Characterization of Macro-Scale and Meso-Scale Performance of Asphalt Concrete Mixtures Under Compression

Researchers at Texas A&M University have developed the Pavement Analysis using Nonlinear Damage Approach (PANDA) for predicting the performance of asphalt concrete mixtures. PANDA offers substantial improvements in mechanistic modeling and simulation of pavement performance over other existing approaches. However, in order to facilitate the use of PANDA, there is a need to develop a systematic approach for determining the input parameters of its constitutive models. In this dissertation, a well-designed experimental testing protocol is developed to characterize the resistance of asphalt concrete mixtures to permanent deformation. This approach involves conducting two experimental tests in order to extract the PANDA model parameters: the dynamic modulus test (DMT) and repeated creep-recovery test at various stresses (RCRT-VS). Then, a systematic analytical approach is used to determine the linear viscoelastic, nonlinear viscoelastic, and viscoplastic PANDA model parameters for different types of asphalt mixtures and at different temperatures, air void contents, and aging levels. The analytical method employs DMT data to determine the long-term linear viscoelastic properties and time-temperature shift factors, and it employs the RCRT-VS data to determine the nonlinear viscoelastic and viscoplastic properties. A significant part of this dissertation focuses on the implementation of the global sensitivity analysis (GSA) approach to determine the sensitivity of the asphalt mixture performance to the PANDA’s input parameters. This analysis is performed in order to reduce the output uncertainty to input uncertainty, focus the experimental methods on evaluating the key parameters that influence performance, and simplify the analytical approach to extract significant model parameters from experimental data. The GSA results show that the viscoelastic nonlinearity parameter (g2), viscoplastic hardening function parameters (k1 and k2), and viscoplasticity-relaxation time (1/ Гvp) are the most significant and sensitive parameters. The PANDA constitutive modeling framework is used to efficiently simulate and predict the viscoelastic and viscoplastic responses of asphalt pavements. Two different scales of asphalt mixture performance are investigated: macro-scale (full dense-graded mixture, DGM) and meso-scale (fine aggregate matrix, FAM, and coarse aggregate matrix, CAM). The computational results show that the FAM controls the viscoelastic response of asphalt mixtures, while the CAM properties primarily influence the viscoplastic response of asphalt mixtures

Characterization of Macro-Scale and Meso-Scale Performance of Asphalt Concrete Mixtures Under Compression

Researchers at Texas A&M University have developed the Pavement Analysis using Nonlinear Damage Approach (PANDA) for predicting the performance of asphalt concrete mixtures. PANDA offers substantial improvements in mechanistic modeling and simulation of pavement performance over other existing approaches. However, in order to facilitate the use of PANDA, there is a need to develop a systematic approach for determining the input parameters of its constitutive models. In this dissertation, a well-designed experimental testing protocol is developed to characterize the resistance of asphalt concrete mixtures to permanent deformation. This approach involves conducting two experimental tests in order to extract the PANDA model parameters: the dynamic modulus test (DMT) and repeated creep-recovery test at various stresses (RCRT-VS). Then, a systematic analytical approach is used to determine the linear viscoelastic, nonlinear viscoelastic, and viscoplastic PANDA model parameters for different types of asphalt mixtures and at different temperatures, air void contents, and aging levels. The analytical method employs DMT data to determine the long-term linear viscoelastic properties and time-temperature shift factors, and it employs the RCRT-VS data to determine the nonlinear viscoelastic and viscoplastic properties. A significant part of this dissertation focuses on the implementation of the global sensitivity analysis (GSA) approach to determine the sensitivity of the asphalt mixture performance to the PANDA’s input parameters. This analysis is performed in order to reduce the output uncertainty to input uncertainty, focus the experimental methods on evaluating the key parameters that influence performance, and simplify the analytical approach to extract significant model parameters from experimental data. The GSA results show that the viscoelastic nonlinearity parameter (g2), viscoplastic hardening function parameters (k1 and k2), and viscoplasticity-relaxation time (1/ Гvp) are the most significant and sensitive parameters. The PANDA constitutive modeling framework is used to efficiently simulate and predict the viscoelastic and viscoplastic responses of asphalt pavements. Two different scales of asphalt mixture performance are investigated: macro-scale (full dense-graded mixture, DGM) and meso-scale (fine aggregate matrix, FAM, and coarse aggregate matrix, CAM). The computational results show that the FAM controls the viscoelastic response of asphalt mixtures, while the CAM properties primarily influence the viscoplastic response of asphalt mixtures

Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy

Rivers transport terrestrial microplastics (MP) to the marine system, demanding cost-effective and frequent monitoring, which is attainable through remote sensing. This study aims to develop and test microplastic concentration (MPC) models directly by satellite images and indirectly through suspended sediment concentration (SSC) as a proxy employing a neural network algorithm. These models relied upon high spatial (26 sites) and temporal (198 samples) SSC and MPC data in the Tisza River, along with optical and active sensor reflectance/backscattering. A feedforward MLP neural network was used to calibrate and validate the direct models employing k-fold cross-validation (five data folds) and the Optuna library for hyperparameter optimization. The spatiotemporal generalization capability of the developed models was assessed under various hydrological scenarios. The findings revealed that hydrology fundamentally influences the SSC and MPC. The indirect estimation method of MPC using SSC as a proxy demonstrated higher accuracy (R2 = 0.17–0.88) than the direct method (R2 = 0–0.2), due to the limitations of satellite sensors to directly estimate the very low MPCs in rivers. However, the estimation accuracy of the indirect method varied with lower accuracy (R2 = 0.17, RMSE = 12.9 item/m3 and MAE = 9.4 item/m3) during low stages and very high (R2 = 0.88, RMSE = 7.8 item/m3 and MAE = 10.8 item/m3) during floods. The worst estimates were achieved based on Sentinel-1. Although the accuracy of the MPC models is moderate, it still has practical applicability, especially during floods and employing proxy models. This study is one of the very initial attempts towards MPC quantification, thus more studies incorporating denser spatiotemporal data, additional water quality parameters, and surface roughness data are warranted to improve the estimation accuracy

Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery

Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images and machine learning (ML), with the Tisza River (Hungary) as a study area. The Very High Resolution (VHR) images obtained from the Google Earth database were employed to recognize some riverine litter spots (a blend of anthropogenic and natural substances). These litter spots served as the basis for training and validating five supervised machine-learning algorithms based on Sentinel-2 images [Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF), Naïve Bays (NB) and Decision Tree (DT)]. To evaluate the generalization capability of the developed models, they were tested on larger unseen data under varying hydrological conditions and with different litter sizes. Besides the best-performing model was used to investigate the spatio-temporal variations of riverine litter in the Middel Tisza. According to the results, almost all the developed models showed favorable metrics based on the validation dataset (e.g., F1-score; SVC: 0.94, ANN: 0.93, RF: 0.91, DT: 0.90, and NB: 0.83); however, during the testing process, they showed medium (e.g., F1-score; RF:0.69, SVC: 0.62; ANN: 0.62) to poor performance (e.g., F1-score; NB: 0.48; DT: 0.45). The capability of all models to detect litter was bounded to the pixel size of the Sentinel-2 images. Based on the spatio-temporal investigation, hydraulic structures (e.g., Kisköre Dam) are the greatest litter accumulation spots. Although the highest transport rate of litter occurs during floods, the largest litter spot area upstream of the Kisköre Dam was observed at low stages in summer. This study represents a preliminary step in the automatic detection of riverine litter; therefore, additional research incorporating a larger dataset with more representative small litter spots, as well as finer spatial resolution images is necessary

The use of single dose of oral misoprostol (600µg) at home in management of first trimester miscarriages in El-Mukala, Yemen

Background: In the management of first trimester miscarriage, the use of oral misoprostol is beneficial for patients as it offers a more discrete and less invasive route for those women who find vaginal administration unacceptable. In spite of high incidence of side-effects from use of oral misoprostol women still found oral route satisfactory.Methods: This study was a prospective cohort study done at El-Mukala maternal and child hospital and Hadhramout maternal and child university hospital in the period between 1st October 2014 and 30th September 2015. All pregnant women (less than 14 weeks) who were diagnosed as an embryonic pregnancy or missed miscarriage were included in the study. Every patient received single dose of oral misoprostol 600 µg in half full stomach at home. The primary outcome measure was complete miscarriage rate.Results: One-hundred women were included in the study. The mean age of study participants was 26.25±4.08 years, the mean BMI was 27.35±3.6 while the mean parity was 2.6±1.5.Ten cases needed emergency surgical evacuation within the period of first 48 hours. Complete miscarriage had occurred in 75 cases, 65 of them in the first 48 hours. Fifteen cases presented by incomplete miscarriage after waiting for one week. They needed surgical evacuation at the end of 7 days due to still considerable intrauterine contents.Conclusions: In our closed community in El-Mukala, Yemen, the use of oral misoprostol in single dose of 600 µg at home as a method for termination of first-trimester miscarriage was effective (75%, success rate), tolerable regarding side effects, has the advantage of high confidentiality and privacy resulting in good satisfaction

Non-invasive Hemodynamic Monitoring of Fluid Resuscitation in Cirrhotic Patients with Acute Kidney Injury

BACKGROUND: Fluid management of patients with liver cirrhosis and acute kidney injury (AKI) is a complex problem requiring accurate assessment of the intravascular volume status and the cause of the AKI. Echocardiography used in various hemodynamic monitoring as a quick, easy, bedside, and non-invasive tool with great sensitivity. AIM: This study aims to evaluate echocardiography as a non-invasive hemodynamic monitoring tool for the assessment of volume status and cardiac function before and after volume expansion in patients with liver cirrhosis presented by AKI. PATIENTS AND METHODS: This study included 120 patients with liver cirrhosis and AKI. All patients were subjected to clinical evaluation, laboratory assessment of kidney and liver functions, and echocardiographic assessment of inferior vena cava (IVC) collapsibility index, left ventricular outflow tract velocity time integral (LVOT VTI) variability index, and cardiac output (CO). RESULTS: Comparison between responders and non-responders to volume resuscitation regarding the echocardiographic data showed that responders had significantly higher IVC collapsibility index, LVOT VTI variability index, and % of CO increase. IVC collapsibility index and LVOT VTI variability index showed good predictive value of fluid responsiveness. CONCLUSIONS: The use of echocardiography is a good tool for hemodynamic monitoring of fluid resuscitation in cirrhotic patients with AKI. The use of echocardiography has limited the use of central venous line only to patients with hemodynamic instability requiring vasoactive support

Volatile organic compounds for the detection of hepatocellular carcinoma : A scoping review

Funding Information: This study is partially funded by the endowment fund NHS Grampian as part of the project (IRAS 250335).Peer reviewe