Implementing Machine Learning with Highway Datasets
| 项目名称： | Implementing Machine Learning with Highway Datasets |
| 摘要： | Every year Maryland Department of Transportation State Highway Administration (MDOT SHA) invests millions of dollars into testing geomaterials to optimize engineering designs. There is a significant cost savings opportunity, by leveraging the historic material testing data with predicative machine learning models to provide estimated values for the engineering characteristics for newly proposed projects. The Office of Materials Technology has developed a deep learning Neural Network to predict drilling data, but this is just a first step. The purpose of this study is to write algorithms to improve, refine and optimize the accuracy of this neural network, and to develop other machine learning models by continually updating, retraining, and optimizing the neural network geometries. The study will evaluate and test additional data sets such as laboratory soil test data, in-situ and geophysics data, as well as others. It will also investigate the opportunity to implement machine learning for other large datasets in OMT such as pavement data, construction history, slope stability, geologic risk, as well as others. |
| 状态： | Active |
| 资金： | 151468 |
| 资助组织： | Maryland Department of Transportation State Highway Administration |
| 项目负责人： | Xiang, Hua |
| 执行机构： | University of Maryland, College Park |
| 开始时间： | 20191023 |
| 预计完成日期： | 20210430 |
| 主题领域： | Data and Information Technology；Geotechnology；Highways；Maintenance and Preservation；Materials；Pavements |
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