Search for: All records

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. 

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. 

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. 

This paper presents an optimization approach based on mixed-integer programming (MIP) to maximize the profit of the Microgrid (MG) while minimizing the risk in profit (RIP) in the presence of demand response program (DRP). RIP is defined as the risk of gaining less profit from the desired profit values. The uncertainties associated with the RESs and loads are modeled using normal, Beta, and Weibull distribution functions. The simulation studies are performed in GAMS and MATLAB for 5 random days of a year. Although DRP increases the total profit of the MG, it can also increase the risk. The simulation results show that RIP is reduced when downside risk constraint (DRC) is considered along with DRP implementation. Considering DRC significantly reduces the percentage of the risk while slightly decreasinz the profit.