Search Results (8)

This study introduces a digital fabrication process for producing recyclable, closed-loop wax formwork for architectural concrete applications with visually rich surface articulation while drastically reducing formwork milling time. As such, this paper presents (a) a circular large-scale production method for wax blocks via a single casting process; (b) four machine-time-optimized surface articulation strategies through CNC toolpath-driven design; (c) the investigation of different coating systems to improve architectural concrete surface quality and to ease demolding; and (d) the integration of robotic concrete shotcreting using a low-CO₂ fine-grain concrete. For the first time, wax formwork technology, characterized by its waste-free approach, has been combined with robotic shotcreting in a digital and automated workflow to fabricate fiber-reinforced, geometrically complex thin-shell concrete elements with distinct surface articulations. To evaluate the process, a series of four thin-shell concrete elements was produced, employing four distinct parametric toolpath-driven designs: linear surface articulation, crossed surface articulation, topology-adapted curve flow surface articulation, and robotic drill topology-adapted surface articulation. Results revealed a possible reduction in milling time of between 77% and 94% compared to traditional milling methods. The optimized toolpaths and design-driven milling strategies achieved a high degree of visual richness, showcasing the potential of this integrated approach for the production of high-quality architectural concrete elements. Full article

The accurate installation position of corrugated pipes is critical for ensuring the quality of prestressed concrete box girders. Given that these pipes can span up to 30 m and are deeply embedded within rebars, manual measurement is both labor-intensive and prone to errors. Meanwhile, automated recognition and measurement methods are hindered by high equipment costs and accuracy issues caused by rebar occlusion. To balance cost effectiveness and measurement accuracy, this paper proposes a method that utilizes an RGB-D camera and deep learning. Firstly, an optimal registration scheme is selected to generate complete point cloud data of pipes from segmented data captured by an RGB-D camera. Next, semantic segmentation is applied to extract the characteristic features of the pipes. Finally, the center points from cross-sectional slices are extracted and curve-fitting is performed to recognize and measure the pipes. A test was conducted in a simulated precast factory environment to validate the proposed method. The results show that under the optimal fitting scheme (BP neural network with circle fitting constraint), the average measurement errors for the three pipes are 2.2 mm, 1.4 mm, and 1.6 mm, with Maximum Errors of −5.8 mm, −4.2 mm, and −5.7 mm, respectively, meeting the standard requirements. The proposed method can accurately locate the pipes, offering a new technical pathway for the automated recognition and measurement of pipes in prefabricated construction. Full article

Fabrication drawings are essential for design evaluation, lean manufacturing, and quality detection of precast concrete (PC) components. Due to the complicated shape of PC components, the fabrication drawing needs to be customized to determine manufacturing dimensions and relevant assembly connections. However, the traditional manual drawing method is time-consuming, labor-intensive, and error-prone. This paper presents a BIM-based framework to automatically generate the readable drawing of PC components using building information modeling (BIM) and multi-agent reinforcement learning (MARL). Firstly, an automated generation method is developed to transform BIM model to view block. Secondly, a graph-based representation method is used to create the relationship between blocks, and a reward mechanism is established according to the drawing readability criterion. Subsequently, the block layout is modeled as a layout optimization problem, and the internal spacing and position of functional category blocks are regarded as agents. Finally, the agents collaborate and interact with the environment to find the optimal layout with the guidance of a reward mechanism. Two different algorithms are utilized to validate the efficiency of the proposed method (MADQN). The proposed framework is applied to PC stairs and a double-sided shear wall to demonstrate its practicability. Full article

Prefabricated construction has pioneered a new model in the construction industry, where prefabricated component modules are produced in factories and assembled on-site by construction workers, resulting in a highly efficient and convenient production process. Within the construction industry value chain, the smoothing and roughening of precast concrete components are critical processes. Currently, these tasks are predominantly performed manually, often failing to achieve the desired level of precision. This paper designs and develops a robotic system for smoothing and roughening precast concrete surfaces, along with a multi-degree-of-freedom integrated intelligent end-effector for smoothing and roughening. Point-to-point path planning methods are employed to achieve comprehensive path planning for both smoothing and roughening, enhancing the diversity of textural patterns using B-spline curves. In the presence of embedded obstacles, a biologically inspired neural network method is introduced for precise smoothing operation planning, and the A* algorithm is incorporated to enable the robot’s escape from dead zones. Experimental validation further confirms the feasibility of the entire system and the accuracy of the machining path planning methods. The experimental results demonstrate that the proposed system meets the precision requirements for smoothing and offers diversity in roughening, affirming its practicality in the precast concrete process and expanding the automation level and application scenarios of robots in the field of prefabricated construction. Full article

Recently, the construction industry has benefited from the increased application of smart construction led by the core technologies of the fourth industrial revolution, such as BIM, AI, modular construction, and AR/VR, which enhance productivity and work efficiency. In addition, the importance of “Off-Site Construction (OSC)”, a factory-based production method, is being highlighted as modular construction increases in the domestic construction market as a means of productivity enhancement. The problem with OSC construction is that the quality inspection of Precast Concrete (PC) members produced at the factory and brought to the construction site is not carried out accurately and systematically. Due to the shortage of quality inspection manpower, a lot of time and money is wasted on inspecting PC members on-site, compromising inspection efficiency and accuracy. In this study, the major inspection items to be checked during the quality inspection are classified based on the existing PC member quality inspection checklist and PC construction specifications. Based on the major inspection items, the items to which AI technology can be applied (for automatic quality inspection) were identified. Additionally, the research was conducted focusing on the detection of cracks, which are one of the major types of defects in PC members. However, accurate detection of cracks is difficult since the inspection mostly relies on a visual check coupled with subjective experience. To automate the detection of cracks for PC members, video images of cracks and non-cracks on the surface were collected and used for image training and recognition using Convolutional Neural Network (CNN) and object detection, one of the deep learning technologies commonly applied in the field of image object recognition. Detected cracks were classified according to set thresholds (crack width and length), and finally, an automated PC member crack detection system that enables automatic crack detection based on mobile and web servers using deep learning and imaging technologies was proposed. This study is expected to enable more accurate and efficient on-site PC member quality inspection. Through the smart PC member quality inspection system proposed in this study, the time required for each phase of the existing PC member quality inspection work was reduced. This led to a reduction of 13 min of total work time, thereby improving work efficiency and convenience. Since quality inspection information can be stored and managed in the system database, human errors can be reduced while managing the quality of OSC work systematically and accurately. It is expected that through optimizing and upgrading our proposed system, quality work for the precise construction of OSC projects can be ensured. At the same time, systematic and accurate quality management of OSC projects is achievable through inspection data. In addition, the smart quality inspection system is expected to establish a smart work environment that enables efficient and accurate quality inspection practices if applied to various construction activities other than the OSC projects. Full article

The Fourth Industrial Revolution represents the beginning of a profound change for the building sector. In the last decade, the perspective of shapes, materials, and construction techniques is evolving fast due to the additive manufacturing technology. On the other hand, even if the technology is growing fast and several 3D printed buildings are being developed worldwide, the potential of concrete 3D printing in building prefabrication remains unexplored. Consequently, the application of new digital fabrication technologies in the construction industry requires a redesign of the construction process and its components. This paper proposes a novel conception, design, and prototyping of a precast building envelope to be prefabricated with extrusion-based 3D concrete printing (3DCP). The new design and conception aim to fully exploit the potential of 3D printing for prefabricated components, especially in terms of dry assembly, speed of implementation, reusability, recyclability, modularity, versatility, adaptability, and sustainability. Beyond the novel conceptual design of precast elements, the research investigated the 3D printable cementitious material based on a magnesium potassium phosphate cement (MKPC), which was devised and tested to ensure good performances of the proposed component. Finally, a prototype has been realised in scale with additive manufacturing technology in order to verify the printability and to optimize the extruder path. This study leads us to believe that the combined use of prefabricated systems, construction automation, and innovative materials can decisively improve the construction industry’s sustainability in the future. Full article

Developments in the automation of construction processes, observable in recent years, is focused on speeding up the construction of buildings and structures. Additive manufacturing using concrete mixes are among the most promising technologies in this respect. 3D concrete printing allows the building up of structure by extruding a mix layer by layer. However, the mix initially has low capacity to transfer loads, which can be particularly troublesome in cases of external components that need to be placed on top such as precast lintels or floor beams. This article describes the application of additive manufacturing technology in the fabrication of a building wall model, in which the door opening was finished with automatic lintel installation. The research adjusts the wall design and printing process, accounting for the rheological and mechanical properties of the fresh concrete, as well as design requirements of Eurocode. The article demonstrates that the process can be planned precisely and how the growth of stress in fresh concrete can be simulated, against the strength level developed. The conclusions drawn from this research will be of use in designing larger civil structures. Furthermore, the adverse effects of concrete shrinkage on structures is also presented, together with appropriate methods of control. Full article

During the construction of prefabricated building, there are some problems such as a time consuming, low-level of automation when precast concrete members are assembled and positioned. This paper presents vision-based intelligent assembly alignment guiding technology for columnar precast concrete members. We study the video images of assembly alignment of the hole at the bottom of the precast concrete members and the rebar on the ground. Our goal is to predict the trajectory of the moving target in a future moment and the movement direction at each position during the alignment process by assembly image sequences. However, trajectory prediction is still subject to the following challenges: (1) the effect of external environment (illumination) on image quality; (2) small target detection in complex backgrounds; (3) low accuracy of trajectory prediction results based on the visual context model. In this paper, we use mask and adaptive histogram equalization to improve the quality of the image and improved method to detect the targets. In addition, aiming at the low position precision of trajectory prediction based on the context model, we propose the end point position-matching equation according to the principle of end point pixel matching of the moving target and fixed target, as the constraint term of the loss function to improve the prediction accuracy of the network. In order to evaluate comprehensively the performance of the proposed method on the trajectory prediction in the assembly alignment task, we construct the image dataset, use Hausdorff distance as the evaluation index, and compare with existing prediction methods. The experimental results show that, this framework is better than the existing methods in accuracy and robustness at the prediction of assembly alignment motion trajectory of columnar precast concrete members. Full article