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In recent decades, the construction industry has undergone a technological shift incorporating innovative technologies, such as robotics. However, information requirements must be met to integrate robotics further. Currently, building information models (BIM) contain substantial project information that can be leveraged for robots to create construction tasks, but for some building systems, the level of development (LOD) is inadequate to support these new requirements. Therefore, this study proposes a framework to increase the LOD of building systems by considering location information (X, Y, Z), orientation, material type, and component I.D. The computational modeler, Dynamo, is leveraged to increase the model’s LOD, extract information, and facilitate robotic task execution in the future. A case study is presented for multiple masonry room configurations developed in Autodesk Revit, where masonry units are generated and placed into design locations based on the geometry of the wall system. The case study used concrete masonry units (CMU) and standard brick. The number of partial-sized and full-sized blocks for each configuration was recorded, along with the computational time required to generate the units. It was observed that room configurations with more openings had longer computational times when compared to rooms constructed from the same material. After running the script, the model is reviewed to ensure accuracy and prevent overlaps or gaps in the model. The workflow provides insight into the methods used to interpret model geometry and extract information. 

Advances in robotics represent a potential shift in the construction industry. Construction planning is planned based on craft work; it is necessary to emphasize external factors such as construction robotics. Improving constructability can enhance design-phase construction opportunities, thereby expanding the potential scope of robot operations. However, robotics are often neglected concerning constructability. Previous studies on constructability concentrated on human-based construction methods; hence, gaps remain in assessing constructability for robotics. To minimize the barriers in robotic construction, this paper presents a method for using a rule-based framework for robotic constructability assessment checks with the help of BIM. Focusing on CANVAS—a drywall finishing robot—this paper applies a BIM-based object-oriented model integrating with ROS to utilize constructability reasoning about robotic operations. A model of rule-checking for robotics in the case study is demonstrated and tested. The availability of design information in the model containing robotics is discussed, showing the need for assessing robotics-related constructability information to support an automated review of robotic constructability assessment. This paper applies a case study to validate use of the framework for robotic constructability assessment in the design phase, leading to an automated constructability assessment of construction robotics. 

Building information modeling (BIM) technology in construction has become increasingly prevalent in recent years, and integrating robotics is seen as a natural step to improve efficiency. To increase the level of development (LOD) of a BIM model to support a construction robot, parametric modeling can be used to create highly detailed models by supplementing and defining the geometric and physical properties of the construction elements, such as the components’ size, shape, and material parameters, which are used as inputs for designing robotic tasks. Component information and data are stored as extractable parameters within the BIM model, allowing a robot to perform highly precise and repeatable tasks. This study develops a framework for implementing computational parametric modeling for masonry wall systems with Dynamo. This study tested six wall configurations constructed of 8″ × 8″ × 16″ concrete masonry units (CMUs). Dynamo successfully interpreted most wall geometries placing full-sized CMUs into the correct design locations. Errors occurred when placing partial-sized CMUs, typically at wall intersections, revealing a need for future refinement. The study shows the careful planning and considerations needed to implement computational modeling to generate model content for creating robotic tasks. 

This paper presents a construction robot schema (CRS) for construction planners to facilitate decision-making and project planning in operating robotics. CRS is a database schema structure that was developed in our previous study, which can facilitate collecting and exchanging data of various construction robots based on the data requirements of the construction domain. We validated the applicability of the schema by the simulation of robotic construction operations. In addition, we conducted interviews with experts from the construction industry to validate the information in CRS. As a result, the schema was validated with minor revisions to some parameters. The characteristics of CRS compared to other types of robot schema are that its development and application are based on the perspective of the construction domain and are designed to cover different construction robots broadly. The conclusions highlight the contributions of the data schema use and applicability for the construction industry. 

In the past, the construction industry has been slow to adopt new technology. There has been a rapid expansion of technologies, often referred to as Industry 4.0, to aid in the use of automation. One challenge paralleling these new technologies is implementing how a robot interprets design information, specifically information from a Building Information Model (BIM). This paper presents a method for identifying and transforming information from BIM to support robotic material placement on the construction site. This research will include a review of what information can be directly extracted from the model and what must be supplemented to the model for the robot to perform defined tasks within a construction site. The construction sites’ dynamic nature poses multiple challenges that must be addressed for the information extracted from a model to be used by a robot in daily construction operations. This research also identifies barriers and limitations based upon current practice, such as different levels of development or model content as well as needed precision within the information provided for a mobile robot to complete a defined task. 

Robotics and automation are still considered a novelty in the U.S. construction industry, as compared to manufacturing, despite its proven advantages for production. Due to the continuing advancement of technology needed, there are limited applications of robotics in construction to date. To better identify the potential tasks that would benefit from the use of robotics on construction sites, we consider methods for assessing the craft labor tasks that occur in construction. In this paper, we decompose construction tasks of an observed activity of installation of stone veneer system and compared two systems of categorizing the construction tasks based on value added assessment and lean (waste) assessment of tasks. The analysis compares the two categorization systems using a matrix which highlights consistency in the alignment of value adding tasks, such as final placement, as well as ineffective tasks with type two muda, but discrepancies emerge regarding the idea of contributory tasks related to logistical support of construction activities. The focus of the discussion is derived from the intersection of contributory tasks with type one muda tasks. The contributory tasks offer an opportunity to reduce the use of craft labor for wasteful tasks elimination by leveraging automation and robotics. 

One of the many ways in which automation may help the construction industry is on-site material management. This paper presents an automated process where materials are selected for staging by detecting construction progress from site images. The materials are then delivered to their respective workface locations by a robot. The effectiveness of the material selection process is assessed using a simulated and physical construction site. We demonstrate that our process is successful under a number of different conditions and environments. Our system contributes to the feasibility of autonomously managing materials on a construction site and reveals potential avenues for future research. 

The adoption of robotics into the construction industry has been much slower than in manufacturing and industrial sectors. Current shortfalls in skilled labor, productivity trends, and ongoing safety challenges point to the need for a drastic shift toward the adoption of robotics as a component of a shift toward industrialized construction. Despite this lag, the interest and development of robotic technology targeting construction has grown in recent years, ranging from the use of drones for tracking to use in offsite fabrication. However, the integration into fundamental site construction requires reconsideration of the information technology infrastructure needed to support detailed task execution information needs in the transition from craft labor to robotic operations. This research presents the identification and mapping of the IT System Architecture required to support BIM to Robotic Construction. Combining elements of the Building Information Modeling architecture and information exchanges with the needed construction task decomposition is required. These elements are mapped to the robotic system elements required for mobile robotic operations. In addition to defining the functions and integration required to support the BIM to Robotic Construction Workflow, shortcomings in existing infrastructure, notably regarding the ability to decompose construction fabrication and assembly means and methods are defined.