Laser-based intelligent perception method for tunnel invasion

Yang, Hao; He, Xuhui; Xu, Xiangyang; Wei, Xiaojun; Wang, Youwu

doi:10.1080/15376494.2021.1979141

Cited by 9 publications

(3 citation statements)

References 50 publications

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“…α is the learning rate, which is one of the parameters that control the extent of parameter update. L is the loss calculated by Equations ( 4)- (7).…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…During the large cross-section tunnel construction, challenges include poor geological conditions, support structure parameters, and effects on the surrounding rocks [3,4]. To overcome all of these difficulties, artificial intelligence (AI) technology has been widely adopted in the tunnel construction process, specifically in the area of AI for poor geological prediction [5], disaster risk evaluation [6], construction decision-making [7], and deformation prediction [8]. Under tunnel construction in complex construction conditions, the values and the times of the structure deformation can directly reflect the stability of the surrounding rock and support structure.…”

Section: Introductionmentioning

confidence: 99%

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The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels

Zhang,

Mei,

Wang

et al. 2024

Applied Sciences

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The deformation of tunnel support structures during tunnel construction is influenced by geological factors, geometrical factors, support factors, and construction factors. Accurate prediction of tunnel support structure deformation is crucial for engineering safety and optimizing support parameters. Traditional methods for tunnel deformation prediction have often relied on numerical simulations and model experiments, which may not always meet the time-sensitive requirements. In this study, we propose a fusion deep neural network (FDNN) model that combines multiple algorithms with a complementary tunnel information encoding method. The FDNN model utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to extract features related to tunnel structural deformation. FDNN model is used to predict deformations in the Capital Ring Expressway, and the predictions align well with monitoring results. To demonstrate the superiority of the proposed model, we use four different performance evaluation metrics to analyze the predictive performance of FDNN, DNN, XGBoost, Decision Tree Regression (DTR), and Random Forest Regression (RFR) methods. The results indicate that FDNN exhibits high precision and robustness. To assess the impact of different data types on the predictive results, we use tunnel geometry data as the base and combine geological, support, and construction data. The analysis reveals that models trained on datasets comprising all four data types perform the best. Geological parameters have the most significant impact on the predictive performance of all models. The findings of this research guide predicting tunnel construction parameters, particularly in the dynamic design of support parameters.

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“…α is the learning rate, which is one of the parameters that control the extent of parameter update. L is the loss calculated by Equations ( 4)- (7).…”

Section: Deep Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels

Zhang,

Mei,

Wang

et al. 2024

Applied Sciences

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show abstract

“…Dhiman et al [ 23 ] proposed two methods primarily based on stereo-vision evaluation of street environments beforehand of the vehicle, and they additionally designed two models for deep-learning-based pothole detection. Intelligent monitoring methods [ 24 , 25 , 26 ] will be an essential guarantee for the realization of smart operation and maintenance.…”

Section: Introductionmentioning

confidence: 99%

Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN

Zhao

Shi

et al. 2022

Sensors

Self Cite

183

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The intelligent crack detection method is an important guarantee for the realization of intelligent operation and maintenance, and it is of great significance to traffic safety. In recent years, the recognition of road pavement cracks based on computer vision has attracted increasing attention. With the technological breakthroughs of general deep learning algorithms in recent years, detection algorithms based on deep learning and convolutional neural networks have achieved better results in the field of crack recognition. In this paper, deep learning is investigated to intelligently detect road cracks, and Faster R-CNN and Mask R-CNN are compared and analyzed. The results show that the joint training strategy is very effective, and we are able to ensure that both Faster R-CNN and Mask R-CNN complete the crack detection task when trained with only 130+ images and can outperform YOLOv3. However, the joint training strategy causes a degradation in the effectiveness of the bounding box detected by Mask R-CNN.

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Intelligent monitoring and residual analysis of tunnel point cloud data based on free-form approximation

Wang

Shi

et al. 2022

Mechanics of Advanced Materials and Structures

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Laser-based intelligent perception method for tunnel invasion

Cited by 9 publications

References 50 publications

The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels

The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels

Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN

Intelligent monitoring and residual analysis of tunnel point cloud data based on free-form approximation

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