GA-NN Monitoring Model and its Application on Surface Settlement

2012 ◽  
Vol 505 ◽  
pp. 453-457
Author(s):  
Tie Sheng Wang ◽  
Hai Yan Li ◽  
Bing Zhang ◽  
Kai Feng Ma
Keyword(s):  
Neural Network ◽  
Influence Factors ◽  
Surface Settlement ◽  
Shield Tunnel ◽  
Tunnel Excavation ◽  
Monitoring Model ◽  
Efficiency And Effectiveness ◽  
Genetic Neural Network ◽  
Set Up ◽  
Under Construction

Combining the advantages of basic genetic algorithm and neural network, analyze and set up GA & NN genetic neural network, explore and study the algorithm. The efficiency and effectiveness of this hybrid training has been significantly improved comparing with the single genetic evolution or BP training method, its versatility is better. The model is applied to predict the deformation of shield tunnel excavation. According to the effects of measured influence factors under construction, it can make the appropriate forecast to the surface settlement which is better than the conventional regression model. It shows that neural networks in the ground during tunneling shield analysis and prediction of settlement is practical and adaptable.

The Research of Ground Deformation Prediction Neural Network Model on City Underground Construction

2014 ◽  
Vol 926-930 ◽  
pp. 708-711
Author(s):  
Wen Xin Zhu
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Influence Factor ◽  
Ground Deformation ◽  
Influence Factors ◽  
Shield Tunnel ◽  
Construction Technology ◽  
Underground Construction ◽  
Deformation Prediction ◽  
Main Influence

This paper sees the underground construction shield tunnel and foundation trench as study object. It has analyzed disturbance mechanism and ground deformation mechanism, which were caused by shield tunnel and building pit construction. Through the system analysis of ground deformation influence factor, it has confirmed main influence factors, like geological environment condition of overall consideration, physical parameter and construction technology. And it has established ground deformation prediction model based on neural network. Then it has made sensitivity analysis of affecting ground deformation factor by neural network hierarchical analytical approach.

Surface Settlement Law Induced by Shield Tunnel Excavation Using Modified Gap Model

2012 ◽  
Vol 170-173 ◽  
pp. 1515-1519
Author(s):  
Wen Bo Li ◽  
Wen Pei Wang ◽  
Lian Jin Tao ◽  
Yin Tao Zhang
Keyword(s):  
Critical Value ◽  
Surface Settlement ◽  
Shield Tunnel ◽  
Gap Model ◽  
Tunnel Excavation ◽  
Maximum Settlement ◽  
Maximum Surface ◽  
Adjacent Buildings ◽  
Maximum Surface Settlement ◽  
Tunnel Depth

GAP model will be modified and programmed by FISH language, which can be used in FLAC2D programs. The modified GAP model is used to study the variation of surface settlement shape with depth, the variation of the maximum settlement value with depth, and the variation of settlement gradient with depth. The results show that: the settlement shape is narrow and deep with the conditions of shallow buried depth of tunnel; on the contrary, the settlement shape is wide and shallow; When the tunnel depth is less than the critical value, the tunnel depth and the maximum surface settlement is approximately linear; when the tunnel depth is greater than the critical value, the curve of maximum surface settlement value with depth becomes flat and with the increase of the tunnel, the surface settlement gradient gradually decreases and eventually tends to zero. It is more reasonable to assess the influence of tunnel excavation near adjacent buildings, using the maximum surface settlement and the settlement gradient as a control standard.

scholarly journals Prediction of Ground Settlement Induced by Slurry Shield Tunnelling in Granular Soils

2020 ◽  
Vol 6 (12) ◽  
pp. 2273-2289
Author(s):  
Mo'men Ayasrah ◽  
Hongsheng Qiu ◽  
Xiedong Zhang ◽  
Mohammad Daddow
Keyword(s):  
High Speed ◽  
Phase 1 ◽  
Field Measurements ◽  
Tunnel Boring Machine ◽  
Surface Settlement ◽  
Shield Tunnel ◽  
Tunnel Excavation ◽  
Theoretical Approaches ◽  
Geotechnical Investigations ◽  
Slurry Shield

Underground structures play an important role in achieving the requirements of rapid urban development such as tunnels, parking garages, facilities, etc. To achieve what is needed, new transportation methods have been proposed to solve traffic congestion problems by using of high-speed railway and subway tunnels. One of the issues in urban spaces due to tunnel excavation is considerable surface settlements that also induce problems for surface structures. There are a variety of published relationships concerned with field measurements and theoretical approaches to evaluating the amount of the maximum surface settlement value due to tunneling. This paper studies the ground surface settlement caused by the Greater Cairo Metro – Line 3 - Phase-1. This project was constructed by a slurry shield Tunnel Boring Machine (TBM). Therefore, this work consists of two parts. The first part presents the details of the project and monitoring results field and laboratory geotechnical investigations in order to determine the soil properties. The second part is to the comparison between the field measurements and theoretical approaches for surface settlement due to tunneling construction. At the end of the works, the results show that the more convenient methods which approach the field measurements, and the major transverse settlement occurs within the area about 2.6 times the diameter of the tunnel excavation. Doi: 10.28991/cej-2020-03091617 Full Text: PDF

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

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