The incorporation of nanotechnology has led to significant strides in the concrete industry, ushering in innovative construction methodologies. Various nanomaterials, including nano-silica (NS), have undergone comprehensive scrutiny as potential partial substitutes for cement in concrete formulations. This article aims to provide a comprehensive overview of the impacts of NS on several mechanical properties of concrete, encompassing compressive, split tensile, and flexural strengths. Additionally, the review delves into the influence of NS on the concrete’s durability, including microstructural characterization and the eradication of structural micropores. NS has demonstrated the capacity to bolster both strength and durability while concurrently diminishing structural micropores. Moreover, this review explores the contemporary status of NS application in cement concrete and presents avenues for prospective research. The assessment of engineering attributes becomes imperative for concrete infused with nano-silica. This encompasses aspects like bond strength, creep, shrinkage, and more. A rigorous evaluation of fresh and hardened properties is necessary to discern the material’s thermal and acoustical characteristics. Such a comprehensive understanding contributes to a holistic evaluation of the material’s adaptability across diverse applications.

Due to the chronic shortage of energy-related analytical data and disintegration of building energy regulations, numerous existing residential buildings in Petra (Jordan) and many cities worldwide suffer from poor building energy design. This paper aims at investigating the potential of applying energy-saving standards in order to improve the whole-building energy consumption of low-rise residential buildings in mild and dry climate zones. Representative buildings were selected based on a field survey. Proposed strategies focused on applicable solutions such as envelope components, and energy-related systems were set. The models were created using Autodesk Revit, and then the results were generated by the EnergyPlus engine. The findings showed that the application of building energy standards greatly impacts the overall energy end-use, where up to 30% reduction can be achieved by applying the Jordanian code, and up to 45% by applying the American standard. This work provides guidance for the residential building industry and policymakers in Jordan and many other countries with similar building characteristics and climate zones.

Transportation Mode Detection (TMD) is important in planning new transportation projects as well as improving existing ones. Therefore, this study aims to develop predictive modes of transportation through the use of smartphone data and smartwatches and the use of machine learning techniques. To achieve the objective of this study, a review of the studies related to the use of algorithms to predict transportation modes was prepared. Besides, on the practical side, the Physical Activity for Smart Travel (PASTA) platform has been developed. Two groups of participants were recruited in Michigan and Texas to obtain the required data for the study. Daily activities have been classified into activities related to transportation (trips) and non-related to transportation (work, home, shopping, etc.), then focusing exclusively on transportation activities to determine their modes. In this study, four machine learning methods were used for prediction (Random Forest, Extreme Gradient Boosting, Artificial Neural Network, and Support Vector Machine) and using the data of physical activities as a new feature not used in prior studies. The accuracy of the methods of transportation mode prediction was compared through the training and testing phases. The results of the predictions were compared with the activities verified by the participants. The results showed that the method of Random Forest performed better than other methods. This study provides the appropriate tools for decision-makers to help them understand people’s travel behaviors.