Concrete slump prediction modeling with a fine-tuned convolutional neural network: hybridizing sea lion and dragonfly algorithms

Shaswat, Kumar

doi:10.1007/s11356-020-12244-3

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…Among them, image‐based concrete surface crack detection and evaluation are widely used (Li et al., 2019; Liu & Gao, 2022; Okazaki et al., 2020). Shaswat (2021) used hybridizing sea lion and dragonfly algorithms to optimize CNN to predict concrete slump values and achieved favorable prediction results. Deng et al.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, image-based concrete surface crack detection and evaluation are widely used (Li et al, 2019;Liu & Gao, 2022;Okazaki et al, 2020). Shaswat (2021) used hybridizing sea lion and dragonfly algorithms to optimize CNN to predict concrete slump values and achieved favorable prediction results. Deng et al (2018) developed a model for estimating the CCS of recycled aggregate concrete by using CNN to learn the deep features of the input variables, which has an enhanced adaptive ability, compared to conventional ANN models.…”

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confidence: 99%

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Hybrid random aggregation model and Bayesian optimization‐based convolutional neural network for estimating the concrete compressive strength

Li,

Pan,

Guo

et al. 2023

Computer aided Civil Eng

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Numerous experimental studies have shown the type and gradation of coarse aggregates effect on the mechanical properties of concrete. The type and gradation of coarse aggregates have not been taken into account in the available machine learning prediction models. In this study, a two‐dimensional concrete microscopic image was generated by using a random aggregate model (RAM), and the coarse aggregate and other concrete ingredients were represented innovatively using polygons and trichromatic chromaticity values in the RAM images. The RAM image set was created by applying this method to represent 1110 sets of different concrete mixes. Then based on the Bayesian optimization algorithm and the image set, a compressive strength prediction model considering the effect of coarse aggregate types and gradations was developed utilizing a convolutional neural network (CNN) model. Meanwhile, an artificial neural network (ANN) compressive strength prediction model was developed using 1110 sets of mix ratio data. The results show that the proposed RAM image generation method has the capability to represent different concrete mix ratios collected in this study. The prediction performance of the CNN compressive strength model considering aggregate types and gradations is better than that of the ANN model. The method can provide a new perspective for predicting other concrete mechanical properties and technically support performance‐based intelligent concrete mix design.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Hybrid random aggregation model and Bayesian optimization‐based convolutional neural network for estimating the concrete compressive strength

Li,

Pan,

Guo

et al. 2023

Computer aided Civil Eng

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“…used neural network to predict the compressive strength and slump of high strength concrete [ 12 ]. Shaswat used hybridizing sea lion and dragonfly algorithms with a fine-tuned convolutional neural network to predict concrete slump [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

A proposed method and monitoring system for evaluating workability of Portland cement concrete during mixing

Xiao¹,

Liu²,

Li³

2022

Heliyon

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“…Fitting outputs are achieved by relying on appropriate learning methods. Recently, the relevant studies have considered optimization algorithms based on support vector models for high-quality concrete design [36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Fusion‐Learning‐Based Optimization: A Modified Metaheuristic Method for Lightweight High‐Performance Concrete Design

2022

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In order to build high-quality concrete, it is imperative to know the raw materials in advance. It is possible to accurately predict the quality of concrete and the amount of raw materials used using machine learning-enhanced methods. An automated process based on machine learning strategies is proposed in this paper for predicting the compressive strength of concrete. Fusion-learning-based optimization is used in the proposed approach to generate a strong learner by pooling support vector regression models. The SVR technique proposes an optimization method for finding the kernel radial basis function (RBF) parameters based on improving the innovative gunner algorithm (AIG). As a result of AIG's diverse solutions, local optima are effectively avoided. Therefore, the novelty of our research is that, in solving the uncertainty of predicted outputs based on integrated models, we use fusion-learning-based optimization to improve regression discrimination. We also collected a standard dataset to analyze the proposed algorithm, and subsequently, the dataset was designed from concrete laboratory tests on 244 samples, seven features, and three outputs. Different regression intensities are determined by correlation analysis of responses. Regression fusion is sufficiently accurate to estimate the number of desired outcomes examined based on the appropriate input data sample. The best quality concrete can be achieved with an error rate of less than 5%.

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Concrete slump prediction modeling with a fine-tuned convolutional neural network: hybridizing sea lion and dragonfly algorithms

Cited by 5 publications

References 37 publications

Hybrid random aggregation model and Bayesian optimization‐based convolutional neural network for estimating the concrete compressive strength

Hybrid random aggregation model and Bayesian optimization‐based convolutional neural network for estimating the concrete compressive strength

A proposed method and monitoring system for evaluating workability of Portland cement concrete during mixing

Fusion‐Learning‐Based Optimization: A Modified Metaheuristic Method for Lightweight High‐Performance Concrete Design

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