Recycled concrete aggregate can be utilized in structural concrete in order to reduce the use of natural resources and the harmful impacts of waste concrete on the environment. This present research aimed to assess the effectiveness of using recycled aggregate concrete with the partial replacement of cement by silica fume (SF) to analyze the microstructural and mechanical properties of recycled aggregate concrete (RAC). In this study, recycled stone was used as coarse aggregate. The main variables of the study included the dosage of silica fume that was employed as a partial replacement of ordinary Portland cement (OPC) with four different percentages: 4%, 8%, 12%, and 16% by weight. Five different mixes were prepared, with four mixes created by varying amounts of silica fume, which were designated as RSACSF4, RSACSF8, RSACSF12, and RSACSF16. The other mix was created as a reference mix without silica fume and designated RSACSF0. Slump test was conducted to investigate the workability of concrete mixes. From the test result, a decreasing trend was found after adding more percentage of SF. Compressive and splitting tensile tests were conducted to analyze the mechanical properties of RSAC at 7 and 28 days. The results showed that the addition of SF improved the performance of RSAC at early and later curing ages, and a 12% addition of SF showed the best result. Scanning electron microscopy and X-ray diffraction analysis were performed to explore SF's microstructural performance and effect on RSAC. The results showed that silica fume showed a positive pozzolanic impact, and when combined with calcium hydroxide, it underwent a secondary hydration reaction that boosted the generation of calcium silicate hydrate and improved the parameters of the interface transition zone. X-ray diffraction analysis showed that silica fume and silica fume have similar pattern intensities. Finally, 12% SF is recommended as a partial replacement for cement in RSAC.

Cyber-attacks have become a significant concern today, particularly in IoT environments where security poses a substantial challenge due to the distributed nature and heterogeneity of protocols. To efficiently detect threats in IoT networks, it is crucial to develop a robust intrusion-detection system (IDS) capable of identifying various modern and traditional attacks with high accuracy. Most existing machine-learning-based intrusion-detection systems for IoT have been trained using outdated datasets that do not accurately reflect IoT scenarios. Additionally, current research does not adequately address which machine-learning classifiers are most suitable for developing an efficient IDS in IoT environments. In our research, we have developed and trained a real-time intrusion-detection system for IoT networks that can detect multiple modern and traditional threats with high accuracy. We created seven instances of real-time IDS using state-of-the-art machine-learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Naïve Bayes, and Artificial Neural Networks. Using the Pearson Correlation Coefficient, we extracted the most relevant features from the BoT–IoT dataset. After rigorous preprocessing, we used these data to train our algorithms. Our trained model, INIDS, is not only up to date and real-time but also capable of accurately identifying multiple categories of attacks specifically related to IoT networks. To achieve maximum accuracy, instead of selecting only one classifier, we evaluated seven advanced machine-learning algorithms and provided a comprehensive comparison of their performance and efficiency in the context of IoT networks. This analysis can guide future researchers in choosing the right machine-learning algorithms for developing IDS. We found that Random Forest is the most robust classifier for IoT-based network intrusion-detection systems, achieving an accuracy of 99.2%. The second-best performer is Naïve Bayes, with an accuracy of 98.8%.