scholarly journals Roughness Modeling for Composite Pavements using Machine Learning

2021 ◽  
Vol 1203 (3) ◽  
pp. 032035
Author(s):  
Rulian Barros ◽  
Hakan Yasarer ◽  
Waheed Uddin ◽  
Salma Sultana
Keyword(s):  
Prediction Models ◽  
Concrete Pavement ◽  
Ride Quality ◽  
Performance Models ◽  
Asphalt Overlay ◽  
Road Users ◽  
Maintenance And Rehabilitation ◽  
Roughness Index ◽  
Roughness Prediction ◽  
Composite Pavements

Abstract A large number of paved highway surfaces comprises composite pavements as a result of concrete pavement rehabilitation that uses an asphalt overlay on top of the concrete surface. Annually, billions of dollars are spent on the maintenance and rehabilitation of road networks. Roughness is one of the several indicators of road conditions used to make objective decisions related to road network management. The irregularities in the pavement surface affecting the ride quality of road users can be described by a standard roughness index defined as the International Roughness Index (IRI). Roughness prediction models can identify rehabilitation needs, analyze rehabilitation effects, and estimate future pavement conditions to implement different Maintenance and Rehabilitation (M&R) activities to extend the pavement life cycle and provide a smooth surface for road users. This study intended to develop pavement performance models to predict roughness for asphalt overlay on concrete pavement sections using the Long-Term Performance Pavement (LTPP) program database. Artificial Neural Networks (ANNs) approach was used to develop roughness prediction models. A total of 52 pavement sections with 592 data points were analyzed. Five models were developed, and the best performing model, Model 5 was found with an average square error (ASE) of 0.0023, mean absolute relative error (MARE) of 12.936, and coefficient of determination (R2) of 0.88. Model 5 utilized one output variable (IRIMean) and 14 input variables (i.e., Initial IRIMean, Age, Wet-Freeze, Wet Non-Freeze, Dry-Freeze, Dry Non-Freeze, Asphalt Thickness, Concrete Thickness, CN Code, ESAL, Annual Air Temperature, Freeze Index, Freeze-Thaw, and Precipitation). The ANN model structure utilized for Model 5 was 14-9-1 (14 inputs, 9 hidden nodes, and 1 output). Environmental impacts and traffic repetitions can cause severe damage to the pavement if timely maintenance and rehabilitation are not performed. By considering the effects of the M&R history of the pavement, it is possible to obtain realistic prediction models for future planning. Therefore, the developed ANN roughness performance models in this paper can be used as a prediction tool for IRI values and guide decision-makers to develop a better M&R plan. Local and state agencies can use available historical traffic and climatological data in the developed models to estimate the change in IRI values. Utilizing these prediction models eliminates time-consuming data collection and post-processing, and consequently, a cost reduction. This low-cost tool will improve the condition assessment and effective M&R scheduling.

Development of a New Faulting Model in Jointed Concrete Pavement using LTPP Data

2019 ◽  
Vol 2673 (5) ◽  
pp. 407-417 ◽  
Author(s):  
Yu Chen ◽  
Robert L. Lytton
Keyword(s):  
Prediction Models ◽  
Information Criterion ◽  
Concrete Pavement ◽  
Pavement Design ◽  
Ride Quality ◽  
Related Factors ◽  
Pavement Roughness ◽  
Proposed Model ◽  
Jointed Concrete Pavement ◽  
Two Stages

Faulting is a major and commonplace distress in jointed concrete pavement (JCP) that can directly cause pavement roughness and adversely influence the ride quality of a vehicle. Faulting also plays an essential role in concrete pavement design. Notwithstanding the importance of faulting, the accuracy and reasonability of the faulting prediction models that have been developed to date remain controversial. Furthermore, the process of faulting over time is still not fully understood. This paper proposes a novel mechanistic-empirical model to estimate faulting depth at joints in the wheel path in JCP. Two stages within the process of faulting were revealed by the model and are introduced in this study. To distinguish the two stages of faulting, an inflection point, as a critical faulting depth, was directly determined by this model and suggested to be an indicator of the initiation of erosion for concrete pavement design. The proposed model was proven accurate and reliable by using long-term pavement performance data. The parameters in the model were statistically calibrated with performance-related factors by Akaike’s Information Criterion for variable selection and performing stepwise regression.

scholarly journals International Roughness Index Modeling For Jointed Plain Concrete Pavement Using Artificial Neural Network

2021 ◽  
Vol 1203 (3) ◽  
pp. 032034
Author(s):  
Salma Sultana ◽  
Hakan Yasarer ◽  
Waheed Uddin ◽  
Rulian Barros
Keyword(s):  
Prediction Model ◽  
Extreme Temperature ◽  
Plain Concrete ◽  
Concrete Pavement ◽  
Ann Model ◽  
International Roughness Index ◽  
Statistical Measures ◽  
Maintenance And Rehabilitation ◽  
Roughness Index ◽  
Artificial Neural

Abstract Climate attributes such as precipitation, extreme temperature, and freeze-thaw cycles along with traffic loads cause pavement distresses. The maintenance need for pavements is decided based on the pavement condition rating such as International Roughness Index (IRI). Generally, an IRI rating less than 2.68 m/km is acceptable, and a rating greater than 2.68 m/km is considered unacceptable and classified as “very poor” condition of the pavement. It is imperative to be able to accurately predict pavement conditions to prepare proper Maintenance and Rehabilitation (M&R) programs for the pavements. This study aims to develop IRI models that can successfully estimate the IRI values for Jointed Plain Concrete Pavement (JPCP) considering the M&R history of the pavements using Artificial Neural Networks (ANNs) approach. The study was carried out with the database collected from Long Term Pavement Performance (LTPP) program. The variables used for the ANN model development are initial IRI, pavement age, concrete pavement thickness, equivalent single axle load (ESAL), climatic region (wet-freeze, wet non-freeze, dry-freeze, dry non-freeze), construction number (CN), and several climatological data. After utilizing various ANN model structures, the best performing ANN model resulted in promising statistical measures (i.e. R2 = 0.87). The IRI prediction model can successfully estimate the increase of IRI values with the increase of ESAL value over time. The IRI prediction model can also estimate the decrease of IRI value after maintenance and rehabilitation. The predicted IRI values with good accuracy will help the local and state agencies to prepare for M&R programs for JPCP pavements and allocate a projected budget accordingly.

Predicting International Roughness Index Based on Surface Distresses in Various Climate and Traffic Conditions Using Laser Crack Measurement System

2021 ◽  
pp. 036119812110179
Author(s):  
Mansour Fakhri ◽  
Seyed Masoud Karimi ◽  
Jalal Barzegaran
Keyword(s):  
Measurement System ◽  
Prediction Models ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
International Roughness Index ◽  
Pavement Maintenance ◽  
Traffic Conditions ◽  
Roughness Index ◽  
Roughness Prediction ◽  
Performance Surface

Roughness is one of the most significant parameters in the evaluation of pavement performance. Surface distresses are among the main factors leading to roughness. The collection and evaluation of roughness data require the application of modern equipment such as road surface profilers. In the absence of such equipment, roughness prediction models that are based on surface distresses might provide a desirable assessment of pavement conditions. This research employs the laser crack measurement system (LCMS) to detect and measure surface distresses and roughness along 268 km of primary roads in Iran. Compared with manual survey, LCMS provides maximum detection and measurement accuracy. Based on the LCMS output, distresses with a higher correlation with the International Roughness Index (IRI) were selected as predictors in linear regression models and artificial neural networks (ANN). The models were developed for 10 m and 100 m length sections of the roads under different climate and traffic conditions. The results indicate that the performance of ANN for the 100 m sections with coefficient of determination ( R2) of 0.82 is superior to other models. The best case was that of using ANN in 100 m sections for regions with moderate climate and medium traffic levels, with a 0.94 correlation. Satisfactory results in field validation of the models demonstrated that agencies can use other methods of data collection (e.g., manual, right of way [ROW]) to assess the surface distresses and roughness condition of their roads from the developed models with minimum spending and without expensive equipment. Such estimates can be employed to make informed decisions in pavement maintenance programs at the network level.

Roughness prediction models using pavement surface distresses in different Canadian climatic regions

2019 ◽  
Vol 46 (10) ◽  
pp. 934-940 ◽  
Author(s):  
Graeme Patrick ◽  
Haithem Soliman
Keyword(s):  
Prediction Models ◽  
Climatic Conditions ◽  
International Roughness Index ◽  
Climatic Regions ◽  
Pavement Surface ◽  
Roughness Index ◽  
Roughness Prediction ◽  
Surface Distress ◽  
The Relationship

The correlation between the international roughness index (IRI) and distress is inherent, as roughness is a function of both the changes in elevation of the distress-free pavement surface and the changes in elevation due to existing surface distress. In this way, a relationship between existing surface distress and IRI may be developed. However, the susceptibility of pavement to various types of surface distress is affected by many factors, including climatic conditions. A model that relates pavement surface distress to IRI for Canada needs to account for climatic conditions in different locations. This paper investigates the relationship between pavement surface distresses and IRI for different climatic conditions in Canada using historical data collected at numerous pavement test section locations sourced from the Long-Term Pavement Performance program database. Developed models were calibrated then validated and found to be statistically significant.

Pavement Distress and Selection of Rehabilitation Alternatives: Michigan Practice

1998 ◽  
Vol 1629 (1) ◽  
pp. 214-225
Author(s):  
Donald C. Wotring ◽  
Gilbert Y. Baladi ◽  
Neeraj Buch ◽  
Steve Bower
Keyword(s):  
Department Of Transportation ◽  
Preventative Maintenance ◽  
Pavement Distress ◽  
Maintenance And Rehabilitation ◽  
Composite Pavements ◽  
Selection Of

The Michigan Department of Transportation (MDOT) practice regarding the preservation, rehabilitation, and preventative maintenance actions for rigid, flexible, and composite pavements is presented and discussed. For each pavement type, the causes of distress and the corresponding MDOT fix alternatives are also presented. Examples of the MDOT practice regarding the selection of maintenance and rehabilitation alternatives for rigid, flexible, and composite pavements are also presented.

scholarly journals Statistics and Artificial Intelligence-Based Pavement Performance and Remaining Service Life Prediction Models for Flexible and Composite Pavement Systems

2020 ◽  
Vol 2674 (10) ◽  
pp. 448-460
Author(s):  
Orhan Kaya ◽  
Halil Ceylan ◽  
Sunghwan Kim ◽  
Danny Waid ◽  
Brian P. Moore
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Decision ◽  
Performance Models ◽  
Remaining Service Life ◽  
Management Decision Making ◽  
Decision Making Processes

In their pavement management decision-making processes, U.S. state highway agencies are required to develop performance-based approaches by the Moving Ahead for Progress in the 21st Century (MAP-21) federal transportation legislation. One of the performance-based approaches to facilitate pavement management decision-making processes is the use of remaining service life (RSL) models. In this study, a detailed step-by-step methodology for the development of pavement performance and RSL prediction models for flexible and composite (asphalt concrete [AC] over jointed plain concrete pavement [JPCP]) pavement systems in Iowa is described. To develop such RSL models, pavement performance models based on statistics and artificial intelligence (AI) techniques were initially developed. While statistically defined pavement performance models were found to be accurate in predicting pavement performance at project level, AI-based pavement performance models were found to be successful in predicting pavement performance in network level analysis. Network level pavement performance models using both statistics and AI-based approaches were also developed to evaluate the relative success of these two models for network level pavement performance modeling. As part of this study, in the development of pavement RSL prediction models, automation tools for future pavement performance predictions were developed and used along with the threshold limits for various pavement performance indicators specified by the Federal Highway Administration. These RSL models will help engineers in decision-making processes at both network and project levels and for different types of pavement management business decisions.

Grouting Material Mix Proportion Test Research of Void the Old Cement Concrete Pavement Slab

2014 ◽  
Vol 912-914 ◽  
pp. 162-167
Author(s):  
Da Wei Lv ◽  
Guan Jun Xu ◽  
Xiao Li Sun
Keyword(s):  
Experimental Studies ◽  
Concrete Pavement ◽  
Service Performance ◽  
Cement Concrete ◽  
Bleeding Rate ◽  
Asphalt Overlay ◽  
Grouting Material ◽  
Mix Proportion ◽  
Cement Concrete Pavement ◽  
Mixing Proportion

The effective treatment of void cement concrete pavement slab through grouting technology is the important premise of ensuring the service performance of asphalt overlay engineer on cement concrete pavement. Based on the current problems of grouting technology, we did experimental studies on the mixing proportion of grout slurry and analyzed the effects of slurry composition and admixture type on mechanical properties including the strength, mobility, viscosity, expansion rate and bleeding rate of the slurry and on the service performance. Based on these, we put forward the control indexes for grouting slurry mix proportion determination which provided guidance for the design and construction quality control of grouting under the old cement concrete pavement slab.

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