Performance of Recycled Porous Hot Mix Asphalt with Gilsonite Additive

Djakfar, Ludfi; Bowoputro, Hendi; Prawiro, Bangun; Tarigan, Nugraha

doi:10.1155/2015/316719

Cited by 11 publications

(12 citation statements)

References 5 publications

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“…Djakfar et al [25] evaluated the performance of porous asphalt mixtures using recycled concrete and investigated the impact of gilsonite addition to the mixture. The results showed that using gilsonite at ranges of 8-10% (the optimum content was 9%) increased the Marshall characteristics of the mixture, especially its stability without lessening the permeability capability of the porous asphalt mixtures.…”

Section: Asphalt Modified With Gilsonite As a Sole Modifiermentioning

confidence: 99%

“…Regarding Marshall tests, the use of gilsonite in asphalt mixtures can improve the Marshall properties of mixtures by increasing Marshall stability [24,27,30,38,42]. Moreover, the stability value continued to increase when the rate of gilsonite increased up to 10% [25,27], enhancing the resistance against deformation of the asphalt mixture [24].…”

Section: Critical Evaluation Of Gilsonite As Asphalt Modifiermentioning

confidence: 99%

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A Review on Improving Asphalt Pavement Service Life Using Gilsonite-Modified Bitumen

et al. 2021

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Hot mix asphalt has various benefits such as good workability and durability. It is one of the most general materials used as asphalt mixtures in road pavements. Asphalt mixtures and binders can be improved by modifying them with various additives. Gilsonite is a natural asphalt hydrocarbon which may be used as an additive to hot mix asphalt. It is used as an asphalt binder modifier (wet process) and an asphalt mixture modifier (dry process) to improve the properties of the mix. It provides the option of improved rheological properties, stability, strength rutting resistance and moisture sensitivity. This paper examines the current research relating to the use of gilsonite to improve the asphalt properties (binder and mixture). The rheological properties of the modified asphalt binders and mechanical properties of the modified asphalt mixtures will be reviewed. The influence of adding gilsonite individually or combined with other additives will be discussed. Furthermore, assessment of the environmental and economic perspectives of the studied asphalt along with some suggestions to improve the asphalt binders and mixtures will be explored.

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Section: Asphalt Modified With Gilsonite As a Sole Modifiermentioning

confidence: 99%

Section: Critical Evaluation Of Gilsonite As Asphalt Modifiermentioning

confidence: 99%

A Review on Improving Asphalt Pavement Service Life Using Gilsonite-Modified Bitumen

et al. 2021

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“…The Marshall quotient was determined for each specimen, as the ratio between Marshall stability and Marshall flow. Despite having already been discussed as partial representativity of the Marshall results with regard to the bituminous mixtures' response [91][92][93][94], such a test is currently used due to the huge experience gained all around the world.…”

Section: Hot MIX Asphalts (Hmas)mentioning

confidence: 99%

Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation

2019

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The present paper discusses the analysis and modeling of laboratory data regarding the mechanical characterization of hot mix asphalt (HMA) mixtures for road pavements, by means of artificial neural networks (ANNs). The HMAs investigated were produced using aggregate and bitumen of different types. Stiffness modulus (ITSM) and Marshall stability (MS) and quotient (MQ) were assumed as mechanical parameters to analyze and predict. The ANN modeling approach was characterized by multiple layers, the k-fold cross validation (CV) method, and the positive linear transfer function. The effectiveness of such an approach was verified in terms of the coefficients of correlation ( R ) and mean square errors; in particular, R values were within the range 0.965 – 0.919 in the training phase and 0.881 – 0.834 in the CV testing phase, depending on the predicted parameters.

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“…Furthermore, the Marshall quotient has been computed per each specimen, equal to the ratio between Marshall stability and Marshall flow. Even if it has been previously outlined the partial representativity of the Marshall data with respect to the asphalt concrete behaviour, such a test is still widely adopted given the large experience Advances in Civil Engineering cumulated over the years [38][39][40][41]. Table 5 gives additional details concerning the correspondence between specimens and gradations shown in Figure 2.…”

Section: Advances In Civil Engineeringmentioning

confidence: 99%

Analysis of the Mechanical Behaviour of Asphalt Concretes Using Artificial Neural Networks

Baldo

Manthos

Pasetto

2018

Advances in Civil Engineering

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The current paper deals with the numerical prediction of the mechanical response of asphalt concretes for road pavements, using artificial neural networks (ANNs). The asphalt concrete mixes considered in this study have been prepared with a diabase aggregate skeleton and two different types of bitumen, namely, a conventional bituminous binder and a polymer-modified one. The asphalt concretes were produced both in a road materials laboratory and in an asphalt concrete production plant. The mechanical behaviour of the mixes was investigated in terms of Marshall stability, flow, quotient, and moreover by the stiffness modulus. The artificial neural networks used for the numerical analysis of the experimental data, of the feedforward type, were characterized by one hidden layer and 10 artificial neurons. The results have been extremely satisfactory, with coefficients of correlation in the testing phase within the range 0.98798–0.91024, depending on the considered model, thus demonstrating the feasibility to apply ANN modelization to predict the mechanical and performance response of the asphalt concretes investigated. Furthermore, a closed-form equation has been provided for each of the four ANN models developed, assuming as input parameters the production process, the bitumen type and content, the filler/bitumen ratio, and the volumetric properties of the mixes. Such equations allow any other researcher to predict the mechanical parameter of interest, within the framework of the present study.

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Performance of Recycled Porous Hot Mix Asphalt with Gilsonite Additive

Cited by 11 publications

References 5 publications

A Review on Improving Asphalt Pavement Service Life Using Gilsonite-Modified Bitumen

A Review on Improving Asphalt Pavement Service Life Using Gilsonite-Modified Bitumen

Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation

Analysis of the Mechanical Behaviour of Asphalt Concretes Using Artificial Neural Networks

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