Application of Artificial Intelligence to Evaluate the Fresh Properties of Self-Consolidating Concrete

Abstract

This paper numerically investigates the required superplasticizer (SP) demand for self-consolidating concrete (SCC) as a valuable information source to obtain a durable SCC. In this regard, an adaptive neuro-fuzzy inference system (ANFIS) is integrated with three metaheuristic algorithms to evaluate a dataset from non-destructive tests. Hence, five different non-destructive testing methods, including J-ring test, V-funnel test, U-box test, 3 min slump value and 50 min slump (T50) value were performed. Then, three metaheuristic algorithms, namely particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution optimization (DEO), were considered to predict the SP demand of SCC mixtures. To compare the optimization algorithms, ANFIS parameters were kept constant (clusters = 10, train samples = 70% and test samples = 30%). The metaheuristic parameters were adjusted, and each algorithm was tuned to attain the best performance. In general, it was found that the ANFIS method is a good base to be combined with other optimization algorithms. The results indicated that hybrid algorithms (ANFIS-PSO, ANFIS-DEO and ANFIS-ACO) can be used as reliable prediction methods and considered as an alternative for experimental techniques. In order to perform a reliable analogy of the developed algorithms, three evaluation criteria were employed, including root mean square error (RMSE), Pearson correlation coefficient (r) and determination regression coefficient (R2). As a result, the ANFIS-PSO algorithm represented the most accurate prediction of SP demand with RMSE = 0.0633, r = 0.9387 and R2 = 0.9871 in the testing phase.

Fresh, Mechanical, and Durability Properties of Self-Compacting Mortar Incorporating Alumina Nanoparticles and Rice Husk Ash

“…The chemical composition of NA is reported in Table 2. Previous research studies have stated that the quality of water can also affect the properties of concrete and cementitious materials [5,[35][36][37][38][39][40][41][42]. However, in this research, distilled and tap water were respectively used for the characterization tests and for sample preparation studies according to the recommendation of some previous studies [43][44][45][46][47][48][49][50][51][52].…”

“…Self-compacting concrete (SCC) technology was introduced by Okamura in 1986 [1]. Over the recent decades, the technology of SCC has been an attractive option for industry practitioners worldwide due to its various benefits [1][2][3][4][5][6]. Enhancing the properties of cement paste and mortar has been the key focus of various research studies conducted in the past.…”

Utilizing Artificial Intelligence to Predict the Superplasticizer Demand of Self-Consolidating Concrete Incorporating Pumice, Slag, and Fly Ash Powders

“…According to the literature, the SP demand as one of the controversial parameters of mix design of SCC has not been investigated as much as fresh and hardened properties. For example, Feng et al [40] examined the SP demand of SCC using hybrid intelligent algorithms and obtained promising results. Additionally, as discussed comprehensively, several studies were performed to predict the different characteristics of concrete using AI models.…”

“…According to [16,40], the selected SCC specimens included fly ash, pumice, and slag as binaries samples with replacement percentages of 10%, 20%, 30%, 40%, and 50% and a water to cement ratio of 38%. In the second series of samples, ternary mixtures of pumice and silica fume with the same water to cement ratio have been employed.…”

Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement

“…A novel combination set of ANFIS was developed in China to predict the number of contaminated cases from COVID-19 [59]. Moreover, hybrid algorithms are typically employed to solve multiobjective problems [60,61]. Therefore, different types of optimisation methods can be integrated with ANFIS to increase the accuracy and improve the performance of the considered algorithm.…”