Prediction of pavement remaining service life based on repetition of load and permanent deformation

Usman, Rudy; Setyawan, Ary; Suprapto, Mamok

doi:10.1088/1757-899x/333/1/012089

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…On the other hand, the prediction of remaining service life due to rutting with standard loads is 77.28% in the first year and 24.79% in the third year. These study results are in line with previous research, stating that the load repetition would increase with pavement age and would experience failure when reaching the allowable load repetition [25].…”

Section: Discussionsupporting

confidence: 93%

Analysis of Remaining Service Life for Flexible Pavement Using Mechanistic-Empirical Methods

Rahmawati¹

2021

GEOMATE

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This study aims to analyze pavement performance to predict the remaining service life and its handling. This research utilized mechanistic-empirical methods, specifically the KENPAVE program to analyze the strain on the pavement, the AASHTO 1993 to predict the remaining service life, and the 2017 Road Pavement Design Manual (MDP 2017) to recommend alternative treatments in the form of new pavement. This study discovered a horizontal tensile strain value of 0.0002998 and a vertical compressive strain of 0.0004022. Using the Asphalt Institute equation, the repetition value of the fatigue cracking load was 1,047,484.63 ESAL, and the repetition value of rutting load was 2,172,928.92 ESAL. Prediction of remaining service life due to traffic loads using the AASHTO 1993 equation yielded a residual service life value of 1.03% fatigue cracking in the second year and the remaining rutting service life of 24.79% in the third year. The results revealed that the pavement was unable to withstand traffic loads according to the planned age. Therefore, MDP 2017 was used to provide alternative treatments and resulted in the alternative handling presented, recommended as a treatment for the study site.

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Section: Discussionsupporting

confidence: 93%

Analysis of Remaining Service Life for Flexible Pavement Using Mechanistic-Empirical Methods

Rahmawati¹

2021

GEOMATE

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“…Traffic load working on the surface of flexible pavement is assumed as evenly distributed static load that the material of pavement will give response which is believed to be critical for design purposes are: Horizontal tensile strain (εt) bottom of the asphalt layer and vertical compressive strain value (εc) on the surface of subgrade [8]. Strain is the unit displacement due to stress, usually expressed as a ratio of the change in dimension to the original dimension (mm/mm or in/in).…”

Section: Fatigue and Rutting Distress Modellingmentioning

confidence: 99%

The Influence of CBR Value and Overloading on Flexible Pavement Mechanistic Response

Tajudin¹,

Setyarini²,

Darmawati³

2019

Proceedings of the 11th Asia Pacific Transportation and the Environment Conference (APTE 2018)

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Flexible pavement distresses are often caused by overloading with the combination of other factors, such as subgrade strength. Therefore, this study was conducted to investigate and analyze the effect of subgrade strength on flexible pavement mechanistic response with normal traffic loading and overloading scenario (25% to 100%) using KENPAVE software. CBR was varied from the range 3% to 10% with an increase of every 0,5%. Horizontal tensile strain was then used to estimate pavement fatigue life (Nf) and vertical tensile strain was then used to estimate pavement rutting life (Nr). The results of mechanistic responses reveal that the horizontal tensile strain and vertical compressive strain all decrease with increasing subgrade CBR value. The addition of load will increase the pressure that the vehicle distributes to the pavement, so that the horizontal tensile strain and vertical compressive strain will be higher. Nf and Nd all increase with decreasing of horizontal tensile strain and vertical compressive strain, which implies that higher CBR value will increase Nf and Nd. Rutting occurs at CBR 3% to 7,5% with the overloading of 75% and 100% while fatigue cracking occurs at CBR 3% to 9,5% with the overloading of 50, 75%, and 100%.

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“…These methods have been developed for use at different levels of a project or network [33]. Generally, each of these methods uses specific pavement data such as PCI [34], PSR, severity, and extent of a particular type of distresses such as edge faulting, fatigue cracking, roughness levels [35], pavement deflection [36], layer thickness, layer modulus, and so forth. Many different methods of analysis have been found in the literature accordingly, including empirical studies based on field data [37], probabilistic methods [38,39], mechanistic-empirical methods [35], or methods based on soft computing analysis using pavement history data [40].…”

Section: Introductionmentioning

confidence: 99%

A Novel Life Prediction Model Based on Monitoring Electrical Properties of Self-Sensing Cement-Based Materials

et al. 2021

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Assessing the damage level in concrete infrastructures over time is a critical issue to plan their timely maintenance with proper actions. Self-sensing concretes offer new opportunities for damage assessment by monitoring their electrical properties and relating their variations to damage levels. In this research, fatigue tests were conducted to study the response of a self-sensing concrete under high-cycle dynamic loading. The concept of G-value was defined as the slope of the voltage response baseline of the self-sensing concrete over time that reflects the damage created under the fatigue-loading test. Based on this definition, log (G)–log (N) curves were obtained using a linear regression approach, with N representing the number of cycles during the fatigue tests. While traditional fatigue curves S-log (N) are used to estimate the remaining life under fatigue loading, log (G)–log (N) diagrams can be used to determine the damage level based on the voltage response of the self-sensing concrete as a function of the loading history. This finding can be useful for the estimation of the lifetime and remaining life of self-sensing concrete structures and infrastructure, eventually helping to optimize the related maintenance operations.

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Prediction of pavement remaining service life based on repetition of load and permanent deformation

Cited by 7 publications

References 2 publications

Analysis of Remaining Service Life for Flexible Pavement Using Mechanistic-Empirical Methods

Analysis of Remaining Service Life for Flexible Pavement Using Mechanistic-Empirical Methods

The Influence of CBR Value and Overloading on Flexible Pavement Mechanistic Response

A Novel Life Prediction Model Based on Monitoring Electrical Properties of Self-Sensing Cement-Based Materials

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