Deep Learning with Spatial Constraint for Tunnel Crack Detection

Li, Qingquan; Zou, Qin; Liao, Jianghai; Yue, Yan; Wang, Song

doi:10.1061/9780784482438.050

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The developed model outperformed the typical U-net network, yielding a pixel accuracy, intersection over union, and dice coefficient of 98.67%, 54.65%, and 68.09%, respectively. Li et al [11] introduced a computer vision-based model for detecting cracks in highway tunnels. A deep convolutional encoder-decoder was designed that comprised the use of Unet architecture for pixel-wise semantic segmentation of cracks.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

et al. 2021

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Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

et al. 2021

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“…In these articles, deep‐learning (DL) model was not used to estimate the mechanical properties of SCC having single fiber and binary, ternary, and quaternary fiber hybridization. The DL has been generally used in crack detection in civil engineering in recent years 15–22 . In the literature, several articles that predict the strength of concrete using DL have been found 23–27 .…”

Section: Introductionmentioning

confidence: 99%

“…The DL has been generally used in crack detection in civil engineering in recent years. [15][16][17][18][19][20][21][22] In the literature, several articles that predict the strength of concrete using DL have been found. [23][24][25][26][27] Jang et al 24 used the digital microscope image to predict compressive strength of concrete with DL.…”

mentioning

confidence: 99%

Estimation of strengths of hybrid FR‐SCC by using deep‐learning and support vector regression models

Kına

Türk

Tanyıldızı

2022

Structural Concrete

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In this work, to estimate the compressive, splitting tensile, and flexural strength of self‐compacting concrete (SCC) having single fiber and binary, ternary, and quaternary fiber hybridization, the deep‐learning (DL) and support vector regression (SVR) models were devised. The fiber content and coarse aggregate/total aggregate ratio (CA/TA) were the variables for 24 designed mixtures. Four different fibers, which were a macro steel fiber, two types of micro steel fibers with different aspect ratio, and polyvinyl alcohol (PVA) fiber, were used in SCC mixtures. The specimens of each mixture were tested to measure the engineering properties for 7, 28, and 90 days. The amount of cement, fly ash, fine aggregate, CA, high‐range water‐reducing admixture, water, macro steel fiber, PVA fiber, two types of micro steel fibers, and curing time were selected as input layers while the output layers were strength results. The experimental results were compared with the estimation results. The engineering properties were estimated using the SVR model with 95.25%, 87.81%, and 93.89% accuracy, respectively. Furthermore, the DL model estimated compressive strength, tensile strength, and flexural strength with 99.27%, 98.59%, and 99.15% accuracy, respectively. It was found that the DL estimated the engineering properties of hybrid fiber–reinforced SCC with higher accuracy than SVR.

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“…The prerequisite for bucket trajectory planning is the detection of bucket position information, so bucket detection is positively correlated with improving the bucket fill factor. Considering the rapid development of deep learning in the target detection field (Li et al., 2019; Ryu & Kim, 2018), deep learning is undoubtedly the right choice to solve both tasks.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

Guan

Chen

Wang

et al. 2022

Computer aided Civil Eng

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The development of autonomous detection technology is imperative in the field of construction. The bucket fill factor is one of the main indicators for evaluating the productivity of construction vehicles. Bucket detection is a prerequisite for bucket trajectory planning. However, previous studies have been conducted under ideal environments, a specific single environment, and several normal environments without considering the actual harsh environments at construction sites. Therefore, seven extreme environments are set in this paper to fill this gap, and an effective method is proposed. First, a novel framework for image restoration under extreme environments is proposed. It applies to all tasks conducted by vision on construction sites. Second, a combination of segmentation and classification networks is used for the first time in this area. Multitask learning is used to discover a positive correlation between fill factor estimation and bucket detection. Furthermore, probabilistic methods and transfer learning were introduced, and excellent results were achieved (97.40% accuracy in fill factor estimation and 99.76% accuracy in bucket detection for seven extreme environments).

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Deep Learning with Spatial Constraint for Tunnel Crack Detection

Cited by 5 publications

References 12 publications

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

Estimation of strengths of hybrid FR‐SCC by using deep‐learning and support vector regression models

A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

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