Using Data Analytics to Determine Best Practices for Winter Maintenance Operations
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University of Akron / OhioLINK
2017
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=akron1516351388406594 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-akron15163513884065942021-08-03T07:05:20Z Using Data Analytics to Determine Best Practices for Winter Maintenance Operations Crow, Mallory Joyce Transportation Civil Engineering Winter Maintenance Probability of Failure Liquid Deicers Plow Blades Linear Regression Cost Analysis In an effort to provide safe roadways for the traveling public, transportation agencies are challenged with combating snow and ice events throughout certain regions of the country. In order to combat these events, winter maintenance fleets are designed to mechanically and chemically remove the snow and ice from the roadways. These snow and ice removal practices require labor, equipment, and materials; therefore, snow and ice removal requires much of the transportation maintenance budget. This dissertation examines how to use winter maintenance operational data in various methods in order to improve, optimize, or justify winter maintenance operations.This dissertation will review three areas within the winter maintenance practices that may benefit from data analytics. The first analysis conducted reviewed the mechanical snow removal equipment, specifically the plow blades. These plow blades makes contact with the roadway and eventually will wear down and need to be replaced. There are several types of blades on the market which have been shown to wear at a lower rate in comparison to the current standard flame harden steel blades. Using these blade data and county data (truck number, lane-miles treated, and weather received), the probability of failure for each blade type at different quantities used per year (1 to 10 blades) for each county may be modeled. Overall these findings present that the specialty blades have a much lower probability of failure due to the low wear rate on each of them. Chemically removing snow and ice from roadways to keep the traveling public safe is highly expensive for transportation agencies. Liquid deicers have been shown to assist in chemical removal process. One common liquid deicer utilized is brine because it is made with NaCl and water in-house at a low cost. Using in-field data and lab data these chemical removal practices may be determined. These results may highly impact winter maintenance operations. The field data analysis consists of an analysis of mean, linear regression, and image processing models were conducted using these extensive field data. These models linear regression also shows that none of the deicers are significantly better then brine. Brine is the less expensive liquid deicer; therefore, when combating weather similar to what was received in 2016-2017, brine is the optimal deicer. These results may be utilized to justify current chemical practices and prevent agencies from purchasing unneeded liquid deicers. The lab testing consist of using the standardized ice melting capacity test (SHRP H-205.2). Using these data, an analysis of means and linear regression models were completed in order to compare the performance of each deicer to the control, brine. As well as, see the effects of time, temperature, and concentration on each individual deicer. The mean analysis shows that deicer D, H and E are significantly different from brine. Deicer H and E have been showed to work much better than brine in these lower temperature (0ºF). These analyses may assist transportation agencies in determining the optimal winter maintenance practices, and potentially result in a cost savings. 2017 English text University of Akron / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=akron1516351388406594 http://rave.ohiolink.edu/etdc/view?acc_num=akron1516351388406594 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Transportation Civil Engineering Winter Maintenance Probability of Failure Liquid Deicers Plow Blades Linear Regression Cost Analysis |
| spellingShingle |
Transportation Civil Engineering Winter Maintenance Probability of Failure Liquid Deicers Plow Blades Linear Regression Cost Analysis Crow, Mallory Joyce Using Data Analytics to Determine Best Practices for Winter Maintenance Operations |
| author |
Crow, Mallory Joyce |
| author_facet |
Crow, Mallory Joyce |
| author_sort |
Crow, Mallory Joyce |
| title |
Using Data Analytics to Determine Best Practices for Winter Maintenance Operations |
| title_short |
Using Data Analytics to Determine Best Practices for Winter Maintenance Operations |
| title_full |
Using Data Analytics to Determine Best Practices for Winter Maintenance Operations |
| title_fullStr |
Using Data Analytics to Determine Best Practices for Winter Maintenance Operations |
| title_full_unstemmed |
Using Data Analytics to Determine Best Practices for Winter Maintenance Operations |
| title_sort |
using data analytics to determine best practices for winter maintenance operations |
| publisher |
University of Akron / OhioLINK |
| publishDate |
2017 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=akron1516351388406594 |
| work_keys_str_mv |
AT crowmalloryjoyce usingdataanalyticstodeterminebestpracticesforwintermaintenanceoperations |
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