Levelling Calibration and Intelligent Real-Time Monitoring of the Assembly Process of a DfD-Based Prefabricated Structure Using a Motion Capture System

Abstract

Conventional measuring techniques and equipment such as the level and total-station are commonly used in on-site construction to measure the position of building elements. However, a motion capture system can measure the dynamic 3D movements of markers attached to any target structure with high accuracy and high sampling rate. Considering the characteristics of prefabricated structures that is composed by lot of discrete building elements, advanced requirements for the on-site assembly monitoring is required. This paper introduces an innovative real-time monitoring technique for the DfD-based (Design for Disassembly) structure with the application of motion capture system and other hardware in an IoT-based BIM system. The design and construction method of the structure system, on-site setup of monitoring system and hardware, data acquisition and analysis method, calibration algorithm as well as the BIM system are further illustrated in the paper. The proposed method is finally applied in a real building project that is composed by thousand discrete building elements and covers a large area of 50*25 m. As demonstrator, such monitoring system is applied in the real construction of a DfD-based prefabricated steel structure in the “Water Cube” (Chinese National Aquatics Centre) in Bei**g. The building process is successfully recorded and displayed on-site with the digital twin model in the BIM system. The construction states of the building elements are gathered with different kind of IoT techniques such as the RfID chips and QR-Codes. With the demand to control the flatness tolerance within 6 mm (within a 25*50 m area), a large area monitoring system was applied in the project and finally reduced the construction time within 20 days. The final tolerance is verified and further discussed2.

This paper is supported by the National Key R&D Program of China (2020YFF0304303).

1 Introduction

Nowadays, construction industry is facing a tremendous digital changing with the wide application of digital and intelligent building techniques. Prefabricated structure system is also greatly challenging the traditional building system and methods with the advantage that lot of building components and be standardized, and hence the design and fabrication process can be greatly improved by the application of BIM system and industrial manufacturing technique. Nevertheless, prefabricated structures also bring a restrict requirement of detail design and a series of building techniques that rely greatly on the assembly process and its accuracy. In this sense, apart from the digital fabrication technique, the digital or advanced assembly technique is of great importance in the present architectural researches and practice.

At the same time, prefabricated structure also brings a new view point of the relationship between architecture and its environment, and of adaptive and sustainable architecture. In this field, DfD (Design for Disassembly) is one of the most systematic theories, and focuses on the hierarchy and composition of the structure system, its components and their connection details, with the purpose to give full play to the performance of the material and to enhance the potential sustainability of a building [1]. Design for disassembly requires both the design process to split building into components system and the construction techniques to assemble the structure. On one hand, DfD system responds to the fast and repeatable installation and damage-free detachment. Therefore, it emphasizes the application of dry joint, including both structural members and enclosure members. On the other hand, due to the highly discretized structural system, the design and construction of such structure relies strongly on the building information model of the component system and the installation process itself. This also makes the on-site assembly procedure and its quality control (accuracy of the installation) very important.

In this research, an innovative on-site building process for the DfD structure system is introduced. The importance the possibility to use BIM models together with the IoT (Internet of Things) techniques is discussed. At the same time, a novel application of motion capture system in the levelling process is present. The main case in this research is a DfD-based quick assembly and disassembly steel structure system, which is mainly used for the construction of the competition platform of the Bei**g 2022 Winter Olympics curling venues. For the first time in the history of the Olympic Games, the curling venue of the Bei**g Winter Olympics has applied a DfD construction system that can be repeatedly disassembled and assembled (Fig. 1). The main swimming pool of the original 2008 Summer Olympics Swimming Centre- “the Water Cube”- is used for reconstruction, trying to achieve a repeatable “winter and summer” scene change for the different uses. Since the construction time of the structural conversion has greatly affected the daily operations of the venues, the efficiency of the structural conversion has become the core issue of this project. At the same time, because curling has extremely high requirements for the flatness of the venue platform (the vertical height difference of any two points of the venue platform within the entire 50*25 m range must be guaranteed within 6 mm), the overall stability of the prefabricated structure and the accuracy of the installation also determines the success of the entire project.

Fig. 1.

Demonstrator DfD-based project of the Olympic curling hall in the Chinese National Aquatics Center (“Water-Cube”). The steel structure should be assembled and disassembled every year to accommodate the scenario changing requirement of the building

2 BIM System and the Motion Capture System

2.1 BIM-Based Construction Control System

The selected research project provides a high complexity of requirement that includes the status monitoring of each building component in the full construction cycle, the real-time dynamic monitoring of the high information of hundreds of plates, the high accurate calibration standard of levelling (±3 mm). Therefore, a complex framework and a powerful construction data analysis platform is needed. The structural design and the component design consider both the possibilities to maintain the integrity of the structure and to make the component small enough for transportation. And this also makes the final discretized building structure composed of thousands of different structural elements.

To solve the problem, the BIM platform is introduced from the designing stage of the structure system. Firstly, a component definition system and its related building information model is built in REVIT. For each component, a customized family is created with several special defined fields to trace its condition, such as the transportation stages, assembly stages as well as the levelling information.

The tracing and monitoring of various components relies mainly on the sensors such as the RFID that gives the status information whether a component is on-site or still in transportation, the motion capture system that provide the height of each top plate as well as the QR Code information that the builders edited at any time. To integrate every different kind of information together, Autodesk REVIT is selected as the main software platform. With the use of the “Rhino-inside-REVIT” technique, a computational data analysis approach could be achieved by using Grasshopper Platform inside REVIT (Fig. 2). By develo** corresponding interfaces in the system, one can get all the information at the same time and interact with the same data system.

Fig. 2.

On-site monitoring system and its workflow

2.2 Motion Capture System

A Motion Capture (MoCap) System comprises markers, cameras, a server, and a computer as the data analyzer and exchanger, as shown in Fig. 3. The infrared-reflective markers are attached to the target objects and the cameras can triangulate the location of the markers and record them with a high sampling rate.

Fig. 3.

Typical system configuration of a motion capture system

To obtain the 3D coordinates of a marker, at least 2 cameras must simultaneously track the positions of the target marker in the 2D images. At the same time, because the accuracy is decreased if the marker image in any camera(s) overlap or are closely positioned, a series of evenly distributed cameras are the common configuration in a MoCap system. All the cameras in a MoCap system must be synchronized to measure the dynamic motions of different individual markers. To achieve the synchronization, a server gets all the real-time 2D information of the cameras and send them to the computer. The software in the computer combines the 2D coordinates together and record all the history data for each marker.

To get the accurate 3D coordinate of the target markers, the real coordinates of the cameras should be calculated. Because there are often many cameras in one system, one simple static coordinate information of the markers is not enough. Therefore, a calibration procedure must be applied. The calibration uses a great amount of data, which is usually sampled by performing with a wand, as shown in Fig. 4. Because the wand provides the basic shape relationship and distances of the markers as a priori and the wand can be easily waved, the calibration method based on the wand can simplify the process and improve the calibration accuracy.

Fig. 4.

Principles of the calibration of a motion capture system

The calibration process can be divided into two processes: static and dynamic. Firstly, static calibration is performed to establish the absolute spatial reference of the coordinate system. In this process, the T-shape of the wand will be used. As shown in Fig. 4, the wand is placed around the target, and the markers appended on the wand form two perpendicular axes (x_w, y_w). These axes are used to define a 3D space coordinated system (x_w, y_w, z_w). The relationship between the 2D image coordinates of the cameras (u, v) and the target 3D model coordinates (x_w, y_w, z_w) is defined by the algorithm as follows [2]:

$$ \begin{gathered} \left( {\begin{array}{*{20}c} u \\ v \\ \end{array} } \right) = \left[ C \right]\left[ {I|0} \right]\left[ {R|T} \right]\left( {\begin{array}{*{20}c} {x_{w} } \\ {y_{w} } \\ {z_{w} } \\ 1 \\ \end{array} } \right) = \hfill \\ \left[ {\begin{array}{*{20}c} {f_{x} } & 0 & {p_{x} } \\ 0 & {f_{y} } & {p_{y} } \\ 0 & 0 & 1 \\ \end{array} } \right]\left[ {\begin{array}{*{20}c} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ \end{array} } \right]\left[ {\begin{array}{*{20}c} {r_{11} } & {r_{12} } & {r_{13} } & {T_{x} } \\ {r_{21} } & {r_{22} } & {r_{23} } & {T_{y} } \\ {r_{31} } & {r_{32} } & {r_{33} } & {T_{z} } \\ 0 & 0 & 0 & 1 \\ \end{array} } \right]\left( {\begin{array}{*{20}c} {x_{w} } \\ {y_{w} } \\ {z_{w} } \\ 1 \\ \end{array} } \right) \hfill \\ \end{gathered} $$

(1)

where [C] is the perspective projection matrix from the camera space to the image space, which is also an intrinsic matrix that consists of the focal length $f_{x}$ and $f_{y}$ and the principal point coordinates $p_{x}$ and $p_{y}$. $\left[ {R|T} \right]$ is the extrinsic matrix that shows the transformation from the wand space to the camera space. It contains the rotation matrix R and the translation vector T.

Three coordinate systems are used in the process to transform the 2D image coordinates to the 3D model coordinates. As shown in Fig. 4, the 2D image coordinates (h_j, v_j)_i of the j th marker from the i th camera are the result of the perspective projection of the camera coordinates. The camera coordinates (x_c, y_c, z_c)_i are the 3D coordinates of the ith camera corresponding to the marker. The wand coordinates (x_wj, y_wj, z_wj) are the virtual 3D coordinates of the jth marker relative to the location of the wand.

The camera matrix $\left[C\right]$ and the extrinsic matrix $\left[R|T\right]$ are optimized and finally determined via a dynamic calibration procedure, which is performed by waving the wand to record multiple coordinates data and to calculate the relative positions and directions of the camera. The accuracy of the reconstruction of the 3D coordinates is dependent on various factors such as the focal length, the lens distortion coefficients and the camera’s resolution. During the calibration, direct linear transformation and nonlinear transformation algorithms are frequently employed [3].

3 Experiments and the Results

3.1 Design of the Structure and the BIM System

The final construction structure is 56.7 m long and 26.7 m wide. The supporting frame body is made of prefabricated steel columns and beams, covered with light-weight precast concrete slab to meet the ice-making conditions (Fig. 5). The structural beams and column members are all high-frequency welded thin-walled H-section steel. The steel used for the members is Q235B grade steel, and the connection is bolted. The connections used are M16 high-strength structural bolts. The surface layer adopts lightweight concrete prefabricated slabs, the main specification is 1*1*1 m, the strength grade is L40. Finally, a total of 1,568 prefabricated slabs and a steel structure of 140 t are used.

Fig. 5.

Design of the DfD-based structure system

All components are defined parametrically in REVIT by means of customized family components through the use of computational design methods in the Grasshopper plugin, so the establishment of the component system database and custom modifications can be quickly realized. In order to realize real-time positioning and tracking and information visualization of the component system, all components in the database are named using unique coding rules. At the same time, in order to realize the collection and processing of information based on IoT theory, a plug-in suitable for reading RFID beacon information has been developed in the relevant Grasshopper platform. Meanwhile, a WECHAT-based Application which is suitable for QR code scanning and information filling has been created. Data crawling and real-time analysis of QR code database information is realized in Grasshopper (Fig. 6). Therefore, the repetitively disassembled steel structure system realizes the real-time perception and analysis of the construction information of the whole process at each component level, and is integrated accordingly through the BIM software platform.

Fig. 6.

Data collection of the QR Code based APP

3.2 On-Site Levelling Calibration

The construction site has an area of 60*25 m, which is far beyond the sensing range of the sensors (such as the resolution range of the infrared cameras of the motion capture system). At the same time, due to the large height difference of the original swimming pool in the site, the motion capture cameras on site can only be placed on the side of the pool bank in a unilateral arrangement, and the single monitoring range can basically reach 12*15 m (Fig. 7). In previous researches, it is also shown that unevenly distributed cameras, different light environment and frequent change of camera visibility would also affect the final accuracy of the MoCap system [4, 5]. And it is helpful to use hybrid MoCap techniques and to work together with traditional surveying and measuring techniques to reduce the system tolerance [6]. Therefore, a calibration of the levelling analysis is required.

Fig. 7.

On-site placement of the motion capture system and the collected point cloud for each marker

In order to solve this problem, a set of coordinate system compensation and correction algorithm based on ICP (Iterative Closest Points) algorithm was developed, and was finally developed as plugins inside Grasshopper . The basic principle is: after the main beams are installed, the top midpoints of the structural column are used to arrange the positioning markers, the relevant column top coordinates are then collected in the MoCap software to establish a grid system. At the same time, the electronic level and the total station were used as reference data, and the total station was used to collect the horizontal XY coordinates, and the electronic level was used to collect the vertical Z coordinates to generate a second set of real coordinate grids for correcting errors. Finally, the corresponding regions of the two sets of grids were fitted, the global tolerance will be eliminated. Together with the BIM model, a real-time levelling support software is further developed (Fig. 8).

Fig. 8.

On-site placement of the motion capture system and the collected point cloud for each marker

At the construction site, the BIM system and related levelling support system were connected to the central control screen, and the construction personnel were assisted to fine-tune the relevant structure through real-time playback. This construction method greatly saved the time required for on-site installation, especially the real-time display of the components’ status and the levelling process, and provided a large amount of data support for the construction planning. Compared with the previous structural conversion construction using traditional level measurement, it reduced the construction time from 40 to 20 days and enhanced the quality of the assembled structure.

In the construction, a part of the construction area that did not use levelling monitoring was reserved as a comparison verification area in all areas. It was tested via scanning with the laser tracing instrument to test all the slab centre coordinates. The results showed that the area with levelling monitoring basically meet the requirements of the project for global and local elevation errors, and the overall accuracy was controlled between −1.5−1.5 mm (Fig. 9). Compared with the levelling monitoring area, the unmonitored area had a larger elevation error, and it was related to the experience of the construction personnel. A large number of measurement points in the area were lower than the global elevation, and the error was in the range of −2 to −10 mm.

Fig. 9.

Verification of the effect of the motion capture levelling system

4 Conclusion

This research presents and discusses the possibility to apply a BIM-based system for the intelligent and sustainable construction of some possible DfD-based structure systems. The system framework and key technologies are shown with an integrated BIM workflow, with corresponding IoT techniques, motion capture system technique and the software interface in the Grasshopper plugin. As demonstrator, such construction method is applied in the China National Aquatics Centre for the rapid and repeatable scene transition from the 2008 summer swimming venue to the 2022 winter curling venue, effectively reducing the required structural construction time and ensuring the structure itself the high standard of elevation control requirements. Under the current social development background, this construction method will provide a potential green, efficient and sustainable energy-saving construction technology, and provide an enlightening direction for the digital development, sustainable development, energy conservation and emission reduction of the future construction industry.