Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

Abstract

The testing procedure in order to determine the precise mechanical testing results in Marshall design is very time consuming. Also, the physical properties of the asphalt samples are obtained by further calculations. Therefore if the researchers can obtain the stability and flow values of a standard mixture with the help of mechanical testing, the rest of the calculations will just be mathematical manipulations. Determination of mechanical testing parameters such as strain accumulation, creep stiffness, stability, flow and Marshall Quotient of dense bituminous mixtures by utilising artificial neural networks is important in the sense that, cumbersome testing procedures can be avoided with the help of the closed form solutions provided in this study. Marshall specimens, prepared by utilising polypropylene fibers, were tested by universal testing machine carrying out static creep tests to investigate the rutting potential of these mixtures. On the very well trained data basis, artificial neural network analyses were carried out to propose five separate models for mechanical testing properties. The explicit formulation of these five main mechanical testing properties by closed form solutions are presented for further use for researches.

Viscoelastic Properties of Asphalt Mixtures with Different Modifiers at Different Temperatures Based on Static Creep Tests

“…To analyze the effect of temperature on viscoelasticity of asphalt mixtures and compare viscoelasticity between different asphalt mixtures, the static creep tests of DBFAM, SBSAM, and AM were performed at 10 • C, 20 • C, 30 • C, 40 • C, and 50 • C. For the static creep tests, the cylindrical asphalt mixture specimens with 100 mm in diameter and 63.5 mm in height were fabricated accordance with the previous researches [30][31][32][33]. And a contact pressure of 200 kPa (σ 0 ) was applied on these specimens after 5-hour drying at experimental temperature.…”

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

“…In general, the closer the value of R is to the unity, the stronger the results of the linear relation between t and y, thus confirming that the training has been completed successfully (if R 1 for the training data set) and that the degree of generalization achieved can be considered optimal (if R 1 for the testing data set). The mean squared error and the correlation coefficient have already been used in previous performance analysis of some ANNs designed to predict the mechanical parameters of HMA mixtures [65,66,[72][73][74][75][76]90].…”

“…ANNs have also been used in other studies to model different mechanical parameters of bituminous mixtures for road pavement; such researches have reported the use of a basic network architecture, a standard approach for the data sampling and adoption of just one type of transfer function, namely the hyperbolic tangent one [65,[72][73][74][75][76][77]. This type of methodological approach has been followed primarily because the data sets were large enough to avoid the use of more complex neural network structure; however, it has been mentioned that more sophisticated ANNs could be useful to improve the prediction performance of neural models [64].…”

Estimation of fatigue lives of fly ash modified dense bituminous mixtures based on artificial neural networks

“…Artificial neural network applications are treated as black-box applications in general. However the studies carried by the author and colleagues in the recent years open this black box and introduces the artificial neural network applications in closed form solutions [25][26][27] .…”