Proper quality assessment of ready-mixed concrete, which is currently the principal material for construction, land engineering and architecture, has an impact on the optimisation and verification of correct functioning of individual stages of the production process. According to the European Standard EN 206 “Concrete–Specification, performance, production and conformity”, obligatory conformity control of concrete is carried out by the producer during its production. In order to verify the quality of concrete, investors generally commission independent laboratory units to perform quality assessment of both concrete mix and hardened concrete, which guarantees a high quality of construction works. One of the essential tools for ensuring the quality of test results is the participation of laboratories in the so-called proficiency testing (PT) or inter-laboratory comparisons (ILC). Participation in PT/ILC programmes is, on the one hand, a tool for demonstrating the laboratory’s performance, on the other hand an aid for maintaining the quality of available concrete tests and validating test methods. Positive evaluation is a confirmation of the laboratory’s capability for performing the tests. The paper presents the results of laboratory proficiency tests carried out by means of inter-laboratory comparisons, as shown in the example of quality assessment of ready-mixed concrete for nine participating laboratories. The tests were performed for concrete of the following parameters: strength class C30/37, consistency S3, frost resistance degree F150, and water resistance degree W8. This involved determining consistencies, air content and density of the concrete mix, and compressive strength of hardened concrete. For the evaluation of laboratory performance results, z-score, ζ-score and E_n-score were applied. The innovation of the proposed study lies in employing both classical and iterative robust statistical methods. In comparison with classical statistical methods, robust methods ensure a smaller impact of outliers and other anomalies on the measurement results. Following the analyses, clear differences were found between the types of detected discrepancy of test results, which occurred due to the nature of individual parameters. For two laboratories, two scores revealed unsatisfactory results for concrete mix consistency. The main reasons can be pouring into the cone-shaped form a concrete mixture that is too dry, or incorrect use of a measuring tool also creating a possibility that the obtained value can be wrongly recorded. Other possible reasons are discussed in the paper. Participation in inter-laboratory comparison programmes is undoubtedly a way to verify and raise the quality of tests performed for concrete mix and hardened concrete, whereas individual analysis of the results allows the laboratory quality system to be improved.

This study proposes an integrated approach to the digital documentation and system-level analysis of heritage buildings, combining LiDAR-based data acquisition, H-BIM modelling and DEMATEL analysis. The novelty of the study lies in combining a Scan-to-BIM workflow with DEMATEL-based system analysis in order to identify causal and dependent stages in the heritage building digitisation process. The research was carried out on two heritage buildings in south-eastern Poland: the Church of St Onuphrius in Posada Rybotycka and a wooden manor house from Brzeziny preserved in the ethnographic park in Kolbuszowa. Terrestrial laser scanning was used to acquire high-resolution point clouds of the buildings, which then provided the basis for developing parametric H-BIM models within a Scan-to-BIM workflow. For the church case study, the geometric accuracy of the Scan-to-BIM output was verified by comparing measurements derived from the point cloud with traditional surveying data. The results confirmed the suitability of Scan-to-BIM for heritage documentation, with an average absolute deviation of approximately 7 mm and a maximum deviation not exceeding 31 mm. DEMATEL analysis was used to examine cause–effect relationships between seven stages of the digitisation process and to determine which stages have the greatest influence on subsequent activities. Preliminary assessment, LiDAR scanning and H-BIM modelling were identified as causal stages, while validation, IoT integration, AI-based predictive analysis and digital twin development were classified as effect stages. The study also outlines how H-BIM models may be extended through IoT sensors and AI-based analytics as a basis for future digital twin development.