Method to Predict Interlayer Shear Strength of Asphalt Pavement Based Upon Improved Back Propagation Neural Network

2022 ◽  
Author(s):  
Tengfei Nian ◽  
Jinggao Li ◽  
Ping Li ◽  
Rui Guo ◽  
Jinguo Ge ◽  
...  
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Asphalt Pavement ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Interlayer Shear ◽  
Interlayer Shear Strength

Prediction of Shear Strength for Subgrade Soil under Freezing-Thawing Based on Back Propagation Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 610-614 ◽  
Author(s):  
Jing Long Chen ◽  
Pei Feng Cheng ◽  
Chuan Jun Yin
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Transfer Function ◽  
Sandy Soil ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Friction Angle ◽  
Back Propagation Neural Network ◽  
Linear Transfer Function ◽  
Propagation Network

Soil samples are taken from two experimental roads in Heilongjiang province for the test. Then a prediction of shear strength is carried out, basing on a three-layer BP (back propagation) network in Matlab, the hidden layer, output layer and training function of which adopt non-linear transfer function tansig, linear transfer function purelin, and trainbfg function respectively. It is found workable to predict factors influencing shearing strength using BP neural network with given soil properties. Prediction results of cohesion strength for clay show a better performance than those for sandy soil, while results of friction angle for sandy soil are better than those for clay. It is indicated that BP neural network does a better work in predicting the friction angle than that of cohesion.

Prediction of Peak Shear Strength of Rock Joints Based on Back-Propagation Neural Network

2021 ◽  
Vol 21 (6) ◽  
pp. 04021085
Author(s):  
Man Huang ◽  
Chenjie Hong ◽  
Jie Chen ◽  
Chengrong Ma ◽  
Changhong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Rock Joints ◽  
Peak Shear Strength

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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