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Application of Geographic Information System and Building Information Modelling: Volume...
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Application of Geographic Information System and Building Information Modelling: Volume II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 16562

Special Issue Editor

Prof. Dr. Jongwon Seo

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: GIS; BIM; construction automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Amid growing interest in the smart construction and operation of the built environment in the construction industry, the roles of GIS and BIM are also becoming increasingly important. GIS (geographical information system) provides spatial location and geospatial information of the city and/or infrastructure systems while BIM (building information modeling) provides information of an individual building structure and facility on a relatively small and detailed scale.

Although GIS and BIM are currently operating on separate platforms, there is much active platform-related research that has attempted to link GIS and BIM during construction operations. Merging GIS and BIM data provides a geospatial element that can be used in the design, construction, and operation of various built environments. Seamless project handover (i.e., sharing of data and information between platforms) between BIM design processes and GIS technologies can bring about improvement in the data managing capacity of smart construction and operation of the built environment. In this regard, this Special Issue invites you to submit original research papers on GIS–BIM connected platforms and services, collaboration between GIS and BIM, and related topics. The papers about the non-traditional use of BIM or GIS through the extended features and/or the connection with other information technology are also welcome. Hence, topics may include but are not limited to the following:

  • Integration of GIS and BIM
  • Application of GIS data towards BIM or vice versa
  • Advancing processes of GIS and BIM for interoperability
  • BIM or GIS data format for interoperability
  • Platform development of GIS and BIM
  • BIM or GIS connected with other information technology
  • Extended features of BIM or GIS

Prof. Dr. Jongwon Seo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GIS
  • BIM
  • geospatial information
  • construction operation
  • construction automation
  • data format
  • platform
  • interoperability

Published Papers (9 papers)

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Research

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27 pages, 10231 KiB  
Article
Digital Twin-Driven Framework for TBM Performance Prediction, Visualization, and Monitoring through Machine Learning
by Kamran Latif, Abubakar Sharafat and Jongwon Seo
Appl. Sci. 2023, 13(20), 11435; https://doi.org/10.3390/app132011435 - 18 Oct 2023
Cited by 6 | Viewed by 1464
Abstract
The rapid development in underground infrastructure is encouraging faster and more modern ways, such as TBM tunneling, to meet the needs of the world. However, tunneling activities generate complex and heterogeneous data, which makes it difficult to visualize the performance of a project. [...] Read more.
The rapid development in underground infrastructure is encouraging faster and more modern ways, such as TBM tunneling, to meet the needs of the world. However, tunneling activities generate complex and heterogeneous data, which makes it difficult to visualize the performance of a project. Advancements in information technology, such as digital twins and machine learning, provide platforms for digital demonstration, visualization, and system performance monitoring of such data. Therefore, this study proposes a digital twin-driven framework for TBM performance prediction through machine learning, visualization, and monitoring. This novel approach integrates machine learning and real-time performance data to predict, visualize, and monitor the status of the tunnel construction progress. A digital twin virtual model of TBM was constructed based on TBM design parameters, the input parameter, boring energy, RPM, torque, thrust force, speed, gripper pressure, total revolution, and Q-value provided to SVR and ANN models to predict the TBM AR and PR, and TBM daily progress was visualized continuously. The predictive performance indices R2 (0.97) and RMSE (0.011) were estimated for AR prediction, showing the accuracy of the proposed model. To demonstrate the proposed framework, this study shows the its effectiveness. By implementing this framework, stakeholders can minimize the risk associated with the cost and schedule of a tunneling project by simultaneously visualizing and monitoring the performance of TBMs through digital twin and machine learning algorithms. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Building Information Modelling: Volume II)
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18 pages, 11701 KiB  
Article
Indoor Clutter Object Removal Method for an As-Built Building Information Model Using a Two-Dimensional Projection Approach
by Sung-Jae Bae and Jung-Yeol Kim
Appl. Sci. 2023, 13(17), 9636; https://doi.org/10.3390/app13179636 - 25 Aug 2023
Viewed by 835
Abstract
Point cloud data are used to create an as-built building information model (as-built BIM) that reflects the actual status of any building, whether being constructed or already completed. However, indoor clutter objects in the point cloud data, such as people, tools, and materials, [...] Read more.
Point cloud data are used to create an as-built building information model (as-built BIM) that reflects the actual status of any building, whether being constructed or already completed. However, indoor clutter objects in the point cloud data, such as people, tools, and materials, should be effectively eliminated to create the as-built BIM. In this study, the authors proposed a novel method to automatically remove indoor clutter objects based on the Manhattan World assumption and object characteristics. Our method adopts a two-dimensional (2D) projection of a 3D point cloud approach and utilizes different properties of indoor clutter objects and structural elements in the point cloud. Voxel-grid downsampling, density-based spatial clustering (DBSCAN), the statistical outlier removal (SOR) filter, and the unsupervised radius-based nearest neighbor search algorithm were applied to our method. Based on the evaluation of our proposed method using six actual scan datasets, we found that our method achieved a higher mean accuracy (0.94), precision (0.97), recall (0.90), and F1 core (0.93) than the commercial point cloud processing software. Our method shows better results than commercial point cloud processing software in classifying and removing indoor clutter objects in complex indoor environments acquired from construction sites. As a result, assumptions about the different properties of indoor clutter objects and structural elements are being used to identify indoor clutter objects. Additionally, it is confirmed that the parameters used in the proposed method could be determined by the voxel size once it is decided during the downsampling process. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Building Information Modelling: Volume II)
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23 pages, 5519 KiB  
Article
Analysis of BIM-Based Quantity Take-Off in Simplification of the Length of Processed Rebar
by Woobin Kwon, Hyeonmin Kim, Heejae Ahn, U-Yeol Park, Chee Kyeong Kim and Hunhee Cho
Appl. Sci. 2023, 13(4), 2468; https://doi.org/10.3390/app13042468 - 14 Feb 2023
Cited by 1 | Viewed by 1478
Abstract
It is important to apply the Length types of Processed Rebar Simplification (LPRS) to rebar work for improving work efficiency and reducing labor in construction fields. However, when used excessively, LPRS can also bring about adverse results, as the increase in the amount [...] Read more.
It is important to apply the Length types of Processed Rebar Simplification (LPRS) to rebar work for improving work efficiency and reducing labor in construction fields. However, when used excessively, LPRS can also bring about adverse results, as the increase in the amount of wasted rebars can scale with the cutting process, leading to an increase in material cost. Therefore, it is crucial to find a proper level of simplification for considering labor and material cost together. In this study, various simplification tests were conducted based on BIM software to quantitatively validate the variation of the amount of rebar and LPRS according to the simplification. These tests were conducted for each member and the shape of the building, using the data of five projects, by dividing the unit of simplified rebar length into three cases. The research analysis showed that simplifying the unit of rebar lengths to 500 mm and 1000 mm increased the amounts of materials at a greater rate, making them undesirable. Further, it was recognized that irregular slabs, compared to regular slabs, more efficiently reduced the number of LPRS when adopting simplification methods. This study is expected to contribute to preventing material costs from increasing excessively by quantitatively analyzing the impact level of simplification on the amount of rebar materials and LPRS. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Building Information Modelling: Volume II)
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19 pages, 7607 KiB  
Article
Vision-Based Activity Classification of Excavators by Bidirectional LSTM
by In-Sup Kim, Kamran Latif, Jeonghwan Kim, Abubakar Sharafat, Dong-Eun Lee and Jongwon Seo
Appl. Sci. 2023, 13(1), 272; https://doi.org/10.3390/app13010272 - 26 Dec 2022
Cited by 6 | Viewed by 3350
Abstract
Advancements in deep learning and vision-based activity recognition development have significantly improved the safety, continuous monitoring, productivity, and cost of the earthwork site. The construction industry has adopted the CNN and RNN models to classify the different activities of construction equipment and automate [...] Read more.
Advancements in deep learning and vision-based activity recognition development have significantly improved the safety, continuous monitoring, productivity, and cost of the earthwork site. The construction industry has adopted the CNN and RNN models to classify the different activities of construction equipment and automate the construction operations. However, the currently available methods in the industry classify the activities based on the visual information of current frames. To date, the adjacent visual information of current frames has not been simultaneously examined to recognize the activity in the construction industry. This paper proposes a novel methodology to classify the activities of the excavator by processing the visual information of video frames adjacent to the current frame. This paper follows the CNN-BiLSTM standard deep learning pipeline for excavator activity recognition. First, the pre-trained CNN model extracted the sequential pattern of visual features from the video frames. Then BiLSTM classified the different activities of the excavator by analyzing the output of the pre-trained convolutional neural network. The forward and backward LSTM layers stacked on help the algorithm compute the output by considering previous and upcoming frames’ visual information. Experimental results have shown the average precision and recall to be 87.5% and 88.52%, respectively. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Building Information Modelling: Volume II)
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24 pages, 7767 KiB  
Article
2D-LiDAR-Sensor-Based Retaining Wall Displacement Measurement System
by Jun-Sang Kim, Gil-yong Lee and Young Suk Kim
Appl. Sci. 2022, 12(22), 11335; https://doi.org/10.3390/app122211335 - 08 Nov 2022
Cited by 2 | Viewed by 1596
Abstract
The displacement of retaining walls is measured using inclinometers in order to evaluate the safety of the wall. However, inclinometers have three problems: they (1) are difficult to install, (2) have local displacement detection, and (3) are measured using manpower. Consequently, a two-dimensional [...] Read more.
The displacement of retaining walls is measured using inclinometers in order to evaluate the safety of the wall. However, inclinometers have three problems: they (1) are difficult to install, (2) have local displacement detection, and (3) are measured using manpower. Consequently, a two-dimensional (2D) LiDAR sensor-based retaining wall displacement measurement system that facilitates installation and three-dimensional (3D) displacement detection (more economically feasible than inclinometers) was developed in order to overcome the aforementioned limitations. The developed system collects 3D point cloud data about the retaining wall by rotating the 2D LiDAR sensor 360° at a constant speed. Laboratory experiments were performed using a simulated deformation model to evaluate the displacement measurement performance of the system, which had a root-mean-square error of 2.82 mm at approximately 20 m. The economic feasibility of the system was analyzed, which revealed that the system was economically feasible, with a benefit/cost ratio and breakeven point of 3.52 and 2.71 years, respectively. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Building Information Modelling: Volume II)
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18 pages, 7292 KiB  
Article
Synthetic Data and Computer-Vision-Based Automated Quality Inspection System for Reused Scaffolding
by Alexander Kim, Kyuhyup Lee, Seojoon Lee, Jinwoo Song, Soonwook Kwon and Suwan Chung
Appl. Sci. 2022, 12(19), 10097; https://doi.org/10.3390/app121910097 - 08 Oct 2022
Cited by 4 | Viewed by 1802
Abstract
Regular scaffolding quality inspection is an essential part of construction safety. However, current evaluation methods and quality requirements for temporary structures are based on subjective visual inspection by safety managers. Accordingly, the assessment process and results depend on an inspector’s competence, experience, and [...] Read more.
Regular scaffolding quality inspection is an essential part of construction safety. However, current evaluation methods and quality requirements for temporary structures are based on subjective visual inspection by safety managers. Accordingly, the assessment process and results depend on an inspector’s competence, experience, and human factors, making objective analysis complex. The safety inspections performed by specialized services bring additional costs and increase evaluation times. Therefore, a temporary structure quality and safety evaluation system based on experts’ experience and independent of the human factor is the relevant solution in intelligent construction. This study aimed to present a quality evaluation system prototype for scaffolding parts based on computer vision. The main steps of the proposed system development are preparing a dataset, designing a neural network (NN) model, and training and evaluating the model. Since traditional methods of preparing a dataset are very laborious and time-consuming, this work used mixed real and synthetic datasets modeled in Blender. Further, the resulting datasets were processed using artificial intelligence algorithms to obtain information about defect type, size, and location. Finally, the tested parts’ quality classes were calculated based on the obtained defect values. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Building Information Modelling: Volume II)
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18 pages, 5013 KiB  
Article
Deep Learning-Based PC Member Crack Detection and Quality Inspection Support Technology for the Precise Construction of OSC Projects
by Seojoon Lee, Minkyeong Jeong, Chung-Suk Cho, Jaewon Park and Soonwook Kwon
Appl. Sci. 2022, 12(19), 9810; https://doi.org/10.3390/app12199810 - 29 Sep 2022
Cited by 8 | Viewed by 2294
Abstract
Recently, the construction industry has benefited from the increased application of smart construction led by the core technologies of the fourth industrial revolution, such as BIM, AI, modular construction, and AR/VR, which enhance productivity and work efficiency. In addition, the importance of “Off-Site [...] Read more.
Recently, the construction industry has benefited from the increased application of smart construction led by the core technologies of the fourth industrial revolution, such as BIM, AI, modular construction, and AR/VR, which enhance productivity and work efficiency. In addition, the importance of “Off-Site Construction (OSC)”, a factory-based production method, is being highlighted as modular construction increases in the domestic construction market as a means of productivity enhancement. The problem with OSC construction is that the quality inspection of Precast Concrete (PC) members produced at the factory and brought to the construction site is not carried out accurately and systematically. Due to the shortage of quality inspection manpower, a lot of time and money is wasted on inspecting PC members on-site, compromising inspection efficiency and accuracy. In this study, the major inspection items to be checked during the quality inspection are classified based on the existing PC member quality inspection checklist and PC construction specifications. Based on the major inspection items, the items to which AI technology can be applied (for automatic quality inspection) were identified. Additionally, the research was conducted focusing on the detection of cracks, which are one of the major types of defects in PC members. However, accurate detection of cracks is difficult since the inspection mostly relies on a visual check coupled with subjective experience. To automate the detection of cracks for PC members, video images of cracks and non-cracks on the surface were collected and used for image training and recognition using Convolutional Neural Network (CNN) and object detection, one of the deep learning technologies commonly applied in the field of image object recognition. Detected cracks were classified according to set thresholds (crack width and length), and finally, an automated PC member crack detection system that enables automatic crack detection based on mobile and web servers using deep learning and imaging technologies was proposed. This study is expected to enable more accurate and efficient on-site PC member quality inspection. Through the smart PC member quality inspection system proposed in this study, the time required for each phase of the existing PC member quality inspection work was reduced. This led to a reduction of 13 min of total work time, thereby improving work efficiency and convenience. Since quality inspection information can be stored and managed in the system database, human errors can be reduced while managing the quality of OSC work systematically and accurately. It is expected that through optimizing and upgrading our proposed system, quality work for the precise construction of OSC projects can be ensured. At the same time, systematic and accurate quality management of OSC projects is achievable through inspection data. In addition, the smart quality inspection system is expected to establish a smart work environment that enables efficient and accurate quality inspection practices if applied to various construction activities other than the OSC projects. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Building Information Modelling: Volume II)
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12 pages, 2856 KiB  
Article
Development of a Remote Collaboration System for Interactive Communication with Building Information Model in Mixed Reality
by Jaehong Cho, Sungpyo Kim, Namyoung Kim and Sanghyeok Kang
Appl. Sci. 2022, 12(17), 8738; https://doi.org/10.3390/app12178738 - 31 Aug 2022
Cited by 2 | Viewed by 1437
Abstract
Remote collaboration for construction site management is challenging. Building Information Modeling (BIM) provides the potential for remote collaboration based on its powerful data compatibility. Furthermore, the recently evolving Mixed Reality technology improves visual perception by superimposing 3D virtual BIM objects on real-world artifacts. [...] Read more.
Remote collaboration for construction site management is challenging. Building Information Modeling (BIM) provides the potential for remote collaboration based on its powerful data compatibility. Furthermore, the recently evolving Mixed Reality technology improves visual perception by superimposing 3D virtual BIM objects on real-world artifacts. This study proposes a remote collaboration system based on BIM in Mixed Reality. This system consists of three-unit systems: (1) Field Operator System (FOS), (2) Communication Server, and (3) Office Manager System (OMS). FOS was developed based on MR smart glasses for a field operator. The field operator can manipulate virtual BIM objects with finger-pointing cues and share the view with an office manager. FOS creates Mixed Reality Capture (MRC) video, the combined image of real-world images of existing artifacts in the construction site with virtual BIM elements superimposed on them, and sends it through the Communication Server to OMS. Thus, the office manager can see the field operator’s view through OMS based on a desktop or tablet PC. The office manager can give instructions to a field operator by voice through OMS. A user test was conducted to evaluate the applicability of the developed prototype system. As a result of the test, it was found that most of the testers had a positive evaluation of the developed system. This paper discusses the development of the BIM and MR-based remote collaboration and the test results. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Building Information Modelling: Volume II)
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Review

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29 pages, 2899 KiB  
Review
A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
by Youngsu Yu, Sihyun Kim, Haein Jeon and Bonsang Koo
Appl. Sci. 2023, 13(23), 12560; https://doi.org/10.3390/app132312560 - 21 Nov 2023
Cited by 1 | Viewed by 1025
Abstract
Numerous studies have developed extensions to the IFC schema to meet the needs of specialized domains or represent nascent technologies, and in turn have expanded the scope of interoperability for BIM data exchanges. However, these studies used varying approaches for IFC extensions and [...] Read more.
Numerous studies have developed extensions to the IFC schema to meet the needs of specialized domains or represent nascent technologies, and in turn have expanded the scope of interoperability for BIM data exchanges. However, these studies used varying approaches for IFC extensions and validation, making it difficult to identify research gaps and agree on legitimate extension protocols. This study collected 64 studies of IFC schema extensions spanning over two decades, from 2001 to 2022. The analysis first focused on categorizing these cases with respect to their target domains and sectors, their purpose and extension approaches, as well as their methods for implementation and validation. Timeline analyses were also conducted to track the temporal trends over the specified period. The results revealed that architectural cases have recently shifted from process to product representations due to new technology adoptions, while infrastructure cases, initially centered on major sector elements, have transitioned towards operation and maintenance processes. The findings also showed the need for a more holistic and organized approach for extensions, as current ad hoc developments were limited to products and processes only applicable for specific sectors. Full article
(This article belongs to the Special Issue Application of Geographic Information System and Building Information Modelling: Volume II)
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