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Full-Text Articles in Engineering
Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury
Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury
Graduate Theses and Dissertations
The transportation sector stands as a significant contributor to greenhouse gas emissions in the United States, with its environmental impact steadily escalating over the past few decades. This has prompted government agencies to facilitate the adoption and usage of low-carbon transportation (LCT) options as alternatives to fossil-fuel-powered transportation. LCTs include modes of transportation that minimize the overall carbon footprint of the transportation sector by relying on energy sources that are environmentally sustainable. These sustainable transportation options have also garnered significant interest in the transportation research community. For government agencies and researchers alike, a comprehensive understanding of the adoption and usage …
Generative Neural Network-Based Defense Methods Against Cyberattacks For Connected And Autonomous Vehicles, M Sabbir Salek
Generative Neural Network-Based Defense Methods Against Cyberattacks For Connected And Autonomous Vehicles, M Sabbir Salek
All Dissertations
The rapid advancement of communication and artificial intelligence technologies is propelling the development of connected and autonomous vehicles (CAVs), revolutionizing the transportation landscape. However, increased connectivity and automation also present heightened potential for cyber threats. Recently, the emergence of generative neural networks (NNs) has unveiled a myriad of opportunities for complementing CAV applications, including generative NN-based cybersecurity measures to protect the CAVs in a transportation cyber-physical system (TCPS) from known and unknown cyberattacks. The goal of this dissertation is to explore the utility of the generative NNs for devising cyberattack detection and mitigation strategies for CAVs. To this end, the …
Exploring Machine Learning In Deep Foundation And Soil Classification Application, Mohammad Moontakim Shoaib
Exploring Machine Learning In Deep Foundation And Soil Classification Application, Mohammad Moontakim Shoaib
LSU Master's Theses
The applicability of several Machine Learning (ML) models was explored in this research to predict the ultimate capacity and load-settlement behavior of axially loaded single-driven piles from Cone Penetration Test (CPT) data. Additionally, a common CPT-based soil behavior type (SBT) classification system was reproduced using those ML models. Eighty static pile load tests and corresponding CPT data close to those pile locations were collected from 34 sites in Louisiana for the deep foundation application. On the other hand, 70 CPT soundings were taken in 14 different parishes across Louisiana for the soil classification application. Specifically, tree-based ML models such as …