilized in networked systems for solving a number of real-world problems. Models utilizing specific sensing modalities achieve impressive performance in understanding human activity and are used in...

Fall 2021

Jafari, Parinaz

The management of project documentation involves processing a large amount of important information embedded in different contract and project specification documents. Although contract-related documentation is critical for effective information flow and—in turn—successful project management, it...

Fall 2020

Yin, Xianfei

The municipal drainage system is a key component of every modern city’s infrastructure. However, as the drainage system ages, its pipes gradually deteriorate at rates that vary based on the conditions of utilization (i.e., intrinsic conditions) and other extrinsic factors such as the presence of...

Spring 2022

Aghaei, Nikoo

Data augmentation is a strong tool for enhancing the performance of deep learning models using different techniques to increase both the quantity and diversity of training data. Cutout was previously proposed, in the context of image classification, as a simple regularization technique that...

Fall 2022

Richard, Daniel

Universal approximation refers to the property of a collection of functions to approximate continuous functions. Past literature has demonstrated that neural networks are dense in continuous functions on compact subsets of finite-dimensional spaces, and this document extends those findings to...

Spring 2021

Linke, Cameron

Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this...

Spring 2016

Neufeld, James, P

Monte Carlo methods are a simple, effective, and widely deployed way of approximating integrals that prove too challenging for deterministic approaches. This thesis presents a number of contributions to the field of adaptive Monte Carlo methods. That is, approaches that automatically adjust the...