Search

The blending degree between virgin asphalt and aged asphalt has a significant effect on road performance of reclaimed asphalt mixture. This study presented an innovative examination of blending behavior between virgin asphalt and aged asphalt incorporating a new bio-based warm-mix rejuvenator (BWR) by utilizing Atomic Force Microscopy (AFM). Through analyzing the variation of several micro-morphology parameters between virgin asphalt and aged asphalt (or recycled asphalt) after blending, an index of regenerative blending degree (RBD) was proposed to quantitatively evaluate their blending behavior, and the effect of various blending temperatures and durations on regenerative blending degree was investigated. The results show that the regenerative blending degree between virgin asphalt and aged asphalt was higher than that between virgin asphalt and recycled asphalt under the same blending condition. A clear linear correlation was observed between the regenerative blending degree calculated by 3D micro-morphology parameters and the dosage of bio-based warm-mix rejuvenator in recycled asphalt, with a correlation coefficient of 0.98. With the increase in blending duration, the regenerative blending degree between virgin asphalt and recycled asphalt increased first and then decreased, but continued to improve with the increase in blending temperature, which indicates that a higher blending temperature and prolonging the blending duration properly have a positive effect on the blending processing between virgin asphalt and recycled asphalt. Compared with the regenerative blending degree calculated by 2D micro-morphology parameters, the regenerative blending degree calculated by 3D micro-morphology parameters is more reasonable to quantify the blending behavior between virgin asphalt and recycled asphalt. [ABSTRACT FROM AUTHOR]

The use of warm-mix recycling technology can reduce the mixing temperature and the secondary aging of binders in reclaimed asphalt pavement (RAP), which is one of the effective ways to recycle high-content RAP. In this study, the penetration, softening point, ductility, and viscosity were used to characterize the conventional physical properties of aged asphalt after regenerating, while a dynamic shear rheometer (DSR), force ductility tester (FDT), and atomic force microscope (AFM) were used to evaluate the rheological performance and micro-morphology of aged asphalt incorporating a new bio-based warm-mix rejuvenator (BWR) and a commercial warm-mix rejuvenator (ZJ-WR). The regeneration mechanism of warm-mix rejuvenators on aged asphalt was analyzed by Fourier transform infrared spectroscopy (FTIR). The results show that the new bio-based warm-mix rejuvenator can restore the conventional physical properties, low-temperature performance, and micro-morphology of aged asphalt with an appropriate dosage, but it has a negative effect on high-temperature performance. In comparison with 2D area parameters, 3D roughness parameters were more accurate in evaluating the variation in micro-morphology of aged asphalt after regeneration. The FTIR analysis results indicate that both the new bio-based warm-mix rejuvenator and the commercial warm-mix rejuvenator regenerate aged asphalt by physical action, and AS=O and AC-H values are more reasonable than the AC=O value for the restoration evaluation of aged asphalt. And the new bio-based warm-mix rejuvenator has a better regeneration effect on the performance and micro-morphology of aged asphalt than the commercial warm-mix rejuvenator. [ABSTRACT FROM AUTHOR]

The aim of this article is to explore the dynamic compaction characteristics of stone mastic asphalt (SMA) and the friction–lubrication effect of internal particles during the superpave gyratory compaction (SGC) process. Firstly, a calculated method for the compaction degree of an asphalt mixture in the gyratory compaction process was defined based on the multiphase granular volume method. Secondly, the gyratory compaction curves of asphalt mixtures were taken based on this calculation method of compaction degree. The dynamic change law of each compaction index (compaction, percentage of air voids, compaction energy index, etc.) during the compaction process was analysed. Finally, the effects of different initial compaction temperatures and different asphalt content on the friction–lubrication effect and compaction characteristics of asphalt mixtures were studied. Research shows that it is reasonable to define the compaction degree by the ratio of the apparent density of the asphalt mixture to the maximum theoretical density of the asphalt mixture during gyratory compaction. The dynamic prediction equations of the compaction degree K and the compaction energy index CEI with the amount of compaction were established, and could effectively predict the compaction degree, percentage of air voids and compaction energy index CEI. The compaction process of the asphalt mixture needed to go through three phases, including periods of rapid growth, slow growth, and stabilisation, and the compaction degree increased by about 10%, 5%, and 1%, in that order, finally tending towards a stable value. The effect of the initial compaction temperature on the forming compaction degree of the asphalt mixture is significant; therefore, it should be controlled strictly in the compaction construction of asphalt mixtures. When the initial compaction temperature of SMA-13 is about 170 °C, the compaction effect is optimal, and the effect of the increase in the amount of compaction at a later stage on the increase in the compaction degree of the asphalt mixture is very low. With the optimal asphalt content, the friction–lubrication effect between the asphalt and aggregate particles is optimal, because it can effectively form an asphalt film, reducing the frictional resistance of the particles moving each other during the compaction process, and the voids will be embedded and filled with each other, finally producing the best compaction result. [ABSTRACT FROM AUTHOR]

Copyright of Multipurpose Utilization of Mineral Resources / Kuangchan Zonghe Liyong is the property of Multipurpose Utilization of Mineral Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rapid advances in technology have led to an increasing demand for this type of information in the field of cultural heritage and architectural conservation. The article aims to use digital technology to obtain, record, store, and display accurate and intuitive information about architectural heritage for daily management and repair of that heritage. This article conducts a comprehensive and in-depth study of the pathology information from the James Jackson Gymnasium, a typical case of the modern educational architectural heritage of Wuhan. Research contents include obtaining point cloud data through 3D scanning, constructing Building Information Modeling (BIM) 3D models and pathological information models to visualize the affected parts, using the Monument Damage Diagnostic System (MDDS) to establish a pathological information map management system, using virtual simulation technology and digital repair technology to diagnose and repair the affected parts, and establishing a systematic architectural pathology information database to explore the causes of architectural pathology from a multidisciplinary perspective. The authors aim to gradually promote this method and build a systematic pathological information database of architectural heritage. This utilizes the sharing characteristics of information technology to transcend the barriers of time and space and provide important support for heritage protection and pathology management. The Architectural Pathology Information Management System is proposed as an innovative solution to promote the sustainable conservation of architectural heritage through digital technology. [ABSTRACT FROM AUTHOR]

High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science. [ABSTRACT FROM AUTHOR]

In order to study the problems of inadequate maintenance measures, inappropriate maintenance time, and unreasonable use of funds in asphalt pavement maintenance of Highway in China, the maintenance of highway pavement is taken as the research object in this study, and a prediction model is established for preventive maintenance performance of highway by using neural network. Firstly, the performance of pavement is evaluated. The pavement performance prediction model is studied, and some mature prediction models are introduced. It is concluded that for the early built highways, the models are used when the acceptance of maintenance and preventive maintenance concepts is poor and the pavement performance shows a decreasing trend, but for the existing maintenance and preventive maintenance sections, the pavement performance detection shows a wave. The dynamic descent section is not suitable. The results show that the forecasting model proposed in this study is consistent with the development trend of the measured results, and can be used to predict the pavement performance under this model. Therefore, a theoretical basis is provided for the investment of highway maintenance funds and the scientific selection of maintenance schemes. The study has important guiding significance for the future highway management units in the selection of maintenance measures, the determination of maintenance timing, and the size of capital investment. [ABSTRACT FROM AUTHOR]

High entropy alloys (HEAs) and compositionally complex alloys (CCAs) have recently attracted great research interest because of their remarkable mechanical and physical properties. Although many useful HEAs or CCAs were reported, the rules of phase design, if there are any, which could guide alloy screening are still an open issue. In this work, we made a critical appraisal of the existing design rules commonly used by the academic community with different machine learning (ML) algorithms. Based on the artificial neural network algorithm, we were able to derive and extract a sensitivity matrix from the ML modeling, which enabled the quantitative assessment of how to tune a design parameter for the formation of a certain phase, such as solid solution, intermetallic, or amorphous phase. Furthermore, we explored the use of an extended set of new design parameters, which had not been considered before, for phase design in HEAs or CCAs with the ML modeling. To verify our ML-guided design rule, we performed various experiments and designed a series of alloys out of the Fe-Cr-Ni-Zr-Cu system. The outcomes of our experiments agree reasonably well with our predictions, which suggests that the ML-based techniques could be a useful tool in the future design of HEAs or CCAs. [ABSTRACT FROM AUTHOR]