Hybrid Krill Herd-ANN Model for Prediction Strength and Stiffness of Bolted Connections

Faridmehr, Iman; Nikoo, Mehdi; Baghban, Mohammad Hajmohammadian; Pucinotti, Raffaele

doi:10.3390/buildings11060229

Cited by 10 publications

(2 citation statements)

References 61 publications

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“…In this instance, MLR analyzes the observed data and fits it into a linear equation to determine the correlation between two or more input variables. Multiple linear regression involves summarizing data and investigating the relationship between variables [ 40 , 41 ]. The general formula for multiple regression models is given in Equation (7) below, where Y is a dependent variable, β 0 is a constant, and β j is a regression coefficient ( i = 1, 2,…, n ).…”

Section: Artificial Intelligence Estimation Of Compressive Strengthmentioning

confidence: 99%

Optimization of Fly Ash—Slag One-Part Geopolymers with Improved Properties

et al. 2023

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One-part geopolymer concrete/mortar is a pre-mixed material made from industrial by-products and solid alkaline activators that only requires the addition of water for activation. Apart from being environmentally friendly, it also reduces complexity and improves consistency in the mixing process, leading to more efficient production and consistent material properties. However, developing one-part geopolymer concrete with desirable compressive strength is challenging because of the complexity of the chemical reaction involved, the variability of the raw materials used, and the need for precise control of curing conditions. Therefore, 80 different one-part geopolymer mixtures were compiled from the open literature in this study, and the effects of the constituent materials, the dosage of alkaline activators, curing condition, and water/binder ratio on the 28-day compressive strength of one-part geopolymer paste were examined in detail. An ANN model with the Levenberg–Marquardt algorithm was developed to estimate one-part geopolymer’s compressive strength and its sensitivity to binder constituents and alkaline dosage. The ANN model’s weights and biases were also used to develop a CPLEX-based optimization method for achieving maximum compressive strength. The results confirm that the compressive strength of one-part geopolymer pastes increased by increasing the Na2O content of the alkaline source and the slag dosage; however, increasing the Na2O content in alkaline sources beyond 6% by fly ash weight led to decreasing the compressive strength; therefore, the optimum alkaline activator dosage by weight of fly ash was to be 12% (i.e., 6% Na2O). The proposed ANN model developed in this study can aid in the production and performance tuning of sustainable one-part geopolymer concrete and mortar for broader full-scale applications.

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Section: Artificial Intelligence Estimation Of Compressive Strengthmentioning

confidence: 99%

Optimization of Fly Ash—Slag One-Part Geopolymers with Improved Properties

et al. 2023

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“…The neural network, a method of machine learning, is widely used in various fields [16][17][18][19][20][21][22][23]. Abuodeh et al [24,25] used machine learning techniques to predict behavior of RC beams and compressive strength of ultra-high-performance concrete.…”

Section: Introductionmentioning

confidence: 99%

Thermal–Mechanical Coupling Evaluation of the Panel Performance of a Prefabricated Cabin-Type Substation Based on Machine Learning

Lei

Ouyang

Wang

et al. 2021

Fire

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The panel performance of a prefabricated cabin-type substation under the impact of fires plays a vital role in the normal operation of the substation. However, current evaluations of the panel performance of substations under fire still focus on fire resistance tests, which seldom consider the relationship between fire behavior and the mechanical load of the panel under the impact of fires. Aiming at the complex and uncertain relationship between the thermal and mechanical performance of the substation panel under impact of fires, this paper proposes a machine learning method based on a BP neural network. First, the fire resistance test and the stress test of the panel is carried out, then a machine learning model is established based on the BP neural network. According to the collected data, the model parameters are obtained through a series of training and verification processes. Meanwhile, the correlation between the panel performance and fire resistance was obtained. Finally, related parameters are input into the thermal–mechanical coupling evaluation model for the substation panel performance to evaluate the fire resistance performance of the substation panel. To verify the correctness of the established model, numerical simulation of the fire test and stress test of the panel is conducted, and numerical simulation samples are predicted by the trained model. The results show that the prediction curve of neural network is closer to the real results compared with the numerical simulation, and the established model can accurately evaluate the thermal–mechanical coupling performance of the substation panel under fire.

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