Automatic recognition of tunnel lining elements from GPR images using deep convolutional networks with data augmentation

Qin, Hui; Zhang, Donghao; Tang, Yu; Wang, Yuanzheng

doi:10.1016/j.autcon.2021.103830

Cited by 89 publications

(40 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Limitations in these applications including limited training data set and the need for the manual labeling of data. Qin et al (2021) developed an automatic recognition of tunnel lining elements from GPR images using deep convolutional networks with data augmentation to facilitate tunnel lining inspection. However, in this study the training and testing GPR images were labeled manually based on personal experience instead of ground truth.…”

Section: Objects and Information Detection On Sitementioning

confidence: 99%

“…During the past few years, AI has been improved and different subsets were developed to provide wider solutions, one of these subsets is deep learning, which is defined as a set of computational models that includes multiple processing layers to learn representations of different types of data with different levels of abstraction (LeCun et al , 2015). During the past a few years, research in adopting deep learning in the construction industry has commenced, the density of these research was focused on using deep learning to detect distresses in buildings and pavements (Hou et al , 2020; Qin et al , 2021). However, deep learning was also considered to develop solutions to automate construction site management tasks including equipment detection, sites health and safety, labor management and progress evaluation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

Elghaish

Matarneh

Alhusban

2021

View full text Add to dashboard Cite

Purpose The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps. Design/methodology/approach The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application. Findings The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks. Practical implications This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world. Originality/value To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.

show abstract

Section: Objects and Information Detection On Sitementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

Elghaish

Matarneh

Alhusban

2021

View full text Add to dashboard Cite

show abstract

“…As a result, the deformation of the foundation pit and retaining wall including the railway subgrade that occurred during the excavation process was confirmed. Qin et al used a deep convolutional network with data augmentation, and deep learning-based automatic recognition was performed for automatic recognition of tunnel lining elements using ground penetrating radar images [10]. This method yielded an accuracy of 95.45% for the initial lining thickness recognition, as confirmed by field experiments.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Automatic Measurement Evaluation System for Subway Structures by Adjacent Excavation

Choi

Kim

Lee³

et al. 2021

Materials

View full text Add to dashboard Cite

This study evaluated the structural stability of subway structures based on adjacent excavations by comparing automatically measured and numerically analyzed data. The reliability of the automated measurement methodology was evaluated by first applying probability statistical analysis to the measured results and then comparing these results with the numerically analyzed results. An improvement in the calculation method evaluation system, including the method of processing and analysis of the automatically measured data of subway structures through the average value of probability density, was proposed. As a result of the field measurement and numerical analysis, the measured results of tunnel displacement and track deformation exhibited some differences. However, it was determined that the construction stage and location where the maximum values of the tunnel displacement and track deformation occurred had similarities.

show abstract

“…Ground penetrating radar (GPR) is a popular geophysical technique and has been widely applied to near-surface investigation [1,2], archaeological prospection [3,4], hydrological investigation [5], lunar exploration [6], and civil engineering [7]. In tunnel detection, GPR is used to detect voids, seepage, and rebar defects [8,9]. In bridge field, GPR is commonly used to measure reinforcement position, concrete thickness, and reinforcement corrosion degree [10,11].…”

Section: Introductionmentioning

confidence: 99%

Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks

et al. 2021

View full text Add to dashboard Cite

Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.

show abstract

Automatic recognition of tunnel lining elements from GPR images using deep convolutional networks with data augmentation

Cited by 89 publications

References 37 publications

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

Improvement of Automatic Measurement Evaluation System for Subway Structures by Adjacent Excavation

Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks

Contact Info

Product

Resources

About