“…It's clear that while there exists a minor variance between the experimental outcomes and the predictions generated by ANN, the disparities in RSM are notably substantial and pertinent. This outcome aligns well with the findings from (Boumaaza et al 29 ), which demonstrated that ANN models outperformed those derived from RSM. Nevertheless, it is crucial to note that the ANN models adeptly approximated the experimental observations.Figure 18.Bar graphs depicting the anticipated inaccuracies and errors for (a), (b) stress and (c), (d) Displacement.…”
Section: Resultssupporting
confidence: 91%
“…The study aims to offer practical recommendations for process parameters, contributing to a robust magnesium composites database for manufacturing and mechanical performance assessment. But, Boumaaza et al 29 investigated using biochar from Washingtonia filifera rachis waste (WFRB) as a sustainable substitute for cement in mortars. They found that substituting 1% of cement with WFRB pyrolyzed at 500°C significantly improved flexural strength (5%), displacement (20%), and flexural modulus (265%) compared to the reference mortar.…”
This study explores the relationship between natural fiber filling density (10%, 15%, 25%) and its impact on the bending properties of polymer compounds reinforced with Diss, Sisal and Luffa fibers. Using advanced techniques like fiber analysis and Fourier transform infrared spectrometry (FTIR), the research reveals that a 25% filling density results in the highest stress values (25.61 MPa, 22.21 MPa and 20.88 MPa) for Diss, Sisal and Luffa compounds, respectively, fostering robust bonds in Diss-reinforced polymers. The Artificial Neural Network (ANN) model demonstrates superior predictive capability with correlation coefficients exceeding 0.99 for stress and displacement, outperforming Response Surface Methodology (RSM). Analysis of Variance (ANOVA) underscores the impact of sample section parameters and fiber rate on stress, establishing the significance of type parameters and fiber rate on displacement. This integration of ANN and RSM represents a paradigm shift in predicting bending mechanical properties, advancing our understanding of composite materials for innovative applications.
“…It's clear that while there exists a minor variance between the experimental outcomes and the predictions generated by ANN, the disparities in RSM are notably substantial and pertinent. This outcome aligns well with the findings from (Boumaaza et al 29 ), which demonstrated that ANN models outperformed those derived from RSM. Nevertheless, it is crucial to note that the ANN models adeptly approximated the experimental observations.Figure 18.Bar graphs depicting the anticipated inaccuracies and errors for (a), (b) stress and (c), (d) Displacement.…”
Section: Resultssupporting
confidence: 91%
“…The study aims to offer practical recommendations for process parameters, contributing to a robust magnesium composites database for manufacturing and mechanical performance assessment. But, Boumaaza et al 29 investigated using biochar from Washingtonia filifera rachis waste (WFRB) as a sustainable substitute for cement in mortars. They found that substituting 1% of cement with WFRB pyrolyzed at 500°C significantly improved flexural strength (5%), displacement (20%), and flexural modulus (265%) compared to the reference mortar.…”
This study explores the relationship between natural fiber filling density (10%, 15%, 25%) and its impact on the bending properties of polymer compounds reinforced with Diss, Sisal and Luffa fibers. Using advanced techniques like fiber analysis and Fourier transform infrared spectrometry (FTIR), the research reveals that a 25% filling density results in the highest stress values (25.61 MPa, 22.21 MPa and 20.88 MPa) for Diss, Sisal and Luffa compounds, respectively, fostering robust bonds in Diss-reinforced polymers. The Artificial Neural Network (ANN) model demonstrates superior predictive capability with correlation coefficients exceeding 0.99 for stress and displacement, outperforming Response Surface Methodology (RSM). Analysis of Variance (ANOVA) underscores the impact of sample section parameters and fiber rate on stress, establishing the significance of type parameters and fiber rate on displacement. This integration of ANN and RSM represents a paradigm shift in predicting bending mechanical properties, advancing our understanding of composite materials for innovative applications.
“…Here are the key components and equations commonly used in RSM: Experimental design: RSM typically employs a design matrix to determine the combination of factor levels for conducting experiments. The most commonly used design is the Central Composite Design (CCD), which consists of a combination of factorial points, axial points, and center points 44 . Response function: The response function represents the relationship between the factors and the response variable.…”
The stiffness (K) and slenderness factor (λ) of a steel plate-based damper has been studied on the basis of elastic-inelastic-plastic buckling (EIP) modes and flexural/shear/flexural-shear failure mechanisms (FSF-S), which has been designed for the improvement of the behavior of concentrically braced frames. Steel plate-based dampers offer significant benefits in terms of mode shapes and failure mechanisms, contributing to improved dynamic performance, enhanced structural resilience, and increased safety of civil engineering structures. Their effectiveness in mitigating dynamic loads makes them a valuable tool for engineers designing structures to withstand extreme environmental conditions and seismic events. This study was undertaken by using the learning abilities of the response surface methodology (RSM), artificial neural network (ANN) and the evolutionary polynomial regression (EPR). Steel plate dampers are special structural designs used to withstand the effect of special loading conditions especially seismic effects. Its design based on the prediction of its stiffness (K) and slenderness factor (λ) cannot be overlooked in the present-day artificial intelligence technology. In this research work, thirty-three entries based on the steel plate damper geometrical properties were recorded and deployed for the intelligent forecast of the fundamental properties (λ and K). Design ratios of the steel plate damper properties were considered and models behavior was recorded. From the outcome of the model, it can be observed that even though the EPR and ANN in that order outclassed the other techniques, the RSM produced model minimization and maximization features of the desirability levels, color factor scales and 3D surface observation, which shows the real model behaviors. Overall, the EPR with R2 of 0.999 and 1.000 for the λ and K, respectively showed to be the decisive model but the RSM has features that can be beneficial to the structural design of the studied steel plate damper for a more robust and sustainable construction. With these performances recorded in this exercise, the techniques have shown their potential to be applied in the prediction of steel damper stiffness with optimized characteristic features to withstand structural stresses.
“…In order to reduce the error increase in the FRP constraint model due to FRP failure, Gian proposed an effective strain analysis model of FRP-confined cylinder concrete columns based on a theoretical analysis and the test data [ 26 ]. Wu and Wei studied the effects of the ultimate tensile strain of FRP and the strength of core concrete on the axial compressive mechanical behaviours of FRP-confined circular concrete columns and proposed an axial compression constitutive model of FRP-confined circular concrete columns based on the test results [ 27 ]. With the depth of research on FRP-confined concrete columns increasing, researchers have carried out a series of research works on FRP-confined non-circular concrete columns.…”
The prediction and control of the mechanical behaviours of fibre-reinforced polymer (FRP)-confined circular concrete columns subjected to axial loading are directly related to the safety of the structures. One challenge in building a mechanical model is understanding the complex relationship between the main parameters affecting the phenomenon. Artificial intelligence (AI) algorithms can overcome this challenge. In this study, 298 test data points were considered for FRP-confined circular concrete columns. Six parameters, such as the diameter-to-fibre thickness ratio (D/t) and the tensile strength of the FRP (ffrp) were set as the input sets. The existing models were compared with the test data. In addition, artificial neural networks (ANNs) and support vector regression (SVR) were used to predict the mechanical behaviour of FRP-confined circular concrete columns. The study showed that the predictive accuracy of the compressive strength in the existing models was higher than the peak compressive strain for the high dispersion of material deformation. The predictive accuracy of the ANN and SVR was higher than that of the existing models. The ANN and SVR can predict the compressive strength and peak compressive strain of FRP-confined circular concrete columns and can be used to predict the mechanical behaviour of FRP-confined circular concrete columns.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
