A major concern with the application of water-foamed asphalt mixtures is the possible performance degradation caused by the presence of inclusion water during production. Inclusion water left in the asphalt mixture after compaction affects the performance of the asphalt mixture. The objectives of this study are to investigate the impact of different foaming conditions on the performance of water-foamed asphalt mixtures prepared using oven-dried aggregates, examine the freeze-thaw resistance of water-foamed asphalt mixtures, and assess the water-foamed asphalt mixture damage level after multiple freeze-thaw cycles through an ultrasonic direct test. The samples for this study were prepared in the laboratory based on the method of Superpave mix design. The control groups were mixed and compacted at 135°C and 148°C, respectively. The water-foamed asphalt binders were prepared at different foaming temperatures, i.e., 120°C and 135°C, and the amount of water agent used in this process was 0.0%–2.0% by mass of asphalt binder. The water-foamed asphalt samples were mixed at various foaming temperatures but compacted at 135°C. Based on the laboratory test results, the foaming temperature and the inclusion water from the foaming process significantly affected the air void and the tensile strength of the asphalt mixture and the void in the mineral aggregate. The freeze-thaw cycle accelerated the destruction of the internal structure of the asphalt mixture and resulted in an increase in porosity and decrease in cohesive strength between the asphalt binder and aggregate. The ultrasonic direct test method was found to be a feasible approach to assessing the potential damage in water-foamed asphalt mixtures.

High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.