Algorithm and software for proactive pothole repair.

Details

• Creators:
  Sadeghi, Leila ; Zhang, Yaguang ; Balmos, Andrew ; Krogmeier, James V. ; Haddock, John E.
• Corporate Creators:
  Purdue University. Joint Transportation Research Program
• Corporate Contributors:
  Indiana Department of Transportation ; United States. Federal Highway Administration
• Subject/TRT Terms:
  AADT Algorithms Pothole Cumulative Distribution Function Pavement Pavement Maintenance Potholes (Pavements) Predictive Models Weather
• Publication/ Report Number:
  FHWA/IN/JTRP-2016/14
• Resource Type:
  Tech Report
• Geographical Coverage:
  Indiana
• Corporate Publisher:
  Purdue University. Joint Transportation Research Program
• Abstract:
  Potholes are a common pavement distress, particularly appearing during the spring freeze-thaw period in northern climates. Potholes
  
  reduce ride quality, and if left unrepaired can lead to rapid pavement deterioration. Typically, when a pothole appears a repair crew is
  
  dispatched to place patch mixture in the hole with the hope that the patch will last until such time as a more permanent repair can be
  
  made. This reactive approach to potholes can often be too late to prevent further pavement damage and also makes it difficult for
  
  repairs crews to be scheduled in the most cost effective manner.
  
  In this study, the relation between traffic loads combined with weather records, such as temperature, freeze-thaw cycles and the
  
  numbers of potholes requiring patching was investigated in an attempt to develop a model to predict pothole formation and distinguish
  
  the routes which are prone to pothole formation before the potholes begin to form. If pothole prediction were possible, this proactive
  
  approach would enable agencies to plan and schedule maintenance activities more cost and time effectively thus increasing ride safety
  
  and mobility.
  
  To achieve the objective, four years of maintenance data from Indiana routes were collected and statistically analyzed to develop a
  
  model to estimate the probability of occurrence of a pothole due to annual average daily traffic and climate. The model indicates how
  
  significant traffic loads combined with weather condition influence the pothole. Also, although traffic loads and weather conditions are
  
  the essentials for potholes to form, the effect of pavement condition on the initiation of new potholes cannot be disregarded.
  
  Additionally, this study began the development of a basic roadway distress evolution model by employing several standard statistical
  
  tools, such as, the empirical cumulative distribution functions (CDF) and the Kolmogorov-Smirnov (KS), to a pavement condition dataset.
  
  The goal of the model was to predict and rank areas of probable future concern by likelihood and severity. The resulting analysis showed
  
  promise but the data resolution was too low to achieve predictions on the desired fine scale.
  
  
• Format:
  PDF
• Funding:
  SPR-3908
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:193e8f8687c97d9d9271e29d32901250d92a7680e7cd55d8acca10898caad009272f5faf83e61e452623e48afbb0f72dbd5ede5997fb12088d8ca83db10e9dbb
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31565/dot_31565_DS1.pdf
• File Type:
  [PDF - 5.74 MB ]