Article
Construction & Building Technology
L. Minh Dang, Hanxiang Wang, Yanfen Li, Yesul Park, Chanmi Oh, Tan N. Nguyen, Hyeonjoon Moon
Summary: This research proposes a deep learning-based tunnel lining crack segmentation framework for automated detection and measurement of cracks. The experimental results demonstrate that the framework performs well.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2022)
Article
Construction & Building Technology
Zhong Zhou, Longbin Yan, Junjie Zhang, Yidi Zheng, Chenjie Gong, Hao Yang, E. Deng
Summary: In order to tackle the challenges of complex environmental interference and multiscale targets in deep learning-based tunnel lining defect identification, a novel segmentation algorithm called MC-TLD is proposed. MC-TLD utilizes a context-enhanced feature encoder to extract global context information, a multiscale attention-based atrous spatial pyramid pooling module to improve feature extraction for different scales of defects, and parameter learnable DUpsampling in the feature decoding module to output more accurate pixel prediction results. Experimental results demonstrate that MC-TLD achieves higher segmentation accuracy compared to other models, making it suitable for tunnel lining multiscale defect identification in complex environments.
CONSTRUCTION AND BUILDING MATERIALS
(2023)
Article
Computer Science, Interdisciplinary Applications
Hanxiang Wang, Yanfen Li, L. Minh Dang, Sujin Lee, Hyeonjoon Moon
Summary: An innovative framework combining weakly supervised learning methods and fully supervised learning methods is proposed for crack detection and segmentation in tunnel images. The proposed method successfully judges the risk levels of detected cracks and performs calculations on different types of cracks. Furthermore, the framework achieves comparable performance to manual annotation-based frameworks, showcasing the effectiveness of weakly supervised learning methods in accurately labeling images.
COMPUTERS IN INDUSTRY
(2021)
Article
Engineering, Multidisciplinary
Pengfei Shi, Fengting Zhu, Yuanxue Xin, Shen Shao
Summary: In this paper, an automatic pavement crack detection method called U(2)CrackNet is proposed. The method uses an encoding and decoding architecture with a two-level nested U-structure. The experimental results show that U(2)CrackNet can obtain clearer and more continuous cracks.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Review
Construction & Building Technology
Shanglian Zhou, Carlos Canchila, Wei Song
Summary: This paper reviews recent developments in deep learning-based crack segmentation methods and investigates their performance under the impact from different image types. Publicly available datasets and commonly adopted performance evaluation metrics are also summarized. Moreover, an image dataset, namely the Fused Image dataset for convolutional neural Network based crack Detection (FIND), was released to the public for deep learning analysis.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Construction & Building Technology
Thai Son Tran, Son Dong Nguyen, Hyun Jong Lee, Van Phuc Tran
Summary: Detecting and measuring cracks on a bridge deck is crucial for preventing further damage and ensuring safety. This study proposes a novel deep learning approach for detecting and segmenting cracks on the bridge deck.
CONSTRUCTION AND BUILDING MATERIALS
(2023)
Article
Construction & Building Technology
Wenlong Ye, Shijie Deng, Juanjuan Ren, Xueshan Xu, Kaiyao Zhang, Wei Du
Summary: In this study, a fast and effective detection method for concrete cracks on slab tracks using dilated convolution and the watershed algorithm was proposed. The results showed that the STCNet I network had advantages in terms of calculation speed and model robustness, and demonstrated strong generalization ability for concrete cracks.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Haiying Zhou, Qi Bai, Xianliang Hu, Ahmad Alhaskawi, Yanzhao Dong, Zewei Wang, Binjie Qi, Jianyong Fang, Vishnu Goutham Kota, Mohamed Hasan Abdulla Hasa Abdulla, Sohaib Hasan Abdullah Ezzi, Hui Lu
Summary: Carpal tunnel syndrome is a common nerve disease that affects adults, causing pain and numbness. MRI can provide an objective view of the patient's condition, but its clinical application is limited due to various challenges. In this study, we propose a deep learning framework called Deep CTS that can efficiently segment the carpal tunnel region.
JOURNAL OF DIGITAL IMAGING
(2022)
Article
Environmental Sciences
Mario Gilcher, Thomas Udelhoven
Summary: The study suggests that convolutional neural networks have better generalization potential compared to pixel-based models, as they can consider object geometry and texture along with phenological development. The UNET classifier achieved the highest F1 scores in temporal validation samples and also had a high average score in spatial validation samples. The theoretical risk of overfitting geometry and memorizing specific shapes has been shown to be insignificant in practical applications.
Article
Construction & Building Technology
L. Minh Dang, Hanxiang Wang, Yanfen Li, Le Quan Nguyen, Tan N. Nguyen, Hyoung-Kyu Song, Hyeonjoon Moon
Summary: This research focuses on implementing computer vision techniques and deep learning to automate crack segmentation and real-life crack length measurement of masonry walls. The experimental results demonstrate that deep learning-based crack segmentation outperforms previous approaches and can provide accurate measurements.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Chemistry, Analytical
Jie Wu, Xiaoqian Zhang
Summary: This paper proposes a tunnel crack detection method based on improved Retinex and deep learning. The image enhancement algorithm is used to improve the contrast information, and an improved VGG19 network model is constructed to achieve efficient segmentation of tunnel crack images. The length and width information of the cracks are obtained using a fast parallel-thinning method. Experimental results show that this method has a shorter detection time and higher detection accuracy.
Article
Computer Science, Artificial Intelligence
Tao Lei, Xiaohong Jia, Dinghua Xue, Qi Wang, Hongying Meng, Asoke K. Nandi
Summary: This article proposes a fuzzy STUDENT'S t-distribution model based on richer spatial combination (FRSC) for image segmentation. FRSC integrates both narrow and wide receptive fields into the objective function, and introduces rich spatial combination under STUDENT'S t-distribution to address the limitations of FCM algorithms with spatial information. Experimental results demonstrate that FRSC provides better segmentation results than state-of-the-art clustering algorithms.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Engineering, Civil
Tianjie Zhang, Donglei Wang, Yang Lu
Summary: This paper presents ECSNet, a customized deep learning model for accelerated real-time pavement crack detection and segmentation. The model achieves high accuracy and efficiency by incorporating novel components such as small kernel convolutional layers and parallel max pooling and convolutional operations. Experimental results on the DeepCrack Dataset demonstrate that ECSNet performs well in terms of accuracy and efficiency compared to other state-of-the-art models.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xinhua Wu, Xiujie Liu
Summary: This paper explores the method of building crack recognition and total quality management based on deep learning, focusing on computer vision technology and the design of building crack classification and segmentation models, which significantly improves the efficiency of building crack detection.
PATTERN RECOGNITION LETTERS
(2021)
Article
Geochemistry & Geophysics
Jun Yue, Dingshun Zhu, Leyuan Fang, Pedram Ghamisi, Yaowei Wang
Summary: A hyperspectral image classification method based on adaptive spatial pyramid constraint (ASPC) has been proposed to improve the generalization ability of the classification model using limited training samples. By adding spatial constraints to both labeled and unlabeled subregions, the method fully explores the spatial-spectral correlation and outperforms existing state-of-the-art methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Construction & Building Technology
Zhi Ding, Xiao Zhang, Shao-Heng He, Yong-Jie Qi, Cun-Gang Lin
Summary: This study investigates the longitudinal behavior of a shield tunnel by designing and constructing a reduced-size indoor model. The results show that the longitudinal settlement of the tunnel follows a normal distribution, with the maximum settlement occurring at the central ring and increasing linearly with the applied load. Stress concentration typically occurs on the side of the tunnel waist under surcharge, resulting in transverse elliptical deformation of the entire structure.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Lucia Lopez-de-Abajo, Marcos G. Alberti, Jaime C. Galvez
Summary: Assessing and predicting concrete damage is crucial for infrastructure management. This study quantifies gas concentrations in urban tunnels to achieve this goal.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Chao He, Yinghao Cai, Chenqiang Pu, Shunhua Zhou, Honggui Di, Xiaohui Zhang
Summary: This paper investigates the impact of river channel excavation on adjacent metro tunnels and proposes protective measures based on an engineering project in Fuzhou, China. A three-dimensional finite element model is developed to calculate the displacements and distortion of tunnels under different excavation sequences and soil reinforcement measures. Real-time monitoring confirms that the vertical displacements and diametrical distortion of tunnels are primarily caused by the excavation of the river above the tunnels, while horizontal displacements are induced by the excavation next to the tunnels. The study recommends a combination of cement slurry with a portal form and concrete with a plate form for soil reinforcement and tunnel protection.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Yaosheng Liu, Ang Li, Feng Dai, Ruochen Jiang, Yi Liu, Rui Chen
Summary: In this study, a hybrid model based on a multilayer perceptron (MLP) and meta-heuristic algorithms was developed to improve blast performance during tunnel excavation. Precise prediction of post-blasting indicators was important for optimization, and a comparison of meta-heuristic algorithms was conducted to find the most suitable model. The results showed that the developed model effectively reduces overbreak areas and quantitatively analyzes the influence of geological conditions.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Xiang Shen, Yifan Chen, Liqiang Cao, Xiangsheng Chen, Yanbin Fu, Chengyu Hong
Summary: In this paper, a machine learning-based method for predicting the slurry pressure in shield tunnel construction is proposed. By considering the influence of fault fracture zones and setting the formation influence coefficient, the accuracy of the prediction is significantly improved.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Shuying Wang, Zihao Zhou, Xiangcou Zheng, Jiazheng Zhong, Tengyue Zheng, Changhao Qi
Summary: A real-time assessment and monitoring approach based on laser scanning technology and point cloud data analysis was proposed to address the hysteresis in assessing the workability of conditioned soils and the inefficiency in estimating the soil volume flow rate in tunnelling practice. The approach was successfully applied in identifying the workability of conditioned soil and its discharge rate in the EPB shield tunnelling project.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Peng Jiang, Benchao Liu, Yuting Tang, Zhengyu Liu, Yonghao Pang
Summary: This study introduces a novel deep learning-based electrical method that jointly inverses resistivity and chargeability to estimate water-bearing structures and water volume. Compared with traditional linear inversion methods, the proposed method demonstrates superiority in locating and delineating anomalous bodies, reducing solution multiplicity.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Haoyu Mao, Nuwen Xu, Zhong Zhou, Chun Sha, Peiwei Xiao, Biao Li
Summary: The study focuses on the delineation of rock mass damage zones and stability analysis of underground powerhouse in Lianghekou hydropower station. ESG monitoring system is used to monitor the inner micro-fracture activity of surrounding rock mass in real-time. Engineering analogy method is adopted to forecast the deformation period of surrounding rock mass and analyze the variation characteristics of seismic source parameters. The research results provide references for similar deep underground excavation engineering in terms of design and construction.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Junling Qiu, Dedi Liu, Kai Zhao, Jinxing Lai, Xiuling Wang, Zhichao Wang, Tong Liu
Summary: This study focuses on the construction surface cracks of large cross-section tunnels in loess strata of China. The mechanism of surface crack formation is analyzed, and factors such as settlement deformation, construction scheme, and surrounding soil environment are identified as the main contributors. Numerical simulations were conducted to gain a deeper understanding of the influence of factors on surface cracks in loess tunnel construction. Specific measures for prevention and treatment of construction surface cracks are proposed to provide new ideas for surface crack control in loess tunnels.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Ting Shang, Jiaxin Lu, Ying Luo, Song Wang, Zhengyu He, Aobo Wang
Summary: The study reveals significant variations in car-following behavior across different types of tunnels and consecutive sections of the same tunnel. As tunnel length increases, the driving stability of following vehicles decreases, but the level of driving safety risk is not positively correlated with tunnel length. Significant vehicle trajectory oscillation is observed within the inner sections of long and extra-long tunnels, and a significant relationship between the acceleration of following vehicles and the location within the tunnel section is found.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Rusi Zeng, Zhongwei Shen, Jun Luo
Summary: The urban underground complexes (UUCs) in China have been effective in solving urban problems, but users have expressed dissatisfaction with the internal physical environment. Personal characteristics and environmental factors play significant roles in determining users' satisfaction.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Gabriel Lehmann, Heiko Kaeling, Sebastian Hoch, Kurosch Thuro
Summary: Analysing and predicting the advance rate of a tunnel boring machine (TBM) in hard rock is important for tunnelling projects. This study focuses on small-diameter TBMs and their unique characteristics, such as insufficient geotechnical information and special machine designs. A database of 37 projects with 70 geotechnically homogeneous areas is compiled to investigate the performance of small-diameter TBMs. The analysis shows that segment lining TBMs have higher penetration rates, and new approaches for the penetration prediction of pipe jacking machines in hard rock are proposed.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Ting Ren, Ming Qiao, Jon Roberts, Jennifer Hines, Yang-Wai Chow, Wei Zong, Adrian Sugden, Mark Shepherd, Matthew Farrelly, Gareth Kennedy, Faisal Hai, Willy Susilo
Summary: Long-term exposure to coal and silica dust during underground tunnelling operations is a growing concern. To bridge the gap between knowledge in dust exposure monitoring and frontline workers, a virtual reality educational tool was developed to visualize ventilation and dust flow characteristics. This tool allows workers to better understand decision-making and best practices for dust controls.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Dong Lin, Zhipeng Zhou, Miaocheng Weng, Wout Broere, Jianqiang Cui
Summary: Metro systems play a vital role in the transportation, economic, environmental and social aspects of cities. The uncertainties in construction, passenger comfort and safety, as well as efficiency and reliability of the metro system, have been widely studied. Metro systems influence urban development and have a positive impact on housing prices, public health, and environmental quality. Further research is needed to fill the research gaps and make recommendations for future studies.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Wei Yu, Bo Wang, Xin Zi, Jie Dong
Summary: In this study, a whole-process analytical theory for the coupled deformation of deep circular tunnel surrounding rock and prestressed yielding anchor bolt (cable) system is derived and validated through numerical simulations. The results show that anchor bolts (cables) can significantly reduce the convergence of surrounding rock, and factors such as support timing and anchor cable length have important effects on the support effectiveness.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)