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The dynamic modulus of hot mix asphalt (HMA) is a fundamental material property that defines the stress-strain relationship based on viscoelastic principles and is a function of HMA properties, loading rate, and temperature. Because of the large number of efficacious predictors (factors) and their nonlinear interrelationships, developing predictive models for dynamic modulus can be a challenging task. In this research, results obtained from a series of laboratory tests including mixture dynamic modulus, aggregate gradation, dynamic shear rheometer (on asphalt binder), and mixture volumetric are used to create a database. The created database is used to develop a model for estimating the dynamic modulus. First, the highly correlated predictor variables are detected, then Principal Component Analysis (PCA) is used to first reduce the problem dimensionality, then to produce a set of orthogonal pseudo-inputs from which two separate predictive models were developed using linear regression analysis and Artificial Neural Networks (ANN). These models are compared to existing predictive models using both statistical analysis and Receiver Operating Characteristic (ROC) Analysis. Empirically-based predictive models can behave differently outside of the convex hull of their input variables space, and it is very risky to use them outside of their input space, so this is not common practice of design engineers. To prevent extrapolation, an input hyper-space is added as a constraint to the model. To demonstrate an application of the proposed framework, it was used to solve design-based optimization problems, in two of which optimal and inverse design are presented and solved using a mean-variance mapping optimization algorithm. The design parameters satisfy the current design specifications of asphalt pavement and can be used as a first step in solving real-life design problems.

Summary Truss optimization is a complex structural problem that involves geometric and mechanical constraints. In the present study, constrained mean‐variance mapping optimization (MVMO) algorithms have been introduced for solving truss optimization problems. Single‐solution and population‐based variants of MVMO are coupled with an adaptive exterior penalty scheme to handle geometric and mechanical constraints. These tools are explained and tuned for weight minimization of trusses with 10 to 200 members and up to 1,200 nonlinear constraints. The results are compared with those obtained from the literature and classical genetic algorithm. The results show that a MVMO algorithm has a rapid rate of convergence and its final solution can obviously outperform those of other algorithms described in the literature. The observed results suggest that a constrained MVMO is an attractive tool for engineering‐based optimization, particularly for computationally expensive problems in which the rate of convergence and global convergence are important.

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Historically, asphalt mixtures in Minnesota have been produced with fine gradations. However, recently more coarse-graded mixtures are being produced as they require less asphalt binder. Thus, it is important that pavement performance for coarse gradations be evaluated. Within this research work, performance evaluation took place with the use of the Dynamic Modulus Test in Indirect Tension mode on coarsegraded mixtures consisting of field cores from 9 different pavements located in five districts of Minnesota. From each pavement's surface layer, 3 specimens were tested at three temperatures; 0.4°C, 17.1°C, and 33.8°C each at nine frequencies ranging between 0.1 Hz and 25 Hz. Additional volumetric characterization of the field mixtures was done to determine asphalt content, air voids, and blended aggregate gradations. Asphalt binders were extracted and recovered for use in determining binder shear complex master curves. Through this information the modified Witczak model was used to create │E*│ master curves which were then compared against the indirect tension (IDT) test │E*│ experimentally created master curves. From the results the Modified Witczak Model needs to be modified for IDT collected dynamic modulus data.