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Welcome to ResearchSpace, the institutional repository of the CSIR. ResearchSpace is an open access electronic archive collecting, preserving and distributing scholarly digital materials created by the CSIR.

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Evaluating the efficacy of laboratory ageing of asphalt mix binders as a prediction for field ageing

(2023-10) O'Connell, Johannes S; Maina, J; Bredenhann, SJ; Marais, H; Komba, J

A Performance-grade Binder Specification (SATS 3208) for South Africa was finalised after CAPSA 2015 and launched at CAPSA 2019. A key feature of the performance-graded binder specification is the regulation of binder performance after long-term ageing, which is simulated in the laboratory using the pressure ageing vessel (PAV). This paper reports how this simulated long-term ageing relates to the ageing of binders in continuously-graded asphalt surfacing mixes in the field. Samples of asphalt surfacing mixes were obtained from ten sites in Gauteng, South Africa, which were constructed 5 to 30 years ago and selected based on the availability of the original binders. An ageing profile was developed for the original binders by characterising their rheology in the original state, after rolling thin film oven (RTFO) ageing and pressure ageing vessel (PAV) ageing after 20 hours, 40 hours and 80 hours. The ageing profiles were compared to the corresponding recovered binders. Rheological parameters used for comparison were Softening Point and Flexural Creep Stiffness / m-Value from the Bending Beam Rheometer (BBR) test.

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Relating the rheology of recovered binders from asphalt surfacing in the field to their fatigue performance

(2024-10) O'Connell, Johannes S; Maina, J; Bredenhann, SJ; Marais, H; Komba, J

G*.Sind and TC are two of many rheological parameters that have been proposed as potential specification properties for control of fatigue performance for hot mix asphalt. This paper assesses the extent of correlation between the values of these parameters and the presence of cracking in the hot mix asphalt surfacing from 11 sites carefully selected to represent a wide range of fatigue performance over periods ranging from 5 to 20 years. The binders were recovered using a modified Abson recovery process that accurately represents the properties of the aged in-situ binder. Results indicate that TC correlates better with the condition of the asphalt surfacing, compared with G*.Sind. The results also demonstrate that although binder fatigue parameters may be an indicator of fatigue performance, the actual fatigue performance is also determined by other factors such as binder film thickness of the mix, traffic loading, climate and rate of ageing.

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Short term ageing of asphalt binder in thin asphalt layers

(2024-03) O’Connell, Johan; Maina, James; VdM Steyn, WJ

The effects of ageing on pavement performance are significant, particularly in terms of fatigue cracking. South Africa has the 10th longest road network in the world, requiring innovative approaches to road construction due to severe budget constraints. Innovative solutions such as thin asphalt concrete layers for surfacing, result in unique ageing rates of the layers, which, in general, have a higher incidence of fatigue cracking than, for example, thicker asphalt concrete layers used in other parts of the world. The objective of this paper is to evaluate how ageing mechanisms affect various asphalt binder properties, and whether they affect them to the same extent or not. Furthermore, the objective of the paper is also to determine the accuracy of the Rolling Thin Film Oven Test (RTFOT) in simulating short-term ageing in the field. The RTFOT provides a relatively good indication of short-term ageing, according to this multi-decade ageing study, and the effect on the asphalt binder properties used as ageing indices depends on the specific property chosen for comparison before and after ageing.

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An analysis of a cryptocurrency giveaway scam: Use case

(2024-06) Botha, Johannes G; Leenen, L

A giveaway scam is a type of fraud leveraging social media platforms and phishing campaigns. These scams have become increasingly common and are now also prevalent in the crypto community where attackers attempt to gain crypto-enthusiasts’ trust with the promise of high-yield giveaways. Giveaway scams target individuals who lack technical familiarity with the blockchain. They take on various forms, often presenting as genuine cryptocurrency giveaways endorsed by prominent figures or organizations within the blockchain community. Scammers entice victims by promising substantial returns on a nominal investment. Victims are manipulated into sending cryptocurrency under the pretext of paying for "verification" or "processing fees." However, once the funds have been sent, the scammers disappear and leave victims empty-handed. This study employs essential blockchain tools and techniques to explore the mechanics of giveaway scams. A crucial aspect of an investigation is to meticulously trace the movement of funds within the blockchain so that illicit gains resulting from these scams can be tracked. At some point a scammer wants to “cash-out” by transferring the funds to an off-ramp, for example, an exchange. If the investigator can establish a link to such an exchange, the identity of the owner of cryptocurrency address could be revealed. However, in organised scams, criminals make use of mules and do not use their own identities. The authors of this paper select a use case and then illustrate a comprehensive approach to investigate the selected scam. This paper contributes to the understanding and mitigation of giveaway scams in the cryptocurrency realm. By leveraging the mechanics of blockchain technology, dissecting scammer tactics, and utilizing investigative techniques and tools, the paper aims to contribute to the protection of investors, the industry, and the overall integrity of the blockchain ecosystem. This research sheds light on the intricate workings of giveaway scams and proposes effective strategies to counteract them.

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Deep learning vs. traditional learning for radio frequency fingerprinting

(2024-05) Otto, A; Rananga, S; Masonta, Moshe T

Radio Frequency (RF) fingerprinting is the theory of identifying a wireless device based on its unique transmitting characteristics. RF fingerprinting uses the validated concept that the physical components and configuration of a transmitting device can result in a distinct wireless emission. This research focuses on the application of machine learning algorithms, specifically Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) for the task of RF fingerprinting. The primary aim of this research paper is to comparatively assess the performance of SVMs and CNNs in RF fingerprinting for wireless device identification, focusing on hyperparameters, accuracy and real-world applicability. The study includes an in-depth implementation and evaluation of the SVMs and CNNs models, considering their performance in a high-dimensional dataset of multiple transmissions and wireless devices. While the CNN model slightly outperformed the SVM in terms of classification accuracy, other metrics such as inference time and training duration made the SVM equally competitive. The high accuracy and competitive inference times affirm the real-world applicability of these models, and their need to be further explored.