Details:

Creators:
Richardson, David N. ; Lusher, Michael ;
Richardson, David N. ; Lusher, Michael ; Less -
Corporate Creators:
Missouri University of Science and Technology. Dept. of Civil, Architectural, and Environmental Engineering ;
Corporate Contributors:
United States. Federal Highway Administration ; Missouri University of Science and Technology. Center for Transportation Infrastructure and Safety ;
United States. Federal Highway Administration ; Missouri University of Science and Technology. Center for Transportation Infrastructure and Safety ; Less -
Subject/TRT Terms:
[+]
Costs
Guidelines
Maintenance Management
Mathematical Models
Minimization
Pavement Maintenance
Pavement Management Systems
Pavement Performance
Research
Roughness

Publication/ Report Number:
cmr 16-004 ;
Resource Type:
Tech Report ;
Geographical Coverage:
Missouri ;
Corporate Publisher:
Missouri. Dept. of Transportation. Division of Construction and Materials ;
Abstract:
Pavement performance models describe the deterioration behavior of pavements. They are essential in a pavement management system if the goal is to make more objective, reliable, and cost-effective decisions regarding the timing and nature of pavement maintenance activities. The general objective of Task 2 was to develop performance models for a variety of pavement families and pavement preservation treatments used by the Missouri Department of Transportation (MoDOT). Using the data collected in Task 1, linear least-squares regression techniques were used to generate deterministic models that predict the International Roughness Index (IRI), the pavement condition measure most widely used today. Family IRI-prediction models were developed for full-depth asphalt (FDA), concrete (PCC), and composite (Comp) pavements. Treatment IRI-prediction models were developed for 1-in. overlays on FDA pavements, chip seals on FDA pavements, and 3.75-in. overlays on PCC pavement. Predictor variables consistently shown to be highly significant in predicting IRI for both FDA and Comp pavements were initial IRI (IRIo or the IRI value right after treatment) and pavement surface age (SA). The majority of the PCC pavement sections selected were so old that IRIo could not be determined (or estimated with any confidence), therefore SA was the dominant predictor variable in the PCC pavement family model. Terminal IRI (IRIt which was the IRI just prior to a treatment) was also a significant predictor of IRI and was directly or indirectly included in the FDA and Comp family and treatment models. Additional significant IRI predictors (depending on the model) were the climate parameters DT32 (days/year that air temperature was below freezing), FT (freeze/thaw cycles per year), and DP01 (days/year that precipitation was at least 0.1–in.), subgrade soil parameters P200 (percent passing the #200 sieve) and Pclay (percent clay-size soil), and LstTrtThk (the last treatment thickness).
Format:
PDF ;
Funding:
TRyy1141 (Task 2) ; 39112 ; DTRT06-G-0014 ;
TRyy1141 (Task 2) ; 39112 ; DTRT06-G-0014 ; Less -
Collection(s):
University Transportation Centers
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:1316e435c48f29d2de70f9ec76f941dc49dc7924ab775f6405a8b14367029546
File Type:

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