The replacement of virgin aggregate with recycled concrete aggregate (RCA) in concrete mixtures offers an eco-strategy to mitigate the environmental limitations linked with traditional recycling techniques of RCA. However, the inferior properties of RCA, in contrast to virgin aggregate, present an obstacle to efficiently proceeding with this approach. Therefore, the aim of this study is to enhance the characteristics of concrete that contains RCA using nano-ZnO particles. Virgin aggregate was replaced with RCA in 30 wt.% and 50 wt.% ratios, followed by the addition of 0.5 wt.% nano-ZnO. The performance of concrete mixtures was evaluated in terms of their physical, mechanical, and durability properties. The addition of nano-ZnO particles to concrete with RCA resulted in refining its pore structure and reducing its water absorption, where the impermeability of concrete with 30 wt.% and 50 wt.% treated RCA decreased by 14.5% and 18%, respectively. Moreover, nano-ZnO treatment increased the compressive strength of mixtures with 30 wt.% and 50 wt.% RCA by 2.8% and 4%, respectively. All mixtures underwent a reduction in their 28-day compressive strength after exposure to a 5% sulphuric acid solution, where concrete with 30 wt.% and 50 wt.% RCA showed 20.2% and 22.8% strength loss, respectively. However, there was a 17.6% and 19.6% drop in the compressive strength of concrete with 30 wt.% and 50 wt.% RCA and treated with nano-ZnO.

Moisture susceptibility is a complex phenomenon that induces various distresses in asphalt pavements and can be assessed by the Retained Stability Index (RSI). This study proposes a robust model to predict the RSI using a hybrid machine learning technique, including Artificial Neural Network (ANN) and Gene Expression Programming. The model is expressed as a simple and direct mathematical function with input variables of mineral filler proportion (F%), water absorption rate of combined aggregate (Ab%), asphalt content (AC%), and air void content (Va%). A relative importance analysis ranked AC% as the most influential variable on RSI, followed by Va%, F%, and Ab%. The experimental RSI results of 150 testing samples of various mixes were utilized along with other data points generated by the ANN to train and validate the proposed model. The model promotes a high level of accuracy for predicting the RSI with a 96.6% coefficient of determination (R²) and very low errors. In addition, the sensitivity of the model has been verified by considering the effect of the variables, which is in line with the results of network connection weight and previous studies in the literature. F%, Ab%, and Va% have an inverse relationship with the RSI values, whereas AC% has the opposite. The model helps forecast the water susceptibility of asphalt mixes by which the experimental effort is minimized and the mixes’ performance can be improved.