Modeling Marshall Stability of light asphalt concretes fabricated using expanded clay aggregate with Artificial Neural Networks

Morova, Nihat; Sargin, Şebnem; Terzi, Serdal; Saltan, Mehmet; Serin, Sercan

doi:10.1109/inista.2012.6246946

Cited by 14 publications

(4 citation statements)

References 8 publications

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“…A broader spectrum of work was performed relating to the development of mathematical and numerical modeling [12][13][14][15][16][17]. Various research studies have used ANN to predict the results of Marshall tests for dense bituminous mixtures modified with polypropylene [8] to model the MS of asphalt concrete for changing temperatures [18]; to model stiffness modulus, MQ and MS of HMA [19]; to model MF, MS, indirect tensile strength, and stiffness of asphalt concrete with progressive conditions of temperature [20]; to determine optimum bitumen content, MS and MQ of asphalt concrete mixtures; to model fluctuation in MS with asphalt content [21]; and to model the MS of expanded clay aggregates used in light asphalt concrete [22]. Morova et al [23] used ANFIS to model MS for fiber-reinforced asphalt mixtures.…”

Section: Introductionmentioning

confidence: 99%

“…The MS and MF of asphalt concrete have been modeled widely using the distinctive features of most common technique of AI, i.e., ANN [6,8,[18][19][20][21][22][23][24][25][26][27][28][29][30][48][49][50][51][52][53]. These algorithms, with their abilities to recognize patterns, result in simplified engineering problems that are complex in nature [11,[54][55][56].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Marshall Flow and Marshall Stability of Asphalt Pavements Using Multi Expression Programming

Awan¹,

Hussain²,

Javed

et al. 2022

Buildings

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The traditional method to obtain optimum bitumen content and the relevant parameters of asphalt pavements entails time-consuming, complicated and expensive laboratory procedures and requires skilled personnel. This research study uses innovative and advanced machine learning techniques, i.e., Multi-Expression Programming (MEP), to develop empirical predictive models for the Marshall parameters, i.e., Marshall Stability (MS) and Marshall Flow (MF) for Asphalt Base Course (ABC) and Asphalt Wearing Course (AWC) of flexible pavements. A comprehensive, reliable and wide range of datasets from various road projects in Pakistan were produced. The collected datasets contain 253 and 343 results for ABC and AWC, respectively. Eight input parameters were considered for modeling MS and MF. The overall performance of the developed models was assessed using various statistical measures in conjunction with external validation. The relationship between input and output parameters was determined by performing parametric analysis, and the results of trends were found to be consistent with earlier research findings stating that the developed predicted models are well trained. The results revealed that developed models are superior and efficient in terms of prediction and generalization capability for output parameters, as evident by the correlation coefficient (R) (in this case >0.90) for both ABC and AWC.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Marshall Flow and Marshall Stability of Asphalt Pavements Using Multi Expression Programming

Awan¹,

Hussain²,

Javed

et al. 2022

Buildings

View full text Add to dashboard Cite

show abstract

“…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%

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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A multi-objective genetic algorithm for the hot mix asphalt problem

Altıok

ALAKARA

Gündüz

et al. 2022

Neural Comput & Applic

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Modeling Marshall Stability of light asphalt concretes fabricated using expanded clay aggregate with Artificial Neural Networks

Cited by 14 publications

References 8 publications

Predicting Marshall Flow and Marshall Stability of Asphalt Pavements Using Multi Expression Programming

Predicting Marshall Flow and Marshall Stability of Asphalt Pavements Using Multi Expression Programming

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

A multi-objective genetic algorithm for the hot mix asphalt problem

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