Marshall stability and flow analysis of asphalt concrete under progressive temperature conditions: An application of advance decision-making approach

Shah, Syyed Adnan Raheel; Anwar, Muhammad Kashif; Arshad, Hunain; Qurashi, Muhammad Ahmed; Nisar, Aqsa; Khan, Asfar Nasir; Waseem, Muhammad

doi:10.1016/j.conbuildmat.2020.120756

Cited by 15 publications

(7 citation statements)

References 37 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

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“…The maximum load was obtained as soon as it became lower, with the flowmeter above the guide rod pulled out from its position. Lastly, the flow rate was measured and recorded while the duration between the water bath specimen removal and maximum load calculation was ensured to be below 30 s [22].…”

Section: Laboratory Tests 241 Marshall and Flow Testsmentioning

confidence: 99%

Aggregate gradation effect on the mechanical properties of asphalt mixtures

Sulaiman,

abed,

Al-Saffar

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The issue of road deterioration in Mosul, Iraq, has become a matter of concern for local communities. The instability of road surfaces under the stress of vehicular traffic necessitates significant intervention to mitigate and manage this problem. This study has developed an asphalt mixture by following the Bailey method for aggregate gradation. The impact of aggregate gradation on the mechanical properties of the asphalt mixture was thoroughly analysed. Additionally, two separate mixtures were conducted using the conventional Marshall method to assess indirect tensile strength, Marshall properties, and susceptibility to moisture damage. The primary objective of this research is to design an aggregate structure that acts as a robust unit capable of withstanding heavy loads, repeated stresses, and high temperatures. The findings of the study indicate that the asphalt mixture created using the Bailey method exhibited superior stability with values of 13.6 kN, compared to the two traditional mixtures (11.8 kN, 9.2 kN), and. However, it is worth noting that the flow values also indicated a relative preference for the Bailey method, with values of and 3.1 mm, compared with 3.85 mm and 3.2 mm of the traditional mixtures. The results of Tensile strength ratio further corroborated the efficacy of Bailey’s technique, with outcomes of 85.1 %, thus enhancing the applicability of this method. This observation is attributed to the concept of granular interlocking, which forms the foundation of the Bailey method.

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“…As the MEP technique offers simple mathematical equations, such as Equations ( 22) and ( 23) for forecasting the MS and MF, the proposed MEP models outperformed the other two models. The overall requirement of time for both MS and MF tests, using the proposed MEP models, is substantially cheaper and faster with the proposed equations than the conventional test approaches [62]. Consequently, the anticipated developed mathematical expressions are feasibly swift procedures to determine the MS and MF of AWC of asphalt pavements.…”

Section: Performance Assessment and Comparison Of Developed Modelsmentioning

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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Marshall stability and flow analysis of asphalt concrete under progressive temperature conditions: An application of advance decision-making approach

Cited by 15 publications

References 37 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

Aggregate gradation effect on the mechanical properties of asphalt mixtures

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

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