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The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

This paper presents the application of grey modeling for thermal spray processing parameter analysis in less data environment.

Based on processing knowledge, key processing parameters of thermal spray process are analyzed and preselected. Then, linear and non-linear grey modeling models are integrated to mine the relationships between different processing parameters.

Model A reveals the linear correlation between the HVOF process parameters and the characterization of particle in-flight with average relative errors of 9.230 percent and 5.483 percent for velocity and temperature.

The prediction accuracies of coatings properties vary, which means that there exists more complex non-linear relationship between the identified input parameters and coating results, or more unexpected factors (e.g. factors from material side) should be further investigated.

According to the modeling case in this paper, method has potential to deal with other diverse modeling problems in different industrial applications where challenge to collecting large quantity of data sets exists.

It is the first time to apply grey modeling for thermal spray processing where complicated relationships among processing parameters exist. The modeling results show reasonable results to experiment and existing processing knowledge.