3D Imaging in Construction and Infrastructure Management: Technological Assessment and Future Research Directions

Wei, Yujie; Kasireddy, Varun; Akinci, Burcu

doi:10.1007/978-3-319-91635-4_3

Cited by 9 publications

(4 citation statements)

References 94 publications

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“…Wei et al carried out feasibility studies on using 3D imaging for construction and infrastructure management [24]. Likewise, Mróz et al (2020) modelled the usefulness of digital image correlation (DIC), for testing concrete's susceptibility to fire spalling [25].…”

Section: Introductionmentioning

confidence: 99%

Progress and Trends in Image Processing Applications in Civil Engineering: Opportunities and Challenges

Salunkhe

Gobinath²,

Vinay³

et al. 2022

Advances in Civil Engineering

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Technological advancements in electronic storage have been trending for cloud computing. The revolution of this computer technology with machine learning and artificial intelligence has created prodigious platforms to the various disciplines of science and technology. Civil engineering is the oldest discipline, and due to the never-ending demand of this domain, it is rapidly adapting to newer computer techniques like image processing, deep learning, big data analysis, neural networks, building information modeling (BIM), unmanned aerial vehicle (UAV) system, digital image correlation (DIC), and many more. In the current paper, we portrayed the primary research and achievements of AI and image processing applications in the civil domain. The paper is divided in two parts. The first part provides analysis of existing methods along with examples relevant to the civil domain where it is incorporated. The second part elaborates scientometric study constituting 605 documents (Science Direct database) published in the last two decades. The bibliometrics are further used for producing analytical frameworks based on publications, citations, top journals, top institutions, and funding sources. This study serves as a guide for readers to identify research gaps and use the review for potential future study.

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Section: Introductionmentioning

confidence: 99%

Progress and Trends in Image Processing Applications in Civil Engineering: Opportunities and Challenges

Salunkhe

Gobinath²,

Vinay³

et al. 2022

Advances in Civil Engineering

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“…The construction industry has been known to be laggard in adopting new technologies, and there is a large effort to facilitate the process of technology adoption by exploring new applications and addressing barriers of technology implementation [72,73]. Exploring new algorithms for enabling drones and robots to recognise objects, defects and edges may facilitate and increase the adoption of new drones and robots in construction [74][75][76][77].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Classical Operators and Fuzzy Logic Algorithms for Edge Detection of Panels at Exterior Cladding of Buildings

et al. 2019

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The automated process of construction defect detection using non-contact methods provides vital information for quality control and updating building information modelling. The external cladding in modular construction should be regularly controlled in terms of the quality of panels and proper installation because its appearance is very important for clients. However, there are limited computational methods for examining the installation issues of external cladding remotely in an automated manner. These issues could be the incorrect sitting of a panel, unequal joints in an elevation, scratches or cracks on the face of a panel or dimensions of different elements of external cladding. This paper aims to present seven algorithms to detect panel edges and statistically compare their performance through application on two scenarios of buildings in construction sites. Two different scenarios are selected, where the building façades are available to the public, and a sample of 100 images is taken using a state-of-the-art 3D camera for edge detection analysis. The experimentation results are validated by using a series of computational error and accuracy analyses and statistical methods including Mean Square Error, Peak Signal to Noise Ratio and Structural Similarity Index. The performance of an image processing algorithm depends on the quality of images and the algorithm utilised. The results show better performance of the fuzzy logic algorithm because it detects clear edges for installed panels. The applications of classical operators including Sobel, Canny, LoG, Prewitt and Roberts algorithms give similar results and show similarities in terms of the average of errors and accuracy. In addition, the results show that the minor difference of the average of the error and accuracy indices for Sobel, Canny, LoG, Prewitt and Roberts methods between both scenarios are not statistically significant, while the difference in the average of the error and accuracy indices for RGB-Sobel and Fuzzy methods between both scenarios are statistically significant. The accuracy of the algorithms can be improved by removing unwanted items such as vegetation and clouds in the sky. The evaluated algorithms assist practitioners to analyse their images collected day to day from construction sites, and to update building information modelling and the project digital drawings. Future work may need to focus on the combination of the evaluated algorithms using new data sets including colour edge detection for automatic defect identification using RGB and 360-degree images.

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“…Reality capturing through 3D imaging, such as laser scanning and RGB-D images, have become an active research topic in the Architectural, Engineering, and Construction (AEC) industries to capture the as-is status of a building [20]. For instance, Scan-to-BIM approaches that convert point clouds captured by a laser scanner to an integrated BIM are widely used for progress monitoring, as-designed and as-is comparison, and facility management [21][22][23].…”

Section: Previous Researchmentioning

confidence: 99%

Supporting Deconstruction Waste Management through 3D Imaging: A Case Study

Wu¹,

Pushkar²,

Akinci³

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Conventional demolition approaches of razing a building at the end of its life-cycle generate a large amount of mingled debris, which is difficult to reuse and recycle. Compared to demolition, deconstruction involves disassembling a building systematically and it is a more environmentally friendly alternative. Recent research studies have focused on the transition from demolition to deconstruction to minimize the amount of generated waste and maximize the amount of recycling and reusing material. However, due to tight schedule requirements, extra labor cost, and the lack of drawings and design information, it is difficult for an owner to estimate the cost and duration of deconstruction ahead of time. 3D imaging technologies, such as laser scanning and image-based 3D reconstruction, provide an opportunity to obtain data about as-is conditions at a job site and hence can potentially help in identifying quantities of materials that will be recycled. Existing 3D imaging workflows have two primary limitations: visibility and appearance ambiguity. First, 3D imaging can only capture visible objects before a deconstruction process starts. Also, data captured before deconstruction or at different times during deconstruction can only include a subset of all building components. Second, building components with similar appearances can be made from different materials, resulting in misclassification and errors in quantity estimation. Only a few case studies have discussed how visibility and appearance ambiguity can affect the usage of 3D imaging in deconstruction waste management. In this paper, the authors aim to illustrate the application of 3D imaging during a small-scale deconstruction project in Pittsburgh. Specifically, the authors documented the waste generated during deconstruction manually and by using two different 3D imaging technologies: laser scanning and image-based registration. We then quantified the number of invisible objects and objects with ambiguous appearances at different stages of deconstruction. Through the comparison between the quantity takeoffs from 3D imagery and the ground truth, the paper aims at providing insights on the following questions: 1) How accurate are the quantity estimation and documentation of two 3D imaging technologies (laser scanning and imagery) compared to the actual waste generated? 2) Does 3D imaging capture all components of interest during deconstruction?

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3D Imaging in Construction and Infrastructure Management: Technological Assessment and Future Research Directions

Cited by 9 publications

References 94 publications

Progress and Trends in Image Processing Applications in Civil Engineering: Opportunities and Challenges

Progress and Trends in Image Processing Applications in Civil Engineering: Opportunities and Challenges

Evaluation of Classical Operators and Fuzzy Logic Algorithms for Edge Detection of Panels at Exterior Cladding of Buildings

Supporting Deconstruction Waste Management through 3D Imaging: A Case Study

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