An artificial neural network base prediction model and sensitivity analysis for marshall mix design

Öztürk, Hande İşık; Saglik, Ahmet; Demir, Birol; Gungor, Ahmet Gurkan

doi:10.14311/ee.2016.224

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…They concluded that the application of the ANN model superpave mix design can take approximately 1.5 to 4.5 days. Ozturk et al (2016) analysed the possible application of ANN for predicting the HMA volumetric properties of mixtures prepared by Marshall Mix design procedures. For modelling purposes, aggregate gradation, bulk specific gravity of aggregates and binder content was used as an input data.…”

Section: Application Of Artificial Neural Network In the Predicting Process Of The Asphalt MIX Propertiesmentioning

confidence: 99%

Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

Androjić

Marović

2017

Can. J. Civ. Eng.

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The oscillation of asphalt mix composition on a daily basis significantly affects the achieved properties of the asphalt during production, thus resulting in conducting expensive laboratory tests to determine existing properties and predicting the future results. To decrease the amount of such tests, a development of artificial neural network and multiple linear regression models in the prediction process of predetermined dependent variables air void and soluble binder content is presented. The input data were obtained from a single laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found that it is possible and desirable to apply such models in the prediction process of the HMA properties. The final aim of the research was to compare results of the prediction models on an independent dataset and analyze them through the boundary conditions of technical regulations and the standard EN 13108-21.

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Section: Application Of Artificial Neural Network In the Predicting Process Of The Asphalt MIX Propertiesmentioning

confidence: 99%

Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

Androjić

Marović

2017

Can. J. Civ. Eng.

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“…Recently, the machine learning approaches have been utilized in several studies to predict models for different aspects of construction materials and civil engineering applications (Murad et al, 2021;Al Bodour et al, 2022;Iftikhar et al, 2022;Momani et al, 2022). The artificial neural networks (ANNs), which is a recent learning machine tool, was also utilized by researchers in asphalt mix design applications (Tapkın et al, 2010;Ozgan, 2011;Singh et al, 2013;Ozturk and Kutay, 2014;Shafabakhsh et al, 2015;Ozturk et al, 2016;Zavrtanik et al, 2016;Pasetto et al, 2019;Fadhil et al, 2022;Othman, 2022). An ANN-based model to predict the Marshall mix volumetric properties has been proposed by Ozturk et al (Ozturk et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The artificial neural networks (ANNs), which is a recent learning machine tool, was also utilized by researchers in asphalt mix design applications (Tapkın et al, 2010;Ozgan, 2011;Singh et al, 2013;Ozturk and Kutay, 2014;Shafabakhsh et al, 2015;Ozturk et al, 2016;Zavrtanik et al, 2016;Pasetto et al, 2019;Fadhil et al, 2022;Othman, 2022). An ANN-based model to predict the Marshall mix volumetric properties has been proposed by Ozturk et al (Ozturk et al, 2016). Ozgan (Ozgan, 2011) modeled the Marshall stability of asphalt mixes under different testing temperatures and exposure time conditions using the ANN technique.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Marshall Test Results for Dense Glasphalt Mixtures Using Artificial Neural Networks

Jweihan

Alawadi

Momani

et al. 2022

Front. Built Environ.

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Asphalt mixture comprising waste glass as an aggregate is referred to as “glasphalt”. Limited studies have been oriented to investigate the Marshall test results of dense-graded glasphalt mixes considering a wide range of variables. This study aims to utilize the artificial neural networks (ANNs) to develop predictive models for Marshall stability and Marshall flow of dense glasphalt mixes based on a large experimental database collected from the literature. Eight independent variables covering the material and mix properties were utilized as inputs in the models. The proposed models resulted in an experimental-to-predicted ratio of 1.00 and 1.00, coefficient of variation of 8.6% and 8.7%, RMSE of 1.63 kN and 0.54 mm, and R-squared of 93.6% and 85.7% for the glasphalt stability and flow models, respectively. Comprehensive parametric analyses have been conducted to further validate the models by investigating the sensitivity of their parameters to the predicted stability and flow values. The analyses revealed some desirable design values that could be considered for a better performance of dense glasphalt mixes. The results indicate that 4% is the desired design air void content of glasphalt mixes. High stability value can be achieved for glasphalt mixes containing a crushed aggregate of 12.5 mm maximum size and 50% glass cullet of 4.75 mm maximum size. Lower viscosity asphalt binder would provide uniformly compacted mixes. Furthermore, glasphalt flow increases as the maximum size of ingredient particles, the penetration grade of asphalt cement, asphalt cement content, and VMA% increase.

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“…In 1948, the Marshall test was widely adopted in many countries with slight modifications from country to country [9]. Till now, it is the most common asphalt mix design method [12], and it is still used in Egypt for estimating the OAC [13]. During the Marshall test, the designer has to prepare at least 15 samples for five bitumen contents (three samples for every bitumen content) and draw the design curves [14,15] to find the OAC that satisfies the following criteria: Maximum stability, Maximum density or unit weight, predefined air voids percent, and a min value for voids in mineral aggregate [9].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the optimum asphalt content using artificial neural networks

Othman¹,

Abdelwahab²

2021

Metall Mater Eng

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The performance of the asphalt mix is significantly influenced by the optimum asphalt content (OAC). The asphalt content is responsible for coating the aggregate surface and filling the voids between the aggregate particles. Thus, the aggregate gradation has a significant influence on the required asphalt content. The Marshall design process is the most common method used for estimating the OAC, and this process is called the asphalt mix design. However, this method is time consuming, labor intensive, and its results are subjected to variations. Thus, this paper employs the artificial neural network (ANN) to estimate the OAC from the aggregate gradation for the two most common gradations used in asphalt mixes in Egypt (3D, 4C). Results show that the proposed ANN can predict the OAC with a coefficient of correlation of 0.98 and an average error of 0.026%. As a result, a new approach for the Marshall test can be adopted using results of the proposed ANN, and only three specimens, instead of fifteen, are prepared and tested for estimating the remaining parameters. This approach saves the time, effort, and resources required for estimating the OAC. Additionally, the ANN was validated with previously developed models, and the ANN shows promising results.

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An artificial neural network base prediction model and sensitivity analysis for marshall mix design

Cited by 7 publications

References 7 publications

Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

Prediction of Marshall Test Results for Dense Glasphalt Mixtures Using Artificial Neural Networks

Prediction of the optimum asphalt content using artificial neural networks

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