A hybrid data-driven and metaheuristic optimization approach for the compressive strength prediction of high-performance concrete

Imran, Muhammad; Khushnood, Rao Arsalan

doi:10.1016/j.cscm.2023.e01890

“…CFNN is a kind of NN that performs similarly to an FFNN. The major difference between FNN and CFNN is that it has a link with the prior HLs and input that provides the benefits of integrating the nonlinear relationships without eliminating the linear relationships between output and input [ 31 ]. Furthermore, it is a standard network since it needs fewer neurons to resolve the problems than FNN, making it efficient and compact.…”

Section: The Proposed Modelmentioning

confidence: 99%

Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment

Khadidos

¹

,

Khadidos

²

,

Alyoubi

³

et al. 2023

Sensors

3

0

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Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter–prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures.

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“…The rationality and applicability of the model were verified through concrete slump and strength tests. Imran [18] , used the artificial bee colony (ABC) algorithm and cascade forward neural network (CFNN) to develop a novel hybrid model for predicting the compressive strength of concrete. The model was validated using performance indicators, and it was found that the proposed hybrid model outperformed other models in all performance indicators.…”

Section: Introductionmentioning

confidence: 99%

Prediction of compressive strength of concrete based on IABC-MLP algorithm

Li,

Zhang,

Gu

et al. 2024

Preprint

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There are many factors that affect the compressive strength of concrete. The relationship between compressive strength and these factors is a complex nonlinear problem. Empirical formulas commonly used to predict the compressive strength of concrete are based on summarizing experimental data of several different mix proportions and curing periods, and their generality is poor. This article proposes an improved artificial bee colony algorithm (IABC) and a multilayer perceptron (MLP) coupled model for predicting the compressive strength of concrete. To address the shortcomings of the basic artificial bee colony algorithm, such as easily falling into local optima and slow convergence speed, this article introduces a Gaussian mutation operator into the basic artificial bee colony algorithm to optimize the initial honey source position and designs an MLP neural network model based on the improved artificial bee colony algorithm (IABC-MLP). Compared with traditional strength prediction models, the ABC-MLP model can better capture the nonlinear relationship of the compressive strength of concrete and achieve higher prediction accuracy when considering the compound effect of multiple factors. The IABC-MLP model built in this study is compared with the ABC-MLP and particle swarm optimization (PSO) coupling algorithms. The research shows that IABC can significantly improve the training and prediction accuracy of MLP. Compared with the ABC-MLP and PSO-MLP coupling models, the training accuracy of the IABC-MLP model is increased by 1.6% and 4.5%, respectively. This model is also compared with common individual learning algorithms such as MLP, decision tree (DT), support vector machine regression (SVR), and random forest algorithms (RF). Based on the comparison of prediction results, the proposed method shows excellent performance in all indicators and demonstrates the superiority of heuristic algorithms in predicting the compressive strength of concrete.

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“…Zhou Hao et al 22 applied the ABC algorithm and SVM algorithm to optimize the concrete mix proportion, establishing a concrete mix proportion optimization model and verified the rationality and applicability of the model through concrete slump and strength tests. Imran, M et al 23 used artificial bee colony (ABC) and cascade forward neural network (CFNN) to optimize and develop a new hybrid model for predicting concrete compressive strength, and model validation using performance indicators showed that the proposed hybrid model was superior to other models in all performance indicators. However, as a random optimization algorithm, the ABC algorithm, similar to other evolutionary algorithms, also has defects of slow convergence speed and easy to fall into local optima.…”

Section: Introductionmentioning

confidence: 99%

Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm

Li,

Zhang,

Gu

et al. 2024

Sci Rep

2

0

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There are many factors that affect the compressive strength of concrete. The relationship between compressive strength and these factors is a complex nonlinear problem. Empirical formulas commonly used to predict the compressive strength of concrete are based on summarizing experimental data of several different mix proportions and curing periods, and their generality is poor. This article proposes an improved artificial bee colony algorithm (IABC) and a multilayer perceptron (MLP) coupled model for predicting the compressive strength of concrete. To address the shortcomings of the basic artificial bee colony algorithm, such as easily falling into local optima and slow convergence speed, this article introduces a Gaussian mutation operator into the basic artificial bee colony algorithm to optimize the initial honey source position and designs an MLP neural network model based on the improved artificial bee colony algorithm (IABC-MLP). Compared with traditional strength prediction models, the ABC-MLP model can better capture the nonlinear relationship of the compressive strength of concrete and achieve higher prediction accuracy when considering the compound effect of multiple factors. The IABC-MLP model built in this study is compared with the ABC-MLP and particle swarm optimization (PSO) coupling algorithms. The research shows that IABC can significantly improve the training and prediction accuracy of MLP. Compared with the ABC-MLP and PSO-MLP coupling models, the training accuracy of the IABC-MLP model is increased by 1.6% and 4.5%, respectively. This model is also compared with common individual learning algorithms such as MLP, decision tree (DT), support vector machine regression (SVR), and random forest algorithms (RF). Based on the comparison of prediction results, the proposed method shows excellent performance in all indicators and demonstrates the superiority of heuristic algorithms in predicting the compressive strength of concrete.

show abstract

A hybrid data-driven and metaheuristic optimization approach for the compressive strength prediction of high-performance concrete

Cited by 14 publications

References 41 publications

Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment

Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment

Prediction of compressive strength of concrete based on IABC-MLP algorithm

Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm

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