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Open AccessArticle

Analysis of BIM-Based Quantity Take-Off in Simplification of the Length of Processed Rebar

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Appl. Sci. 2023, 13(4), 2468; https://doi.org/10.3390/app13042468 - 14 Feb 2023

Cited by 1 | Viewed by 704

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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Open AccessArticle

Vision-Based Activity Classification of Excavators by Bidirectional LSTM

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Appl. Sci. 2023, 13(1), 272; https://doi.org/10.3390/app13010272 - 26 Dec 2022

Viewed by 2082

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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Open AccessArticle

2D-LiDAR-Sensor-Based Retaining Wall Displacement Measurement System

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Jun-Sang Kim

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Gil-yong Lee

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Young Suk Kim

Appl. Sci. 2022, 12(22), 11335; https://doi.org/10.3390/app122211335 - 08 Nov 2022

Viewed by 714

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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Open AccessArticle

Synthetic Data and Computer-Vision-Based Automated Quality Inspection System for Reused Scaffolding

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Appl. Sci. 2022, 12(19), 10097; https://doi.org/10.3390/app121910097 - 08 Oct 2022

Viewed by 942

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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Open AccessArticle

Deep Learning-Based PC Member Crack Detection and Quality Inspection Support Technology for the Precise Construction of OSC Projects

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Appl. Sci. 2022, 12(19), 9810; https://doi.org/10.3390/app12199810 - 29 Sep 2022

Cited by 2 | Viewed by 1136

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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Open AccessArticle

Development of a Remote Collaboration System for Interactive Communication with Building Information Model in Mixed Reality

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Appl. Sci. 2022, 12(17), 8738; https://doi.org/10.3390/app12178738 - 31 Aug 2022

Viewed by 817

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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Open AccessArticle

Impact of Virtual Reality-Based Design Review System on User’s Performance and Cognitive Behavior for Building Design Review Tasks

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Appl. Sci. 2022, 12(14), 7249; https://doi.org/10.3390/app12147249 - 19 Jul 2022

Cited by 2 | Viewed by 1279

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Virtual reality (VR) can potentially enhance various design and construction assessment intensive tasks, such as construction design and review. However, it may lead to cognitive overload, adversely affecting the participants’ performance. It is critical to understand the effects of VR cognitive behavior for [...] Read more.

Virtual reality (VR) can potentially enhance various design and construction assessment intensive tasks, such as construction design and review. However, it may lead to cognitive overload, adversely affecting the participants’ performance. It is critical to understand the effects of VR cognitive behavior for implementing VR technology in the construction industry. The principal objective of this study was to investigate the participants’ cognitive load (CL), task performance (TP), and situational awareness (SA) in the VR environment for the evaluation of building design review tasks. Participants were asked to review the design task based on their memory knowledge and understanding in one of the three environments: paper-based, monitor-based, and immersive virtual environment. Participants’ CL was measured using the National Aeronautics and Space Administration Task Load Index (NASA TLX), TP was evaluated on completion time and the number of errors correctly detected, and situational awareness (SA) was assessed using the Situational Awareness and Review Technique (SART). The statistical results show a high CL and better performance in the immersive virtual environment. These findings can contribute to a better understanding of cognitive process characteristics and capabilities for design review activities in the VR environment. Full article

(This article belongs to the Special Issue Application of Geographic Information System and Building Information Modelling: Volume II)

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Open AccessArticle

Development and Assessment of an Intelligent Compaction System for Compaction Quality Monitoring, Assurance, and Management

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Appl. Sci. 2022, 12(14), 6855; https://doi.org/10.3390/app12146855 - 06 Jul 2022

Cited by 1 | Viewed by 1080

Abstract

The successful quality control and quality assurance of compaction operations are vital for the long-term performance of earth structures. Traditional in situ measurement methods are in practice for assessing compaction project specifications. These methods have several shortcomings and cannot provide complete compaction quality [...] Read more.

The successful quality control and quality assurance of compaction operations are vital for the long-term performance of earth structures. Traditional in situ measurement methods are in practice for assessing compaction project specifications. These methods have several shortcomings and cannot provide complete compaction quality information. With the advancement in automation and information technology in the construction section, intelligent compaction roller technology has the potential to solve this problem. However, this technology still has many problems and needs more comprehensive studies for its implementation. This study focuses on the development of a Web-GIS-based intelligent compaction system and the assessment of the practical application of this technology. The developed system consists of major components—namely, system hardware and software—to provide real-time compaction information and an effective management system. An experimental study was conducted to assess the correlation between the developed system’s compaction quality and traditional measurement methods. The linear regression analysis identifies the strong correlation and promises the feasibility of an intelligent compaction system instead of a traditional in situ test for compaction quality control. Two implementation case studies of real-time projects are presented to validate and demonstrate the practicality of the developed system. Full article

(This article belongs to the Special Issue Application of Geographic Information System and Building Information Modelling: Volume II)

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