Method for determining load magnitude and location from the plastic deformation of fixed beams using a neural network

Yin, Jian; Gu, Jinyu; Chen, Yongdang; Tang, Wenbin; Zhang, Feng

doi:10.1177/00368504211003385

Cited by 3 publications

(3 citation statements)

References 47 publications

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“…The ANN-based model has been widely applied to other engineering fields, a backpropagation ANN (BP-ANN) is constructed to more accurately identify the information of magnitude and location of the load resulting in a fixed beam plastic deformation. 83 An expanded dataset helps establish a well-developed BP-ANN model that could be used to improve the accuracy of prediction. Also, an ANN algorithm was performed to construct a prediction model of breakthrough extruding force regarding the great-scale extrusion operation.…”

Section: Computational Studymentioning

confidence: 99%

Machine learning in the evaluation and prediction models of biochar application: A review

et al. 2023

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This article reviews recent studies applying machine learning (ML) approaches to biochar applications. We first briefly introduce the general biochar production process. Various aspects are contained, including the biochar application in the elimination of heavy metals and/or organic compounds and the biochar application in environmental and economic scopes, for instance, food security, energy, and carbon emission. The utilization of ML methods, including ANN, RF, and NN, plays a vital role in evaluating and predicting the efficiency of biochar absorption. It has been proved that ML methods can validly predict the adsorption effectiveness of biochar for water heavy metals with higher accuracy. Moreover, the literature proposed a comprehensive data-driven model to forecast biochar yield and compositions under various biomass input feedstock and different pyrolysis criteria. They said a 12.7% improvement in prediction accuracy compared to the existing literature. However, it might need further optimization in this direction. In summary, this review concludes increasing studies that a well-trained ML method can sufficiently reduce the number of experiment trials and working times associated with higher prediction accuracy. Moreover, further studies on ML applications are needed to optimize the trade-off between biochar yield and its composition.

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Section: Computational Studymentioning

confidence: 99%

Machine learning in the evaluation and prediction models of biochar application: A review

et al. 2023

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“…Iteratively update center parameter, width parameter, and weight, and then calculate the loss. When the loss is within the acceptable range, stop training [23].…”

Section: Training Of Evaluation Modelmentioning

confidence: 99%

Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network

Jian-ping

Gao

2021

Complexity

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Due to the lack of maintenance support samples, maintenance support effectiveness evaluation based on the deep neural network often faces the problem of small sample overfitting and low generalization ability. In this paper, a neural network evaluation model based on an improved generative adversarial network (GAN) and radial basis function (RBF) network is proposed to amplify maintenance support samples. It adds category constraint based on category probability vector reordering function to GAN loss function, avoids the simplification of generated sample categories, and enhances the quality of generated samples. It also designs a parameter initialization method based on parameter components equidistant variation for RBF network, which enhances the response of correct feature information and reduces the risk of training overfitting. The comparison results show that the mean square error (MSE) of the improved GAN-RBF model is 5.921 × 10 − 4 , which is approximately 1/2 of the RBF model, 1/3 of the Elman model, and 1/5 of the BP model, while its complexity remains at a reasonable level. Compared with traditional neural network evaluation methods, the improved GAN-RBF model has higher evaluation accuracy, better solves the problem of poor generalization ability caused by insufficient training samples, and can be more effectively applied to maintenance support effectiveness evaluation. At the same time, it also provides a good reference for evaluation research in other fields.

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“…Chen et al 22 proposed a machine learning method that predicted the position, velocity, and load duration of a rigid body impact on a hemispherical shell subject to permanent plastic deformation based on a reverse engineering method. In addition, in the previous study work, 23 we used the reverse engineering method combined with the artificial neural network algorithm to study the magnitude and position of the static load on the large deformation of the one-dimensional beam. The effectiveness of the method is verified by comparing with the calculation results of the finite element model (FEM).…”

Section: Introductionmentioning

confidence: 99%

Method for identifying the impact load condition of thin-walled structure damage based on PSO-BP neural network

Song

Chen

2022

Science Progress

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Thin-walled structures (TWS) were widely used in engineering equipment, and may be subjected to impact loads to produce different degrees of structural damage during application. However, it is a difficult problem to determine the impact load conditions for these structural damages. In this study, we developed a novel method of identifying the impact load condition of the thin-walled structure damage, which is based on particle swarm optimization-backpropagation (PSO-BP) neural network. First, the known impact position and velocity are applied to the finite element model (FEM) of the TWS to produce permanent plastic deformation, and to fit the characteristic shape of the deformation is needed by invoking the multivariate polynomial function. Then, the method is devoted to build a basic data set. With impact position and velocity as input and function coefficients as output, a model of extended PSO-BP neural network is established. Besides, the basic sample set is expanded to solve the lack of samples. Ultimately, utilizing the expanded total sample set as training data, function coefficients, impact position and velocity will be outputted. On the basis of the known functional coefficients of deformed surfaces, a model of predictive PSO-BP neural network is established and predicted. Furthermore, we predicted the collision position and velocity using a conventional BP neural network in the same way. Finally, the predicted impact position and velocity is compared with the analysis results of the FEM, which verifies that the PSO-BP neural network algorithm has high accuracy.

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Method for determining load magnitude and location from the plastic deformation of fixed beams using a neural network

Cited by 3 publications

References 47 publications

Machine learning in the evaluation and prediction models of biochar application: A review

Machine learning in the evaluation and prediction models of biochar application: A review

Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network

Method for identifying the impact load condition of thin-walled structure damage based on PSO-BP neural network

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