Evaluation and optimization of red pine needle-reinforced roller-compacted concrete by bioinspired algorithms

Abstract

This paper discusses the improvement of roller compacted concrete by the addition of red pine needle (PN) fibers optimized by bioinspired techniques. The purpose was to determine a perfect set of mechanical characteristics, such as flexural, compressive, and splitting tensile strengths, by determining proper correlations between the water-cement ratio, superplasticizer, and fiber volume. Based on the data obtained with the help of experiments, it was shown that optimizing the PN fibers added to RCC with nature-inspired algorithms (particle swarm optimization, genetic algorithms, simulated annealing and artificial neural networks) yields positive results in terms of improving a number of characteristics, including flexural strength, fracture toughness, and durability. In addition, adopting PN fibers has certain environmental benefits. All methods under analysis showed good results, with the artificial neural network (ANN) being superior in terms of predicting the parameters of RCC. The discussed research confirms the effectiveness of such optimization methods for determining the best proportions for RCC using PN fibers. At the same time, future studies could further develop sustainable and mechanically strong RCC formulations.