Advanced Modeling and Design Methodology for Pavements using Plasticity-Based Shakedown Theory

Pavement design is a process intended to find the most economical combination of layer thickness and material type for the pavement, taking into account the properties of the subgrade soil and the traffic to be carried during the service life of the road. The currently prevalent methods of pavement analysis and design, however, are more or less empirical in U.S., which possess the shortcoming that the important type of pavement distress of rutting related to the accumulation of plastic or permanent deformations cannot be effectively considered. This project proposes an exploratory study on the application of the plasticity theory-based shakedown concept to the analysis and design of pavements under repeated loading, with a more realistic incorporation of the roughness impact of the top pavement layer on the dynamic amplification of vehicle loading as well as on the elastic stress responses in the underlying subsoils. Numerical results from the newly developed vehicle-road coupling model show that the total vehicle load amplification factor ranges from 0.88 to 1.16 under different roughness levels and traveling speeds. This indicates the necessity and importance of incorporating the factors of roughness/vehicle speed in the pavement response analysis. Extensive parametric analyses for the shakedown limit show that increases in the pavement cohesion strength and internal friction angle and in the pavement thickness have a positive influence on the calculated shakedown limit value. The analysis results also indicate that there generally exists an optimal Young’s modulus ratio between the pavement and subsoil, for which a maximum shakedown load of the pavement system will be reached. The outcomes of this project on one hand add contributions to the development of a more rational theoretical framework for the pavement design/analysis. On the other hand, the shakedown design approach can prevent the flexible pavement from excessive rutting failure, and hence is of great practical value for prediction/design of the vehicle load, traveling speed, and layer thickness that is required to warrant shakedown state of the pavements (i.e., no excessive rutting) in the long run

Modified stress and temperature-controlled direct shear apparatus on soil-geosynthetics interfaces

In this paper, a bespoke stress and temperature controlled direct shear apparatus to test soil-geosynthetics interfaces is introduced. By adopting the apparatus, a series of ‘rapid loading’ shear tests and creep tests were conducted on the Clay – Geosynthetic Drainage layer (GDL) interfaces to assess the functionality of the apparatus. The experimental results indicate that, the modified apparatus can allow the shear deformation behaviour of soil-geosynthetics interfaces under environmental stress during thermal and drying-wetting cycles to be investigated, with a reliable performance. The resistance of Clay-GDL interfaces to shear deformation under the rapid loading of shear stress decreases after drying-wetting cycle and at elevated temperature. In the creep tests, the interfaces subjected to drying-wetting cycles and thermal cycles fail under a lower shear stress level than that of the interfaces without experiencing drying-wetting cycles and thermal cycles, respectively. The impacts of drying cycles on the horizontal displacement is significantly larger than that of wetting cycles. The first drying cycle has the largest impacts on the horizontal displacement than those of the following drying cycles. The impacts of drying alone on the horizontal displacement of Clay-GDL interfaces during drying cycles are small, and the main influence factor is the elevated temperature

Comparative study of hybrid artificial intelligence approaches for predicting peak shear strength along soil-Geocomposite drainage layer interfaces

Peak shear strength of soil-Geocomposite Drain Layer (GDL) interfaces is an important parameter in the designing and operating related engineering structures. In this paper, a database compiled from 316 large direct shear tests on soil-GDL interfaces has been established. Based on this database, five different machine learning models: Back Propagation Artificial Neural Network (BPANN) and Support Vector Machine (SVM), with hyperparameters optimised by Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA), respectively, and Extreme Learning Machine (ELM) optimised by Exhaustive Method, were adopt to assess the peak shear strength of soil-GDL interfaces. Then, a comprehensive investigation and comparison of the predictive performance for the models was conducted. Also, based on the selected optimal machine learning model, sensitivity analysis was conducted, and an empirical equation developed based on it. The research indicated that GA and PSO could significantly increase forecasting precision in a small number of iterations. The BPANN model optimised by PSO has the highest forecasting precision based on the statistics criteria: Root-Mean-Square Error, Correlation Coefficient, Coefficient of Determination, Wilmot’s Index of Agreement, and Mean Absolute Percentage Error. The normal stress has the biggest impact on the peak shear strength, followed by drainage core type, moisture saturation of the soil layer, shearing surface, soil type, consolidation condition, geotextile specification, soil density and drainage core thickness, and the ranking is affected partly by the data distribution of input parameters in the database based on mechanism analysis. An empirical equation developed from the optimal model was proposed to estimate the peak shear strength, which provides convenience for geotechnical engineering personnel with limited knowledge of machine learning technique

Identification by PCR signature-tagged mutagenesis of attenuated Salmonella Pullorum mutants and corresponding genes in a chicken embryo model

A key feature of the fowl-specific pathogen Salmonella Pullorum is its vertical transmission to progeny via the egg. In this study, PCR signature-tagged mutagenesis identified nine genes of a strain of S. Pullorum that contributed to survival in the chicken embryo during incubation. The genes were involved in invasion, cell division, metabolism and bacterial defence. The competition index in vivo and in vitro together with a virulence evaluation for chicken embryos of all nine mutant strains confirmed their attenuation

A parametric study of 3D printed polymer gears

The selection of printing parameters for 3D printing can dramatically affect the dynamic performance of components such as polymer spur gears. In this paper, the performance of 3D printed gears has been optimised using a machine learning process. A genetic algorithm (GA)–based artificial neural network (ANN) multi-parameter regression model was created. There were four print parameters considered in 3D printing process, i.e. printing temperature, printing speed, printing bed temperature and infill percentage. The parameter setting was generated by the Sobol sequence. Moreover, sensitivity analysis was carried out in this paper, and leave-one cross validation was applied to the genetic algorithm-based ANN which showed a relatively accurate performance in predictions and performance optimisation of 3D printed gears. Wear performance of 3D printed gears increased by 3 times after optimised parameter setting was applied during their manufacture

Photoacoustic Identification of Laser-induced Microbubbles as Light Scattering Centers for Optical Limiting in Liquid Suspension of Graphene Nanosheets

Liquid suspensions of carbon nanotubes, graphene and transition metal dichalcogenides have exhibited excellent performance in optical limiting. However, the underlying mechanism has remained elusive and is generally ascribed to their superior nonlinear optical properties such as nonlinear absorption or nonlinear scattering. Using graphene as an example, we show that photo-thermal microbubbles are responsible for the optical limiting as strong light scattering centers: graphene sheets absorb incident light and become heated up above the boiling point of water, resulting in vapor and microbubble generation. This conclusion is based on direct observation of bubbles above the laser beam as well as a strong correlation between laser-induced ultrasound and optical limiting. In-situ Raman scattering of graphene further confirms that the temperature of graphene under laser pulses rises above the boiling point of water but still remains too low to vaporize graphene and create graphene plasma bubbles. Photo-thermal bubble scattering is not a nonlinear optical process and requires very low laser intensity. This understanding helps us to design more efficient optical limiting materials and understand the intrinsic nonlinear optical properties of nanomaterials

Screening for natural and derived bio-active compounds in preclinical and clinical studies: one of the frontlines of fighting the coronaviruses pandemic

Background: Starting December 2019, mankind faced an unprecedented enemy, the COVID-19 virus. The world convened in international efforts, experiences and technologies in order to fight the emerging pandemic. Isolation, hygiene measure, diagnosis, and treatment are the most efficient ways of prevention and intervention nowadays. The health organizations and global care systems screened the available resources and offered recommendations of approved and proposed medications. However, the search for a specific selective therapy or vaccine against COVID-19 remains a challenge. Methods: A literature search was performed for the screening of natural and derived bio-active compounds which showed potent antiviral activity against coronaviruses using published articles, patents, clinical trials website (https://clinicaltrials.gov/) and web databases (PubMed, SCI Finder, Science Direct, and Google Scholar). Results: Through the screening for natural products with antiviral activities against different types of the human coronavirus, extracts of Lycoris radiata (L’H´er.), Gentiana scabra Bunge, Dioscorea batatas Decne., Cassia tora L., Taxillus chinensis (DC.), Cibotium barometz L. and Echinacea purpurea L. showed a promising effect against SARS-CoV. Out of the listed compound Lycorine, emetine dihydrochloride hydrate, pristimerin, harmine, conessine, berbamine, 4`-hydroxychalcone, papaverine, mycophenolic acid, mycophenolate mofetil, monensin sodium, cycloheximide, oligomycin and valinomycin show potent activity against human coronaviruses. Additionally, it is worth noting that some compounds have already moved into clinical trials for their activity against COVID-19 including fingolimod, methylprednisolone, chloroquine, tetrandrine and tocilizumab. Conclusion: Natural compounds and their derivatives could be used for developing potent therapeutics with significant activity against SARS-COV-2, providing a promising frontline in the fighting against COVID-19

Rime length, stress, and association domains

Every regular Chinese syllable has a syllable tone (the tone we get when the syllable is read in isolation). In some Chinese languages, the tonal pattern of a multisyllabic expression is basically a concatenation of the syllable tones. In other Chinese languages, the tonal pattern of a multisyllabic expression is determined solely by the initial syllable. I call the former M -languages (represented by Mandarin) and the latter S -languages (represented by Shanghai). I argue that there is an additional difference in rime structures between the two language groups. In S-languages, all rimes are simple, i.e., there are no underlying diphthongs or codas. In M-languages, all regular rimes are heavy. I further argue that a syllable keeps its underlying tones only if it has stress. Independent metrical evidence tells us that heavy rimes may carry inherent stress. Thus, in M-languages, all regular syllables are stressed and retain their underlying tones (which may or may not undergo further changes). In contrast, in S-languages, regular rimes do not carry inherent stress; instead, only those syllables that are assigned stress by rule can keep their underlying tones and hence head a multisyllabic tonal domain.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42998/1/10831_2005_Article_BF01440582.pd