Author
Shuai Zhao
Bio: Shuai Zhao is an academic researcher from Tongji University. The author has contributed to research in topics: Perceptron & Convolutional neural network. The author has an hindex of 4, co-authored 6 publications receiving 71 citations.
Papers
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TL;DR: The accuracy, F1 score, and intersection over union (IoU) for the proposed method are superior than those for the FCN, RGA, and OA with respect to 503 test images.
63 citations
TL;DR: A deep learning (DL)-based method for the instance segmentation of cracks from shield tunnel lining images using a mask region-based convolutional neural network (Mask R-CNN) incorporated with a morphological closing operation, and a relative optimal model is found.
Abstract: This paper presents a deep learning (DL)-based method for the instance segmentation of cracks from shield tunnel lining images using a mask region-based convolutional neural network (Mask R-CNN) in...
32 citations
TL;DR: An integrated deep learning method of water leakage inspection using tunnel lining point cloud data from mobile laser scanning that achieves automated detection and evaluation of tunnel lining water leakages in 3D space, which provides the inspectors with an intuitive overall 3D view of the detected water leakage and the leakage information.
Abstract: On-site manual inspection of metro tunnel leakages has been faced with the problems of low efficiency and poor accuracy. An automated, high-precision, and robust water leakage inspection method is vital to improve the manual approach. Existing approaches cannot provide the leakage location due to the lack of spatial information. Therefore, an integrated deep learning method of water leakage inspection using tunnel lining point cloud data from mobile laser scanning is presented in this paper. It is composed of three parts as follows: (1) establishment of the water leakage dataset using the acquired point clouds of tunnel linings; (2) automated leakage detection via a mask-region-based convolutional neural network; and (3) visualization and quantitative evaluation of the water leakage in 3D space via a novel triangle mesh method. The testing result reveals that the proposed method achieves automated detection and evaluation of tunnel lining water leakages in 3D space, which provides the inspectors with an intuitive overall 3D view of the detected water leakages and the leakage information (area, location, lining segments, etc.).
32 citations
TL;DR: A deep learning-based approach that extends the PANet model by adding a semantic branch which refines the process of crack evaluation to reduce inaccuracies associated with crack discontinuities and image skeletonization demonstrates its superiority at mitigating crack disjoint problems and skeletonization error.
30 citations
TL;DR: By using the proposed approach, the leakage‐area and scaling defects can be automatically classified and quantified with an overall accuracy of 89.3%, which is quite promising compared to the inherent uncertainty in geotechnical engineering.
27 citations
Cited by
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TL;DR: This study presented the state of practice of DL in geotechnical engineering, and depicted the statistical trend of the published papers, as well as describing four major algorithms, including feedforward neural, recurrent neural network, convolutional neural network and generative adversarial network.
Abstract: With the advent of big data era, deep learning (DL) has become an essential research subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful feature learning and expression capabilities compared with the traditional machine learning (ML) methods, which attracts worldwide researchers from different fields to its increasingly wide applications. Furthermore, in the field of geochnical engineering, DL has been widely adopted in various research topics, a comprehensive review summarizing its application is desirable. Consequently, this study presented the state of practice of DL in geotechnical engineering, and depicted the statistical trend of the published papers. Four major algorithms, including feedforward neural (FNN), recurrent neural network (RNN), convolutional neural network (CNN) and generative adversarial network (GAN) along with their geotechnical applications were elaborated. In addition, a thorough summary containing pubilished literatures, the corresponding reference cases, the adopted DL algorithms as well as the related geotechnical topics was compiled. Furthermore, the challenges and perspectives of future development of DL in geotechnical engineering were presented and discussed.
194 citations
TL;DR: This paper aims to present the state-of-the-art development and future trends of BIM, machine learning, computer vision and their related technologies in facilitating the digital transition of tunnelling and underground construction.
92 citations
TL;DR: An overview of ML techniques for structural engineering is presented in this article with a particular focus on basic ML concepts, ML libraries, open-source Python codes, and structural engineering datasets.
89 citations
TL;DR: The study concluded that the deep learning instance segmentation model performs better than conventional machine learning models and deep learning semantic segmentation models in detection and segmentation.
Abstract: The visual similarity of oil slick and other elements, known as look-alike, affects the reliability of synthetic aperture radar (SAR) images for marine oil spill detection. So far, detection and discrimination of oil spill and look-alike are still limited to the use of traditional machine learning algorithms and semantic segmentation deep learning models with limited accuracy. Thus, this study developed a novel deep learning oil spill detection model using computer vision instance segmentation Mask-Region-based Convolutional Neural Network (Mask R-CNN) model. The model training was conducted using transfer learning on the ResNet 101 on COCO as backbone in combination with Feature Pyramid Network (FPN) architecture for feature extraction at 30 epochs with 0.001 learning rate. Testing of the model was conducted using the least training and validation loss value on the withheld testing images. The model’s performance was evaluated using precision, recall, specificity, IoU, F1-measure and overall accuracy values. Ship detection and segmentation had the highest performance with overall accuracy of 98.3%. The model equally showed a higher accuracy for oil spill and look-alike detection and segmentation although oil spill detection outperformed look-alike with overall accuracy values of 96.6% and 91.0% respectively. The study concluded that the deep learning instance segmentation model performs better than conventional machine learning models and deep learning semantic segmentation models in detection and segmentation.
81 citations
TL;DR: In this article, the coupled effect of the soil spatial variations and the disturbance of ground surface on the existing tunnel using random finite difference method (RFDM) is investigated, where the soil Young's modulus is highlighted and simulated with horizontally stratified anisotropic random field that is discretized by the Karhunen-Loeve expansion.
67 citations