Development of a Full-Depth Wheel Tracking Test for Asphalt Pavement Structure: Methods and Performance Evaluation

The rutting performance of asphalt pavement structure relies on the high temperature properties of asphalt mixture as well as the pavement structure and thickness. In order to investigate the influence of the structure and thickness, a full-depth wheel tracking test is developed in this research by improving the conventional wheel tracking test apparatus. The newly proposed test method is capable of varying its load speed and load size, controlling its specimen temperature gradient, and simulating the support conditions of actual asphalt pavement. The full-depth wheel tracking test based rutting performance evaluation of different asphalt pavement structures indicates that it is not reasonable to explain the rutting performance of asphalt pavement structure from the point of view of single-layer asphalt mixture rutting performance. The developed full-depth wheel tracking test can be used to distinguish rutting performance of different asphalt pavement structures, and two of five typical asphalt pavement structures commonly used in Shanxi Province were suggested for use in practical engineering

Molecular Dynamics Simulation to Investigate the Interaction of Asphaltene and Oxide in Aggregate

The asphalt-aggregate interface interaction (AAI) plays a significant role in the overall performances of asphalt mixture, which is caused due to the complicated physicochemical processes and is influenced by various factors, including the acid-base property of aggregates. In order to analyze the effects of the chemical constitution of aggregate on the AAI, the average structure C65H74N2S2 is selected to represent the asphaltene in asphalt and magnesium oxide (MgO), calcium oxide (CaO), aluminium sesquioxide (Al2O3), and silicon dioxide (SiO2) are selected to represent the major oxides in aggregate. The molecular models are established for asphaltene and the four oxides, respectively, and the molecular dynamics (MD) simulation was conducted for the four kinds of asphaltene-oxide system at different temperatures. The interfacial energy in MD simulation is calculated to evaluate the AAI, and higher value means better interaction. The results show that interfacial energy between asphaltene and oxide reaches the maximum value at 25°C and 80°C and the minimum value at 40°C. In addition, the interfacial energy between asphaltene and MgO was found to be the greatest, followed by CaO, Al2O3, and SiO2, which demonstrates that the AAI between asphalt and alkaline aggregates is better than acidic aggregates

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Highly Self-Adaptive Path-Planning Method for Unmanned Ground Vehicle Based on Transformer Encoder Feature Extraction and Incremental Reinforcement Learning

Path planning is an indispensable component in guiding unmanned ground vehicles (UGVs) from their initial positions to designated destinations, aiming to determine trajectories that are either optimal or near-optimal. While conventional path-planning techniques have been employed for this purpose, planners utilizing reinforcement learning (RL) exhibit superior adaptability within exceedingly complex and dynamic environments. Nevertheless, existing RL-based path planners encounter several shortcomings, notably, redundant map representations, inadequate feature extraction, and limited adaptiveness across diverse environments. In response to these challenges, this paper proposes an innovative and highly self-adaptive path-planning approach based on Transformer encoder feature extraction coupled with incremental reinforcement learning (IRL). Initially, an autoencoder is utilized to compress redundant map representations, providing the planner with sufficient environmental data while minimizing dimensional complexity. Subsequently, the Transformer encoder, renowned for its capacity to analyze global long-range dependencies, is employed to capture intricate correlations among UGV statuses at continuous intervals. Finally, IRL is harnessed to enhance the path planner’s generalization capabilities, particularly when the trained agent is deployed in environments distinct from its training counterparts. Our empirical findings demonstrate that the proposed method outperforms traditional uniform-sampling-based approaches in terms of execution time, path length, and trajectory smoothness. Furthermore, it exhibits a fivefold increase in adaptivity compared to conventional transfer-learning-based fine-tuning methodologies

Multiscale Validation of the Applicability of Micromechanical Models for Asphalt Mixture

Asphalt mixture is more complicated than other composite materials in terms of the higher volume fraction of aggregate particles and the viscoelastic property of asphalt matrix, which obviously affect the applicabilities of the micromechanical models. The applicabilities of five micromechanical models were validated based on the shear modulus of the multiscale asphalt materials in this paper, including the asphalt mastic, mortar, and mixture scales. It is found that all of the five models are applicable for the mastic scale, but the prediction accuracies for mortar and mixture scales are poorer. For the mixture scale, all models tend to overestimate at the intermediate frequencies but show good agreement at low and high frequencies except for the Self-Consistent (SC) model. The Three-Phase Sphere (TPS) model is relatively better than others for the mortar scale. The applicability of all the existing micromechanical models is challenged due to the high particle volume fraction in the multiscale asphalt materials as well as the modulus mismatch between particles and matrix, especially at the lower frequencies (or higher temperatures). The particle interaction contributes more to the stiffening effect within higher fraction than 30%, and the prediction accuracy is then deteriorated. The higher the frequency (or the lower the temperature) is, the better the model applicability will be

Evaluation of Geometric Characteristics of Fine Aggregate and Its Impact on Viscoelastic Property of Asphalt Mortar

It has long been recognized that fine aggregate (FA) plays a crucial role in the performance of asphalt mixture, especially for the viscoelastic behavior. In this research, 13 types of FA (1 natural sand, 5 stone chips, and 7 machine-made sands) were selected for investigation. Three indirect indicators (uncompact void content test, flow time test, and standard test method for index of aggregate particle shape and texture ASTM D3398) and three types of direct indicators (form, angularity, and texture) were employed to evaluate the geometric characteristics of FA and conduct comprehensive studies on the indicator system. Meanwhile, the effects of FA geometrical properties on the viscoelastic behavior of asphalt mortar were investigated. The results show that only the form indicator ratio of equivalent ellipse axis (E) and angularity indicator surface parameter (SP) can effectively distinguish different types of fine aggregates. The correlation analysis reveals that the parameters of the four elements in the Burgers model are negatively related to the form index (E) but positively related to the angularity index (SP), while the parameter retardation time ( τ r ) exhibits the opposite. This indicates that the use of less flat-elongated and more angular FA can increase both the overall stiffness and elastic component of asphalt mortar