Artificial neural network (ANN) models are developed in this study to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design application. A database is developed containing grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulus results for 97 soils from 16 different counties in Oklahoma. Of these, 63 soils (development data set) are used in training, and the remaining 34 soils (evaluation data set) from two different counties are used in the evaluation of the developed models. A commercial software, STATISTICA 7.1, is used to develop four different feedforward-type ANN models: linear network, general regression neural network, radial basis function network, and multilayer perceptrons network (MLPN). In each of these models, the input layer consists of seven nodes, one node for each of the independent variables, namely moisture content (w) , dry density ( γd ) , plasticity index (PI), percent passing ...

In this study, a four-layer feed-forward neural network is constructed and applied to determine a mapping associating mix design and testing factors of asphalt concrete samples with their performance in conductance to flow or permeability. To generate data for the neural network model, a total of 100 field cores from 50 different mixes (two replicate cores per mix) are tested in the laboratory for permeability and mix volumetric properties. The significant factors that affect asphalt permeability are identified using simple and multiple regression analysis. The analyses results show that permeability of an asphalt concrete is affected mainly by five factors: (1) air void ( Va ) ; (2) the grain size through which 10% materials pass ( d10 ) ; (3) the grain size through which 30% materials pass ( d30 ) ; (4) saturation, or the CoreLok Infiltration Coefficient (CIC); and (5) effective asphalt to dust ratio ( Pbe ∕ P0.075 ) . The significant factors are then used to define the domain of a neural network. Regar...

Over the past few years, many regression-based and artificial neural network (ANN)-based models have been developed to estimate the dynamic modulus of hot mix asphalt (HMA). These models use the gradation of aggregates and the volumetric properties of compacted samples as input variables to the model. However, none of these models use aggregate shape parameters (i.e., angularity, texture, form, and sphericity) in the development of the model. Recently, researchers have expressed concerns that the shape parameters of aggregates need to be considered in the estimation of dynamic modulus. The primary objective of this study was to develop an ANN-based model for the estimation of dynamic modulus of HMA using aggregate shape parameters. The dynamic modulus of 20 different HMA mixes composed of various sources, sizes, types of aggregates, and different volumetric properties were measured in the laboratory. The shape parameters of different sizes of coarse and fine aggregates were measured with an automa...

Adequate compaction of asphalt pavements during their construction is essential to the long-term performance of the pavement. Current quality control techniques determine the quality at a limited number of points and are not indicative of the overall quality of the pavement. In this paper, the intelligent asphalt compaction analyzer (IACA) is used to estimate the density of an asphalt pavement during its construction and thereby determine the overall quality of compaction. A comparison of these estimates with the percent within limits (PWL) calculations based on roadway cores demonstrates that the IACA can be effectively used as a nondestructive quality assurance (QA) tool. Further, since the IACA continuously estimates the density of the asphalt in real time, inadequate compaction can be addressed during the construction, thereby improving the overall quality of pavement. Thus, the IACA can also serve as a valuable quality control (QC) tool during the construction.

Continuous real-time estimating of compaction quality during the construction of a hot mix asphalt (HMA) pavement is addressed in this paper. The densification of asphalt pavements during construction usually is accomplished by using vibratory compactors. During compaction, the compactor and the asphalt mat form a coupled system whose dynamics are influenced by the changing stiffness of the mat. The measured vibrations of the compactor along with process parameters such as lift thickness, mix type, mix temperature, and compaction pressure can be used to predict the asphalt mat density. Contrary to existing techniques in the literature in which a model is developed to fit experimental data and to predict mat density, a neural network-based approach is adopted that is model-free and uses pattern-recognition techniques to estimate density. The neural network is designed to read the entire frequency spectrum of roller vibrations and to classify these vibrations into different levels. The intelligent asphalt c...