3D Visualization of Airport Pavement Quality Based on BIM and WebGL Integration

Liu, Zhen; Gu, Xingyu; Dong, Qiao; Tu, Shanshan; Li, Shuwei

doi:10.1061/jpeodx.0000280

Cited by 30 publications

(10 citation statements)

References 22 publications

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“…Combined with the basic principle of differential expression and Three.js, asphalt pavement 3D model display and damage details rendering were realized in the Web browser of BIM model [32].…”

Section: Methodsmentioning

confidence: 99%

Road detection and three-dimensional visualization management using deep learning and BIM integration

Liu,

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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Asphalt pavement detection is a problem characterized by low efficiency, high manual input, and high cost. A 3D visualization management platform for road infrastructures based on artificial intelligence and information technology is presented in this study. Initially, images of cracks and potholes were processed digitally and segmented using image processing methods. A pavement condition index (PCI) is calculated by combining pavement distress identification with cracks and potholes. After that, ground penetrating radar (GPR) was used to detect the road structures, and then Canny edge algorithm was performed to process the original GPR images. Cracks, looseness, and poor interlayering were identified with the object detection model and data enhancement methods. Inverse time migration (3D) imaging of the internal diseases was performed in three dimensions (3D). Based on the results of the object detection model, F1 score and mAP were both above 80% for road distress detection. As a final step, a BIM model of asphalt pavement was generated using Revit software, and 3D asphalt pavement model rendering and quality details were displayed in Web-BIM browser, suggesting that this method can be used to maintain highways in a fast, intelligent, and scientific manner.

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Section: Methodsmentioning

confidence: 99%

Road detection and three-dimensional visualization management using deep learning and BIM integration

Liu,

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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“…Literature case studios and practical trials appoint major breakthroughs in the combined use of different tools. In particular, Autodesk Revit software ® [23][24] is used to define parametric-adaptive families for each specific unit, in order to digitally reproduce geometrical properties and storing informative ones. Autodesk Dynamo ® [24] is implemented to automatically replicate the previous attributes' assignment of each physical element starting from the corresponding Revit parametric family and interacting with simple .xls format input data, directly extracted from survey campaigns.…”

Section: Methodsmentioning

confidence: 99%

“…Adopting a BIM approach would in fact allow the modeling of the assets' digital twins, achieved by discretizing airside and landside in homogeneous zones virtually replicated through parametric elements, each with its own geometric and informative properties. At this stage it is essential to automatically implement the results of monitoring and survey campaigns (using destructive and/or Non-Destructive Testing (NDT) techniques) to shape and assign specific, up-to-date geometric and informative properties to each parametric element developed, given the enormous quantity of datasets generally collected in an areal infrastructure such as airside [19][20][21][22][23]. Of fundamental importance is the phase of testing and validation of the automatic parametric object definition and assignment, to ensure the actual compatibility with the survey outputs and correct reproduction of the pavement asset in a BIM-oriented environment.…”

Section: Aim and Objectivesmentioning

confidence: 99%

“…Another significant difference in terms of progress achieved landside, at the expense of the airside's evolution, needs to be mentioned. Numerous are in fact the literature efforts pursued in developing landside's BIM models, highlighting the results in terms of optimization and efficiency in terms of clash detection, monitoring, safety, and construction [7,[19][20][21][22][23]. As the landside area is mainly built vertically, resembling effectively the construction's levels, which is typical of building engineering, it is evident that the landside has inherited all the progresses and technological advances pursued in structural engineering.…”

Section: Introduction and Statement Of The Problemmentioning

confidence: 99%

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Survey of airport facilities: novel approaches

Bertolini,

Bianchini Ciampoli,

Pinto

et al. 2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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Airports are very specific infrastructures requiring ad hoc design, construction and management approaches. Indeed, they typically include different types of structures, which are handled with remarkably different techniques. Particularly, landside elements (e.g. terminals, technical facilities, real estate areas…) are usually treated as building structures, whereas airside items (e.g. runways, aprons) are generally managed according to specific pavement engineering standards and procedures. At interfaces between these blocks, difficulties are experienced by designers and maintenance operators, who have to interact with different work methodologies at the same time.In addition, such a scenario is even compounded by the fact that the airport environment is strictly subject to a number of constraints and regulations due to safety and security reasons. This implies that, in comparison to other infrastructures, a huge amount of remote sensing and non-destructive survey data is regularly collected in airports to be used as support to decisions by the facility manager in matter of both airport extension projects and maintenance of the existing asset.In such a context, novel perspectives are being fostered by the recent spread of Building Information Modeling (BIM) approaches in design and management processes for transport infrastructures. Given the complexity of the airport case, digital environments capable of containing both land-and air-side data, monitoring survey reports and limitations acting in the different areas of the asset are expected to drive the following achievements:▪ enhancement of the accuracy of information at the land-side/air-side interfaces;▪ increase of actions planning efficiency through management of integrated monitoring datasets; ▪ strengthening of constraints and limitations effectiveness by means of the simulation of the interference between worksite activities and obstacles-free volumes.This study reports on the actual potential and challenges related to a digital model at a land-side/air-side interface area in an airport. Particularly, the study has focused on the digitalization of the multi-source non-destructive testing (NDT) survey conducted over an apron area located in Italy.

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“…On the other hand, researchers have performed many studies of the standardization of road testing and maintenance [33,34]. The group criteria of technical guidelines for ground-penetrating radar detection of the internal condition of highway asphalt pavement has been published by the China Highway and Transportation Society (CHTS) [35], which has provided a scientific reference for future exploration.…”

Section: Introductionmentioning

confidence: 99%

Application of Combining YOLO Models and 3D GPR Images in Road Detection and Maintenance

Liu

Wu²,

et al. 2021

Remote Sensing

Self Cite

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Improving the detection efficiency and maintenance benefits is one of the greatest challenges in road testing and maintenance. To address this problem, this paper presents a method for combining the you only look once (YOLO) series with 3D ground-penetrating radar (GPR) images to recognize the internal defects in asphalt pavement and compares the effectiveness of traditional detection and GPR detection by evaluating the maintenance benefits. First, traditional detection is conducted to survey and summarize the surface conditions of tested roads, which are missing the internal information. Therefore, GPR detection is implemented to acquire the images of concealed defects. Then, the YOLOv5 model with the most even performance of the six selected models is applied to achieve the rapid identification of road defects. Finally, the benefits evaluation of maintenance programs based on these two detection methods is conducted from economic and environmental perspectives. The results demonstrate that the economic scores are improved and the maintenance cost is reduced by $49,398/km based on GPR detection; the energy consumption and carbon emissions are reduced by 792,106 MJ/km (16.94%) and 56,289 kg/km (16.91%), respectively, all of which indicates the effectiveness of 3D GPR in pavement detection and maintenance.

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3D Visualization of Airport Pavement Quality Based on BIM and WebGL Integration

Cited by 30 publications

References 22 publications

Road detection and three-dimensional visualization management using deep learning and BIM integration

Road detection and three-dimensional visualization management using deep learning and BIM integration

Survey of airport facilities: novel approaches

Application of Combining YOLO Models and 3D GPR Images in Road Detection and Maintenance

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