Managing Pavement Maintenance and Rehabilitation Projects under Budget Uncertainties

2014 ◽  
Vol 14 (6) ◽  
pp. 92-100 ◽  
Author(s):  
(David)Wei FAN ◽  
Feng WANG
Keyword(s):  
Pavement Maintenance ◽  
Maintenance And Rehabilitation

Establishing optimal project-level strategies for pavement maintenance and rehabilitation – A framework and case study

2012 ◽  
Vol 44 (5) ◽  
pp. 565-589 ◽  
Author(s):  
Muhammad Irfan ◽  
Muhammad Bilal Khurshid ◽  
Qiang Bai ◽  
Samuel Labi ◽  
Thomas L. Morin
Keyword(s):  
Pavement Maintenance ◽  
Maintenance And Rehabilitation ◽  
Project Level

Detection of Pavement Maintenance Treatments using Deep-Learning Network

2021 ◽  
pp. 036119812110078
Author(s):  
Lu Gao ◽  
Yao Yu ◽  
Yi Hao Ren ◽  
Pan Lu
Keyword(s):  
Deep Learning ◽  
Measurement Errors ◽  
Short Term Memory ◽  
Learning Networks ◽  
Pavement Maintenance ◽  
Time Period ◽  
Learning Techniques ◽  
Maintenance And Rehabilitation ◽  
History Of ◽  
Deep Learning Network

Pavement maintenance and rehabilitation (M&R) records are important as they provide documentation that M&R treatment is being performed and completed appropriately. Moreover, the development of pavement performance models relies heavily on the quality of the condition data collected and on the M&R records. However, the history of pavement M&R activities is often missing or unavailable to highway agencies for many reasons. Without accurate M&R records, it is difficult to determine if a condition change between two consecutive inspections is the result of M&R intervention, deterioration, or measurement errors. In this paper, we employed deep-learning networks of a convolutional neural network (CNN) model, a long short-term memory (LSTM) model, and a CNN-LSTM combination model to automatically detect if an M&R treatment was applied to a pavement section during a given time period. Unlike conventional analysis methods so far followed, deep-learning techniques do not require any feature extraction. The maximum accuracy obtained for test data is 87.5% using CNN-LSTM.

scholarly journals Performance and Microstructure of Cold Recycled Mixes Using Asphalt Emulsion with Different Contents of Cement

Materials ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 2548 ◽  
Author(s):  
Yanhai Yang ◽  
Ye Yang ◽  
Baitong Qian
Keyword(s):  
Low Temperature ◽  
Temperature Stability ◽  
Hydration Products ◽  
High Temperature Stability ◽  
Asphalt Emulsion ◽  
Moisture Susceptibility ◽  
Pavement Maintenance ◽  
Maintenance And Rehabilitation ◽  
Low Temperature Cracking ◽  
Temperature Crack

Cold recycled mixes using asphalt emulsion (CRME) is an economical and environmentally-friendly technology for asphalt pavement maintenance and rehabilitation. In order to determine the optimum range of cement contents, the complex interaction between cement and asphalt emulsion and the effects of cement on performance of CRME were investigated with different contents of cement. The microstructure and chemical composition of the fracture surface of CRME with different contents of cement were analyzed in this paper as well. Results show that the high-temperature stability and moisture susceptibility of CRME increased with the contents of cement increasing. The low-temperature crack resistance ability gradually increased when the content of cement is increased from 0% to 1.5%. However, it gradually decreased when the content of cement is increased from 1.5% to 4%. Cold recycled mixes had better low-temperature cracking resistance when the contents of cement were in the range from 1% to 2%. The results of microstructure and energy spectrum analysis show that the composite structure is formed by hydration products and asphalt emulsion. The study will be significant to better know the effects of cement and promote the development of CRME.

Development of Structural Condition Index to Support Pavement Maintenance and Rehabilitation Decisions at Network Level

2003 ◽  
Vol 1827 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Zhanmin Zhang ◽  
German Claros ◽  
Lance Manuel ◽  
Ivan Damnjanovic
Keyword(s):  
Evaluation Method ◽  
Surface Condition ◽  
Condition Index ◽  
Structural Condition ◽  
Pavement Management ◽  
Structural Deformation ◽  
Pavement Maintenance ◽  
State Highway ◽  
Structural Reinforcement ◽  
Maintenance And Rehabilitation

Every year, state highway agencies apply large amounts of seal coats and thin overlays to pavements to improve the surface condition, but these measures do not successfully address the problem. Overall pavement condition continues to deteriorate because of the structural deformation of pavement layers and the subgrade. To make effective decisions about the type of treatment needed, one should take into consideration the structural condition of a pavement. Several different structural estimators can be calculated by using falling weight deflectometer data and information stored in the Pavement Management Information System (PMIS) at the Texas Department of Transportation. The analysis considers pavement modulus and structural number as the structural estimators of a pavement. The evaluation method is based on the sensitivity of the structural estimators to deterioration descriptors. The deterioration per equivalent single-axle load of all major scores stored in the Texas PMIS is proposed as the primary indicator of pavement deterioration. In addition, the use of the structural condition index is recommended as a screening tool to discriminate between pavements that need structural reinforcement and those that do not. This index is calibrated for use in maintenance and rehabilitation analysis at the network level.

A semi-Markov formulation of the pavement maintenance optimization problem

1993 ◽  
Vol 20 (3) ◽  
pp. 436-447 ◽  
Author(s):  
Dale M. Nesbit ◽  
Gordon A. Sparks ◽  
Russell D. Neudorf
Keyword(s):  
Pavement Management ◽  
Maintenance Optimization ◽  
Infrastructure Management ◽  
Pavement Maintenance ◽  
Pavement Deterioration ◽  
Maintenance And Rehabilitation ◽  
Computationally Intensive ◽  
Asset Depreciation ◽  
Special Case ◽  
Project Level

The problem of determining optimal pavement maintenance and rehabilitation strategies is a special case of a more general problem termed the asset depreciation problem. Perhaps the most general formulation and solution of the asset depreciation problem is the semi-Markov formulation. This paper illustrates how the semi-Markov formulation and solution of the general asset depreciation problem can be applied to pavements. The semi-Markov formulation, like the Markov formulation, characterizes pavement deterioration probabilistically and represents human intervention (maintenance and rehabilitation) as slowing or modifying the basic probabilities of deterioration. The Markov formulation, first implemented for the state of Arizona, is shown to be a special case of the more general, less computationally intensive semi-Markov formulation. The application of the semi-Markov formulation is illustrated at the project level for a heavy-duty pavement in Manitoba. Key words: asset depreciation, infrastructure management, pavement management, probabilistic modelling, Markov, semi-Markov, maintenance optimization, project level.

Life cycle assessment of asphalt pavement maintenance and rehabilitation techniques: a study for the City of St. John’s

2020 ◽  
Vol 47 (12) ◽  
pp. 1320-1326 ◽  
Author(s):  
Md Rakibul Alam ◽  
Kamal Hossain ◽  
Ali Azhar Butt ◽  
Tim Caudle ◽  
Carlos Bazan
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Environmental Impacts ◽  
Impact Category ◽  
Pavement Maintenance ◽  
Environmental Footprints ◽  
Maintenance And Rehabilitation ◽  
Maintenance Techniques ◽  
Acidification Potential ◽  
The City

Although pavement maintenance and rehabilitation (M&R) techniques are usually examined in economic terms, there is a growing need to address their environmental footprints. The objective of this study is to assess the environmental impacts of M&R techniques. Life cycle assessment (LCA) can help in the decision-making process of selecting suitable maintenance techniques based on their environmental impacts. This study investigates: patching, rout & sealing, hot in-place recycling, and cold in-place recycling. Global warming potential (GWP), acidification potential, human health particulate, eutrophication potential, ozone depletion potential, and smog potential are estimated as environmental impacts for each maintenance activity. Materials, equipment use (for construction and M&R), and transportation were the main elements considered. A sensitivity test is performed to identify the significant factors for the LCA. The study concluded that GWP was the most important impact category. Rout & sealing and cold in-place recycling produced the lowest GWP emissions. Notably, pavement patching and hot in-place recycling showed significant detrimental environmental impacts.

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