• International conference on construction engineering and project management
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• 2005.10a
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• pp.569-574
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• 2005

This paper presents the core technology of Construction Business Process Automation to model and automate construction business processes. Business Process Reengineering (BPR) and Automation (BPA) have been recognized as one of the important aspects in construction business management. However, BPR requires a lot of efforts to identify, document, implement, execute, maintain, and keep track thousands of business processes to deliver a project. Moreover, existing BPA technologies used in existing Enterprise Resource Planning (ERP) systems do not lend themselves to effective scalability for construction business process management. Application of Workflow and Object Technologies would be quite effective in implementing a scalable enterprise application for construction community. This paper present the technologies and methodologies for automating construction business processes by addressing how: 1) Automated construction management tasks are developed as software components, 2) The process modeling is facilitated by dragging-and dropping task components in a network, 3) Raising business requests and instantiating corresponding process instances are delivered, and 4) Business process instances are executed by using workflow technology based on real-time simulation engine. This paper presents how the construction business process automation is achieved by using equipment reservation and cancellation processes simplified intentionally.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.42-52
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• 1997

Computer aided process planning and Offline programming are decisive factors in successful implementation of automated robotic production. However, conventional offline programming procedure has proven ineffective due to time-consuming teaching process for robot programming and due to inefficient system modeling. The paper presents an efficient procedure to semi-automatically generate robot job programs for ship block welding applications. In the research, the teaching positions are automatically determined by predefined rules which are functions of the type and the dimensions of the given welding section of ship block. And a sequence of robot movements and welding conditions such as welding type, welding current, welding speed, and welding torch orientation, are determined by use of Standard Program which is experimentally proved to work well for the welding wection group. Finally, a robot program for the welding section is generated automatically. Based on the algorithm, a offline automatic teaching software is developed. The paper presents also the algorithm and structure of the software.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.299-303
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• 2015

Flexible space is an adaptable space that has been increasingly used in many office and academic buildings as it increases the use of the space available and reduces the unnecessary building area. However, the architectural, engineering and construction (AEC) industry lacks a formalized method that helps architects predict and update the space utilization of flexible space during the project development, as such prediction aims to maximize the use of the building space available without exceeding the target utilization policy. Consequently, current manual utilization prediction results in lower accuracy level and limits the maximized use of the flexible space, which has multiple space-use types that affect the prediction of utilization. To address this problem, we identified eight space-use type differentiators (SUTDs) based on the literature review and observations and discussed the use of them in automated space-use analysis (SUA), which can predict the utilization of flexible space via a computer program. This research builds on SUA and contributes to flexible space planning by providing a means of a more comprehensive and accurate SUA.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• International Journal of High-Rise Buildings
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• v.11 no.2
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• pp.87-102
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• 2022

In Japan, the population progresses to the extreme aging society and it is entering the phase of the population decrease while the population increase is continuing in the world. The construction market is expected to shrink accordingly, however the situation of labor shortage is expected to continue at a faster rate, because the aging of construction workers is progressing and new younger labor force cannot be secured. In order to supplement the labor shortage, it is required to progress mechanization, automation, labor saving, and efficiency improvement by utilizing the information well in each stage in a series of flow of planning, design, construction, operation, and disassembly in one building. The measures to maintain and expand the construction market by the new efficiency improvement techniques which enhance the utilization degree of building information are required. Currently, the elemental technologies which utilized BIM (Building Information Modeling) are accumulated by advancing digitization in each phase. DX (Digital transformation) in the construction industry can be achieved by the technology maturing and having a series of continuities. It is anticipated that this will evolve to a new method which is unprecedented. Present status of BIM and mechanized constructions in Taisei Construction are introduced, and future prospect is described.

• Journal of Navigation and Port Research
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• v.48 no.2
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• pp.116-124
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• 2024

Automatic docking of small planing ship is a critical aspect of maritime operations, requiring accurate prediction of motion states to ensure safe and efficient maneuvers. This study investigates the use of Artificial Neural Network (ANN) to predict motion state of a small planing ship to enhance navigation automation in port environments. To achieve this, simulation tests were conducted to control a small planing ship while docking at various heading angles in calm water and in waves. Comprehensive analysis of the ANN-based predictive model was conducted by training and validation using data from various docking situations to improve its ability to accurately capture motion characteristics of a small planing ship. The trained ANN model was used to predict the motion state of the small planning ship based on any initial motion state. Results showed that the small planing ship could dock smoothly in both calm water and waves conditions, confirming the accuracy and reliability of the proposed method for prediction. Moreover, the ANN-based prediction model can adjust the dynamic model of the small planing ship to adapt in real-time and enhance the robustness of an automatic positioning system. This study contributes to the ongoing development of automated navigation systems and facilitates safer and more efficient maritime transport operations.

• Progress in Medical Physics
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• v.17 no.1
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• pp.24-31
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• 2006

Automated analysis software was developed to measure the magnitude of the intrafractional and interfractional errors during breast radiation treatments. Error analysis results are important for determining suitable planning target volumes (PTV) prior to Implementing breast-conserving 3-D conformal radiation treatment (CRT). The electrical portal imaging device (EPID) used for this study was a Portal Vision LC250 liquid-filled ionization detector (fast frame-averaging mode, 1.4 frames per second, 256X256 pixels). Twelve patients were imaged for a minimum of 7 treatment days. During each treatment day, an average of 8 to 9 images per field were acquired (dose rate of 400 MU/minute). We developed automated image analysis software to quantitatively analyze 2,931 images (encompassing 720 measurements). Standard deviations ($\sigma$) of intrafractional (breathing motion) and intefractional (setup uncertainty) errors were calculated. The PTV margin to include the clinical target volume (CTV) with 95% confidence level was calculated as $2\;(1.96\;{\sigma})$. To compensate for intra-fractional error (mainly due to breathing motion) the required PTV margin ranged from 2 mm to 4 mm. However, PTV margins compensating for intefractional error ranged from 7 mm to 31 mm. The total average error observed for 12 patients was 17 mm. The intefractional setup error ranged from 2 to 15 times larger than intrafractional errors associated with breathing motion. Prior to 3-D conformal radiation treatment or IMRT breast treatment, the magnitude of setup errors must be measured and properly incorporated into the PTV. To reduce large PTVs for breast IMRT or 3-D CRT, an image-guided system would be extremely valuable, if not required. EPID systems should incorporate automated analysis software as described in this report to process and take advantage of the large numbers of EPID images available for error analysis which will help Individual clinics arrive at an appropriate PTV for their practice. Such systems can also provide valuable patient monitoring information with minimal effort.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2003.09a
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• pp.63-63
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• 2003

Purpose: Planning target volume (PTV) for tumors in abdomen or thorax includes enough margin for breathing-related movement of tumor volumes during treatment. We developed a simple and handy method, which can reduce PTV margins in patients with moving tumors, respiratory motion reduction device system (RMRDs). Materials and Methods: The patients clinical database was structured for moving tumor patients and patient setup error measurement and immobilization device effects were investigated. The system is composed of the respiratory motion reduction device utilized in prone position and abdominal presser (strip device) utilized in the supine position, moving phantom and the analysis program, which enables the analysis on patients setup reproducibility. It was tested for analyzing the diaphragm movement and CT volume differences from patients with RMRDs, the magnitude of PTV margin was determined and dose volume histogram (DVH) was computed using a treatment planning software. Dose to normal tissue between patients with RMRDs and without RMRDs was analyzed by comparing the fraction of the normal liver receiving to 50% of the isocenter dose(TD50). Results: In case of utilizing RMRDs, which was personally developed in our hospital, the value was reduced to $5pm1.4 mm$, and in case of which the belt immobilization device was utilized, the value was reduced to 3$pm$0.9 mm. Also in case of which the strip device was utilized, the value was proven to reduce to $4pm.3 mm$0. As a result of analyzing the TD50 is irradiated in DVH according to the radiation treatment planning, the usage of the respiratory motion reduction device can create the reduce of 30% to the maximum. Also by obtaining the digital image, the function of comparison between the standard image, automated external contour subtraction, and etc were utilized to develop patients setup reproducibility analysis program that can evaluate the change in the patients setup. Conclusion: Internal organ motion due to breathing can be reduced using RMRDs, which is simple and easy to use in clinical setting. It can reduce the organ motion-related PTV margin, thereby decrease volume of the irradiated normal tissue.

• Korean Journal of Construction Engineering and Management
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• v.18 no.6
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• pp.26-37
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• 2017

Preliminary scheduling at the initial stage of planning phase is usually performed with limited information and details. Therefore, the reliability and accuracy of preliminary scheduling is affected by personal experiences and skills of the schedule planners, and it requires enormous managerial effort (or workload). Reusing of historical data of the similar projects is important for efficient preliminary scheduling. However, understanding the structure of historical data and applying them to a new project requires a great deal of experience and knowledge. In this context, this paper propose a framework and methodology for automated preliminary schedule generation based on historical database. The proposed methodology and framework enables to automatically generate CPM schedules for airport projects in the early planning stage in order to enhance the reliability and to reduce the workload by using structured knowledge and experience.

• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.90-105
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• 2004

Machines for crack sealing automation have been continually developed since the early 1990's because of the effectiveness of crack sealing that would be able to improve safety, quality and productivity. It has been considered challenging problem to detect crack network in pavement which includes noise (oil marks, skid marks, previously sealed cracks and inherent noise). Moreover, it is required to develop crack network mapping and modeling algorithm in order to accurately inject sealant along to the middle of cut crack network. The primary objective of this study is to propose machine vision algorithms (digital image processing algorithm and path planning algorithm) for fully automated pavement crack sealing. It is anticipated that the effective use of the proposed machine vision algorithms would be able to reduce error rate in image processing for detecting, mapping and modeling crack network as well as improving quality and productivity compared to existing vision algorithms.