A hybrid optimized learning‐based compressive performance of concrete prediction using GBMO‐ANFIS classifier and genetic algorithm reduction

Abstract

High performance concrete (HPC) is a type of concrete that cannot be produced using conventional methods. The exact percentage of materials used in the production of this concrete is one of the challenges facing civil engineers so that if ingredients are not in proportion, the strength of concrete is undermined. In the present study, attempts have been made to find an intelligent model to predict the quality of HPC. As a result of regression analysis, the automatic recognition would be affected by inferential estimation. Hence, to increase classification accuracy, first extracted feature is rearranged based on expectation–maximization clustering algorithm and then feature vector size is reduced using genetic algorithm. The proposed classification is adaptive neuro‐fuzzy inference system, which is optimized by Gases Brownian Motion Optimization and able to predict outputs at an acceptable level in limited reiterations. The split ratio of data during learning and testing steps was 0.9 and 0.1, as measured by K‐fold cross‐validation method. Computation of criteria such as mean square error and mean absolute percentage error in the algorithm indicated the desirable performance of the proposed method.

Influence of parameter uncertainty on the low‐carbon design optimization of reinforced concrete continuous beams

“…Different algorithms can be applied to realize the defined low-carbon design optimization including (1) exhaustive methods that examine all possible solutions; (2) classical analytic methods, such as Lagrange multipliers, interpolation methods, and linear programming; (3) gradient search methods, such as the steepest descent method and quasi-Newton method; and (4) heuristic or metaheuristic methods, such as GAs, 57,58 particle swarm optimization (PSO), 59 and the harmony search method. 60 The GAs can handle discrete design variables and nonlinear and inequality constraints and have been successfully employed to solve various engineering problems such as structural design, production scheduling, and automatic control.…”

Optimal fractional-order PID control design for time-delayed multi-input multi-output seismic-excited structural system

“…GBMO algorithm, due to having powerful exploration technique in the gases Brownian movement, showed its ability over the particle swarm optimization, genetic algorithm, imperialist competitive algorithm, and gravitational search algorithm for high dimensions optimization problems (Abdechiri et al, 2013; Elyas et al, 2014; Etedali et al, 2018; Rahchamani et al, 2021).…”

New insight into the prediction of strength properties of cementitious mortar containing nano‐ and micro‐silica based on porosity using hybrid artificial intelligence techniques

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