Investigation and Evaluation of Roller-Compacted Concrete Environmentally Friendly Mix with an Emphasis on Rigid Pavement and Compressive Strength

Abstract

The RCC constituents are plain concrete including aggregates, water, cement, dispersants, and additives (admixtures). However, some RCC features, such as zero-slump, roller bearing capacity, and RCC placing, have overcome the weaknesses in plain concrete. Despite the similarities between plain concrete and RCC, RCC mix design cannot be achieved by applying a few simple changes to plain concrete mix design, such as reducing water volume, changing the water-cement ratio, increasing the number of fine aggregates, etc. Given the importance of the topic, this paper aims to investigate and evaluate the RCC mix with an emphasis on rigid pavement and compressive strength. To achieve this by using the library (desk research) method, a comparison was first made between concrete (rigid) and asphalt (flexible) pavements. Then, the position of RCC in concrete pavements, the selection of RCC consumables, and the determination of the mix design were investigated. The results showed that one of the most important mechanical properties of concrete is compressive strength which is strongly affected by the concrete mix design. Parameters such as cement grade, water-cement ratio, cement substitutes, etc. affect concrete compressive strength. It is difficult to predict concrete compressive strength due to the existence of many parameters. In mix design proportioning methods, it is attempted to reduce production costs. It is time-consuming and costly to use building code methods for calculating mix design and the building code conditions and assumptions must be met for all concrete components. Hence, artificial intelligence (AI)-based methods such as fuzzy logic and artificial neural networks (ANNs) are being used successfully today. These models have generally been derived from laboratory experiments and investigations.