Search

Scientists and engineers are consistently working to enhance the performance attributes of asphalt concrete mixtures. Pavement condition monitors, despite their high cost and long-term inaccuracies, are utilized for assessing pavement conditions and determining the level of deterioration. Consequently, they provide crucial data for the design, cost estimations, and development of pavement maintenance programmers. In recent times, there have been significant technological advancements and the introduction of new characteristics, such as the self-sensing feature. This study utilized this feature to build hot asphalt mixtures with several functionalities. The incorporation of conductive elements into asphalt mixtures enhances their electrical and mechanical characteristics. The objective of this study is to develop a hot asphalt mixture with electrical conductivity capable of detecting applied loads through conducting experimental tests such as the Marshall and compression tests. The asphalt grade used was between 40 and 50, and the aggregates were in proportions that met the Iraqi requirement. The asphalt mixture contained 2.5% steel fibers by volume, which were added to investigate their impact on the functional performance of the asphalt mixtures. An analysis was conducted on the samples’ behavior during the tests, revealing a discernible alteration in the electrical resistance measurement. This alteration demonstrated that the asphalt mixture detected the weights exerted upon it. The findings also indicated a rise in the Marshall stability metric. The advanced asphalt mixtures and their novel features allow for the monitoring of pavement conditions. Through the resolution of monitoring device issues, they additionally offer superior performance and extended lifespan.

This study aimed to analyze the influence of different methodologies to determine the maximum specific gravity on the Superpave mix design method. The comparative analysis focused on the volumetric parameters, on the choice of the design particle size and, consequently, on the respective design asphalt binder contents of the studied asphalt mixtures. Regarding the particularities of the research, there were no significant differences in the design binder contents obtained using the two adopted methodologies for determining the maximum specific gravity. From a practical point of view, the adoption of any of the methods interfered little with the results of the application of the Superpave mix design method. This fact demonstrates a quality of this method since similar researches based on the Marshall design method evidences the sensitivity of the protocol for determining the asphalt binder content in relation to the used type of maximum specific gravity.

In the recent decades, different kinds of fiber materials are used for improving the asphalt mixture performance. Meanwhile, different kinds of fiber are used excessively due to their desirable physical and chemical properties and their easier application. The main purpose of this research is to evaluate the characteristics of the asphalt mixture while using basalt fiber and glass fiber. In order to provide asphalt samples, these two types of fibers are used in different percentages. In this way, 42 samples (with different percentages of fiber and bitumen) were made using Marshal Hammer. In the next step, while constructing 63 asphalt samples using a gyratory device, then mix asphalt conventional tests include the determination of indirect tensile strength, moisture sensitivity test, and resilient modulus and creep tests performed. The results of this research indicate that using these two types of fibers increased the percentage of optimum bitumen and marshal resistance. At best, adding 0.1% glass fiber resulted in 13% increase in marshal resistance. Finally, ANFIS-GUI was used to estimate the experimental result and the feasibility of employing neural fuzzy network to predict the laboratory data have been evaluated

Overburdens are stones that lack the required properties of minerals to be extracted and are piled adjacent to mine, while they may be used as aggregates in the asphalt mixture. Set-tlement is a cheap mineral composed of iron oxide, silicone and aluminum oxide, application of iron, silica and aluminum as aggregates in pavement industry has great effect on per-formance of asphalt mixtures. In this study, we investigated the effect of using Sillimanite aggregate material as an alter-native for Limestone aggregates on resilient modulus, Mar-shall Strength and skid resistance parameters at various tem-peratures. The modeling was carried out by using finite ele-ment software based on the elastic behavior of the layers to determine the effect of elasticity modulus variations due to the change in aggregate type on the pavement response. The re-sults indicated that the use of Sillimanite aggregates in the asphalt mixture led to a lower optimum bitumen percent than Limestone material; and also, in the asphalt mixture made of Sillimanite aggregates with optimum bitumen percent, Mar-shall Strength was increased by 15% and the resilient modulus by 25% compared to the mixture with Limestone material. Application of Sillimanite material in the asphalt mixture could enhance the friction resistance of the mixture against the abrasion resulting from traffic loading due to its higher hardness compared to Limestone material. The results of nu-merical analysis showed that the aggregate’s type had the lowest effect on the critical tensile strain at the bottom of the asphalt layer, then came the percentage of bitumen in the mix-ture and, finally, the bonding of the layers had the highest ef-fect.

Copyright of Dyna is the property of Universidad Nacional de Colombia, Medellin, Facultad de Minas 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.)

In the recent decades, different kinds of fiber materials are used for improving the asphalt mixture performance. Meanwhile, different kinds of fiber are used excessively due to their desirable physical and chemical properties and their easier application. The main purpose of this research is to evaluate the characteristics of the asphalt mixture while using basalt fiber and glass fiber. In order to provide asphalt samples, these two types of fibers are used in different percentages. In this way, 42 samples (with different percentages of fiber and bitumen) were made using Marshal Hammer. In the next step, while constructing 63 asphalt samples using a gyratory device, then mix asphalt conventional tests include the determination of indirect tensile strength, moisture sensitivity test, and resilient modulus and creep tests performed. The results of this research indicate that using these two types of fibers increased the percentage of optimum bitumen and marshal resistance. At best, adding 0.1% glass fiber resulted in 13% increase in marshal resistance. Finally, ANFIS-GUI was used to estimate the experimental result and the feasibility of employing neural fuzzy network to predict the laboratory data have been evaluated. [ABSTRACT FROM AUTHOR]

El artículo presenta el cambio que experimenta el módulo resiliente, la deformación permanente y la resistencia a fatiga de una mezcla de concreto asfáltico cuando es expuesta a las condiciones climáticas de la ciudad de Bogotá D.C. (Colombia) durante 47-48 meses. Las mezclas fueron fabricadas utilizando dos cementos asfálticos (CA) fabricados en Colombia: CA 80-100 y CA 60-70. La tendencia general de las mezclas con el tiempo de exposición es experimentar un aumento en los valores de rigidez debido principalmente a procesos de endurecimiento por envejecimiento del ligante asfáltico. El aumento en rigidez genera incremento en la resistencia al fenómeno de ahuellamiento y en la vida de fatiga de la mezcla analizada cuando ésta sea utilizada para conformar capas asfálticas gruesas en el pavimento. Con base en la evolución del módulo resiliente con el tiempo de exposición al medio ambiente se propone una ecuación para predecir de manera aproximada el cambio que experimenta la vida a fatiga de la mezcla.The present paper introduces the evolution of resilient modulus, permanent strain and fatigue life of an asphaltic concrete mixture exposed under weather conditions in Bogota D.C. (Colombia) during 47-48 months. Mixtures were elaborated by using two asphaltic cements (CA) made in Colombia: CA 80100 and CA 60-70. Mixtures generally tend to increase their stiffness values after an exposure period, mainly due to hardening and ageing processes undergone by asphaltic binder. A stiffness gain leads to increase endurance against rutting phenomenon and fatigue life of the analyzed mixture employed to lay thick asphaltic layers on the pavement. Based on the resilient modulus evolution due to exposure under environmental conditions, an equation is proposed to approximately predict the evolution of fatigue life endured by a mixture.

The DCNNs framework for the HMA dynamic modulus prediction • A deep convolution neural networks technique is newly adapted for E* prediction. • The first pre-trained DL model for pavement materials is constructed. • Laboratory effort reduction attained via transfer learning of pre-trained DL model. • The robustness of the methodology is statistically verified. Evaluating the hot mix asphalt (HMA) expected performance is one of the significant aspects of highways research. Dynamic modulus (E*) presents itself as a fundamental mechanistic property that is one of the primary inputs for mechanistic-empirical models for pavements design. Unfortunately, E* testing is an expensive and complicated task that requires advanced testing equipment. Moreover, a significant source of difficulty in E* modeling is that many of the factors of variation in the HMA mixture components and testing conditions significantly influence the predicted values. For each laboratory practice, a vast number of mixes are required to estimate the E* accurately. This study aims to extend the knowledge/practice of other laboratories to a target one in order to reduce the laboratory effort required for E* determination while attaining accurate E* prediction. Therefore, the transfer learning solution using deep learning (DL) technology is adopted for the problem. By transfer learning, instead of starting the learning process from scratch, previous learnings that have been gained when solving a similar problem is used. A deep convolution neural networks (DCNNs) technique, which incorporates a stack of six convolution blocks, is newly adapted for that purpose. Pre-trained DCNNs are constructed using a large data set that comes from different sources to constitute cumulative learning. The constructed pre-trained DCNNs aim to dramatically reduce the effort elsewhere (target lab) when it comes to the E* prediction problem. Then, a laboratory effort reduction justification is investigated through fine toning the constructed pre-trained DCNNs using a limited amount of the target lab data. The performance of the proposed DCNNs is evaluated using representative statistical performance indicators and compared with well-known predictive models (e.g., η- based Witczak 1-37A, G,δ -based Witczak 1-40D and G -based Hirsch models). The proposed methodology proves itself as an excellent tool for the E* prediction compared with the other models. Moreover, it could preserve its accurate performance with less data input using the transferred learning from the previous phase of the solution. [ABSTRACT FROM AUTHOR]