Open AccessTime-Series Data Mining of Minimum Design Height for River Bridge Deck Using Seasonal Trend Decomposition
Author(s) -
Haiyan Xie,
Xi Huang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1284/1/012042
Subject(s) - bridge (graph theory) , series (stratigraphy) , time series , stability (learning theory) , deck , correlation coefficient , computer science , statistics , environmental science , civil engineering , data mining , engineering , mathematics , geology , structural engineering , machine learning , medicine , paleontology
Using the traditional calculation of minimum design height (MDH) could not provide the effective decision support for engineers under complex situations. This research proposes a time-series data mining system for the decomposition and analysis of seasonal trends of hydrologic fluctuations, using stage-discharge curves, Log-Pearson frequency curves, and plotting positions. The research purpose is to provide the decision support based on precise time series for bridge design and feasibility analysis. Prior to conducting the gage-height design for bridges, this study investigates the construction site of the bridge at Kinston Mines of Illinois River in the United States and processes the correlation tests between gage heights and hydrologic discharge. In addition, the study simulates and verifies the system outcomes using Mann-Kendall Mutation, stability tests, and non-linear regressions. The results show the correlation coefficient of 0.9719 for the discharge trends from 1994 to 2017. In addition, the study selects the best-fitted theoretical frequency curve based on the most appropriate convergence to the empirical frequency curve. Overall, this research achieves satisfactory results for the MDH design and has substantial influence for engineers and designers to meet design requirements and save project costs. The data mining method sheds light upon data analysis for engineering design.