Framing and Evaluating the Best Practices of IFC-Based Automated Rule Checking: A Case Study

This research reviews recent advances in the domain of Automated Rule Checking (ARC) and argues that current systems are predominantly designed to validate models in post-design stages, useful for applications such as e-permitting. However, such a design-check-separated paradigm imposes a burden on designers as they need to iteratively fix the fail-to-pass issues. Accordingly, the study reviews the best-practices of IFC-based ARC systems and proposes a framework for ARC system development, aiming to achieve proactive bottom-up solutions building upon the requirements and resources of end-users. To present and evaluate its capabilities, the framework is implemented in a real-life case study. The case study presents all the necessary steps that should be taken for the development of an ARC solution from rule selection and analysis, to implementation and feedback. It is explained how a rule checking problem can be broken down into separate modules implemented in an iterative approach. Results show that the proposed framework is feasible for successful implementation of ARC systems and highlight that a stable data standard and modeling guideline is needed to achieve proactive ARC solutions. The study also discusses that there are some critical limitations in using IFC which need to be addressed in future studies.

РАЗРАБОТКА ПРОТОТИПА ПРОГРАММНОГО МОДУЛЯ АВТОМАТИЗИРОВАННОЙ ГЕНЕРАЦИИ КВАЛИФИКАЦИОННЫХ ЯЧЕЕК ДЛЯ КОНТРОЛЯ ПРАВИЛ ПРОЕКТИРОВАНИЯ ИС

Разработан прототип программного модуля генератора квалификационных ячеек для автоматизированного контроля геометрических правил проектирования DRC. Проведено тестирование прототипа генератора в реальных рабочих условиях проектирования. The paper presents a prototype of software module of the QA-cells Generator for automated Design Rule Checking. The QA-Cells Generator has been tested in the real workplace within actual microelectronic industrial design.

Deep learning-based extraction of predicate-argument structure (PAS) in building design rule sentences☆

This paper describes an approach to extracting a predicate-argument structure (PAS) in building design rule sentences using natural language processing (NLP) and deep learning models. For the computer to reason about the compliance of building design, design rules represented by natural language must be converted into a computer-readable format. The rule interpretation and translation processes are challenging tasks because of the vagueness and ambiguity of natural language. Many studies have proposed approaches to address this problem, but most of these are dependent on manual tasks, which is the bottleneck to expanding the scope of design rule checking to design requirements from various documents. In this paper, we apply deep learning-based NLP techniques for translating design rule sentences into a computer-readable data structure. To apply deep learning-based NLP techniques to the rule interpretation process, we identified the semantic role elements of building design requirements and defined a PAS for design rule checking. Using a bidirectional long short-term memory model with a conditional random field layer, the computer can intelligently analyze constituents of building design rule sentences and automatically extract the logical elements. The proposed approach contributes to broadening the scope of building information modeling-enabled rule checking to any natural language-based design requirements.

A semantic-based framework for automated rule checking in healthcare construction projects

Healthcare projects are known for having a high degree of complexity. Furthermore, the design of healthcare facilities is highly constrained by regulations containing a wide range of requirements. Using BIM for automated rule checking has been pointed out as an opportunity to improve requirements management in these projects. However, most existing research is focused on hard-coded approaches or on limited sets of requirements. The aim of this investigation is to propose a semantic-based framework for automated rule checking in the context of healthcare design. An empirical study was conducted in the redevelopment of a university hospital, using Design Science Research as a methodological approach. Results indicate that the nature of regulations and the subjectivity of requirements have a major impact on the possibility of their translation into logical rules, which is needed to enable automated checking. The main theoretical contribution is a taxonomy for automated rule checking and information transformation.