As precast concrete based rapid construction becomes more commonplace and standardized in the construction industry, checking the conformity of dimensional and surface qualities of precast concrete elements to the specified tolerances has become ever more important to prevent construction failures. Moreover, as BIM gains popularity due to increasing demand for information technology (IT) in the construction industry, autonomous and intelligent QA techniques that are interoperable with BIM and a systematic data storage and delivery system for dimensional and surface QA of precast concrete elements is urgently needed. The current method for dimensional and surface QA of precast concrete element relies largely on manual inspection and contact-type measurement devices, which are time consu...[ Read more ]

As precast concrete based rapid construction becomes more commonplace and standardized in the construction industry, checking the conformity of dimensional and surface qualities of precast concrete elements to the specified tolerances has become ever more important to prevent construction failures. Moreover, as BIM gains popularity due to increasing demand for information technology (IT) in the construction industry, autonomous and intelligent QA techniques that are interoperable with BIM and a systematic data storage and delivery system for dimensional and surface QA of precast concrete elements is urgently needed. The current method for dimensional and surface QA of precast concrete element relies largely on manual inspection and contact-type measurement devices, which are time consuming and costly. To overcome the limitations of the current precast concrete QA method, this study aims to develop intelligent precast concrete QA techniques based on 3D laser scanning and BIM technology. There are four research cores investigated in this study, which are (1) dimensional and surface QA techniques, (2) BIM based QA data storage and management framework (3) scan parameter optimization for accurate QA and (4) validation through field tests.

Two QA techniques are developed in this study. Firstly, a non-contact measurement technique that automatically measures and assessed the dimensional qualities of precast concrete elements is developed using a 3D laser scanner. A robust edge extraction algorithm, which is able to extract only the scan points within the edges of a target precast concrete element, is developed based on a unique characteristic of scan points captured from the laser scanner. Moreover, to increase the dimensional estimation accuracy, a compensation model is employed to account for the dimension losses caused by the mixed pixel problem of laser scanners. Experimental tests on a lab scale specimen as well as lab scale actual precast concrete elements are performed to validate the effectiveness of the proposed technique. Secondly, a surface QA technique that simultaneously localizes and quantifies surface defects on precast concrete surfaces is developed. Defect sensitive features, which have complementary properties to each other, are developed and combined for improved localization and quantification of surface defects on precast concrete elements. A defect classifier is also developed to automatically determine whether the investigated surface region is damaged, where the defect and its size is located. To validate the robustness of the proposed surface QA technique, numerical simulations and experiments are conducted.

For data storage and management for QA of precast concrete elements, a BIM-based data storage and management framework is proposed. The framework aims to answer four essential questions for precast concrete QAs, which are (1) what the inspection checklists should be; (2) what the quality inspection procedure should be employed; (3) which kind of laser scanner is appropriate and which scan parameters are optimal for the intended quality inspection; and (4) how the inspection data should be stored and delivered. The feasibility of the proposed framework for dimensional and surface QA of precast concrete elements is investigated through case studies where dimensional errors and surface defects within lab-scale precast slabs are detected and those QA data are systematically stored and managed with help of BIM.

In scan parameter optimization, a method of selecting optimal scan parameters of a laser scanner is proposed to ensure that the proposed dimensional QA technique provides satisfactory accuracy. It was found in the experimental results of the previous study that dimensional estimation accuracy is largely influenced by scan parameters, especially in the incident angle between the laser scanner and target object. Hence, to find optimal scan parameters for dimensional QA, a simulation model that estimates the laser beam position of the laser scanner is developed by constructing the geometric position of the laser beam and contaminating the measurement noise of the laser beam into the mathematical laser beam position. Comparison tests with experiments are conducted to validate the laser beam model, and parametric studies with different scan parameters are implemented based the developed model to find optimal scan parameters.

Finally, this research validates the effectiveness of the proposed QA techniques and the data storage and management system through field tests. In the field test, two types of full-scale precast concrete slabs with complex geometries are scanned in a precast concrete factory and dimensional QA checklists including dimension and positions are inspected. The challenges encountered during the data analysis of the full-scale test are discussed and addressed. In addition, a comparison test with the conventional deviation analysis is conducted and the robustness of the developed dimensional QA technique is demonstrated. Furthermore, a cloud-BIM web-service is employed to investigate the potential of the proposed data storage and management system for QA of precast concrete elements.

Keywords: 3D laser scanning, building information modeling (BIM), dimensional quality assessment, precast concrete element, surface quality assessment