Quality Assessment
Scanning technology (3D laser scanner technique) measures the dimension of a precast concrete panel using extraction algorithms to measure boundary points with a compensation model to increase measurement accuracy (Kim et al., 2013). 3D laser scanning converts CAD models into BIM, which identifies scanned objects by ensuring precision for rebar mapping before actual drilling as well as rebar detailing for installing rebar cages (Hajian & Brandow, 2012). Before shipping precast concrete elements, dimensional quality assurance (DQA) is carried out to ensure that there are no dimensional defects using a scanner in an automated process that saves time and labor input (Kim et al, 2016).
Computer Aided Design (CAD) can be improved using an Iterative closes point (ICP) calibration technique, which is a point matching technique that improves the implementation process (Bosché, 2010). The deviation of the point cloud data can adequately analyze determining up to nearly six times extra errors saving up to 40 percent of the times used compared to the physical measurement (Cronin et al., 2000). This enables a user to assess quality assurance by analyzing error between the 3D and as-is built model (Son, Bosché & Kim, 2015:Anil et al, 2011, January). Using 3D scanner and information relayed from the point cloud, the display of a wall is cast in three dimensions, which enables detection of smoothness of the wall (Shih & Wang, 2004, September ). The as-is built model and 3D laser scanned data provides a qualitative, detailed, timely, and comprehensive information than the physical measurement thus efficient and effective.(Tang et al., 2011, January). The as-is build models provide a more accurate analysis of BIM as shown with the accuracy analysis hence can be used for quality assessment (Kalyan et al., 2016). The 3D laser scanner can automatically and accurately measure the width, squareness, and length of the precast concrete panel (Kim & Chang, 2014: Kim et al., 2013). Conforming to dimension can be automatically controlled with the as-is built dimension of the 3D BIM accurately and efficiently detailing geometric planes and lines before the actual construction (Bosche, 2012).
Progress monitoring
In order to remove redundant parameter in a model, a statistical parameter can be employed without interfering with the integrity as well as the accuracy of the model (Gordon & Lichti, 2007). Using remote sensing technology, the progress of the construction can be accurately measured using a fully automated technique that employs 4D BIM in accord with 3D data received ( Kim et al., 2013). The accuracy of the project can be improved through a revision process that aims to optimize measurement to using 3D data from the as-built BIM for precision and accuracy . Point and plan based deformation can be improved by using terrestrial laser scanners because it can capture 3D models with setting up makers to a ratio of a millimeter to sub-millimeter level (Chow et al., 2012). An automated system integrating 3D model, 4D model, work schedule, and point cloud can (Zhang & Arditi, 2013) effectively monitor the progress based on a percentage of completion using a laser scanner.
Change detection
3D laser scanner relay data to point cloud inform of geometric data exhibiting points as plane and lines detecting changes in the location and position of the surface, which then adjusted (Yeung, 2014). Using 4 Dimensional Augmented Reality (D4 AR) models the user is able to monitor the progress and well defects between as-is built and as-planned side by side on a site photograph detailing the orientation and location (Golparvar-Fard et al., 2009). Point cloud data can be obtained by iterating a 3D laser scanner to the as-is built model to avoid error detections which ensure that BIM generates accurate and precise data (Giel & Issa, 2011). Scan-vs BIM system is more efficient in detecting MEP (Bosche et al., 2015) when built as-planned and as-built as depicted on the geometrical placement as well as determining the completeness of pipe by identifying and recognizing their measurements.
References
Anil, E. B., Tang, P., Akinci, B., & Huber, D. (2011, January). Assessment of quality of as-is building information models generated from point clouds using deviation analysis. In Proceedings of SPIE.
Bosché, F. A Laser Scanning-based Approach to Automated Dimensional Compliance Control in Construction.
Bosché, F. (2010). Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction. Advanced engineering informatics, 24(1), 107-118.
Bosché, F., Ahmed, M., Turkan, Y., Haas, C. T., & Haas, R. (2015). The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components. Automation in Construction, 49, 201-213.
Chow, J. C., Ebeling, A., & Teskey, W. F. (2012). Point-based and plane-based deformation monitoring of indoor environments using terrestrial laser scanners. Journal of Applied Geodesy, 6(3-4), 193-202.
Cronin, J. J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of retailing, 76(2), 193-218.
Giel, B., & Issa, R. R. A. (2011). Using laser scanning to access the accuracy of as-built BIM. In Computing in Civil Engineering (2011) (pp. 665-672).
Gordon, S. J., & Lichti, D. D. (2007). Modeling terrestrial laser scanner data for precise structural deformation measurement. Journal of Surveying Engineering, 133(2), 72-80.
Golparvar-Fard, M., Peña-Mora, F., & Savarese, S. (2009). D4AR–a 4-dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication. Journal of information technology in construction, 14(13), 129-153.
Hajian, H., & Brandow, G. (2012). As-built documentation of structural components for reinforced concrete construction quality control with 3D laser scanning. In Computing in Civil Engineering (2012) (pp. 253-260).
Kim, M. K., Wang, Q., Park, J. W., Cheng, J. C., Sohn, H., & Chang, C. C. (2016). Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and BIM. Automation in Construction, 72, 102-114.
Kalyan, T. S., Zadeh, P. A., Staub-French, S., & Froese, T. M. (2016). Construction Quality Assessment Using 3D as-built Models Generated with Project Tango. Procedia Engineering, 145, 1416-1423.
Shih, N. J., & Wang, P. H. (2004, September). Using point cloud to inspect the construction quality of wall finish. In Proceedings of the 22nd eCAADe Conference (pp. 573-578).
Kim, M. K., Sohn, H., & Chang, C. C. (2013). Active dimensional quality assessment of precast concrete using 3D laser scanning. In Computing in Civil Engineering (2013) (pp. 621-628).
Kim, C., Son, H., & Kim, C. (2013). Automated construction progress measurement using a 4D building information model and 3D data. Automation in Construction, 31, 75-82.
Kim, M. K., Sohn, H., & Chang, C. C. (2014). Automated dimensional quality assessment of precast concrete panels using terrestrial laser scanning. Automation in Construction, 45, 163-177.
Son, H., Bosché, F., & Kim, C. (2015). As-built data acquisition and its use in production monitoring and automated layout of civil infrastructure: A survey. Advanced Engineering Informatics, 29(2), 172-183.
Shih, N. J., & Wang, P. H. (2004, September). Using point cloud to inspect the construction quality of wall finish. In Proceedings of the 22nd eCAADe Conference (pp. 573-578).
Tang, P., Anil, E. B., Akinci, B., & Huber, D. (2011). Efficient and effective quality assessment of as-is building information models and 3D laser-scanned data. In Computing in Civil Engineering (2011) (pp. 486-493).
Yeung, J., Nahangi, M., Shahtaheri, Y., Haas, C., Walbridge, S., & West, J. (2014). Comparison of methods used for detecting unknown structural elements in three-dimensional point clouds. In Construction Research Congress 2014: Construction in a Global Network (pp. 945-954).
Zhang, C., & Arditi, D. (2013). Automated progress control using laser scanning technology. Automation in Construction, 36, 108-116.
