Deep learning-based visual defect-inspection system for reinforced concrete bridge substructure: a case of Thailand’s department of highways

Kruachottikul, Pravee; Cooharojananone, Nagul; Phanomchoeng, Gridsada; Chavarnakul, Thira; Kovitanggoon, K.; Trakulwaranont, Donnaphat

doi:10.1007/s13349-021-00490-z

Cited by 39 publications

(8 citation statements)

References 20 publications

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“…To develop a successful user driven application, we applied the unified theory of acceptance and use of technology (UTAUT) to identify the key factors that influence behavioral intention to use of the mobile application. The fundamental specifications were derived from our previous research on mobile applications for construction process quality control [6], AI for visual bridge defect-inspection system [7], and related literature reviews in the area of teamwork, project management, and visual inspection. Building on this body of work, the current study proposes a modified technology acceptance model UTAUT research framework, which identifies parameters that influence user intention to use the product by adding a new VIS factor that leads to either performance expectancy or effort efficacy.…”

Section: Discussionmentioning

confidence: 99%

“…The development of a collection of small services in the microservice architecture can best be explained with a sequence diagram. The sequence diagram of the AI assisted bridge visual defect inspection microservice collection was derived from our prior study [7], which starts from the user uploading an image and defect information through the system. This original image is stored in Amazon S3 bucket whereas the defect information is recorded on the image table.…”

Section: Microservice Developmentmentioning

confidence: 99%

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Development of a User-Centric Bridge Visual Defect Quality Control Assisted Mobile Application: A Case of Thailand’s Department of Highways

et al. 2021

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Digital innovations have changed the way many industries operate, but the construction industry has been slow to adopt these technologies. However, challenges such as low productivity, project overruns, labor shortages, and inefficient performance management have motivated Thailand’s Department of Highways to adopt digital innovations to build a competitive advantage. Because this industry requires a large work force, obstacles to collaboration can result in ineffective project management. We aimed to improve collaboration on bridge inspections that typically requires the involvement of many people, personal judgement, and extensive travel to survey bridges across the country. One major challenge is to standardize human judgement. To address these challenges, we developed a user-centric bridge visual defect quality control mobile application to improve collaboration and assist field technicians to conduct visual defect inspection. Our results can be used as a case study for other construction firms to embrace digital transformation technologies. This research also demonstrates the new-product development process using the new technology in known markets innovation development and technology acceptance model. We offer several recommendations for future research, including other infrastructure applications.

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Section: Discussionmentioning

confidence: 99%

Section: Microservice Developmentmentioning

confidence: 99%

Development of a User-Centric Bridge Visual Defect Quality Control Assisted Mobile Application: A Case of Thailand’s Department of Highways

et al. 2021

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“…Facial images analysed by those methods will be capture the variation in shape and intensity. Nevertheless, the applied of deep learning algorithm, especially CNNs and Deep Neural Networks (DNNs) in age estimation has become more and more popular [7] [8]. Particularly, the work of using ordinal regression and multiple output CNNs for this task by Niu.…”

Section: Related Workmentioning

confidence: 99%

Transfer and Ensemble Learning in Real-time Accurate Age and Age-group Estimation

Dang¹,

Mai²,

Nguyen³

2022

Adv. sci. technol. eng. syst. j.

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Aging is considered to be a complex process in almost every species' life, which can be studied at a variety of levels of abstraction as well as in different organs. Not surprisingly, biometric characteristics from facial images play a significant role in predicting human's age. Specifically, automatic age estimation in real-time situation has begun to affirm its position as an essential process in a vast variety of applications. In this paper, two approaches are addressed as solutions for such application: prediction of accurate age and age group by using the two most fundamental techniques in the domain of deep learningconvolutional neural networks (CNNs) and deep neural networks (DNNs). In summary, this work can be split into two main key contributions. By applying a novel hierarchical aggregation built on the base of neural network developed from the training dataset, in the first stage, features extraction, the convolutional activation features are extracted from the captured facial image. As soon as this part is done, the features classification step is performed, in which Softmax Regression (SR) and majority vote classifiers are applied to predict accurate age and age group respectively. The effectiveness of the designed model was showed satisfactorily in the experimental results, which emphasizes the promising of the solution and indicates another direction for future development of algorithms and models in the field of machine learning.

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“…Several studies highlighted the issue of the limited number of image data sets. 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.…”

Section: Objects and Information Detection On Sitementioning

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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Deep learning-based visual defect-inspection system for reinforced concrete bridge substructure: a case of Thailand’s department of highways

Cited by 39 publications

References 20 publications

Development of a User-Centric Bridge Visual Defect Quality Control Assisted Mobile Application: A Case of Thailand’s Department of Highways

Development of a User-Centric Bridge Visual Defect Quality Control Assisted Mobile Application: A Case of Thailand’s Department of Highways

Transfer and Ensemble Learning in Real-time Accurate Age and Age-group Estimation

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

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