This research addresses the time-consuming and error-prone nature of manual code compliance checking in Building Information Modeling (BIM) by introducing a Large Language Model (LLM)-driven approach to semi-automate this critical process. The developed system integrates LLMs such as GPT, Claude, Gemini, and Llama, with Revit software to interpret building codes, generate Python scripts, and perform semi-automated compliance checks within the BIM environment. Case studies on a single-family residential project and an office building project demonstrated the system's ability to reduce the time and effort required for compliance checks while improving accuracy. It streamlined the identification of violations, such as non-compliant room dimensions, material usage, and object placements, by automatically assessing relationships and generating actionable reports. Compared to manual methods, the system eliminated repetitive tasks, simplified complex regulations, and ensured reliable adherence to standards. By offering a comprehensive, adaptable, and cost-effective solution, this proposed approach offers a promising advancement in BIM-based compliance checking, with potential applications across diverse regulatory documents in construction projects.

When survivors from January's wildfires in Los Angeles County apply to rebuild their homes, their first interaction might be with a robot. Artificial intelligence will aid city and county building officials in reviewing permit requests, an effort to speed up a process already being criticized as too slow. "The current pace of issuing permits locally is not meeting the magnitude of the challenge we face," Gov. Gavin Newsom said when announcing the AI deal in late April. Some 13,000 homes were lost or severely damaged in the Eaton and Palisades fires, and many families are eager to return as fast as they can. Just eight days after the fire began and while it was still burning, the city received its first home rebuilding application in Pacific Palisades.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Two decades ago, the fire marshal in Glendale, Arizona, was concerned that the elevators in a new stadium wouldn't be large enough to accommodate a 7-foot stretcher held flat. Tilting a stretcher to make it fit in the cab, the marshal worried, might jeopardize the treatment of a patient with a back injury. Maybe our elevators should be bigger, he thought. The marshal put this idea to the International Code Council, the organization that governs the construction of American buildings. After minor feedback and minimal research (the marshal measured three stretchers in the Phoenix area), the suggestion was incorporated into the ICC's model code.

Interpreting regulatory documents or building codes into computer-processable formats is essential for the intelligent design and construction of buildings and infrastructures. Although automated rule interpretation (ARI) methods have been investigated for years, most of them highly depend on the early and manual filtering of interpretable clauses from a building code. While few of them considered machine interpretability, which represents the potential to be transformed into a computer-processable format, from both clause- and document-level. Therefore, this research aims to propose a novel approach to automatically evaluate and enhance the machine interpretability of single clause and building codes. First, a few categories are introduced to classify each clause in a building code considering the requirements for rule interpretation, and a dataset is developed for model training. Then, an efficient text classification model is developed based on a pretrained domain-specific language model and transfer learning techniques. Finally, a quantitative evaluation method is proposed to assess the overall interpretability of building codes. Experiments show that the proposed text classification algorithm outperforms the existing CNN- or RNN-based methods, improving the F1-score from 72.16% to 93.60%. It is also illustrated that the proposed classification method can enhance downstream ARI methods with an improvement of 4%. Furthermore, analyzing the results of more than 150 building codes in China showed that their average interpretability is 34.40%, which implies that it is still hard to fully transform the entire regulatory document into computer-processable formats. It is also argued that the interpretability of building codes should be further improved both from the human side and the machine side.

As a vital stage of automated rule checking (ARC), rule interpretation of regulatory texts requires considerable effort. However, interpreting regulatory clauses with implicit properties or complex computational logic is still challenging due to the lack of domain knowledge and limited expressibility of conventional logic representations. Thus, LLM-FuncMapper, an approach to identifying predefined functions needed to interpret various regulatory clauses based on the large language model (LLM), is proposed. First, by systematically analysis of building codes, a series of atomic functions are defined to capture shared computational logics of implicit properties and complex constraints, creating a database of common blocks for interpreting regulatory clauses. Then, a prompt template with the chain of thought is developed and further enhanced with a classification-based tuning strategy, to enable common LLMs for effective function identification. Finally, the proposed approach is validated with statistical analysis, experiments, and proof of concept. Statistical analysis reveals a long-tail distribution and high expressibility of the developed function database, with which almost 100% of computer-processible clauses can be interpreted and represented as computer-executable codes. Experiments show that LLM-FuncMapper achieve promising results in identifying relevant predefined functions for rule interpretation. Further proof of concept in automated rule interpretation also demonstrates the possibility of LLM-FuncMapper in interpreting complex regulatory clauses. To the best of our knowledge, this study is the first attempt to introduce LLM for understanding and interpreting complex regulatory clauses, which may shed light on further adoption of LLM in the construction domain.

In debates about the future of work, technology is often portrayed as the villain. One recent study calculated that 38 percent of jobs in the United States were at a "high risk" of being automated during the next decade. In the construction industry, predictions are especially dire: estimates of robot-fueled joblessness range from 24 percent in Britain to 41percent in Germany. Borja García de Soto is an Assistant Professor of Civil Engineering at New York University Abu Dhabi (NYUAD), a Global Network Assistant Professor of Civil and Urban Engineering at the NYU Tandon School of Engineering, and Director of NYUAD's S.M.A.R.T. Construction Research Group. There is no question that automation will change the way people work, but for some sectors of the economy, change is long overdue.

For many architects, the hardest part of their job starts after they finish designing a building, when the onerous process of code compliance begins. Written to ensure the safety and accessibility of buildings, codes dictate everything from the height and depth of stairs and where railings end, to the amount of floor space in front of toilets and the height of windows. Regulations are constantly updated, which means that even the most diligent team of architects often miss violations, resulting in costly delays. Y Combinator alum UpCodes wants to help them by using artificial intelligence, including natural language processing, to create what the San Francisco-based startup describes as "the spellcheck for buildings." Called UpCodes AI, the program is a plug-in that scans 3D models created with building information modeling (BIM) data and alerts architects about potential issues.