Engineering Commons^™

Reliability Improvement On Feasibility Study For Selection Of Infrastructure Projects Using Data Mining And Machine Learning, Xi Hu

Theses and Dissertations

With the progressive development of infrastructure construction, conventional analytical methods such as correlation index, quantifying factors, and peer review are no longer satisfactory in support for decision-making of implementing an infrastructure project in the age of big data. This study proposes using a mathematical model named Fuzzy-Neural Comprehensive Evaluation Model (FNCEM) to improve the reliability of the feasibility study of infrastructure projects by using data mining and machine learning. Specifically, the data collection on time-series data, including traffic videos (278 Gigabytes) and historical weather data, uses transportation cameras and online searching, respectively. Meanwhile, the researcher sent out a questionnaire for …

Evaluation And Forecast Of Energy Consumption In Different Sectors Of The United States Using Artificial Neural Networks, Arash Kialashaki

Theses and Dissertations

The United States is a country which consumes a vast amount of energy. In order to keep the development of the United States sustainable (diverse and productive over the time) energy planning should be carried out comprehensively and precisely. This dissertation presents a specific mathematical modeling approach towards energy demand modeling of the United States and forecast future energy demand. To generate more detailed and accurate results, this dissertation investigates the energy demand of each sector separately using the analysis of trend for unique set of independent parameters which affect the energy demand in that sector.

In solving a forecast …

Design And Optimization Of Peristaltic Micropumps Using Evolutionary Algorithms, Ravi Bhadauria

Theses and Dissertations

A design optimization based on coupled solid–fluid analysis is investigated in this work to achieve specific flow rate through a peristaltic micropump. A micropump consisting of four pneumatically actuated nozzle/diffuser shaped moving actuators on the sidewalls is considered for numerical study. These actuators are used to create pressure difference in the four pump chambers, which in turn drives the fluid through the pump in one direction. Genetic algorithms along with artificial neural networks are used for optimizing the pump geometry and the actuation frequency. A simple example with moving walls is considered for validation by developing an exact analytical solution …