Study of the Properties and Modification Mechanism of SBS-Modified Asphalt by Dry Process.

SBS (styrene-butadiene-styrene block copolymer) is a thermoplastic elastomer with properties most similar to rubber. SBS asphalt modifier is mainly composed of a styrene-butadiene-styrene block copolymer with a certain amount of additives and stabilizers. SBS-modified asphalt binder has always been the most commonly used pavement material both domestically and internationally. However, conventional wet-process SBS-modified asphalt binder requires manufacturers to produce it in advance and transport it to a mixing plant for blending. This has provided an opportunity for unscrupulous businesses to reduce the amount of SBS by adding other substances, allowing inferior asphalt binder to pass inspections undetected. At the same time, conventional wet-process SBS-modified asphalt tends to undergo phase separation and experience a decline in performance as the storage time increases. However, dry-process SBS-modified asphalt can be directly added at the mixing plant, effectively addressing the issues associated with conventional wet-process SBS-modified asphalt. It also helps to reduce environmental pollution to a certain extent. This study investigates the extraction process of dry-process SBS-modified asphalt binder. It clarifies the performance and modification mechanisms of two types of dry-process SBS-modified asphalt binder at different dosages through various testing methods, including basic indicators, rheological properties, infrared spectroscopy, and fluorescence microscopy. The results indicate that due to the incorporation of oil, crosslinker, solubilizer, and other substances into dry-process SBS modifier, there is a small amount of chemical reaction with asphalt in the melting process. The high- and low-temperature properties and fatigue properties of the two dry-process SBS-modified asphalt binders at a 7% dosage are close to wet SBS-modified asphalt binder at a 5% dosage.

Evaluation of electromagnetic scattering characteristics of construction solid waste-A theoretical study of solid waste identification.

The surge in urban development has resulted in a substantial accumulation of construction solid waste (CSW). However, prevailing identification methods of CSW remain predominantly two-dimensional in scope and need to be more efficient. This study employs an approach, combining simulation and experimental analyses, to delve into the factors influencing the electromagnetic scattering characteristics of CSW, investigating the feasibility of employing Synthetic Aperture Radar (SAR) to recognize CSW. The findings show that the computational time of MLFMM and PO is only 3.28 % and 0.029 % of MM among different simulation methods. The results underscore the collective impact of material types, surface structures, and curvature on the scattering characteristics of CSW. The difference in average intensity between different materials can reach up to 13 dB. Exploiting these distinctions in scattering enables the precise identification of high-value waste components, such as intact bricks and steel bars, within the intricate landscape of CSW.

Multi-Task Segmentation and Classification Network for Artery/Vein Classification in Retina Fundus.

Automatic classification of arteries and veins (A/V) in fundus images has gained considerable attention from researchers due to its potential to detect vascular abnormalities and facilitate the diagnosis of some systemic diseases. However, the variability in vessel structures and the marginal distinction between arteries and veins poses challenges to accurate A/V classification. This paper proposes a novel Multi-task Segmentation and Classification Network (MSC-Net) that utilizes the vessel features extracted by a specific module to improve A/V classification and alleviate the aforementioned limitations. The proposed method introduces three modules to enhance the performance of A/V classification: a Multi-scale Vessel Extraction (MVE) module, which distinguishes between vessel pixels and background using semantics of vessels, a Multi-structure A/V Extraction (MAE) module that classifies arteries and veins by combining the original image with the vessel features produced by the MVE module, and a Multi-source Feature Integration (MFI) module that merges the outputs from the former two modules to obtain the final A/V classification results. Extensive empirical experiments verify the high performance of the proposed MSC-Net for retinal A/V classification over state-of-the-art methods on several public datasets.

An Extension Network of Dendritic Neurons.

Deep learning (DL) has achieved breakthrough successes in various tasks, owing to its layer-by-layer information processing and sufficient model complexity. However, DL suffers from the issues of both redundant model complexity and low interpretability, which are mainly because of its oversimplified basic McCulloch-Pitts neuron unit. A widely recognized biologically plausible dendritic neuron model (DNM) has demonstrated its effectiveness in alleviating the aforementioned issues, but it can only solve binary classification tasks, which significantly limits its applicability. In this study, a novel extended network based on the dendritic structure is innovatively proposed, thereby enabling it to solve multiple-class classification problems. Also, for the first time, an efficient error-back-propagation learning algorithm is derived. In the extensive experimental results, the effectiveness and superiority of the proposed method in comparison with other nine state-of-the-art classifiers on ten datasets are demonstrated, including a real-world quality of web service application. The experimental results suggest that the proposed learning algorithm is competent and reliable in terms of classification performance and stability and has a notable advantage in small-scale disequilibrium data. Additionally, aspects of network structure constrained by scale are examined.

Particle Swarm Optimization-Based Approach for Optic Disc Segmentation.

Fundus segmentation is an important step in the diagnosis of ophthalmic diseases, especially glaucoma. A modified particle swarm optimization algorithm for optic disc segmentation is proposed, considering the fact that the current public fundus datasets do not have enough images and are unevenly distributed. The particle swarm optimization algorithm has been proved to be a good tool to deal with various extreme value problems, which requires little data and does not require pre-training. In this paper, the segmentation problem is converted to a set of extreme value problems. The scheme performs data preprocessing based on the features of the fundus map, reduces noise on the picture, and simplifies the search space for particles. The search space is divided into multiple sub-search spaces according to the number of subgroups, and the particles inside the subgroups search for the optimal solution in their respective sub-search spaces. The gradient values are used to calculate the fitness of particles and contours. The entire group is divided into some subgroups. Every particle flies in their exploration for the best solution. During the iteration, particles are not only influenced by local and global optimal solutions but also additionally attracted by particles between adjacent subgroups. By collaboration and information sharing, the particles are capable of obtaining accurate disc segmentation. This method has been tested with the Drishti-GS and RIM-ONE V3 dataset. Compared to several state-of-the-art methods, the proposed method substantially improves the optic disc segmentation results on the tested datasets, which demonstrates the superiority of the proposed work.

Quantifying Hole-Edge Crack of Bolt Joints by Using an Embedding Triangle Eddy Current Sensing Film.

Hole-edge crack quantification of bolt joints is critical for monitoring and estimating structural integrity of aircraft. The paper proposes a new triangle eddy current sensor array for the purpose of increasing the level of quantifying hole-edge crack parameters, especially, the crack angle. The new senor array consists of triangular coils instead of planar rectangular coils. The configuration of the novel sensor array, including the excitation current directions and the excitation winding shape, is optimized by simulation. The ability of the proposed sensing film to identify the crack parameters has been verified by finite element simulations and experiments. Results shows that triangular coils with same current directions in circumferentially adjacent coils and opposite current directions in axially adjacent coils achieve better performance in sensor linearity and resolution compared to rectangular coils. In addition, it has also been proved that the sensing film has a good potential to identify the crack depth and length.

Improving Flame Retardancy of Epoxy Resin Nanocomposites by Carbon Nanotubes Grafted CuAl-Layered Double Hydroxide Hybrid.

To solve the issues of the poor dispersion performance of the inorganic flame retardant filler in epoxy resin (EP) matrix, the three-dimensional (3D) hybrid carbon nanotubes-copper aluminum (sodium dodecyl sulfate)-layered double hydroxide (CNTs-OLDH) is designed and synthesized by co-precipitation. The results indicate that the CNTs-OLDH hybrid with 3D-structure is successfully fabricated and well dispersed in EP matrix. The thermostability of EP/CNTs-OLDH nanocomposites is raised and the residue is obviously increased. When the amount of CNTs-OLDH is only 4 wt%, limit oxygen index value of EP/CNTs-OLDH nanocomposites reaches 28.5. Compared with pure EP, the heat release, smoke and gas of EP/FePP nanocomposites are inhibited by CNTs-OLDH hybrid, and the PHRR, THR and SPR values of EP/4CNTs-OLDH nanocomposites decrease by 41.7%, 27.8% and 31.7%. The improved fire retardant performances and thermal stability are attributed to the excellent homogeneous dispersion, the network structure formed by the 3D hybrid in the matrix and the outstanding flame retardant effect of CNTs and OLDH.

Reasonable thickness design of expressway pavement structures based on gray relation analysis of subgrade soil improvement.

During the design of pavement structures, determining a reasonable thickness for pavement layers is critical. When an expressway is to be built in an area with a poor soil foundation, a reasonable subgrade treatment will make the pavement more durable. However, determining the optimal thickness of subgrade treatment is a difficult task for designers. A thicker treatment means a huge cost increase for the project, whereas a thinner treatment cannot achieve significant improvement in the mechanical behavior of pavement structures. This study used the finite-element method to analyze the mechanical response of real field pavement with subgrade treatments at certain depths. The study used an orthogonal design and gray relational theory to analyze the design indicators and make a better design for the pavement structure of a field expressway. The numerical calculation index and theoretical analysis results show that the treatment depth of subgrade soil has a significant influence on the stresses in an asphalt pavement structure and the bottom tensile strains of the asphalt layers. Therefore, designing a pavement structure with equal structural strength, using a reasonable depth for the cement-treated subgrade, instead of increasing the asphalt layer's thickness, is a more cost-effective solution.

Identification and Compensation Technique of Non-Uniform Temperature Field for Lamb Wave-and Multiple Sensors-Based Damage Detection.

Lamb wave-based damage detection for large-scale composites is one of the most prosperous structural health monitoring technologies for aircraft structures. However, the temperature has a significant effect on the amplitude and phase of the Lamb wave signal so that temperature compensation is always the focus problem. Especially, it is difficult to identify the damage in the aircraft structures when the temperature is not uniform. In this paper, a compensation method for Lamb wave-based damage detection within a non-uniform temperature field is proposed. Hilbert transform and Levenberg-Marquardt optimization algorithm are developed to extract the amplitude and phase variation caused by the change of temperature, which is used to establish a data-driven model for reconstructing the reference signal at a certain temperature. In the temperature compensation process, the current Lamb wave signal of each exciting-sensing path under the estimated structural condition is substituted into the data-driven model to identify an interpolated initial temperature field, which is further processed by an outlier removing algorithm to eliminate the effect of damage and get the actual non-uniform temperature field. Temperature compensation can be achieved by reconstructing the reference signals within the identified non-uniform temperature field, which are used to compare with the current acquired signals for damage imaging. Both simulation and experiment were conducted to verify the feasibility and effectiveness of the proposed non-uniform temperature field identification and compensation technique for Lamb wave-based structural health monitoring.

Analysis of Adhesive Characteristics of Asphalt Based on Atomic Force Microscopy and Molecular Dynamics Simulation.

Asphalt binder is a very important building material in infrastructure construction; it is commonly mixed with mineral aggregate and used to produce asphalt concrete. Owing to the large differences in physical and chemical properties between asphalt and aggregate, adhesive bonds play an important role in determining the performance of asphalt concrete. Although many types of adhesive bonding mechanisms have been proposed to explain the interaction forces between asphalt binder and mineral aggregate, few have been confirmed and characterized. In comparison with chemical interactions, physical adsorption has been considered to play a more important role in adhesive bonding between asphalt and mineral aggregate. In this study, the silicon tip of an atomic force microscope was used to represent silicate minerals in aggregate, and a nanoscale analysis of the characteristics of adhesive bonding between asphalt binder and the silicon tip was conducted via an atomic force microscopy (AFM) test and molecular dynamics (MD) simulations. The results of the measurements and simulations could help in better understanding of the bonding and debonding procedures in asphalt-aggregate mixtures during hot mixing and under traffic loading. MD simulations on a single molecule of a component of asphalt and monocrystalline silicon demonstrate that molecules with a higher atomic density and planar structure, such as three types of asphaltene molecules, can provide greater adhesive strength. However, regarding the real components of asphalt binder, both the MD simulations and AFM test indicate that the colloidal structural behavior of asphalt also has a large influence on the adhesion behavior between asphalt and silicon. A schematic model of the interaction between asphalt and silicon is presented, which can explain the effect of aging on the adhesion behavior of asphalt.

Modelling and laboratory studies on the adhesion fatigue performance for thin-film asphalt and aggregate system.

Adhesion between asphalt and aggregate plays an important role in the performance of asphalt mixtures. A low-frequency adhesion fatigue test was proposed in this paper to study the effect of environment on the asphalt-aggregate adhesion system. The stress-based fatigue model had been utilized to describe the fatigue behavior of thin-film asphalt and aggregate system. The factors influencing the adhesion fatigue performance were also investigated. Experiment results show that asphalt has more important effect on the adhesion performance comparing with aggregate. Basalt, which is regarded as hydrophobic aggregates with low silica content, has better adhesion performance to asphalt binder when compared with granite. The effects of aging on the adhesion fatigue performance are different for PG64-22 and rubber asphalt. Long-term aging is found to reduce the adhesion fatigue lives for rubber asphalt and aggregate system, while the effect of long-term aging for aggregate and PG64-22 binder system is positive. Generally the increased stress amplitude and test temperature could induce greater damage and lead to less fatigue lives for adhesion test system.