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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).
Lessons learned from the supply curve approach
Published in John Harvey, Imad L. Al-Qadi, Hasan Ozer, Gerardo Flintsch, Pavement, Roadway, and Bridge Life Cycle Assessment 2020, 2020
A.A. Butt, J.T. Harvey, A. Saboori, C. Kim, M.T. Lozano, A.M. Kendall
Pavement condition affects the fuel economy as well as the GHG emissions of vehicles through rolling resistance (that is, energy loss due to the interaction of a vehicle and the pavement). The International Roughness Index (IRI) is a measure of roughness. Vehicle fuel use increases on rougher pavement surfaces. Caltrans and most other US states currently use a single IRI value to trigger maintenance and rehabilitation (M&R) treatment for all segments in their entire highway network. An alternative approach is to keep roads in a smoother condition (that is, keeping roughness lower) through more frequent M&R treatments where the volume of traffic and resultant fuel savings is sufficient to compensate for the GHG emissions from increased intensity of treatments. The LCC for Caltrans of keeping pavement with higher traffic volumes smoother may be the same or lower because the cost of treatment to restore smoothness to a pavement is often less if the pavement is not as badly damaged. To implement this strategy to reduce GHG emissions, the road network is divided into lane-segments (the Caltrans PMS considers each lane separately, and a lane-segment is a length of one lane with a relatively homogenous pavement structure, climate region, and traffic) based on each segment’s traffic volume, and then an “optimized” IRI trigger value is identified per lane-segment that minimizes the total GHG emissions resulting from the treatment process and the smoothness-induced fuel use improvement.
Data collection, processing, and database management
Published in Zongzhi Li, Transportation Asset Management, 2018
The IRI was developed by the World Bank in the 1980s to define a characteristic of the longitudinal profile. The commonly recommended units are meters per kilometer (m/km) or millimeters per meter (mm/m). The IRI is based on the average rectified slope (ARS), which is a filtered ratio of a standard vehicle's accumulated suspension motion (in mm, inches, etc.) divided by the distance traveled by the vehicle during the measurement (km, mi, etc.). IRI is then equal to ARS multiplied by 1000. For example, if the accumulated suspension motion of a standard vehicle is 60 mm and the distance traveled is 1 km, the ARS is 0.06 mm/m and the IRI is 60. The IRI was measured by using response type devices, which can be grouped into four categories: (i) rod and level survey and the dipstick profiler; (ii) profilograph; (iii) response-type road roughness meters (RTRRMs); and (iv) profiling devices.
Life-cycle cost analysis of rehabilitation strategies for asphalt pavements based on probabilistic models
Published in Road Materials and Pavement Design, 2023
Miaomiao Zhang, Hongren Gong, Rui Xiao, Xi Jiang, Yuetan Ma, Baoshan Huang
Compared to preservative maintenance that only maintains pavement conditions, rehabilitation is a structural or functional enhancement of pavement, which can significantly extend the service life (Hall et al., 2002). This study will concentrate on flexible pavement rehabilitation with asphalt concrete overlay when the pavement performance deteriorates to a point where rehabilitation is required. In addition, pavement roughness (in international roughness index, IRI) was selected as the pavement performance indicator in this study. IRI is often used to indicate overall flexible pavement surface condition, which may be caused by pavement construction or other distress (Lin et al., 2003). Roughness is an important pavement characteristic because it affects both driving quality and rehabilitation costs. In general, IRI does not increase much unless the overall pavement performance deteriorates to a severe level and requires major rehabilitation to provide serviceability (Prasad et al., 2013). Therefore, IRI can be regarded as an indicator of overall pavement condition, and the development process of IRI provides a good reference to manage overlay rehabilitation practices.
Estimating pavement roughness using a low-cost depth camera
Published in International Journal of Pavement Engineering, 2022
Waleed Aleadelat, Khaled Aledealat, Khaled Ksaibati
The IRI can be estimated by applying the Quarter Car Model (QCM) on a vertical road profile according to ASTM E1926-98 (ASTM 2008). Figure 5 shows the Quarter Car Model (QCM) that was applied to the established vertical profile from the depth stream. This model is used to stimulate a vehicle response over a road profile at 50 mph. According to this model, accumulating the vehicle response (i.e. suspension motion) and dividing it by the travelled distance will result in the IRI value of that travelled distance. The QCM parameters can be described as follows: ms is the sprung mass which represents a portion of a vehicle body mass that is supported by one wheel.mu is the unsprung mass which represents the mass of the wheel and part of the suspension.Cs is the suspension damping rate.Ks is the suspension spring rate.Kt is the tire spring rate.
A model for predicting the deterioration of the international roughness index
Published in International Journal of Pavement Engineering, 2022
Arieh Sidess, Amnon Ravina, Eyal Oged
The roughness of the road is assessed in terms of the longitudinal profile. The American Society of Testing and Material (ASTM) defines a longitudinal profile as the perpendicular deviations of the pavement surface from an established parallel to the lane direction, usually the wheel tracks (ASTM 1989). Pavements roughness is measured by many devices and each one can provide different units like International Roughness Index (IRI, m/km or inch/mile), Quarter car Index (QI) or roughness metre counts (counts/km). Due to the several techniques employed for roughness measurement, and in order to avoid the dependency on the longitudinal profile measurement device, the World Bank conducted in 1982 the International roughness experiments in Brazil. These experiments introduced the model of the IRI (Sayers et al. 1986). IRI represents the accumulated suspension of the vehicle, divided by the distance travelled during the same period, following the algorithm proposed by Sayers (1995). Since IRI has become the most common international roughness standard.