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The construction industry has traditionally been a labor-intensive industry. Typically, labor cost takes a significant portion of the total project cost. In spite of the good pay, there was a big gap recently between demand and supply in construction trades position. A survey shows that more than 80% of construction companies in the Midwest of US are facing workforce shortage and suffering in finding enough skilled trades people to hire. This workforce shortage is also nationwide or even worldwide in many places. Construction automation provides a potential solution to mitigate this problem by seeking to replace some of the demanding, repetitive, and/or dangerous construction operations with robotic automation. Currently, robots have been used in bricklaying or heavy-lifting operations in the industry, and other uses remain to be explored. In this paper, the authors proposed a feasibility breakdown structure (FBS)-based robotic system method that can be used to test the feasibility of performing target construction operations with specific robotic systems, including a top-down work breakdown structure and a bottom-up set of feasibility analysis components based on literature search and/or simulation. The proposed method was demonstrated in testing the use of a KUKA robot and a Fetch robot to perform rebar meshmore »construction. Results showed that the overall workflow is feasible, whereas certain limitations presented in path planning. In addition, a smooth and timely information flow from the Fetch robot sensor and computer vision-based control to the two robots for a coordinated path planning and cooperation is critical for such constructability.« less

The construction industry is known for its masculine culture where workplace discrimination, biases, and harassment exist. While interventions such as greater workplace diversity, equity and inclusion programs, and mentoring initiatives are directed toward fostering career engagement and employee retention, women continue to leave professional positions in the construction industry. Using an ethnographic methodology, the aim of this study was to identify and examine the dynamics involved in the perseverance of professional women working in the construction industry. In-depth interviews were conducted, and a qualitative approach toward gathering data was utilized. Consistent questions were posed to the participants primarily through synchronous communications, and specific construction companies and professional women employees were asked to participate. Results suggest that women in leadership positions who previously experienced harassment had male interventionists, and are now serving as the primary interventionists for younger women in their companies. Further results suggest increased women’s participation is realized by forming multiple supportive organizational structures within the construction workplace culture and enacting zero-tolerance guidelines to curb inappropriate or harassing behavior. These research findings underscore the need for further exploration of novel interventional mechanisms toward greater retention of women in the industry.

Robotic automation of construction tasks is a growing area of research. For robots to successfully operate in a construction environment, sensing technology must be developed which allows for accurate detection of site geometry in a wide range of conditions. Much of the existing body of research on computer vision systems for construction automation focuses on pick-and-place operations such as stacking blocks or placing masonry elements. Very little research has focused on framing and related tasks. The research presented here aims to address this gap by designing and implementing computer vision algorithms for detection and measurement of building framing elements and testing those algorithms using realistic framing structures. These algorithms allow for a stationary RGB-D camera to accurately detect, identify, and measure the geometry of framing elements in a construction environment and match the detected geometry to provided building information modeling (BIM) data. The algorithms reduce identified framing elements to a simplified 3D geometric model, which allows for robust and accurate measurement and comparison with BIM data. This data can then be used to direct operations of construction robotic systems or other machines/equipment. The proposed algorithms were tested in a laboratory setting using an Intel RealSense D455 RGB-D camera, and initialmore »results indicate that the system is capable of measuring the geometry of timber-frame structures with accuracy on the order of a few centimeters.« less

To allow full automation of building code compliance checking with different building design models and codes/regulations, input building design models need to be automatically validated. Automated architecture, engineering, and construction (AEC) object identification with high accuracy is essential for such validation. For example, in order to check egress requirements, exits of a building (and their presence or absence) need to be identified automatically through object identification. To address that, the authors propose a new AEC object identification algorithm that can identify needed code checking concepts from building design models based on the invariant signatures of AEC objects, which consisted of Cartesian points-based geometry, relative location and orientation, and material mechanical properties. Building design models in industry foundation classes (IFC) format are processed into invariant signatures, which can fully represent the model data and convert them into computable representations to support automated compliance reasoning. A systematic implementation of the above invariant signatures-based object identification algorithm can be used to automatically conduct building design model validation for code compliance checking preparation. An experimental testing on Chapters 4 and 8 of the International Building Code 2015 and a convenience store design model showed the model validation using the proposed identification algorithms successfully validatedmore »ceiling and interior door concepts. Comparing to the manual validation used in current practice, this new object identification algorithm is more efficient in supporting model validation for automated building code compliance checking.« less

Offsite construction (e.g., wood modular houses) has many advantages over traditional stick-built construction, ranging from schedule/cost reduction to improvement in safety and quality of the built product. Unlike stick-built, offsite construction demands higher levels of design and planning coordination at the early stages of the construction project to avoid cost overruns and/or delays. However, most companies still rely on 2D drawings in the development of shop drawings, which are required for the fabrication of the building components such as walls and roofs. In practice, the process of developing shop drawings is usually based on manually interpreting the 2D drawings and specifications, which is time-consuming, costly, and prone to human errors. A 3D information model can improve the accuracy of this process. To help achieve this, the authors developed a semi-automated method that can process 2D orthographic views of building components and convert them to 3D models, which can be useful for fabrication. The developed 3D information model can be further transformed to building information models (BIMs) to support collaboration amongst users and data exchanges across platforms. The developed method was evaluated in the development of wall components of a student apartment project in Kalamazoo, MI. Experimental results showed that themore »developed method successfully generated the 3D information model of the wall components. A time comparison with the state-of-the-art practices in developing the wall components was performed. Results showed that the developed method utilized approximately 22% of the time it took the state-of-the-art manual method to generate the 3D models.« less

To facilitate a better understanding of building codes, the visualization of the embedded structures of the provisions and requirements of the codes is needed. Existing research efforts in building code compliance checking mostly do not purposefully represent building codes in formats that facilitate human understanding and interaction with the codes, such as XML and hypertext (text with links to other text). Visual programming commonly represents building codes more visually as flowcharts. However, flowcharts are static, and the generation of flowcharts is still manual. To address this lack of interactive visual representation of building code requirement structures, this paper proposes an automated building code structure extraction and visualization method for visualizing building code contents in a way that clearly shows the inter-connections between requirements and allows intuitive user interaction. In this method, to extract the chapter-section-subsection hierarchical structure and cross-reference structure, a new extraction method named Building Code Network Generator (BCNG) is proposed to automatically generate an interactive visualization using a directed network. The performance of the proposed BCNG was empirically tested on Chapters 5 and 10 of the International Building Code 2015, with a resulting precision, recall, and F1-score of 99.4%, 96.3%, and 97.8%, respectively. In addition, the extracted hierarchicalmore »and cross-reference structures were displayed using an open-source network visualization tool to facilitate human understanding and interactions with the building code requirements in automated compliance checking systems.« less