Prediction of the mechanical properties of concrete using developed hybrid neural networks

Abstract

This study focuses on the production of durable and high-quality concrete that aligns with the United Nations sustainable development goals (SDGs). Specifically, it aims to fulfil SDG 9 (industry, innovation and infrastructure) and SDG 11 (sustainable cities and communities). However, producing fibre-reinforced ultra-high-performance self-compacting concrete (FRUHPSCC) presents a challenge in achieving the desired mechanical properties. As a result, constructing numerous trial samples increases cost and time. To address this issue, an artificial neural network (ANN) can accurately predict the FRUHPSCC's mechanical properties. The study utilised garnet and basalt aggregates, nanosilica, steel fibre and other components to make FRUHPSCC and tested its compressive and tensile strengths and microstructure. By utilising a data set of experimental results, five types of ANN were developed with different training algorithms, as were five hybridised types of ANN employing the grasshopper optimisation algorithm (GOA), that predicted the compressive strength of this type of concrete. The results indicated that their predictions were highly accurate, and the hybridisation of ANNs with GOA increased prediction accuracy further. Notably, the network that combined the training function trainlm and GOA produced the highest prediction accuracy, showing that ANNs can predict FRUHPSCC's compressive strength accurately while reducing production cost and time.