Predictive models for mechanical properties of expanded polystyrene (EPS) geofoam using regression analysis and artificial neural networks

Akiş, Ebru; Güven, Gül; Sadigh, Bahram Lotfi

doi:10.1007/s00521-022-07014-w

Cited by 11 publications

(5 citation statements)

References 38 publications

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“…Two polarizing motors are symmetrically installed under the tray, and when the polarizing motors vibrate, the tray is driven to vibrate so that the grain particles lay flat, solving the problem of grain particles stacking on each other. In order to prevent the grain particles from vibrating out at the edge of the tray, a 5 mm high edge is set around each side of the tray to make the grain particles vibrate and lay flat inside the tray [16][17].…”

Section: Gm Detection Devicementioning

confidence: 99%

Prediction Model of Grain Mold Probability Based on Naive Bayes Algorithm

2021

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Grain is by far the largest amount of food stored in the world. The early prediction of grain mold (GM) is mainly to be able to detect it in time before its significant growth activity to avoid causing loss of stored grain. Microorganisms are tiny and not visible to the naked eye in the early stage, if microbial colonies can be observed in the grain pile, these parts of the grain often already have serious deterioration, mold metabolism is more hazardous to produce mycotoxins, which has a more adverse impact on the safe storage of grain, in fact, as long as there is the growth of microorganisms such as mold in the grain, even if the colonies of mold are not visible to the naked eye, the grain quality will have significant changes. In order to ensure the quality of grain output, the best way is to prevent GM. In this regard, this paper suggests a model for predicting the probability of GM based on the Naive Bayes(NB) algorithm, cultivating GM samples, and comparing the prediction errors of these three models with the PSO-LSSVM model and ARIMA model to predict the probability of mold in samples, we know that the prediction error(PE) of the NB model is the smallest, which means that the difference between the predicted value of GM and the real value of the model is small, and the accuracy of using this model for prediction is more reliable. The accuracy of the model is more reliable.

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Section: Gm Detection Devicementioning

confidence: 99%

Prediction Model of Grain Mold Probability Based on Naive Bayes Algorithm

2021

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“…At present, China has mastered the position, speed and accuracy of some robot arms based on artificial intelligence control. Some scholars have studied the structure and function of artificial neural system and proposed to use computers as network units, take neurons as the center, and use their self-learning ability instead of relying on external information to obtain the knowledge needed in the brain's working process [7]. And the support vector machine plays a very important role in the whole brain network system.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Control Method of Construction Machinery Manipulator Based on Neural Network

2023

KME

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Neural network is one of the important basic knowledge in computer, chemistry and information science, microelectronics technology and other fields. It has good universality in dealing with nonlinear system problems, and is widely used in industrial automation and intelligent control. This paper mainly introduces the mathematical model, algorithm design and implementation method of the construction machinery robot arm, and studies and analyzes its kinematics. Based on the neural network and artificial neuron theory, this paper constructs the robot arm position recognition technology in the object feature database for the characteristics and requirements of the subject. Finally, the function of the robot arm control model is tested, and the test results show that the arm support of the model has a long elongation distance, which can meet the production requirements.

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“…Multiple researchers have developed correlations relating the initial modulus or the tangent modulus to density and strain rate [15,16] and through regression analysis [4,17]. In addition to that method, Akis [18] introduced artificial neural networks to predict the initial modulus and the compressive strength values at 1%, 5% and 10% strain, and both methods yield satisfactory results with a coefficient of correlation values greater than 0.901. In terms of the compressive strength correlating to density and strain rate, Vilau [19] developed four coefficients for EPS foam with densities of 11, 15, 20 and 25 kg/m 3 under low and high strain rates.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Compressive and Flexural Properties and Stacking Strength of Expanded Polystyrene Boxes: Experimental and Simulation Study

Jónsdóttir

Arason

et al. 2023

Applied Sciences

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Expanded polystyrene (EPS) boxes are used for the packaging of perishable and vulnerable goods during transportation; for instance, fresh fish fillets. It is important to minimize the weight and cost of the packaging materials while maximizing strength to avoid damage to the packaging and the product itself. EPS boxes have to withstand considerable loading, which arises due to rough handling and stacking during transport. This work focused on the compressive and flexural properties and stacking strength of 3 kg capacity EPS boxes with densities of 22 and 23 kg/m3, by combining experiments and simulation. Material properties were obtained from the compression test, and the behavior of EPS boxes under stacking load was investigated through both experiments and finite element simulations. The influences of density and different sample preparation methods on material properties and stacking strength were investigated. The results indicated that, with the density increasing by 1 kg/m3, the initial modulus rises 10–15% and the compressive strength increases by 7–8% in the compression test, while in the flexure test, the rupture stress increases by 3–7%. Additionally, an increase of around 2% was observed for the specimens cut with a hot wire compared to those cut with a table saw. However, because the failure mechanism for a box as a whole differs from that of small units in the compression and flexure tests, density has less of an impact on stacking strength. Finally, a good agreement was obtained between the simulation and stacking strength test results.

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Predictive models for mechanical properties of expanded polystyrene (EPS) geofoam using regression analysis and artificial neural networks

Cited by 11 publications

References 38 publications

Prediction Model of Grain Mold Probability Based on Naive Bayes Algorithm

Prediction Model of Grain Mold Probability Based on Naive Bayes Algorithm

Optimization of Control Method of Construction Machinery Manipulator Based on Neural Network

Assessment of Compressive and Flexural Properties and Stacking Strength of Expanded Polystyrene Boxes: Experimental and Simulation Study

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