Is this project an undergraduate, graduate, or faculty project?
Graduate
Loading...
individual
What campus are you from?
Daytona Beach
Authors' Class Standing
Julian Jesso, Graduate Student
Lead Presenter's Name
Julian Jesso
Faculty Mentor Name
Dr. Dan Su, P.E., Ph.D.
Abstract
Over the past two decades, extremely heavy vehicles, or superloads, have been increasingly utilized to transport heavy loads, such as prestressed concrete girders, automotive presses, transformers, wind turbine components, and other substantial loads. Since these superload have a significant effect on the infrastructure system in comparison to regularly permitted vehicles, they should be subject to special consideration in the permitting and operation process. Despite the great research effort that has been made to improve the superload permitting process, few studies have been performed on the characterization and prediction of superload. Superload has its own distinct characteristics that differ from other vehicle loads. Thus, there is a need to better understand the characteristics of superload and to develop a procedure to predict vital superload attributes for enhanced accuracy in the permitting process.
In this research, the major focus was to develop an analytical procedure for the characterization and prediction of superload using advanced gradient boosting machine (GBM) learning algorithms. Weigh-in-Motion (WIM) data collected from 31 sites in Florida over 10 years were used as the database for this study. The raw data was processed with a newly established procedure and the superload data was extracted. A comprehensive analytical technique was developed using GBM with regression, classification trees, and time series modeling. This methodical procedure was specifically altered to accommodate the unique features of superload data. By applying the new procedure, the characterization of superload was performed for Florida WIM sites and the prediction of various key parameters, such as gross vehicle weight, average axle weight, and maximum axle weight, were accurately predicted using all parameters as well as limited vehicle configuration parameters. The study concluded that, provided vehicle spacing information, superload gross vehicle weight, average axle weight, and maximum axle weight can be predicted using the GBM algorithm with mean absolute percentage error (MAPE) as low as 3.60%.
Did this research project receive funding support from the Office of Undergraduate Research.
Yes, Spark Grant
Characterization and Prediction of Superload in Florida Using Gradient Boosting Machine Learning Algorithm
Over the past two decades, extremely heavy vehicles, or superloads, have been increasingly utilized to transport heavy loads, such as prestressed concrete girders, automotive presses, transformers, wind turbine components, and other substantial loads. Since these superload have a significant effect on the infrastructure system in comparison to regularly permitted vehicles, they should be subject to special consideration in the permitting and operation process. Despite the great research effort that has been made to improve the superload permitting process, few studies have been performed on the characterization and prediction of superload. Superload has its own distinct characteristics that differ from other vehicle loads. Thus, there is a need to better understand the characteristics of superload and to develop a procedure to predict vital superload attributes for enhanced accuracy in the permitting process.
In this research, the major focus was to develop an analytical procedure for the characterization and prediction of superload using advanced gradient boosting machine (GBM) learning algorithms. Weigh-in-Motion (WIM) data collected from 31 sites in Florida over 10 years were used as the database for this study. The raw data was processed with a newly established procedure and the superload data was extracted. A comprehensive analytical technique was developed using GBM with regression, classification trees, and time series modeling. This methodical procedure was specifically altered to accommodate the unique features of superload data. By applying the new procedure, the characterization of superload was performed for Florida WIM sites and the prediction of various key parameters, such as gross vehicle weight, average axle weight, and maximum axle weight, were accurately predicted using all parameters as well as limited vehicle configuration parameters. The study concluded that, provided vehicle spacing information, superload gross vehicle weight, average axle weight, and maximum axle weight can be predicted using the GBM algorithm with mean absolute percentage error (MAPE) as low as 3.60%.