Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

Abstract

Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles.

Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN

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Analyzing Uniaxial Compressive Strength of Concrete Using a Novel Satin Bowerbird Optimizer

“…Metaheuristic optimizers have provided effective solutions to many engineering analyses suchlike geotechnical issues [18], environmental risks [19], energy efficiency [20], etc. Concerning the simulation of concrete parameters, scholars have used famous algorithms like particle swarm optimization (PSO) for shear strength [21], firefly algorithm (FA) for creep strain [22], ant lion optimization (ALO) for slump [23] modeling. Zhang and Wang [24] proposed an optimized version of the least square support vector regression (LSSVR) for analyzing the bond strength of composite joints (fiber-reinforced polymers).…”

Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm

“…In essence, this problem could be viewed as a combinatorial optimization problem, and meta-heuristics algorithms are often employed to deal with this kind of problem. For instance, Huiyan J, Xiaoqi M, et al combined improved fruit fly optimization algorithm with support vector machine and used it to classify pancreatic cancer [21], ant colony optimization were utilized for the selection of accounting models, graph anonymization and robot rescue mission [22][23][24], Reddy G T, Srivastava G et al applied hybrid genetic algorithms to the diagnosis of heart disease [25], and many other evolutionary algorithms have been applied to solve optimization problems in various fields [26][27][28]. There is no doubt that a lot of researches have been conducted around the multi-threshold segmentation handled with evolutionary algorithms, such as hybrid whale optimization algorithm is employed in Kapur entropy for multi-threshold segmentation [29], combine particle swarm optimization algorithm with Tsallis entropy for multi-threshold segmentation [30], ant colony optimization algorithm is employed in OTSU to quickly search for multiple thresholds in images [31], some other optimization algorithms are also used for multi-threshold segmentation, such as water cycle algorithm [32], cuckoo search algorithm [33], knee evolutionary algorithm [34], differential evolution algorithm [35], and bat algorithm [36].…”