การเปรียบเทียบประสิทธิภาพของอุปกรณ์ถ่ายภาพจากความแม่นยำของแบบจำลองสามมิติพ้อยท์คลาวด์สำหรับการประเมินความก้าวหน้าโครงการก่อสร้าง

Suchanard  Charoenchad; Korb Srinavin; Wuttipong Kusonkhum

Authors

Suchanard Charoenchad Students, Master of Engineering Program in Civil Engineering, Faculty of Engineering, Khon Kaen University
Korb Srinavin Associate Professor, Department of Civil Engineering, Faculty of Engineering, Khon Kaen University
Wuttipong Kusonkhum Assistant Professor, Department of Civil Engineering, Faculty of Engineering, Khon Kaen University

Keywords:

Point Cloud 3D model, Construction progress monitoring, Photogrammetry technology

Abstract

This research uses experimental research to study a construction progress evaluation using 3D point cloud modeling technology. This is because inspection and control of construction work is an important step to achieve quality work that meets the planned project objectives. The researcher is therefore aware of the importance of evaluating construction progress and studying the application of 3D point cloud modeling technology as well. This research study has the objective to compare the performance of imaging equipment used to collect data, including mirrorless cameras, mobile phone cameras and tablet cameras. This research collected photographic data of box-shaped objects and then processed them into a 3D point cloud model. and compare the dimensions of the three-dimensional point cloud model, including width, length, height, area and volume with the actual object dimensions obtained from the measurements. As a preliminary experiment, a comparison between the size of real objects and the size of 3D object models created from point clouds was performed. In conclusion, the results of finding the error value of the three-dimensional modeling experiment of the box when comparing the distance, area, and volume of FUJIFILM X-A2 camera, iPhone 11 Pro Max mobile phone camera, and iPad 8th generation tablet camera were not more than ±1.0 percent, which the error value is within the acceptable accuracy criteria for evaluating the work progress.

References

Alizadehsalehi S, Yitmen I. A concept for automated construction progress monitoring: Technologies adoption for benchmarking project performance control. Arab J Sci Eng. 2018;43(12):7073-85.

PMI Association Thailand Chapter. PMBOK guide. 5th ed. Bangkok: Project Management Institute; 2014. Translated by PMI Association Thailand Chapter.

Solihin W, Eastman C. Classification of rules for automated BIM rule checking development. Autom Constr. 2015;53:69-82.

Golparvar-Fard M, Bohn J, Teizer J, Savarese S, Peña-Mora F. Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques. Autom Constr. 2011;20(8):1143-55.

Teizer J, Vela PA. Personnel tracking on construction sites using video cameras. Adv Eng Inform. 2009;23(4):452-62.

Kim J, Lee S, Ahn H, Seo D, Park S, Choi C. Feasibility of employing a smartphone as the payload in a photogrammetric UAV system. ISPRS J Photogramm Remote Sens. 2013;79:1-18.

Leung S, Mak S, Lee B. Using a real-time integrated communication system to monitor the progress and quality of construction works. Autom Constr. 2008;17(6):749-57.

Srisuwan C. Photogrammetry in architectural conservation: Literature review and possible applications for Thai traditional architecture. NAJUA Hist Archit Thai Archit. 2012;9:158-85. Thai.

Waisurasingh C. Photogrammetry. 1st ed. Khon Kaen: Center for Geoinformatics for Local Development, Faculty of Engineering, Khon Kaen University; 2020. Thai.

Omari S, Moselhi O. Data acquisition from construction sites for tracking purposes. J Eng Constr Archit Manag. 2009;16(5):490-503.

Olsen MJ, Kuester F, Chang BJ, Hutchinson TC. Terrestrial laser scanning-based structural damage assessment. J Comput Civ Eng. 2010;24(3):264-72.

Yang J, Park W, Vela P, Golparvar M. Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future. Adv Eng Inform. 2015;29(2):211-24.

Abeid J, Arditi D. Time-lapse digital photography applied to project management. J Constr Eng Manag. 2002;128(6):530-5.

Kim S, Kim S, Lee DE. Sustainable application of hybrid point cloud and BIM method for tracking construction progress. Sustainability. 2020;12(10):4106.

Shirowzhan S, Sepasgozar S, Liu C. Monitoring physical progress of indoor buildings using mobile and terrestrial point clouds. Constr Inf Technol. 2018:602-11.

Agisoft LLC. Useful tips on image capture: How to get an image dataset that meets Metashape requirements? [Internet]. 2020 [updated 2020 Jun 15; cited 2024 Jun 1]. Available from: http://www.agisoft.com/downloads/user-manuals/

Rattanapongwanich S, Srinavin K, Kusonkhum W, Leungbootnak N, Charnwasunuth P. Accuracy of 3-D model based on point cloud: A new technology for construction progress evaluation. Int J Eng Technol. 2020;12(2):27-30.

Pučko Z, Šuman N, Rebolj D. Automated continuous construction progress monitoring using multiple workplace real time 3D scans. Adv Eng Inform. 2018;38:27-40.

ASPRS positional accuracy standards for digital geospatial data released. Photogramm Eng Remote Sens. 2015;81(4):277.

Shen Y, Lindenbergh R, Wang J. Change analysis in structural laser scanning point clouds: The baseline method. IEEE Sens J. 2017;17(1):26.