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.

The ability to accurately develop subject-specific, input causation models, for blood glucose concentration (BGC) for large input sets can have a significant impact on tightening control for insulin dependent diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead to an effective artificial pancreas (i.e., an automatic control system that delivers exogenous insulin) under extreme changes in critical disturbances. These disturbances include food consumption, activity variations, and physiological stress changes. Thus, this paper presents a free-living, outpatient, multiple-input, modeling method for BGC with strong causation attributes that is stable and guards against overfitting to provide an effective modeling approach for feedforward control (FFC). This approach is a Wiener block-oriented methodology, which has unique attributes for meeting critical requirements for effective, long-term, FFC.

Permanent deformation is a major load-associated distress occurring in flexible pavement systems and increases with load repetitions affecting road roughness, serviceability, and the international roughness index (IRI). Early detection of rutting is necessary for maintenance and rehabilitation activities, but due to the complex behavior of asphalt mixtures, accurately predicting the permanent deformation of asphalt pavement is difficult. Historically, multivariate regression modeling and recently, artificial neural networks (ANNs) are used widely for material properties prediction. The ability to model accurately the response variable is adversely affected when inputs have pairwise correlations. To overcome this barrier, principal component analysis (PCA), as a dimensionality reduction technique, can be used to produce uncorrelated linear combinations of the original inputs as illustrated in this work using 83 (i.e., samples) laboratory compacted specimens from the State of Wisconsin. Asphalt binder, aggregate, and mix properties are obtained and used as the model inputs. The response parameter is the accumulated strain at the corresponding flow number. Using the developed pseudo inputs from PCA, a multivariate regression and an ANN model are generated and were able to fit the test cases with r_fit $r_{\mathit{fit}}$ <!--mathContainer--> <!--Loading Mathjax--> of 0.8 and 0.97 respectively. The developed machine learning-based framework is shown to be a capable tool in estimating the rutting behavior of asphalt mixture.