Evaluating the Influence of Carbon Fiber on the Mechanical Characteristics and Electrical Conductivity of Roller-Compacted Concrete Containing Waste Ceramic Aggregates Exposed to Freeze-Thaw Cycling

Traditional methods of removing snow and ice from pavements using chemicals are combined with mechanical removal that involves a lot of manpower, advanced machinery, chemicals that are harmful to the environment, and damage to pavements. Furthermore, annually, large quantities of ceramic materials become waste due to their fragile nature during processing, transport, and installation, and their accumulation in the nature has brought about environmental and health-related concerns. Therefore, the study aims to investigate the effect of using waste ceramic as a replacement for fine aggregate in roller compacted concrete (RCC) and the application of carbon fiber to improve the mechanical properties and electrical conductivity of RCC. To achieve this goal, several tests such as compressive strength, indirect tensile strength, electrical resistance, chloride ion penetration, specific gravity, and skid resistance tests were carried out on the fabricated samples before and after freeze-thaw cycling exposure. The experimental results illustrated that replacing waste ceramics with fine-grained aggregate increased the compressive strength and tensile strength of RCC. Furthermore, carbon fiber increased tensile strength but had no noticeable influence on compressive strength. Freeze-thaw conditioning led to a reduction in the compressive and tensile strength regardless of the aggregate type and carbon fiber utilization. In the samples containing waste ceramic aggregate, the electrical conductivity was reduced, and by adding carbon fiber, its electrical conductivity was increased. Exposure to freeze-thaw cycling resulted in an increase in electrical resistance and the passing charge. Waste ceramic incorporation created a similar mixture in terms of skid resistance, while in contrast, the carbon fiber slightly reduced the skid resistance. In addition, freeze-thaw conditioning resulted in an increase in the skid resistance. Besides, in this study, kernelized support vector regression (KSVR) and radial bias function (RBF) neural network models were proposed to estimate the indirect tensile strength (ITS) and compressive strength (CS) values. The results showed that both models have high performance in estimating these values, but RBF was a more efficient model.