Prediction of the marshall stability of reinforced asphalt concrete with steel fiber using fuzzy logic

Serin, Sercan; Morova, Nihat; Saltan, Mehmet; Terzi, Serdal; Karaşahin, Mustafa

doi:10.3233/ifs-130873

Cited by 6 publications

(4 citation statements)

References 29 publications

(25 reference statements)

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“…The selection of the parameters affecting them significantly is the initial step in developing the appropriate models. After running many trials and comprehensive literature reviews [44,60,[63][64][65][66][116][117][118], MS and MF have been shown to be dependent on the following eight parameters: Both ANN and the ANFIS models were created in the MATLAB R2020b environment, utilizing the NN and FL toolbox, respectively. For the training of both models for MS and MF, 239 (70%) data points were used by a random distribution of the data, whilst the remaining 30% data points, i.e., 104, were set aside for testing and validation (15% each), in order to check the precision and generalization capability of the trained models predicting, MS and MF [119].…”

Section: Model Structure and Performancementioning

confidence: 99%

“…Furthermore, the test of MS and MF takes time, while their determination in the laboratory is also time-consuming and costly [57,58]. A number of studies have previously employed basic input parameters for the prediction of the MS and MF of asphalt pavements using ANN and ANFIS approaches [57,[59][60][61][62][63][64][65][66]. As a result, the goal of this research study is the construction of models that reliably predict the MS and MF of asphalt pavements using major input parameters that are determined simply and economically.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

et al. 2022

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The conventional method for determining the Marshall Stability (MS) and Marshall Flow (MF) of asphalt pavements entails laborious, time-consuming, and expensive laboratory procedures. In order to develop new and advanced prediction models for MS and MF of asphalt pavements the current study applied three soft computing techniques: Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multi Expression Programming (MEP). A comprehensive database of 343 data points was established for both MS and MF. The nine most significant and straightforwardly determinable geotechnical factors were chosen as the predictor variables. The root squared error (RSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), coefficient of determination (R2), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed the rising order of input significance of MS and MF. The results of parametric analysis (PA) were also found to be consistent with previous research findings. The findings of the comparison showed that ANN, ANFIS, and MEP are all reliable and effective methods for the estimation of MS and MF. The mathematical expressions derived from MEP represent the novelty of MEP and are relatively reliable and simple. Roverall values for MS and MF were in the order of MEP > ANFIS > ANN with all values over the permissible range of 0.80 for both MS and MF. Therefore, all the techniques showed higher performance, possessed high prediction and generalization capabilities, and assessed the relative significance of input parameters in the prediction of MS and MF. In terms of training, testing, and validation data sets and their closeness to the ideal fit, i.e., the slope of 1:1, MEP models outperformed the other two models. The findings of this study will contribute to the choice of an appropriate artificial intelligence strategy to quickly and precisely estimate the Marshall Parameters. Hence, the findings of this research study would assist in safer, faster, and more sustainable predictions of MS and MF, from the standpoint of time and resources required to perform the Marshall tests.

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Section: Model Structure and Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

et al. 2022

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“…So far, many kinds of fibers had been applied in asphalt concrete. These fibers included the steel fibers [4,5], basalt fibers [6], polypropylene fibers [7,8], glass fibers [9], natural fibers [10,11], and thermoplastic fibers [12]. Among these researches, Chen and Xu [13] and Qian et al [14] determined effects of polyester fiber, polyacrylonitrile fiber, lignin fiber, and aramid fiber on the asphalt binder.…”

Section: Introductionmentioning

confidence: 99%

“…In order to determine fibers' effect on asphalt mixtures directly, Serin et al [4,5] used steel fiber to modify the mixture. They suggested that the reinforced mixture had the best performance when the bitumen and fiber content were 5.5% and 0.75%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Reinforced Mechanism of Fiber Reinforced Asphalt Concrete Based on Micromechanical Modeling

Gao

Guo

et al. 2017

Advances in Materials Science and Engineering

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Short fibers have been widely used to prepare the fiber reinforced asphalt concrete (FRAC). However, internal interactions between fiber and other phases of asphalt concrete are unclear although experimental methods have been used to design the FRAC successfully. In this paper, numerical method was used to investigate the reinforced mechanism of FRAC from microperspective. 2D micromechanical model of FRAC was established based on Monte Carlo theory. Effects of fiber length and content on stress state of asphalt mortar, effective modulus, and viscoelastic deformation of asphalt concrete were investigated. Indirect tensile stiffness modulus (ITSM) test and uniaxial creep test were carried out to verify the numerical results. Results show that maximum stress of asphalt mortar is lower compared to the control concrete when the fiber length is longer than 12 mm. Fiber reduces the stress level of asphalt mortar significantly. Fiber length has no significant influence on the effective modulus of asphalt concrete. Fiber length and content both have notable impacts on the viscoelastic performance of FRAC. Fiber length should be given more attention in the future design of FRAC except the content.

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Evaluation of Fatigue and Rutting Behaviour of Hot Mix Asphalt Containing Rock Wool

et al. 2020

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Prediction of the marshall stability of reinforced asphalt concrete with steel fiber using fuzzy logic

Cited by 6 publications

References 29 publications

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

Investigation on Reinforced Mechanism of Fiber Reinforced Asphalt Concrete Based on Micromechanical Modeling

Evaluation of Fatigue and Rutting Behaviour of Hot Mix Asphalt Containing Rock Wool

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