Machine-interpretable standards for quality assessment and structural design in the construction industry

Introduction . The digitalization of the construction industry involves a transition from traditional text-based standards to SMART (Standards Machine Applicable, Readable and Transferable) standards. Machine-interpretable standards that enable systems to independently interpret and apply regulatory requirements are a pressing priority. Aim . To explore the implementation of machine-interpretable standards in quality assessment processes for construction products and structural designs, along with related issues; to present practical benefits of applying the standards. Materials and methods. An ontology describing key material parameters and their interrelationships was developed to provide an automated analysis of rolled reinforcement test data. A system of machine-interpretable documentation was implemented in the StatBIM platform. Data from laboratory reports were converted into a structured JSON-LD format with semantic markup for subsequent automatic verification of compliance with standards. Results . The implementation of machine-interpretable standards in the StatBIM platform has reduced the processing time for rolled reinforcement test results by 70% and completely eliminated errors associated with manual data entry. The system demonstrated its ability to automatically detect non-compliance with standards, for example, in terms of tensile strength according to GOST 34028-2016. Conclusions . The application of machine-interpretable standards in practice confirmed their high efficiency for automating quality control in the construction industry. Their widespread implementation requires a comprehensive approach, including the development of open ontologies, training of specialists, and the creation of an adapted regulatory framework, which will contribute to technological sovereignty and the digital transformation of the industry.