scholarly journals Compaction Density Evaluation Model of Sand-Gravel Dam Based on Elman Neural Network With Modified Particle Swarm Optimization

2022 ◽  
Vol 9 ◽  
Author(s):  
Biao Liu ◽  
Yufei Zhao ◽  
Wenbo Wang ◽  
Biwang Liu

The compaction density of sand-gravel materials has a strong gradation correlation, mainly affected by some material source parameters such as P5 content (material proportion with particle size greater than 5 mm), maximum particle size and curvature coefficient. When evaluating the compaction density of sand-gravel materials, the existing compaction density evaluation models have poor robustness and adaptability because they do not take into full consideration the impact of material source parameters. To overcome the shortcomings of existing compaction density models, this study comprehensively considers the impact of material source parameters and compaction parameters on compaction density. Firstly, asymmetric data were fused and a multi-source heterogeneous dataset was established for compaction density analysis. Then, the Elman neural network optimized by the adaptive simulated annealing particle swarm optimization algorithm was proposed to establish the compaction density evaluation model. Finally, a case study of the Dashimen water conservancy project in China is employed to demonstrate the effectiveness and feasibility of the proposed method. The results show that this model performs high-precision evaluation of the compaction density at any position of the entire working area which can timely correct the weak area of compaction density on the spot, and reduce the number of test pit tests.

2021 ◽  
Vol 192 ◽  
pp. 3060-3069
Author(s):  
Barkat Ali ◽  
Saima Anwar Lashari ◽  
Wareesa Sharif ◽  
Abdullah Khan ◽  
Kamran ullah ◽  
...  

2021 ◽  
Vol 11 (5) ◽  
pp. 2137 ◽  
Author(s):  
Tian-Yau Wu ◽  
Chi-Chen Lin

The objective of this research is to investigate the feasibility of utilizing the Elman neural network to predict the surface roughness in the milling process of Inconel 718 and then optimizing the cutting parameters through the particle swarm optimization (PSO) algorithm according to the different surface roughness requirements. The prediction of surface roughness includes the feature extraction of vibration measurements as well as the current signals, the feature selection using correlation analysis and the prediction of surface roughness through the Elman artificial neural network. Based on the prediction model of surface roughness, the cutting parameters were optimized in order to obtain the maximal feed rate according to different surface roughness constraints. The experiment results show that the surface roughness of Inconel 718 can be accurately predicted in the milling process and thereafter the optimal cutting parameter combination can be determined to accelerate the milling process.


2020 ◽  
Vol 39 (6) ◽  
pp. 8713-8721
Author(s):  
Luo Yuan ◽  
Zhao Xiaofei ◽  
Qiu Yiyu

At present, the evaluation of normal teaching order and teaching quality has been seriously interfered by the impact of COVID-19. In order to ensure the quality of art classroom teaching, this article uses BP neural network technology to build a model for art teaching quality evaluation during the epidemic. Based on the introduction of the BP neural network model and the problems of art teaching quality evaluation, the article focuses on the art teaching quality evaluation indicators and the BP neural network algorithm and process. In addition, the article also uses an empirical method to verify the effect of the BP network model training method, and obtains the expected effect. Finally, it discusses the problem of information processing in art teaching evaluation.


Author(s):  
M. G. De Giorgi ◽  
D. Bello ◽  
A. Ficarella

The identification of the water cavitation regime is an important issue in a wide range of machines, as hydraulic machines and internal combustion engine. In the present work several experiments on a water cavitating flow were conducted in order to investigate the influence of pressures and temperature on flow regime transition. In some cases, as the injection of hot fluid or the cryogenic cavitation, the thermal effects could be important. The cavitating flow pattern was analyzed by the images acquired by the high-speed camera and by the pressure signals. Four water cavitation regimes were individuated by the visualizations: no-cavitation, developing, super and jet cavitation. As by image analysis, also by the frequency analysis of the pressure signals, different flow behaviours were identified at the different operating conditions. A useful approach to predict and on-line monitoring the cavitating flow and to investigate the influence of the different parameters on the phenomenon is the application of Artificial Neural Network (ANN). In the present study a three-layer Elman neural network was designed, using as inputs the power spectral density distributions of dynamic differential pressure fluctuations, recorded downstream and upstream the restricted area of the orifice. Results show that the designed neural networks predict the cavitation patterns successfully comparing with the cavitation pattern by visual observation. The Artificial Neural Network underlines also the impact that each input has in the training process, so it is possible to identify the frequency ranges that more influence the different cavitation regimes and the impact of the temperature. A theoretical analysis has been also performed to justify the results of the experimental observations. In this approach the nonlinear dynamics of the bubbles growth have been used on an homogenous vapor-liquid mixture model, so to couple the effects of the internal dynamic bubble with the other flow parameters.


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