Abstract Machine learning (ML) techniques are often employed for the accurate prediction of
the compressive strength of concrete. Despite higher accuracy, previous ML models failed to …

In this study, an efficient implementation of machine learning models to predict compressive
and tensile strengths of high-performance concrete (HPC) is presented. Four predictive …

Resilient modulus (MR) plays the most critical role in the evaluation and design of flexible
pavement foundations. MR is utilised as the principal parameter for representing stiffness …

The compressive and tensile strength of high-performance concrete (HPC) is a highly
nonlinear function of its constituents. The significance of expert frameworks for predicting the …

The article presents a deep neural network model for the prediction of the compressive
strength of foamed concrete. A new, high‐order neuron was developed for the deep neural …

In this study, a new hybrid model, namely the Electromagnetism-based Firefly Algorithm-
Artificial Neural Network (EFA-ANN), is proposed to forecast the energy consumption in …

The use of supplementary cementitious materials such as ground granulated blast furnace
slag (GGBFS) in concrete mixtures provides many technical and economic benefits. The use …

Time series forecasting has been widely used to determine the future prices of stock, and the
analysis and modeling of finance time series importantly guide investors' decisions and …

Many metaheuristic approaches have been developed to select effective features from
different medical datasets in a feasible time. However, most of them cannot scale well to …

Using recycled aggregate in concrete is one of the best ways to reduce construction
pollution and prevent the exploitation of natural resources to provide the needed aggregate …