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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. 

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. 

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. 

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. 

The construction industry has undergone a technological shift. Technology advancements have made robots a topic of discussion in construction. One challenge to overcome is how the robot receives information from designed BIM models. This study describes the methods employed for parametric modeling and generating model content of wall systems in Autodesk Revit added with a Dynamo script. Coordinates are determined for components based on model geometry and dimensions. Once generated, components are placed with the required material based on wall parameters. This research develops a method to add components based on wall materials from a traditionally modeled BIM extracting information such as location, object identifier (ID), type, and orientation which is formatted to transfer to the robot based on the needs of the robotic system as a list of tasks in a comma-separated values (.CSV) file. This study details the development process and early implementation of the Dynamo script. 

This study gathered data into a construction robot schema (CRS) with an initial data structure that can be used to collect and exchange various construction robots’ information based on the data requirements of construction planners for robotics operations. To develop the CRS, the study conducted a systematic literature review using the Web of Science database to filter and identify relevant papers which were published from 2018 to 2022. Based on 279 eligible papers, the study identified significant information which involved data requirements of the construction domain on robotics using Nvivo software. To structure the information, the study summarized the information into parameters then categorized, defined, matched data types, and exemplified for these parameters. All the parameters were grouped into four categories, including ontological properties, operational requirements, activity, and safety. As a result, CRS supports data structure including 4 categories and 35 parameters with corresponding definitions, data types, examples, and references. 

Advances in construction robotics represent a potential shift in building design and construction. In general, construction robotics are usually deployed directly onto construction sites without systematically evaluating the design constructability for robotic applications. Literature on constructability suggest that ignoring it during design will cause rework, inefficiency, and higher cost. Although previous studies have widely discussed design constructability, they mainly focus on traditional human craft-based construction methods. Whereas a gap still exists in design constructability assessment for construction robotics. This paper presents an initial analytical framework for constructability assessment for construction robotics during the design phase. Specifically, we summarize factors that impact robotic constructability based on robotic features, design features, work constraints, and piloted an automated constructability checking system for robotics. Additionally, this study takes CANVAS, a drywall finishing robot, as case study to create a framework in simulation environment and the results demonstrate the potential value of the proposed framework. 

The development of robotics in the Architecture, Engineering, and Construction (AEC) industry has emerged in recent years in response to technology advances and industry challenges such as workforce shortages. Construction robotics has the potential to increase construction productivity and accuracy as well as reduce accidents and costs. However, their introduction to construction sites creates new challenges. Previous studies have shown that robots can cause major changes in construction workflow, scope, and methods. Construction robotics introduce key changes to the work process and the sequence of construction tasks. The traditional planning approach for work break down structure and scheduling assigns resources for construction activities based on human labor and craft methods. Despite this, the capabilities of robotics relative to construction resource planning, sequencing, and work scope has not been fully studied. To address this, the implementation of robotics in construction projects needs a new approach to organizing work packages (WP). With the inclusion of robotics as a resource, planning parameters such as methods and sequence will change both the scope and accordingly the work packaging for construction. This paper aims to systematically identify the potential impacts of robots on construction processes, as well as how those changes influences work packaging. The methodology is based on data integration and content analysis from literature review and collected interviews with project participants about real-world construction projects. The paper discusses how construction robots impact the work package approach and categorizes the affected factors. These factors include the work area, sequence and priority of construction activities, safety management, allocation of risk responsibility for tasks, interaction with other trades, and required materials. 

Building Information Modeling (BIM) is a critical data source for constructing new structures depicting the inner workings of the systems and components in detail. However, current modeling practices are based on traditional construction methods, resulting in insufficient details within the BIM model to support robotic construction for many building systems. The model’s level of development (LOD) needs to be increased to facilitate the changes in data requirements. One method that allows for increased LOD is computational modeling; however, many factors can influence the process. Therefore, this study investigates challenges for implementation to increase the LOD for building to enable robotic construction. Dynamo is used as the computational modeling software in conjunction with Autodesk Revit to accomplish this. A process was created to place various components, such as concrete masonry units (CMUs), in their final design location and extract information utilizing these platforms for masonry construction. However, challenges were met during this process, including material naming conventions, tolerance/specification inputs, wall openings/lintels, and component/material libraries. The challenges presented during the implementation of the Dynamo mirror what the literature shows for supporting technological infrastructure BIM and mobile robot construction. To accomplish this research, an extensive literature review was completed, along with documentation of challenges during the development and implementation of the script. 

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.