Performance evaluation of coarse-graded field mixtures using dynamic modulus results gained from testing in indirect tension mode of testing

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. It is of critical importance to obtain the dynamic modulus of asphalt pavements under repetitive traffic loading to predict its performance and service life. The indirect tension mode can measure the dynamic modulus of each layer of field cores without the dimensional requirement, e.g. a height of 6-inch is required for the traditional uniaxial test mode. Within this research work, performance evaluation took place with the use of the Dynamic Modulus Test in Indirect Tension mode on coarse-graded 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; 0.4ðC, 17.1ðC, and 33.8ðC 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. According to the results the Modified Witczak Model needs to be modified for IDT collected dynamic modulus data. Another focus of this research is developing an accurate finite element (FE) model using mixture elastic modulus and asphalt binder properties to predict dynamic modulus of asphalt concrete mix in indirect tension mode. An Artificial Neural Network is used to back-calculate the elastic modulus of asphalt mixtures. The developed FE model was verified against experimental results of field cores from nine different pavement sections from five districts in Minnesota. It is demonstrated that the ANN modeling is a powerful tool to back-calculate the elastic modulus and FE model is capable of accurately predicting dynamic modulus

Application of optimization and machine learning techniques in predicting pavement performance and performance-based pavement design

Maintenance, rehabilitation, and reconstruction of highway system are the major expenses in a state general expenditure. The emergence of predicting pavement performance and detecting the current state of the pavement health encourages pavement agencies to develop an accurate, efficient, and intelligent model to predict the remaining life of a pavement. Relating pavement condition, surface distresses, and structural properties, to a set of predictors including material properties, traffic loading, environmental factors, etc. through mathematical expressions is called performance modeling. To measure and predict pavement performance, a reproducible, authoritative, and field calibrated condition evaluating system is required. However, in the existence of numerous important predictors and their interrelationships, developing a predictive model for pavement performance is not a trivial task. The present study tackles the problem of developing a pavement performance predictive model in two ways. First, a machine learning-based predictive framework is developed based on the laboratory-produced performance data. The developed framework is implemented to predict the amount of permanent deformation in asphalt pavement as well as the asphalt pavement dynamic modulus. The developed framework is then used to solve a performance-based pavement design problem along with an evolutionary optimization algorithm. In the second approach, the structural behavior of a gantry crane way pavement at intermodal facilities is investigated by assessing the interactions between pavement, subgrade, and operational loading conditions. The pavement structural response to the crane load is measured through the installed strain gages in the field and used to validate a finite element-based model through an inverse analysis. The validated model is implemented to predict the fatigue life of the pavement structure as well as maintenance, rehabilitation and design recommendation for the existing and new crane way pavement sections

Performance Evaluation of Coarse-Graded Field Mixtures Using Dynamic Modulus Results Gained from Testing in the Indirect Tension Mode

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

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Application of optimization and machine learning techniques in predicting pavement performance and performance-based pavement design

Maintenance, rehabilitation, and reconstruction of highway system are the major expenses in a state general expenditure. The emergence of predicting pavement performance and detecting the current state of the pavement health encourages pavement agencies to develop an accurate, efficient, and intelligent model to predict the remaining life of a pavement. Relating pavement condition, surface distresses, and structural properties, to a set of predictors including material properties, traffic loading, environmental factors, etc. through mathematical expressions is called performance modeling. To measure and predict pavement performance, a reproducible, authoritative, and field calibrated condition evaluating system is required. However, in the existence of numerous important predictors and their interrelationships, developing a predictive model for pavement performance is not a trivial task. The present study tackles the problem of developing a pavement performance predictive model in two ways. First, a machine learning-based predictive framework is developed based on the laboratory-produced performance data. The developed framework is implemented to predict the amount of permanent deformation in asphalt pavement as well as the asphalt pavement dynamic modulus. The developed framework is then used to solve a performance-based pavement design problem along with an evolutionary optimization algorithm. In the second approach, the structural behavior of a gantry crane way pavement at intermodal facilities is investigated by assessing the interactions between pavement, subgrade, and operational loading conditions. The pavement structural response to the crane load is measured through the installed strain gages in the field and used to validate a finite element-based model through an inverse analysis. The validated model is implemented to predict the fatigue life of the pavement structure as well as maintenance, rehabilitation and design recommendation for the existing and new crane way pavement sections.</p

Performance evaluation of coarse-graded field mixtures using dynamic modulus results gained from testing in indirect tension mode of testing

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. It is of critical importance to obtain the dynamic modulus of asphalt pavements under repetitive traffic loading to predict its performance and service life. The indirect tension mode can measure the dynamic modulus of each layer of field cores without the dimensional requirement, e.g. a height of 6-inch is required for the traditional uniaxial test mode. Within this research work, performance evaluation took place with the use of the Dynamic Modulus Test in Indirect Tension mode on coarse-graded 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; 0.4à °C, 17.1à °C, and 33.8à °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. According to the results the Modified Witczak Model needs to be modified for IDT collected dynamic modulus data. Another focus of this research is developing an accurate finite element (FE) model using mixture elastic modulus and asphalt binder properties to predict dynamic modulus of asphalt concrete mix in indirect tension mode. An Artificial Neural Network is used to back-calculate the elastic modulus of asphalt mixtures. The developed FE model was verified against experimental results of field cores from nine different pavement sections from five districts in Minnesota. It is demonstrated that the ANN modeling is a powerful tool to back-calculate the elastic modulus and FE model is capable of accurately predicting dynamic modulus.</p

Principal Component Neural Networks for Modeling, Prediction, and Optimization of Hot Mix Asphalt Dynamics Modulus

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