Feasibility of computational intelligent techniques for the estimation of spring constant at joint of structural glass plates: a dome-shaped glass panel structure

Hussain, Saddam; Chen, Pei-Shan; Koizumi, Nagisa; Rufa’i, Imran; Rotimi, Abdulazeez; Malami, Salim Idris; Abba, S.I.

doi:10.1007/s40940-022-00209-6

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…Further, sensitivity analysis was done to figure out which parameters were most valuable and vital to the target variable [5], [6], [7]. This step is the most important AI-based step to take to achieve the best results with a minimal amount of input according to several sources [5], [6], [8], [9]. Next is using the models or their different combinations in processing the data.…”

Section: Methodsmentioning

confidence: 99%

Testing Artificial Intelligence Models for Solar Cook Stove Performance Prediction: Case Study of Agadez, Niger

Danjuma,

Kawu,

Abba Bashir

et al. 2024

Kbes

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Solar cook stoves are becoming an increasingly popular and sustainable option for cooking food, especially in areas where access to traditional cooking fuels is limited or where deforestation is a concern. However, the performance of solar cook stoves can be affected by various factors such as weather conditions, time of day, and geographic location. The developed stove was tested in Agadez, Niger Republic on 9th December 2022, in the cold season, and obtained a temperature of 76.4 ◦C with radiation at noon of 727 W/m2 and a total daily radiation of 5044 W/m2. To improve the performance of solar cook stoves, researchers have turned to artificial intelligence (AI) based models, specifically artificial neural networks (ANN), for prediction. In this research, ANN was adopted to predict the performance of solar cook stoves. The ANN model employed the use of feed-forward back propagation as network type, trainlm as a training function, learngdm as learning function, and Mean Square Error (MSE) as performance function, best result was obtained with 2 layers, 10 neurons in the hidden layer and tansig as a transfer function. For the algorithms, Levenberg-Marrquadt was used in training. The experimental data were divided by 70% for training and 30% for testing. Based on the various interpretations provided, it appears that the AI based models for the prediction of Solar Cook Stove performance have shown promising results. In the sensitivity analyses, M11 was the function for the Test Temperature, and M12 was for Test Temperature and Solar Radiation. Model M11: Training Phase: MSE = 0.1, RMSE = 0.2, Testing Phase: MSE = 0.05, RMSE = 0.15, Model M12, Training Phase: MSE = 0.05, Testing Phase: MSE = 0.05, RMSE= 0.1, RMSE = 0.45, Model M12 has a higher RMSE during testing compared to Model M11, indicating lower predictive accuracy. The scatter plots of observed versus predicted values for both M11 and M12 indicate a fairly strong positive correlation. Furthermore, the radar plots of goodness of fit (measured by R-squared values) suggest that both models perform relatively well, with M12 showing a higher degree of fit compared to M11. The error of fit graph, presented as a bar plot, also shows that the testing phase for M12 is significantly more accurate, with a much lower mean squared error (MSE) and root mean squared error (RMSE) compared to M11.

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Section: Methodsmentioning

confidence: 99%

Testing Artificial Intelligence Models for Solar Cook Stove Performance Prediction: Case Study of Agadez, Niger

Danjuma,

Kawu,

Abba Bashir

et al. 2024

Kbes

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“…In this regard, it is widely known that specific modeling techniques and verification methods are required to analyze and maximize the robustness, ultimate resistance, serviceability, and stability of glass members, which are typically made of a rather brittle/vulnerable material [2], but are indeed expected to ensure safe and efficient load-bearing structural performances (Figure 1). For this reason, especially in the last two decades, a wide set of literature studies have been carried out to improve and support the structural analysis and design of glass members or systems under various loading and boundary configurations of technical significance, such as columns, beams, plates in compression or shear [3][4][5][6], etc. Regardless the specific loading and boundary configuration and the intrinsic brittleness of glass material, a major attention goes to the design of laminated glass (LG) because the thermoplastic polymers commonly used to bond the glass sheets together are significantly temperature and load-time sensitive [2].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Regularization Backpropagation Neural Network for Glass Beams in Lateral–Torsional Buckling

Hussain,

Bedon,

Kumar

et al. 2023

Advances in Civil Engineering

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The lateral–torsional buckling (LTB) performance assessment of laminated glass (LG) beams is a remarkably critical issue because—among others—it can involve major consequences in terms of structural safety. Knowledge of LTB load-bearing capacity (in terms of critical buckling load Fcr and corresponding lateral displacement dLT), in this regard, is, thus, a primary step for more elaborated design considerations. The present study examines how machine learning (ML) techniques can be used to predict the response of laterally unrestrained LG beams in LTB. The potential and accuracy of artificial neural networks (ANN), based on ML methods, are addressed based on validation toward literature data. In particular, to detect the best-performing data-driven ML model, the load-bearing capacity of LG beams (i.e., Fcr and dLT) is set as output response, while geometric properties (length, width, thickness) and material features (for glass and interlayers) are used as input variables. A major advantage is taken from a literature database of 540 experiments and simulations carried out on two-ply LG beams in LTB setup. To determine the best-performing ANN model, different strategies are considered and compared. Additionally, the Bayesian regularization backpropagation (trainbr) algorithm is used to optimize the input–output relationship accuracy. The suitability of present modeling strategy for LG beams in LTB is quantitatively discussed based on error and performance trends.

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“…Another example of the Lap assembling method is shown in Figure 2 In common, the Finite element method (FEM) can analyze a glass panel by meshing it into many elements, as in the meshed glass panel shown in Figure 3. However, it is difficult to analyze the whole structure with FEM because all the glass panels consisting in the structure have to mesh and analyzed (Hadimam et al, 2008;Overed et al, 2007;Hussain et al, 2022;Honfi et al, 2014), which may cost more Central Processing Unit (CPU) time. Accordingly, a methodology for structural design and mechanical analysis for the whole structure is proposed, which transforms the glass panels into equivalent beams of the same bending stiffness and axial stiffness (Chen & Tsai, 2019;Giaralis & Spanos, 2010).…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Spring Constants of Structural Glass Panel Joints Under In-Plane Loading

Hussain¹,

Chen²,

Koizumi³

et al. 2023

JST

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Commonly, the columns and beams of glass panels are frequently subjected to in-plane loading, in which their joints will transfer the in-plane forces. Therefore, it is necessary to investigate the spring constants of the joints of these glass panels for the mechanical analysis of the structures. However, few issues were published on this subject, so estimating the spring constants of glass structure joints is important. Devote themselves to proposing methods to evaluate the spring constants of the joints of structural glass panels. This study tests two types of glass panels with thicknesses of 12 mm and 19 mm based on static and cycling loading. In addition, two types of Cushions: (1) aluminum and (2) rubber with a hardness of 65 and 90 degrees, are set between steel bolt(s) and glass panel(s) for the experiments. The spring constants are determined by the ratios of measured loads and the displacements between the glass panels and bolts. In addition, the authors proposed an equation to evaluate the bending spring constant from its axial spring constant determined by the loading tests. The experimental results showed that the joints with the aluminum cushion appear exactly non-linear elasticity while loading and unloading. Also, the pin junction within the central region (no Curve) is 0.6mm. It is also determined that aluminum (cushion) slides of approximately ±0.3mm under compression and tension. While loading (Tension/compression) is incremental, rubber acts nonlinearly but linear as unloaded.

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Feasibility of computational intelligent techniques for the estimation of spring constant at joint of structural glass plates: a dome-shaped glass panel structure

Cited by 5 publications

References 47 publications

Testing Artificial Intelligence Models for Solar Cook Stove Performance Prediction: Case Study of Agadez, Niger

Testing Artificial Intelligence Models for Solar Cook Stove Performance Prediction: Case Study of Agadez, Niger

Bayesian Regularization Backpropagation Neural Network for Glass Beams in Lateral–Torsional Buckling

Experimental Study on Spring Constants of Structural Glass Panel Joints Under In-Plane Loading

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