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Abstract: Abstract In recent years, research on extrusion-based 3D concrete printing (3DCP) has gained traction. Ongoing research aims to improve sustainability and accuracy of 3DCP as maintaining print quality and material consistency during the process remains a challenge. To address this challenge, vision-based sensing technologies have been implemented as monitoring and controlling systems. This paper presents a systematic review on the use of vision-based sensing technologies primarily in 3DCP, as well as extrusion-based 3D printing with other materials. The goal is to provide a comprehensive analysis of the implementation and effectiveness of the vision-based sensing technologies in enhancing the accuracy and stability of 3DCP. With a focus on the technologies and methodologies employed, the paper presents the trends and gaps in the analyzed literature, centering on 3DCP. The literature reviewed suggests that implementing vision-based sensing technologies as monitoring systems can improve the accuracy and structural stability of the printed parts, which results in increased safety and decreased material waste, environmental impact, time, and labor. PubDate: 2024-08-22
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Abstract: Abstract ZeroWaste as a project aims to reposition existing timber building stock within a circular economy framework, reframing them as valuable resources for reuse rather than disposal. This paper presents the computational methods and physical construction outcomes of the project, showcasing how the circular economy principles of reuse and reduced material consumption can be actualized through cooperative robotic fabrication. Initially, a pavilion-scale prototype, mirroring conventional North American stick frame construction, is constructed. A robotic cell, equipped with three large-scale robotic arms and 3D cameras, is used to generate precise as-built geometric data on the prototype, which is used for planning robotic processes. A novel topological representation of a structure, the support hierarchy graph, is developed and used to generate candidate fabrication sequences. These sequences are then assessed for robotic execution and structural feasibility. Leveraging the cooperative nature of the setup, these sequences are planned without requiring external scaffolding for structural stability as the robots are used to provide temporary support during the fabrication process. Three physical case studies validate the developed computational and cooperative robotic workflow. In Phase 1 we perform a small-scale disassembly intervention by planning the removal of a single member. In Phase 2 we expand the disassembly scope to the full South wall and perform a minor reassembly at the end of the disassembly sequence. In Phase 3 we expand the scope further, disassembling all remaining members in the West wall and roof sub-structures while performing a concurrent one-to-one reassembly process, where each member removed is placed back into the structure to turn the typical stud wall into a stiffer lattice structure. The successful completion of the three case studies demonstrates the potential for existing buildings to serve as reservoirs of reusable materials through scaffold-free cooperative robotic disassembly and reassembly. PubDate: 2024-08-18
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Abstract: Abstract This paper presents the development of algorithms for high-level control and intelligent path planning of multi-rotor aerial vehicles (MAVs) in the tasks of inspecting civil infrastructure. After revisiting the multicopter modeling, we describe the hierarchy of high-level control for MAVs and develop optimization algorithms for generating optimal paths and enabling automatic flight during inspection tasks, making use of the digital twin technology. A co-simulation framework is then established to simulate and evaluate inspection mission scenarios, integrating these essential components. Real-world examples from built infrastructure illustrate this concept. An advantage of this approach is its ability to rigorously test, validate, verify, and evaluate MAV operations under abnormal conditions without requiring physical implementation or field tests. This significantly reduces testing efforts throughout the development cycle, ensuring optimal cooperation, safety, smoothness, fault tolerance, and energy efficiency. The methodology is validated through simulations and real-world inspection of a monorail bridge. PubDate: 2024-08-13
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Abstract: Abstract In architectural and construction robotics research, we now have powerful technologies whose histories are only partially understood. Their ubiquity is matched by persistent historical narratives around their invention that have built up over time through repetition. Appearing in historical surveys and background research for theses and dissertations, the narratives of these tools are infrequently challenged—a situation that has implications for the conception and execution of the research projects that employ them. How do we begin to center narrative and politics in the context of a specialized area of research like construction robotics' In this investigation, we interrogate a set of iconic and influential robotics projects to expand the knowledge base around them and avoid inadvertently perpetuating harmful practices: Ross Ashby’s Homeostat, Grey Walter’s Tortoises, George Devol’s Programmed Article Transfer (Unimate), and Stanford Research Institute’s Mobile Automaton (Shakey). To arrive at a different understanding of these familiar works, we propose an alternative framework—a reconfiguration of definitions of efficiency and utility that we refer to as “robot excess.” Employing the novel method of movement as a hermeneutic device to examine these, we find that certain movements were interpreted as valuable and worthy of study and documentation, while others were considered excessive and, therefore, practically irrelevant. Further, we show that the observation, characterization, and interpretation of these excess movements relied as much on qualitative factors—in conjunction with the narratives we uncover—as on the definition and quantification of traditional machine attributes like efficiency or utility. This research aims to uncover less conventional takes on some commonplace historical narratives and, through doing so, to foster more informed (and inclusive) approaches to the implementation of constantly evolving technologies. PubDate: 2024-08-09
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Abstract: Abstract Optimization of resources is essential for sustainability in construction. Construction automation contributes to optimal resource utilization. However, not much research has been carried out to quantify the benefits of automation in construction and to identify the most appropriate level of automation in a given project. This paper presents a methodology to identify optimal automated construction processes through model composition and stochastic search. This is applicable for both on-site and off-site construction. Through model composition, millions of potential solutions are generated. These are automatically evaluated through discrete event simulation; and performance parameters such as cost and time are computed. Through a multi-criteria decision making algorithm called RR-PARETO4, the best automated solution is identified taking into account trade-offs among conflicting objectives that are acceptable to the user. The methodology is demonstrated using a case study which shows its potential application at the stage of planning, pre-construction and during construction of the project. Through multi-criteria decision making, appropriate automated construction systems can be identified, and overall project performance can be increased. PubDate: 2024-08-09
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Abstract: Abstract In Japan, the number of skilled shaft excavator engineers is decreasing. To complete the Linear Central Shinkansen line of 286 km between Tokyo and Nagoya, an AI-equipped shaft excavator was prototyped to absorb the tacit knowledge of highly skilled engineers. The AI can predict penetration resistance and optimally controlling the excavator. This not only reduces drilling time, enhancing sustainability, but also cuts CO2 emissions by half. Datasets are built based on standard penetration tests and an ensemble–ensemble method with 16 determinants is used, achieving a prediction accuracy of 0.9. This paper presents a case study that AI capabilities are there to fill the gap, extend the skills or meet the shortage in the labor market. Trust of AI in fairness is addressed by calculating fairness as a benchmark with a variety of fairness metrics from all disciplines. From an information management perspective, this paper explores methods for managing the tacit knowledge of highly skilled, diminishing workers in civil engineering to enhance the sustainability of services and products. Tacit knowledge can drive innovation to boost sustainability. PubDate: 2024-07-25
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Abstract: Abstract This case study examines Obayashi Corporation’s fleet management system (FMS), an advanced remote, and semi-automatic operation system for construction equipment. The FMS integrates robotics and autonomous technology to enhance efficiency, safety, and productivity in construction operations during excavation. Through a Robotics Evaluation Framework (REF), product, organization, and process data as well as four variables: safety, quality, schedule, and cost were used to compare the FMS’s performance to its manual counterpart. The analysis reveals that the FMS reduces unergonomic periods by 68% and cuts labor expenses by 31% per zone, and by 18% in the total test volume. The FMS, however, faces considerable challenges in terms of schedule, with a 145% increase in total duration compared to the conventional method. Assuming all four variables in the REF are of equal importance to the contractor, the sensitivity analysis indicates the FMS is superior to human operation, even considering the system’s setbacks. Overall, the study highlights the potential benefits of reduced human error, labor savings, improved safety, repeatability, and modular technology. This analysis provides insights for innovators in the construction industry seeking to introduce digital and automation technologies. Future work should analyze this technology’s performance in other locations to assess its adaptability and the robustness of the results obtained. PubDate: 2024-07-25
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Abstract: Abstract This paper proposes an automatic method for excavator working cycle recognition using supervised classification methods and motion information obtained from four inertial measurement units (IMUs) attached to moving parts of an excavator. Monitoring and analyzing tasks that have been performed by heavy-duty mobile machines (HDMMs) are significantly required to assist management teams in productivity and progress monitoring, efficient resource allocation, and scheduling. Nevertheless, traditional methods depend on human observations, which are costly, time-consuming, and error-prone. There is a lack of a method to automatically detect excavator major activities. In this paper, a data-driven method is presented to identify excavator activities, including loading, trenching, grading, and idling, using motion information, such as angular velocities and joint angles, obtained from moving parts, including swing body, boom, arm, and bucket. Firstly, a dataset lasting 3 h is collected using a medium-rated excavator. One experienced and one inexperienced operator performed tasks under different working conditions, such as different types of material, swing angle, digging depth, and weather conditions. Four classification methods, including support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and naive Bayes, are off-line trained. The results show that the proposed method can effectively identify excavator working cycles with a high accuracy of 99%. Finally, the impacts of parameters, such as time window, overlapping configuration, and feature selection methods, on the classification accuracy are comprehensively analyzed. PubDate: 2024-07-24
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Abstract: Abstract This paper presents a pioneering solution to the task of integrating mobile 3D LiDAR and inertial measurement unit (IMU) data with existing building information models or point clouds, which is crucial for achieving precise long-term localization and mapping in indoor, GPS-denied environments. Our proposed framework, SLAM2REF, introduces a novel approach for automatic alignment and map extension utilizing reference 3D maps. The methodology is supported by a sophisticated multi-session anchoring technique, which integrates novel descriptors and registration methodologies. Real-world experiments reveal the framework’s remarkable robustness and accuracy, surpassing current state-of-the-art methods. Our open-source framework’s significance lies in its contribution to resilient map data management, enhancing processes across diverse sectors such as construction site monitoring, emergency response, disaster management, and others, where fast-updated digital 3D maps contribute to better decision-making and productivity. Moreover, it offers advancements in localization and mapping research. Link to the repository: https://github.com/MigVega/SLAM2REF, Data: https://doi.org/10.14459/2024mp1743877. PubDate: 2024-07-05
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The gyroid is a triply periodic minimal surface (TPMS) that efficiently distributes stress under compressive loading in all Cartesian orientations. Despite the gyroid’s geometric ability to evenly distribute load, it has yet to be more broadly introduced to concrete additive manufacturing (AM) due to the difficulty of printing steep, doubly curved overhangs with a cementitious material. Consequently, the employment of the gyroid TPMS in AM has been limited to small and nano-scale applications. However, for doubly curved 3D printed concrete (3DPC) structures, the feasibility of the print is determined by the relationship between geometry, tool path design, and the mechanical and rheological properties of the concrete material being extruded. Using a 6-axis robotic arm with an accelerator-injection extruder as an end-effector, this research examines the fabrication limitations to construct a 3DPC gyroid. The methods of this paper will present: (1) a parametric method to digitally model a gyroid TPMS through non-uniform rational basis spline (NURBS) surfaces followed by (2) a series of geometric density studies for robotic fabrication, which informed the design of (3) a continuous tool path. Using these findings, (4) several samples were printed to test the overhang limits of the 3DPC gyroid samples. Finally, one of the overhang samples was prepared for a series of compression tests, which demonstrated that the 3DPC gyroid structure could support over 1000 kilonewtons. Though large variability was observed in the performance of three gyroid samples tested, the research demonstrates that steep overhangs can be printed in concrete for gyroid-based structures. PubDate: 2024-06-17 DOI: 10.1007/s41693-024-00124-y
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Abstract: Abstract A major challenge within construction robotics lies in deploying robots for building tasks directly on construction sites. Recent years have seen a surge in innovative solutions, particularly focused on creating mechanical bricklayer to automate wall construction as much as possible. However, most of these solutions involve the use of heavy industrial robots and complex systems that are difficult to calibrate and program. In this paper, the authors introduce a prototype of an automated, lightweight system for brick wall construction that is straightforward to calibrate and program. To validate the proposed approach, a full-scale demonstrator along with its control logic is presented. Experimental results, displayed at a prominent industry expo, demonstrate the viability of the proposed system. Additionally, the system was evaluated using the Construction Automation and Robotics for Sustainability Assessment Method (CARSAM), which provides a structured approach to examine the environmental, social, technological, and economic dimensions of sustainability in the context of advanced construction technologies. By applying this method, stakeholders can better understand the broader implications of integrating such technologies into construction practices, guiding more informed decisions towards sustainable development. PubDate: 2024-06-07 DOI: 10.1007/s41693-024-00123-z
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Abstract: Abstract Underground mines pose significant challenges, including hazardous working conditions, limited access, and the need to ensure the safety of human workers. Digital transformation through the integration of modern technologies is essential to mitigate these challenges and enhance the overall safety and efficiency of mining operations. This paper addresses the pressing need for 5G connectivity for the digital transformation of underground mines and demonstrates its application through a live 3D point cloud mapping by a mobile robot. The results of the experiments conducted to validate the network’s performance for such a use-case are presented. The first experiment involved testing the latency of the network over a test drift at various loads. The second experiment involved mapping the drift and streaming the 3D point cloud map of the drift over the 5G network. These initial experiments emphasize the potential of the 5G-enabled automation in underground mines and holds promise in digitalizing underground mining operations. PubDate: 2024-05-16 DOI: 10.1007/s41693-024-00114-0
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Abstract: Abstract Automated robotic construction of wood frames faces significant challenges, particularly in the perception of large studs and maintaining tight assembly tolerances amidst the natural variability and dimensional instability of wood. To address these challenges, we introduce a novel multi-modal, multi-stage perception strategy for adaptive robotic construction, particularly for wood light-frame assembly. Our strategy employs a coarse-to-fine method of perception by integrating deep learning-based stud pose estimation with subsequent stages of pose refinement, combining the flexibility of AI-based approaches with the precision of traditional computer vision techniques. We demonstrate this strategy through experimental validation and construction of two different wall designs, using both low- and high-quality framing lumber, and achieve far better precision than construction industry guidelines suggest for designs of similar dimension. PubDate: 2024-05-09 DOI: 10.1007/s41693-024-00122-0
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Abstract: Abstract Concrete 3D printing is an emerging technology with great potential to revolutionize the construction industry with respect to productivity, cost, time, quality, and sustainability. However, the concrete mix used for 3D printing has very high cement content, leading to a high carbon footprint of concrete 3D printed elements. The filler slab is a technique used to reduce raw material consumption by introducing materials like clay pots as filler. However, clay pots have high carbon footprints. Furthermore, a high level of supervision is needed to make sure that clay pots do not come into contact with rebar cages. Replacing clay pots with compressed polyethylene waste can help reduce the carbon footprint of 3D-printed slabs. This paper proposes an automated methodology to 3D print concrete slabs with recycled plastic waste as filler. A gantry-type 3D printer with an integrated pick-and-place function is used. A case study is used to bring out the savings in carbon footprint of the proposed system. A cradle-to-gate life cycle assessment is performed to compare three different scenarios: conventional slab, filler slab with clay pots, and filler slab with polyethylene (PE) waste. The case study results indicate that the proposed methodology can reduce the consumption of material and carbon emissions related to it. It also avoids the emissions due to incineration and improper management of polyethylene wastes. The findings of the paper provide a direction toward leveraging the benefits of concrete 3D printing and automation towards sustainable construction. PubDate: 2024-04-30 DOI: 10.1007/s41693-024-00119-9
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Abstract: Abstract A stable, low-latency, and high-bandwidth communication infrastructure is indispensable for effective teleoperation or automated control of construction machinery. Despite the vital importance of this aspect, limited exploration has been undertaken thus far. Our work presents a comprehensive study that begins by detailing the strategic deployment of a 5G network, underscoring its tailored features and functionalities designed specifically to meet the demanding requirements of construction sites. Through a series of diverse experiments involving different types of full-scale construction machines, we vividly demonstrate the tangible benefits of 5G technology in this context. Leveraging comparative studies with WiFi technology and real-world tests, this methodology highlights the improvements in communication facilitated by 5G networks. This holistic exploration not only fills a critical gap in understanding the potential of 5G in construction machinery communication but also offers a roadmap for leveraging this technology to further develop the construction industry. PubDate: 2024-04-29 DOI: 10.1007/s41693-024-00121-1
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Abstract: Abstract User acceptance is crucial for successfully adopting robotic technologies in the architecture, engineering, and construction (AEC) sector. Previous studies have focused on domestic, service, and industrial robots, leaving the construction domain unexplored. In this study, we set out to empirically understand how various dimensions of technology, environment, robot, and user lead to AEC stakeholders’ intention to use construction robots. We apply the fuzzy-set qualitative comparative analysis approach to capture the complexity of human behavior and the interdependencies across dimensions. For the data collection, we sampled 216 cases in Switzerland, Germany, and Austria evaluating three scenarios of human–robot interaction. Our analysis identifies three distinct user profiles—the lifelike robot user, the utilitarian robot user, and the lifelike-utilitarian robot user. The results show that human–robot peering may be a fundamental solution to increase user acceptance. By testing the effect of user characteristics, we also discover a lifelike-utilitarian type of robot that is more appealing to female AEC stakeholders. The study contributes to the construction robotics literature by providing tailored design and implementation strategies. It points to future research avenues such as user experience and social factors for exploring the impact of robotics and artificial intelligence in AEC. PubDate: 2024-04-26 DOI: 10.1007/s41693-024-00115-z
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Abstract: TimberSLAM (TSLAM) is an object-centered, tag-based visual self-localization and mapping (SLAM) system for monocular RGB cameras. It was specifically developed to support a robust and augmented reality pipeline for close-range, noisy, and cluttered fabrication sequences that involve woodworking operations, such as cutting, drilling, sawing, and screwing with multiple tools and end-effectors. By leveraging and combining multiple open-source projects, we obtain a functional pipeline that can map, three-dimensionally reconstruct, and finally provide a robust camera pose stream during fabrication time to overlay an execution model with its digital-twin model, even under close-range views, dynamic environments, and heavy scene obstructions. To benchmark the proposed navigation system under real fabrication scenarios, we produce a data set of 1344 closeups of different woodworking operations with multiple tools, tool heads, and varying parameters (e.g., tag layout and density). The evaluation campaign indicates that TSLAM is satisfyingly capable of detecting the camera’s millimeter position and subangular rotation during the majority of fabrication sequences. The reconstruction algorithm’s accuracy is also gauged and yields results that demonstrate its capacity to acquire shapes of timber beams with up to two preexisting joints. We have made the entire source code, evaluation pipeline, and data set open to the public for reproducibility and the benefit of the community. Graphic abstract PubDate: 2024-04-16 DOI: 10.1007/s41693-024-00118-w
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Abstract: Abstract There is a growing need to understand how locally sourced earthen materials can be processed to build more efficiently and sustainably. Earthen formworks combined with 3D printing technologies present a unique opportunity for the concrete construction sector to address the wastefulness and complexity of custom formworks. The current state-of-the-art projects in academia and industry demonstrate that earthen formwork strategies effectively address this challenge, but remain burdened by upscaling issues such as production speed. This research bridges the gap by exploring strategies for 3D Printed earth formworks to efficiently produce structural elements using custom self-compacting and set-on-demand concrete mixtures. A first base earth mix is developed for reduced shrinkage and later modified via a plasticizer for increased green strength, forming the final mix. Two mix iterations are deployed in two corresponding strategies where concrete is cast into the earth formwork in a dry or plastic state. The methods highlighting the setups for 3D printing and procedures for appropriate material processing such as slump flow, shrinkage and rheology are presented. The results are explored via two column prototypes leading to a final demonstrator for a 2 m high reinforced concrete column. Conclusions are drawn on the implications of the two casting strategies, the current persisting challenges and the crucial next steps for development. Thus, the research provides a foundation for how clay formworks can be upscaled effectively for more sustainable production of complex concrete structures. PubDate: 2024-04-12 DOI: 10.1007/s41693-024-00120-2
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Abstract: Abstract The integration of lean construction and construction robotics is an important research topic because it can significantly improve the construction industry’s efficiency, eliminate waste, and increase productivity. However, there is currently a scarcity of research on this subject, which makes it a key area for future studies. This study conducts a scientometric review analysis of lean construction and construction robotics, focusing on academic publications worldwide between 2012 and 2024. This study considers keywords, co-authors, co-citations, and country/region analyses. This study includes 842 articles from journals and conference papers obtained from Scopus, where 736 papers are related to lean construction, 194 are related to construction robotics, and 18 contain both lean construction and construction robotics. Network maps are built and analyzed to determine the major areas of interest within each field, be that lean construction or construction robotics. The study found that research in both domains is becoming more popular and that there is room for advancement in the intersection of lean construction and construction robotics, particularly around offsite construction. Based on these findings, it may be assumed that future studies including both subjects will grow. PubDate: 2024-04-04 DOI: 10.1007/s41693-024-00117-x
Hybrid journal (It can contain Open Access articles)
