Novel Evolutionary-Optimized Neural Network for Predicting Fresh Concrete Slump

Abstract

Accurate prediction of fresh concrete slumps is a complex non-linear problem that depends on several parameters including time, temperature, and shear history. It is also affected by the mixture design and various concrete ingredients. This study investigates the efficiency of three novel integrative approaches for predicting this parameter. To this end, the vortex search algorithm (VSA), multi-verse optimizer (MVO), and shuffled complex evolution (SCE) are used to optimize the configuration of multi-layer perceptron (MLP) neural network. The optimal complexity of each model was appraised via sensitivity analysis. Various statistical metrics revealed that the accuracy of the MLP was increased after coupling it with the above metaheuristic algorithms. Based on the obtained results, the prediction error of the MLP was decreased by up to 17%, 10%, and 33% after applying the VSA, MVO, and SCE, respectively. Moreover, the SCE emerged as the fastest optimizer. Accordingly, the novel explicit formulation of the SCE-MLP was introduced as a capable model for the practical estimation of fresh concrete slump, which can assist in project planning and management.

Predictive analysis of concrete slump using a stochastic search-consolidated neural network

“…For example, when it comes to hybrid MLs, proper selection of hyperparameters (e.g., the number of hidden neurons, population size, etc.) is important and challenging [ 54 , 55 ]. In this work, this task was tackled by referring to (i) the previous applications of the models to see what are key hyperparameters, (ii) the experts' and authors’ experience, and (iii) conducting extensive sensitivity analysis.…”

“…They stated that the BBO-MLP with RS of 21 showed to have the most accurate model compared to other stated models. Safayenikoo et al [ 55 ] used three different metaheuristic algorithms namely shuffled complex evolution (SCE), the vortex search algorithm (VSA), and multi-verse optimizer (MVO) for enhancing the configuration of multi-layer perceptron (MLP) neural network. Based on the obtained results, they have shown that the prediction error in the case of MLP was considerably reduced around 33 % after using the SCE algorithm.…”

Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization

“…It is used for building various structural elements, from foundations to columns and beams [7,8]. Hereupon, engineers have conducted studies to analyze the behavior of concrete in terms of various parameters, such as ion penetration resistance [9], seismic behavior [10], load-bearing capacity [11], slump and workability [12], etc.…”

Concrete Slump Prediction Based on Hybrid Optimization XGBoost Algorithm

“…Moayedi et al utilized the ant lion optimizer (ALO) to fine-tune neural networks in the field of concrete slump prediction, and their model performed well in approximating concrete slump [3] . Hamed Safayenikoo et al employed vortex search algorithm (VSA), multi-verse optimizer (MVO), and shuffled complex evolution algorithm (SCE) to optimize the configuration of a multi-layer perceptron (MLP) neural network, achieving a 33% reduction in prediction error [4].…”