Plain asphalt typically is an insulator to the flow of electric current. It can be modified to conductive asphalt by adding various recyclable and environment-friendly conductive additives in it. Such asphalt can provide smart and multifunctional environmentally sustainable applications in the pavement industry. Its production and performance behavior parameters are, however, yet to be entirely understood. This study presents a review of literature on conductive asphalt using systematic literature review and scientometric analysis techniques to holistically understand conductive asphalt and current research developments in this field. The objective was to perform a critical review and scientometrically characterize the published research studies. Literature was acquired from credible research databases for the study duration from 2009 to 2022, and these were subsequently using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol to identify the most relevant documents. Sixty-two bibliographic articles were consequently selected for the study. Systematic review identified the research themes and techniques adopted in the field of conductive asphalt technology, and the scientometric analysis quantified the characteristics of the articles. VOSViewer was utilized for visualizing the key findings of the quantitative analysis. Development of conductive asphalt has great research potential and improving its piezoresistivity and conductive network is the future research focus of smart asphalt technology. An experimental study was also conducted, and the results were presented. A dataset of 75 Marshall asphalt specimens of various mix proportions was compiled to assess the Marshall parameters, volumetrics, and their electrical resistivity. 60/70 penetration grade bitumen was used along with five waste tire metal fiber based modified asphalt mixes with contents 0%, 0.375%, 0.75%, 1.125%, and 1.5%. This review, along with experimental investigations, provided an in-depth understanding of conductive asphalt concrete’s behavior, the emerging trends to support future studies, and helped to identify the current major research themes and the corresponding challenges.

The Urban Heat Island (UHI) phenomenon, in which the temperature in urban regions is higher relative to that in surrounding rural areas, is primarily attributed to human activities and urban development. This temperature escalation poses various challenges, impacting energy consumption, human well-being, and the overall urban environment. This study explores the potential of phase change materials (PCMs) as an addition to asphaltic mixes in alleviating the UHI effect. PCMs can store and release thermal energy through phase transition and exhibit valuable thermal properties for temperature management. In the context of UHI, PCMs play a pivotal role in alleviating the surplus heat amassed in urban areas. This study evaluates the effectiveness of shape-stabilized PCM-impregnated aggregates to reduce pavement temperature. The mechanical and thermal performance of the four different bituminous mixes has been evaluated in this study. A decrease in marshall stability while an increase in marshall flow has been observed with the increase in the proportion of PCM-impregnated aggregates in the mix. Based on the thermal analysis of PCM-modified mixes, a temperature drop of 5.03 °C and 2.9 °C in core temperature was observed in the case of 70% and 35% replacement with PCM-impregnated aggregates. The introduction of PCM-impregnated aggregates into the mix lowers the rate of increase and decrease in temperature of the asphaltic mix in direct sunlight. Electrical resistivity increased by around 4% and 8.6% for 35% and 70% replacement as compared to control sample.

Ensuring access to safe drinking water is a critical concern, particularly in regions with limited resources. This study evaluates groundwater potability using a range of machine learning models, including logistic regression, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and Random Forest, as well as deep learning models such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Feedforward Neural Networks (FNNs), and Long Short-Term Memory (LSTM). We collected thirty groundwater samples from residential and industrial locations in Jaen, Kano State, Nigeria, focusing on nine crucial physicochemical parameters: electric conductivity, pH, total dissolved solids, calcium, magnesium, chloride, zinc, manganese, and copper. Machine learning models, such as Logistic Regression and Random Forest, achieved accuracy scores of 0.833. They were closely followed by deep learning models, such as ANNs, with an accuracy score of 0.833, and LSTM, which scored 0.666. KNN and SVC provided moderately accurate predictions, scoring 0.667, while CNN and FNN achieved lower scores of 0.333 and 0.5, respectively. This study represents a significant step toward ensuring safe drinking water for communities and preserving the sustainability of natural resources.

The Marshall stability and flow of asphalt mixes are key performance indicators of their durability and suitability for use in the pavement industry. Achieving the optimal bitumen content and volumetric properties through mix design is critical and depends on the characteristics of the materials used. Recycling waste materials in asphalt is also vital for promoting environmental sustainability. The development of machine learning models plays a crucial role in predicting the performance of such asphalt mixes. This study explores the use of a machine learning approach to predict the performance of waste tyre metal fibre-modified asphalt mixes. A dataset consisting of 75 experimental data points from various mix proportions was compiled to train and test the model. The study used 60/70 penetration grade bitumen and five modified mixes with waste tyre metal fibre (WTMF) contents of 0%, 0.375%, 0.75%, 1.125%, and 1.5%. Decision tree regression was effectively employed to establish the relationship between the input variables. The predictive ability of the model was assessed using R-squared, adjusted R-squared, and mean absolute error. The input parameters included fibre content, bitumen content, aggregate percentage, and porosity. Analysis of the input variables showed that stability decreased while flow increased with higher fibre and bitumen contents. With an R² of 0.901 for training and 0.937 for testing phases, decision tree regression proved to be an effective model for predicting the performance of these modified asphalt mixes.