Detecting Structural Components of Building Engineering Based on Deep-Learning Method

Hou, Xueliang; Zeng, Ying; Xue, Jingguo

doi:10.1061/(asce)co.1943-7862.0001751

Cited by 36 publications

(13 citation statements)

References 34 publications

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“…The YOLO framework has been successfully used in several diverse applications of civil engineering, such as pedestrian detection [46], real-time face detection [47], license plate detection [48], spilled load detection on freeways [49], pothole detection [50], traffic load distribution detection [51], worker and heavy construction equipment identification on site [52], building component identification [53], rebar diameter estimation [54], building footprint estimation [55], traffic management [56], pavement distress detection [57], crack detection [58][59][60][61], and maintenance [62][63][64]. The following sections describe the details of the structure of YOLOv5.…”

Section: Proposed Model For the Detection Of Cracks And Determination...mentioning

confidence: 99%

Two-stage method based on the you only look once framework and image segmentation for crack detection in concrete structures

Mishra

Jain

Singh

et al. 2022

Archit. Struct. Constr.

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Detecting the presence of cracks and identifying their severity are crucial tasks for determining the structural health of a concrete building. In this study, we develop a two-stage automated method based on the You Only Look Once (YOLOv5) deep learning framework for the identification, localization, and quantification of cracks in the concrete structures. In the first stage, cracks are identified and localized using bounding boxes, while in the second stage, the length of cracks and, therefore, the damage severity are determined. The developed deep learning model is trained using 4500 annotated images from a total of 40000 images of size 227 × 227 pixel, which are obtained from an open-source dataset collected at various campus buildings of Middle East Technical University (METU). The concept of transfer learning (i.e., pre-trained weights) is used for the training, which drastically reduces the training time. The detection and localization accuracy of this model is measured in terms of the average precision, average recall, and F1-score. The YOLOv5 model achieves the mean average precision (mAP_0.5) of 95.02%. A ResNet model is also developed just to capture the supremacy of the YOLOv5 model. The proposed method can help in identifying structural anomalies through real-time monitoring that must be urgently repaired and thus can be used in high-quality civil infrastructure monitoring systems.

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Section: Proposed Model For the Detection Of Cracks And Determination...mentioning

confidence: 99%

Two-stage method based on the you only look once framework and image segmentation for crack detection in concrete structures

Mishra

Jain

Singh

et al. 2022

Archit. Struct. Constr.

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“…In summary, although deep learning has been used in many engineering fields [60,61], it has less been used for detecting external wall deterioration. Moreover, integrating UAV and deep learning applications may increase the practical value of automated external wall deterioration detection.…”

Section: Cnn Use For Building Deterioration Detectionmentioning

confidence: 99%

Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance

Kung

Pan

Wang

et al. 2021

Advances in Civil Engineering

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Several natural and human factors are responsible for the defacement of the external walls and tiles of buildings, and the related deterioration can be a public safety hazard. Therefore, active building maintenance and repair processes are essential for ensuring building sustainability. However, conventional inspection methods are time-, cost-, and labor-intensive processes. Therefore, herein, this study proposes a convolutional neural network (CNN) model for image-based automated detection and localization of key building defects (efflorescence, spalling, cracking, and defacement). Based on a pretrained CNN VGG-16 classifier, this model applies class activation mapping for object localization. After identifying its limitations in real-life applications, this study determined the model’s robustness and ability to accurately detect and localize defects in the external wall tiles of buildings. For real-time detection and localization, this study applied this model by using mobile devices and drones. The results show that the application of deep learning with UAV can effectively detect various kinds of external wall defects and improve the detection efficiency.

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“…For example, Kruachottikul et al (2021) reported the challenge of the limited number of image data sets in developing their deep learning-based visual defect-inspection system for reinforced concrete bridge substructures. Hou et al (2020) study failed to make a comparison of multiple sets of test experiments to determine the proposed system effectiveness and generalizability due to limited number of data sets. Wang et al (2019) recommend to expanding the database to overcome this limitation and enhance the accuracy of their proposed system.…”

Section: Application Of "Deep Learning"mentioning

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%

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

Elghaish

Matarneh

Alhusban

2021

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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.

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Detecting Structural Components of Building Engineering Based on Deep-Learning Method

Cited by 36 publications

References 34 publications

Two-stage method based on the you only look once framework and image segmentation for crack detection in concrete structures

Two-stage method based on the you only look once framework and image segmentation for crack detection in concrete structures

Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance

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

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