Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete

Abstract

Ever since their presentation in the late 80s, self-compacting concrete (SCC) has been well received by researchers. SCC can flow under their weight and exhibit high workability. Nonetheless, their nonlinear behavior has made the prediction of their mix properties more demanding. Furthermore, the complex relationship between mixed proportions and rheological and mechanical properties of SCC renders their behavior prediction challenging. Soft computing approaches have been shown to optimize and reduce uncertainties, and therefore in this paper, we aim to address these challenges by employing artificial neural network (ANN) models optimized using the grey wolf optimizer (GWO) algorithm. The optimized model proved to be more accurate than genetic algorithms and multiple linear regression models. The results indicate that the four most influential parameters on the compressive strength of SCC are the cement content, ground granulated blast furnace slag, rice husk ash, and fly ash.

Compressive Strength Prediction of Self-Compacting Concrete-A Bat Optimization Algorithm Based ANNs

“…e 11 influencing parameters of the compressive strength of selfcompacting concrete are cement (C), limestone powder (LP), fly ash (F), ground granulated blast furnace slag (GGBFS), silica fume (SF), rice husk ash (RHA), coarse aggregate (CA), fine aggregate (FA), water (W), new generation superplasticizers (SP) as chemical admixtures, and viscosity modifying admixtures (VMA). e target quantity is the 28-day compressive strength of the self-compacting concrete specimens [2,48].…”

“…Divergence and long training time can arise from feeding the ANN with input variables with various ranges [34]. Input and output data were normalized into a span of −1 to 1 utilizing (6) to mitigate the adverse effects [48] Y…”

Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams

“…Te input is the 6×1 vector, a (1) . Te shear strength is determined by the following equations [7,37]: a (2) � tan h ϑ (1) × a (1) + b 1 􏼐 􏼑, a ( ) � tan h ϑ (2) × a (2) + b 2 􏼐 􏼑, a (4) � tan h ϑ ( ) × a ( ) + b 􏼐 􏼑,…”

Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete

“…Recently, due to the recent advancements in Artificial Intelligence (AI), Machine Learning (ML) techniques have come out as an interesting tool for modelling that is suitable for a wide-ranging variety of scientific domains such as materials engineering (Worden and Manson, 2007;Cao and Li, 2018;Cui et al, 2018;Musumeci et al, 2018;Ebid and Deifalla, 2021;Andalib et al, 2022;Nawaz et al, 2022;Pandey et al, 2022). Keeping this in mind, an inclination has recently been made towards employing ML approaches for concrete strength prediction (Aiyer et al, 2014;Asteris et al, 2016;Sonebi et al, 2016;Dutta et al, 2017;Bayrami, 2022;Sarkhani Benemaran et al, 2022;Shah et al, 2022;Wang and Wu, 2022).…”