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Performance evaluation of composite pavements on Mississippi highways via machine learning
Published in Inge Hoff, Helge Mork, Rabbira Garba Saba, Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields, Volume 2, 2022
R. Barros, H. Yasarer, Y.M. Najjar
Road networks are one of the largest public infrastructure assets of a country, they provide public mobility and freight transport to secure the nation’s economy and prosperity (Sultana et al. 2021). Annually, transportation agencies spend billions of dollars for the maintenance and rehabilitation (M&R) of their road networks. One of the most difficult tasks for state transportation agencies, such as the Mississippi Department of Transportation (MDOT), is to maintain roads and highways in acceptable conditions to meet the public’s needs and safety concerns (Tennant Duckworth 2020). If timely M&R is not performed, it may lead the pavement to poor conditions, causing an uncomfortable ride experience for road users (Barros et al. 2021a). To assess pavement surface condition and ride quality, Performance Condition Rating (PCR) and International Roughness Index (IRI) are the two widely used measures worldwide. Pavement performance models assist agencies to predict how a pavement deteriorates over time due to traffic, environmental conditions, and M&R history, being an important part of the pavement management system (PMS). The use of prediction models allows decision-makers to develop a better budget allocation plan and M&R schedule (Barros et al. 2022).
Composite pavement roughness modeling for LTPP wet freeze climate region using machine learning
Published in Inge Hoff, Helge Mork, Rabbira Garba Saba, Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields, Volume 3, 2022
R. Barros, H. Yasarer, W. Uddin, S. Sultana, Y. Najjar
A large portion of the paved highways in the U.S. comprises composite pavements. This type of pavement is commonly a result of concrete pavement rehabilitation, where an asphalt layer is overlaid on a concrete surface (Chen et al. 2015). In the U.S., pavement performance models are required for state highway agencies to assist in their pavement management decision-making processes (Kaya et al. 2020). Performance models bring key features to a successful pavement management system (PMS) (Elhadidy et al. 2021) providing an estimation of pavement conditions and rehabilitation needs and allowing agencies to prioritize road sections that are in the worst conditions. Performance models are easy to understand and can provide deeper insights converting performance indices into operational measures to inform how long and how well the road will continue to serve the users (Kaya et al. 2020). Numerous pavement performance indices have been developed in the last three decades; however, the international roughness index (IRI) is the most well-recognized performance index (Bashar and Torres-Machi 2021; Zeiada et al. 2020). The IRI expresses the irregularities in the pavement surfaces that affect the ride quality, and it is useful for making objective decisions related to the management of road networks (Jaafar 2019a; Sayers et al. 1986).
Aggregating network-level pavement performance data based on Gaussian Mixture Models
Published in Sandra Erkens, Xueyan Liu, Kumar Anupam, Yiqiu Tan, Functional Pavement Design, 2016
Road authorities are facing the challenges of aging highways and deteriorating networks with limited resources (Haider and Dwaikat, 2013, Simpson et al., 2013, Santos et al., 2015). It is widely recognized that pavement management, which is implemented using pavement management system (PMS), is vital in helping road authorities to manage their own road networks effectively and efficiently (Fernandes and Neves, 2014, Dong et al., 2015). There are generally two major administrative levels in pavement management: the network level and the project level (Haas et al., 1994, Chu and Chen, 2012). The network level deals with the pavement network as a whole and is generally concerned with high-level decisions relating to network-wide planning, policy and budget. The project-level pavement management deals with smaller constituent sections within the network and is intended to predict pavement deterioration, select the appropriate preservation activity, and develop the optimal preservation schedule for a specific pavement segment (Zhang et al., 2013, Wu and Flintsch, 2009).
A two-step sequential automated crack detection and severity classification process for asphalt pavements
Published in International Journal of Pavement Engineering, 2022
Thai Son Tran, Van Phuc Tran, Hyun Jong Lee, Julius Marvin Flores, Van Phuc Le
In pavement management system (PMS), pavement condition surveying is essential in determining an appropriate rehabilitation method based on the current condition of the pavement (Zhang et al. 2016b). Usually in a PMS network level, the pavement condition is evaluated by quantifying and evaluating pavement surface cracks. This is done by surveying stretch of roads using a vehicle with an attached camera capturing pavement surface images. These surveyed images are then evaluated and analysed further by determining the total cracks present on the captured images. Depending on the agency, pavement surface cracks are calculated differently wherein some use crack percentage of the road without considering the crack type or severity level meanwhile other agencies use both crack type and severity level. A common method in quantifying crack percentages is by visually and manually inspecting surveyed images which is uneconomical and time-consuming. Because of this, several studies have been conducted to develop and propose an automated pavement crack detection system addressing the limitations of the previously mentioned method.
Developing an approach for measuring the intensity of cracking based on geospatial analysis using GIS and automated data collection system
Published in International Journal of Pavement Engineering, 2021
Mansour Fakhri, Reza Shahni Dezfoulian, Amir Golroo, Bahador Makkiabadi
The condition of the road pavement has a significant effect on the safety and comfort of users. The pavement management system is employed to assess the pavement condition and to ensure the acceptable serviceability level. Pavement management is considered as a systematic approach to analysing and minimising the cost of maintenance needs. In fact, activities such as the assessment of pavement condition, the determination of maintenance and rehabilitation activities, and the prioritisation of treatments, are components of the pavement management system (Shah et al. 2013, Fakhri and Shahni Dezfoulian 2019). Being aware of the pavement distress data such as cracking intensity can lead to a cost-effective maintenance management system. Manual, automated, and semi-automated methods can be applied to collect and measure the characteristics of cracking. The repeatability and compatibility of the results have led the use and development of automated data collection method (Haas et al. 1984). The selection and prioritisation of maintenance actions, the prediction of pavement condition, and the optimisation of budget allocation are performed using performance indices and surface distresses such as cracking. The most common indices in this regard are International Roughness Index (IRI), Pavement Serviceability Index (PSI), Pavement Condition Index (PCI), Pavement Condition Rating (PCR), the Riding Comfort Index (RCI), and Distress Manifestation Index (DMI) (Ningyuan et al. 2011).
In-situ structural analysis of lightweight cellular concrete subbase flexible pavements
Published in Road Materials and Pavement Design, 2023
Abimbola Grace Oyeyi, Frank Mi-Way Ni, Susan Tighe
Even though there are a few LCC implementations in road construction, complete post-construction performance test methods and evaluations are still lacking. The Ministry of Transportation in Ontario (MTO) currently uses pavement condition surveys, ratings, and performance category assignments based on surface distresses and ride quality to evaluate pavement performance/condition in the pavement management system (MTO, 2013). Because LCC is not commonly used as a subbase material, questions about how to measure its thermal insulation, freeze-thawing resistance, stability, as well as how LCC contributes to the prevention of rutting, surface cracks, and their elongation, and whether the presence of LCC in the subbase influences pavement performance indicators, arise.