Assessment of composite beam performance using GWO–ELM metaheuristic algorithm

Ma, Runqian; Karimzadeh, Misagh; Ghabussi, Aria; Zandi, Yousef; Baharom, Shahrizan; Selmi, Abdellatif; Maureira-Carsalade, Nelson

doi:10.1007/s00366-021-01363-1

Cited by 19 publications

(8 citation statements)

References 212 publications

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“…MLP employs the back propagation algorithm (also known as BPA). The capacity of an ANN [ 137 , 138 , 139 , 140 , 141 ] to acquire the broader relationship among variables is the focus of the ANN testing presented in this study. A vast number of basic processing elements (PE) known as neurons make up the ANN.…”

Section: Methodsmentioning

confidence: 99%

Application of Polyacrylic Hydrogel in Durability and Reduction of Environmental Impacts of Concrete through ANN

Peng

Zandi

et al. 2022

Gels

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While adding superabsorbent polymer hydrogel particles to fresh concrete admixtures, they act as internal curing agents that absorb and then release large amounts of water and reduce self-desiccation and volumetric shrinkage of cement that finally result in hardened concrete with increased durability and strength. The entrainment of microscopic air bubbles in the concrete paste can substantially improve the resistance of concrete. When the volume and distribution of entrained air are adequately managed, the microstructure is protected from the pressure produced by freezing water. This study addresses the design and application of hydrogel nanoparticles as internal curing agents in concrete, as well as new findings on crucial hydrogel–ion interactions. When mixed into concrete, hydrogel particles produce their stored water to power the curing reaction, resulting in less volumetric shrinkage and cracking and thereby prolonging the service life of concrete. The mechanical and swelling performance qualities of the hydrogel are very sensitive to multivalent cations found naturally in concrete mixes, such as aluminum and calcium. The interactions between hydrogel nanoparticles and alkaline cementitious mixes are described in this study, while emphasizing how the chemical structure and shape of the hydrogel particles regulate swelling behavior and internal curing efficiency to eliminate voids in the admixture. Moreover, in this study, an artificial neural network (ANN) was utilized to precisely and quickly analyze the test results of the compressive strength and durability of concrete. The addition of multivalent cations reduced swelling capacity and changed swelling kinetics, resulting in fast deswelling behavior and the creation of a mechanically stiff shell in certain hydrogel compositions. Notably, when hydrogel particles were added to a mixture, they reduced shrinkage while encouraged the creation of particular inorganic phases within the void area formerly held by the swelled particle.

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Section: Methodsmentioning

confidence: 99%

Application of Polyacrylic Hydrogel in Durability and Reduction of Environmental Impacts of Concrete through ANN

Peng

Zandi

et al. 2022

Gels

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“…Therefore, it has been widely used for the optimization of various intelligence algorithms. For example, the hybrid GWO-ELM algorithm [23,24] has been successfully used for solving complex problems in different areas and shows better performance than other algorithms.…”

Section: No Notationsmentioning

confidence: 99%

Forecasting the Collapse-Induced Ground Vibration Using a GWO-ELM Model

et al. 2022

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Blasting demolition is a popular method in the area of building demolishing. Due to the complex process of the building components’ collapse, it is difficult to predict the collapse-induced ground vibrations. As the accuracy of the empirical equation in predicting the collapse-induced ground vibration is not high, there is a significant risk of damage to the surrounding structures. To mitigate this risk, it is necessary to control and predict the peak particle velocity (PPV) and dominant frequency of ground vibration with higher accuracy. In this study, the parameters on the PPV and frequency of collapse-induced ground vibration are analyzed based on the Hertz theory. Then, fall tests are performed to simulate the collapse process of structural components and to investigate the characteristics of influential parameters on PPV and frequency. Using kernel density estimation (KDE) and Pearson correlation, the PPV and frequency are correlated with the distance from the falling point to the monitored point (R) and the mass of the falling structural component (M). Using recorded ground vibration data, the PPV and frequency are predicted using an extreme learning machine in combination with gray wolf optimization. The efficiency of the proposed algorithm is compared with other predictive models. The results indicate that the accuracy pre-diction of the proposed algorithm is better than those of plain extreme learning machines and the empirical equations, which indicates that the approach can be applied for PPV and frequency prediction of collapse-induced ground vibrations during blasting demolition.

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“…Considerable research has been done on the development of metaheuristic algorithms, and some of them have become well known for their effectiveness in solving optimization problems [44][45][46]. e metaheuristic algorithms primarily vary in the balance between their two main phases-exploration and exploitation [47].…”

Section: Metaheuristic Optimization Algorithm: Jellyfish Searchmentioning

confidence: 99%

Jellyfish Search-Optimized Deep Learning for Compressive Strength Prediction in Images of Ready-Mixed Concrete

Chou

Tjandrakusuma

Liu

2022

Computational Intelligence and Neuroscience

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Most building structures that are built today are built from concrete, owing to its various favorable properties. Compressive strength is one of the mechanical properties of concrete that is directly related to the safety of the structures. Therefore, predicting the compressive strength can facilitate the early planning of material quality management. A series of deep learning (DL) models that suit computer vision tasks, namely the convolutional neural networks (CNNs), are used to predict the compressive strength of ready-mixed concrete. To demonstrate the efficacy of computer vision-based prediction, its effectiveness using imaging numerical data was compared with that of the deep neural networks (DNNs) technique that uses conventional numerical data. Various DL prediction models were compared and the best ones were identified with the relevant concrete datasets. The best DL models were then optimized by fine-tuning their hyperparameters using a newly developed bio-inspired metaheuristic algorithm, called jellyfish search optimizer, to enhance the accuracy and reliability. Analytical experiments indicate that the computer vision-based CNNs outperform the numerical data-based DNNs in all evaluation metrics except the training time. Thus, the bio-inspired optimization of computer vision-based convolutional neural networks is potentially a promising approach to predict the compressive strength of ready-mixed concrete.

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Assessment of composite beam performance using GWO–ELM metaheuristic algorithm

Cited by 19 publications

References 212 publications

Application of Polyacrylic Hydrogel in Durability and Reduction of Environmental Impacts of Concrete through ANN

Application of Polyacrylic Hydrogel in Durability and Reduction of Environmental Impacts of Concrete through ANN

Forecasting the Collapse-Induced Ground Vibration Using a GWO-ELM Model

Jellyfish Search-Optimized Deep Learning for Compressive Strength Prediction in Images of Ready-Mixed Concrete

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