Applying Ai Technology to Recognize Bim Objects and Visible Properties for Achieving Automated Code Compliance Checking

Abstract

Automated code compliance checking is an effective approach for assessing the quality of building information modeling (BIM) models. Various automated code compliance checking systems have emerged, wherein users need to input all information accurately according to BIM modeling guidelines, in order to ensure the accuracy of checking results. However, as this process involves human inputs, it is difficult to ensure that each input is accurate. In the case of errors or missing inputs, the checking results will be erroneous. Although automated checking systems can be developed accurately, it is difficult to apply these systems practically. Therefore, this paper proposes the application of AI technology to recognize BIM objects and visible properties, in order to improve the operability of automated code compliance checking. The two necessary elements – object names and properties – could be automatically extracted to a certain extent, following the application of the proposed method to the automated code checking process. The error rate of the input could also be reduced, thus making the application of the code checking system more practically feasible. The proposed recognition method for BIM objects and visible properties is also expected to be used widely in BIM-based building e-submission systems and BIM-based forward designs.

Incorporating smart objects into BIM: A comprehensive review

“…When specific parameters are defined for smart objects in the BIM environment, it becomes possible to develop interactive 3D models that interface with artificial intelligence (AI) recognition systems. This integration improves model capabilities for intelligent recognition and interaction with the objects [25].…”

“…The findings from RQ4, designed to uncover the advantages and challenges associated with the implemented processes, are consolidated and elaborated upon in the discussion section. [8], [9], [11], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43] (2) Construction [3], [5], [15], [38], [41], [42], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59],…”

“…They can embed predefined rules or integrate data related to specific code requirements, automatically checking these rules during the design phase. This helps identify potential code violations early in the process [25,28,34]. Beyond providing real-time feedback on other design elements, smart objects can be programmed to adapt to changes in building codes.…”

Automated code compliance checking research based on BIM and knowledge graph

“…In terms of geometric computation and data consistency correspondence, the mapping between the model and the rule code is performed through a modular algorithm design that allows different modules to be used overlappingly, and the mapping rules alleviate the linguistic ambiguity between the information; the review process of the BIM model is realized through consistency comparison 32 , 33 . Since the object of review in the code review process presented in the knowledge graph is "entities" rather than the BIM model itself, it is necessary to bind the derived component information in the model to the entities in the knowledge graph for compliance review, and the logic of automatic code consistency checking involves two parts, i.e., mapping rules and checking rules.…”