Search for: All records

Automated checking of the compliance of building information modeling (BIM)-based building designs with relevant codes and regulations requires bridging the semantic gap between the Industry Foundation Classes (IFC) schema and the natural language. In most of the existing automated compliance checking (ACC) systems, the integration of the IFC schema and natural language is realized through hardcoding or predefined rules, ontologies, or dictionaries. These methods require intensive manual engineering effort and are often rigid and difficult to generalize. There is, thus, a need for an automated and meanwhile flexible and generalizable information integration method. To address this need, this paper leverages transformer-based language models to learn the semantic representations of concepts in the building information models (BIMs) and regulatory documents. An automated IFC-regulatory information integration approach based on these learned semantic representations is proposed. The preliminary experimental results show that the proposed approach achieved promising performance—an accuracy of 80%—on integrating IFC and regulatory concepts.

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 digital and integrated representation of the physical and functional characteristics of buildings enabled by building information modeling (BIM) provides a computational environment for automated compliance checking (ACC) of building designs. The integration of natural language processing (NLP) and artificial intelligence (AI) with BIM brings further opportunities for ACC – it can empower BIM with text analytics and AI capabilities, thereby injecting intelligence and automation in the compliance checking processes. This chapter highlights emerging approaches that aim to facilitate and harness the marriage of BIM, NLP, and AI to enable the next generation of automated compliance checking systems (ACC) systems. This chapter (1) reviews different types of BIM-based ACC systems that leverage NLP and AI techniques, (2) discusses how NLP and AI techniques are applied in regulatory text analytics tasks and BIM information analytics tasks in the context of ACC, and (3) discusses the future trends of BIM-based ACC systems.

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

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.