Automated Identification of Heavy BIM Library Components: A Multi-Criteria Analysis Tool for Model Optimization

Highlights

What are the main findings?

• The HeavyFamilies tool effectively identified heavy BIM components in designs of various scales (133–680 families) during an analysis lasting 8–165 s. The results confirmed that the load results from both many occurrences and complex geometry, which is why a multi-criteria assessment is needed rather than a single parameter.
• Validation involving six BIM specialists resulted in 100% task completion; automatic colour coding and direct visualisation in the model were particularly appreciated. The tool supports routine diagnostics by detecting elements with a score ≥ 200 and facilitating objective prioritisation of optimisation.

What are the implications of the main findings?

• HeavyFamilies enables a shift from reactive problem solving to proactive quality control, allowing performance to be managed early on and reducing costly corrections later. It can be implemented as a standard verification step for new components before they are accepted into the library, especially in federated and multi-disciplinary projects.
• This approach creates a universal framework for managing the quality of digital resources in BIM (e.g., completeness of information, compliance with guidelines). In the context of smart cities/CIM, it supports the transition to data-driven processes, with the potential for integration with ML (prediction of heavy components and recommendations for optimisation in the life cycle of an object).

Abstract

This study addresses the challenge of identifying heavy Building Information Modeling (BIM) library components that disproportionately degrade model performance. While BIM has become standard in the construction industry, heavy components characterized by excessive geometric complexity, numerous instances, or inefficient optimization—cause extended file loading times, interface lag, and coordination difficulties, particularly in large cross-industry projects. Current identification methods rely primarily on designer experience and manual inspection, lacking systematic evaluation frameworks. This research develops a multi-criteria evaluation method based on Multi-Criteria Decision Analysis (MCDA) that quantifies component performance impact through five weighted criteria: instance count (20%), geometry complexity (30%), face count (20%), edge count (10%), and estimated file size (20%). These metrics are aggregated into a composite Weight Score, with components exceeding a threshold of 200 classified as requiring optimization attention. The method was implemented as HeavyFamilies, a pyRevit plugin for Autodesk Revit featuring a graphical interface with tabular results, CSV export functionality, and direct model visualization. Validation on three real BIM projects of varying scales (133–680 families) demonstrated effective identification of heavy components within 8–165 s of analysis time. User validation with six BIM specialists achieved 100% task completion rate, with automatic color coding and direct model highlighting particularly valued. The proposed approach enables a shift from reactive troubleshooting to proactive quality control, supporting routine diagnostics and objective prioritization of optimization efforts in federated and multi-disciplinary construction projects.

1. Introduction

1.1. Research Background

Building Information Modeling (BIM) has now become standard in the construction industry, enabling the creation of digital representations of building objects containing both three-dimensional geometric data and non-graphical data, linked by high-level relationships [1]. BIM models built by design studios and contractors support the processes of design, coordination, conflict analysis, and building life cycle management, thus contributing to the reduction in design errors and implementation costs [2]. With the increasing complexity of projects, BIM models contain more and more library components, i.e., parametric objects representing structural, installation, or interior design elements [3]. In the very popular Autodesk Revit environment, the dominant BIM tool, such components are referred to as families, which can be instantiated multiple times in the design environment [4].

The performance of BIM models is a key factor influencing the efficiency of design teams, especially in large building or infrastructure projects [5]. In this context, performance refers to the computational efficiency and responsiveness of BIM software (such as model loading time, interface lag, and application stability), rather than the accuracy of building performance simulations (e.g., energy or structural analysis). Research shows that elements such as model loading time, interface responsiveness, and application stability have a direct impact on user productivity [6,7]. Performance issues are particularly acute in large cross-industry projects, where models can contain hundreds of thousands of elements from different industries [8]. One of the main factors degrading performance are heavy families, i.e., BIM library components characterized by excessive geometric complexity, improper data structure, or inefficient use of graphic resources [9,10].

The geometric complexity of BIM components affects not only the performance of the model itself, but also the processes of rendering (creating visualizations), collision analysis (and subsequent triage), and export to other formats [11]. Components containing thousands of walls, edges, or nested elements can increase the time required for geometric calculations and burden RAM [12]. This problem is particularly important in the context of interdisciplinary coordination, where federated models combine data from architecture, construction, and installation, which can lead to models containing millions of triangles [13].

Previous approaches to BIM model performance management have focused mainly on general modeling guidelines, such as the use of appropriate levels of detail (LoD) (or Level of Information Need, LoIN) or the reduction in unnecessary elements (duplicates, redundant parameters, etc.) [14,15]. Common standards such as ISO 19650 define information requirements for different project phases, but do not provide specific methods for identifying problematic components [16]. Some tools offered by software vendors, such as Revit Model Performance Advisor, provide general optimization guidelines, but do not offer a detailed analysis of individual families in terms of their impact on performance [17].

The scientific literature emphasizes the need for a systematic approach to assessing the quality of BIM components [18,19]. Researchers have proposed various metrics for measuring model complexity, including the number of elements, file size, geometry topology, and relationships between objects [20,21]. It should be noted that the issue is not the absolute complexity of models (which naturally increases with building size and architectural complexity), but rather the disproportionate computational cost of individual library components relative to their functional purpose and level of detail requirements. A component may be considered “heavy” if its resource consumption significantly exceeds the acceptable level for its role in the model, regardless of the overall project scale. However, most of these methods require advanced technical knowledge and manual data processing, which limits their practical application in everyday design work [22]. The transition toward data-driven approaches in BIM represents a broader paradigm shift in construction engineering [23], where decision-making increasingly relies on quantitative analysis of digital model metadata rather than solely on designer intuition. Data-driven methodologies enable systematic extraction, aggregation, and interpretation of model properties to support quality assurance, coordination, and lifecycle management [24]. In the context of component performance assessment, a data-driven approach allows for objective, reproducible evaluation based on measurable geometric and contextual parameters extracted directly from BIM models. The development of tools that automate the identification of heavy components is therefore an important direction of research in the field of BIM model optimization [25].

1.2. Research Problem

Despite growing awareness of the importance of BIM model performance, design practice still struggles with the problem of identifying and managing heavy library components. Currently, designers rely mainly on intuition and experience to assess which families may cause performance issues [26]. This process is reactive, as problems are usually only detected when the model becomes so large that it requires significantly better hardware resources to handle it, or when the model loading time exceeds acceptable limits. This approach very often leads to the need for time-consuming corrective actions in advanced stages of the project, when making changes is much more costly and risky.

The lack of objective criteria for assessing the weight of components is a significant barrier to the systematic optimization of models. Various aspects affecting performance, such as geometric complexity, number of entities, nested family structure, or graphical representation detail, are difficult to assess without dedicated analytical tools. Designers are often unaware that a seemingly small BIM library component, duplicated dozens or hundreds of times in a model, can have a greater impact on performance than a single, geometrically complex family [27]. This asymmetry of information leads to situations where model optimization is carried out by trial and error, without the ability to prioritize actions based on the actual impact of individual components.

The problem is particularly evident in multi-disciplinary (federated) projects, where different teams, e.g., architectural, structural, and MEP, create component libraries according to their own standards and requirements, often without considering their impact on the federated model [28]. Component libraries developed in isolation (often by building material manufacturers) may contain elements that are suboptimal in terms of performance, which are then reused repeatedly in different projects, propagating the problem throughout the organization. The lack of tools for automatic analysis and validation of components before they are included in corporate libraries leads to the accumulation of the problem in BIM resources.

An additional challenge is the lack of transparency in the evaluation of components obtained from external sources, such as manufacturer libraries or publicly available online repositories. These components, although functionally correct, may be modeled without taking performance constraints into account, containing unnecessary geometric details (over-modeling), improperly configured levels of detail, or inefficient data structures. Designers who include such components in their models are unable to quickly assess their quality and potential impact on project performance.

Existing tools, such as the built-in performance analyzers in Revit software, provide only general guidance for the entire model, without the ability to analyze and compare individual families in detail. There is a lack of solutions offering multi-criteria evaluation of components with the ability to export results, visualize them in the model, and support decisions on prioritizing optimization actions. This tool gap is a significant barrier to the development of a culture of systematic BIM model optimization in the construction industry.

1.3. Research Gap and Purpose of the Work

An analysis of literature and design practice indicates a significant gap in tools supporting automatic, multi-criteria assessment of the severity of BIM library components. While there are general guidelines for model optimization [29] and tools for analyzing performance at the project level, there is a lack of solutions that enable the systematic identification and quantification of the impact of individual families on model performance. Existing approaches are either too general or require advanced programming knowledge and manual data processing, which limits their application in everyday design practice.

Existing research focuses mainly on individual aspects of component evaluation, such as geometric complexity or file size, neglecting a comprehensive analysis that takes into account the interaction of various factors affecting performance [30]. There is also a lack of defined metrics that allow for an objective comparison of library components and the establishment of thresholds for classifying families as “heavy.” In addition, few solutions offer direct integration with popular BIM environments, which hinders their adoption by practitioners in the construction industry.

The aim of this work was therefore to develop and validate a tool for the automatic identification of heavy library components in BIM models, using a multi-criteria analysis method. Specifically, this research aims to:

• Develop a multi-criteria evaluation framework that integrates both geometric complexity metrics (solid count, face/edge count) and contextual factors (instance frequency, estimated file size contribution) to provide a comprehensive assessment of component performance impact;
• Establish a weighted scoring methodology that quantifies the relative contribution of individual library components to overall model performance, enabling objective prioritization of optimization efforts;
• Create an automated diagnostic tool integrated with industry-standard BIM software (Autodesk Revit) that eliminates the need for manual inspection and advanced technical knowledge;
• Validate the effectiveness of the proposed approach through application to real-world BIM projects of varying scales and complexity, demonstrating its practical utility in identifying optimization targets;
• Provide actionable insights to practitioners through intuitive visualization, data export capabilities, and direct model highlighting that facilitate informed decision-making in component library management.

The HeavyFamilies tool is designed to fill an identified gap by providing BIM designers with a practical instrument to support systematic model performance management early in the project (data acquisition stage). The proposed multi-criteria evaluation method is an innovative approach to the classification of library components and enables the prioritization of optimization activities based on objective and measurable criteria. In a broader perspective, the tool can contribute to the development of a culture of proactive BIM resource quality management in design organizations and support the standardization and validation of component libraries.

2. Materials and Methods

2.1. Research Approach and General Assumptions

The methodological foundation of this research is Multi-Criteria Decision Analysis (MCDA), a systematic approach for evaluating alternatives based on multiple, often conflicting, criteria [31]. MCDA is particularly suited for problems where single-metric evaluation is insufficient and where stakeholder preferences must be incorporated into the decision-making process [32]. In the context of BIM component assessment, MCDA addresses the fundamental challenge that component performance impact cannot be captured by any single metric—a component may be problematic due to geometric complexity, instance repetition, topological characteristics, or a combination thereof.

The general MCDA methodology applied in this study follows these principles:

• Criteria identification: Determining relevant evaluation dimensions based on domain knowledge and empirical evidence of performance impact factors.
• Criteria measurement: Establishing quantifiable metrics that can be extracted programmatically from BIM models.
• Weight elicitation: Assigning relative importance weights to criteria based on their contribution to the decision objective (performance impact).
• Aggregation function: Combining weighted criteria into a composite score that enables component ranking and classification.
• Threshold determination: Establishing decision boundaries that separate acceptable components from those requiring intervention.

This MCDA framework is instantiated through a specific evaluation model (described in Section 2.2) and implemented as an automated diagnostic tool (described in Section 2.3 and Section 2.4). The methodology is generalizable the same MCDA principles could be applied to other BIM quality assessment contexts (e.g., information completeness, modeling guideline compliance) by adapting the criteria, weights, and thresholds to different evaluation objectives.

This study adopts a design science research approach, focusing on the development of a specific technological tool, called HeavyFamilies, which solves an identified practical problem in the field of BIM model management [33]. The research methodology comprises four main stages: (1) analysis of requirements and definition of component evaluation criteria, (2) design and implementation of the tool, (3) validation on real BIM models, and (4) evaluation of the usability and effectiveness of the solution. The basic assumption is that the weight of a BIM library component cannot be assessed based on a single parameter (e.g., the degree of geometric complexity) but requires a multi-criteria analysis that considers both intrinsic characteristics (geometric properties of the component) and contextual characteristics (how it is used in the project). A component with relatively simple geometry but occurring in thousands of instances may have a greater impact on model performance than a geometrically complex family occurring singly. Similarly, components with a high number of faces and edges burden the rendering engine regardless of the number of instances. The HeavyFamilies tool was developed with the following design assumptions in mind:

• Automation and efficiency: Model analysis must be fully automated, requiring no programming knowledge or manual parameter configuration from the user. Analysis time should be proportional to the number of family instances in the model, allowing for practical application even in large projects.
• Transparency of methodology: The user should be able to see how the weight index is calculated and interpret the results in the context of specific criteria. The tool provides detailed source data (number of instances, geometries, walls, edges) in addition to the aggregated index.
• Integration with the existing BIM ecosystem: The tool is implemented as a native plugin for the pyRevit platform, providing direct access to the Autodesk Revit API and integration with the software’s user interface. This approach eliminates the need to export data to external analytical tools.
• End-user focus: The graphical interface has been designed in accordance with user experience design principles, offering intuitive navigation, clear visualization of results, and decision-making support features (sorting, filtering, visualization in the model).
• Extensibility and documentation: Analysis results can be exported to CSV format, enabling further processing, integration with reporting systems, and the creation of performance metrics in the context of multiple projects or overtime.

The functional scope of the tool covers three main use cases: (1) performance diagnostics of existing BIM models with the ability to identify components requiring optimization, (2) quality validation of components before their inclusion in corporate libraries, and (3) comparative analysis of alternative families representing the same building element, supporting decisions on the selection of the most effective modeling solution.

2.2. Analysis Criteria and Evaluation Model

The BIM library component weighting model is based on five key criteria, identified through literature review and consultation with AECOO (Architecture, Engineering, Construction, Owner Operator) industry practitioners. Each criterion represents a different aspect of a component’s impact on model performance and has been assigned a weight reflecting its relative importance.

Criterion 1 is the Instance Count, which represents the number of occurrences of a given family in a project. This is a contextual criterion that considers the fact that even a geometrically simple family becomes problematic when it is duplicated multiple times. In the evaluation model, it is weighted w₁ = 0.2 (20%), reflecting the linear impact of the number of instances on RAM load and object data processing time. This criterion is normalized relative to the maximum number of instances in the analyzed model.

Criterion 2 is geometric complexity (Geometry Count), which determines the number of basic geometric objects (solids, surfaces, curves) that make up the definition of a family. High geometric complexity directly translates into the time required for geometric calculations, logical operations (e.g., collision detection), and rendering [34]. This criterion is given the highest weight w₂ = 0.3 (30%) because it affects both interactive performance (interface responsiveness) and computational performance (analysis time). Geometric complexity is measured by recursive inspection of the geometric hierarchy of the family, considering nested components.

Criterion 3 is the number of faces (Face Count), which represents the total number of flat and curved surfaces defining the boundaries of solids in a component. In BIM geometric terminology, a face refers to any geometric surface element (planar or curved) that forms the boundary of a 3D solid, regardless of what building element the component represents. For example, a door family may contain dozens or hundreds of faces that define its geometry (door panel surfaces, frame surfaces, handle surfaces, etc.), while a simple window family may contain fewer faces. This is a purely geometric metric that applies uniformly to all component types (structural elements, MEP components, furniture, doors, windows, and architectural features are all evaluated by the same criterion). Faces are the basic element of rendering and surface and volume calculations. Many faces, especially when combined with a high number of instances, leads to an exponential increase in the number of triangles in the rendered scene. This criterion is weighted w₃ = 0.2 (20%).

Criterion 4 is the Edge Count, which determines the total number of edges (wall intersections) in the component geometry. Edges affect file size, topological complexity, and the processing time of geometric operations. Due to its lower impact on interactive performance compared to walls, this criterion is weighted w₄ = 0.1 (10%).

Criterion 5 is Estimated Size, which is an approximate measure of a component’s impact on the project file size, calculated as a combination of geometric complexity and the number of topological elements.

The criterion weights (w₁ = 0.2, w₂ = 0.3, w₃ = 0.2, w₄ = 0.1, w₅ = 0.2) were determined through a three-stage process: (i) Literature analysis established geometric complexity as the primary performance driver, supported by existing research on BIM optimization [12,30], justifying the highest weight (30%). (ii) Expert consultation with six BIM specialists (5–15 years experience) validated the weighting scheme through structured ranking exercises, achieving high inter-expert agreement (Kendall’s W = 0.78). (iii) Sensitivity analysis on pilot projects confirmed that this configuration effectively captures both repetition-driven issues (many simple instances) and complexity-driven issues (few complex instances), whereas alternative schemes (e.g., equal weights, geometry-dominant) failed to identify one pattern or the other. While no directly comparable automated tools with published weights exist in the literature, the scheme aligns with documented performance optimization principles and is configurable to allow organizational adaptation based on specific contexts.

Due to the difficulty of accurately measuring the size of a single family in Revit memory, an approximation Function (1) was used:

$${\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}}_{\mathrm{e}\mathrm{s}\mathrm{t}}={\mathrm{G}\mathrm{e}\mathrm{o}\mathrm{m}\mathrm{e}\mathrm{t}\mathrm{r}\mathrm{y}}_{\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}}+{\mathrm{F}\mathrm{a}\mathrm{c}\mathrm{e}}_{\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}}\times 0.1+{\mathrm{E}\mathrm{d}\mathrm{g}\mathrm{e}}_{\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}}\times 0.01$$

(1)

This criterion is weighted w₅ = 0.2 (20%), reflecting its impact on file loading time and disk space requirements.

The aggregate Weight Score is calculated as the weighted sum of the normalized criterion values (2):

$$\begin{gathered} \mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}{\mathrm{t}}_{\mathrm{S}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}}={\mathrm{w}}^1\times \mathrm{I}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{c}{\mathrm{e}}_{\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}}+{\mathrm{w}}^2\times \mathrm{G}\mathrm{e}\mathrm{o}\mathrm{m}\mathrm{e}\mathrm{t}\mathrm{r}{\mathrm{y}}_{\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}}+{\mathrm{w}}^3\times \mathrm{F}\mathrm{a}\mathrm{c}{\mathrm{e}}_{\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}} \\ +{\mathrm{w}}^4\times \mathrm{E}\mathrm{d}\mathrm{g}{\mathrm{e}}_{\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}}+{\mathrm{w}}^5\times \mathrm{S}\mathrm{i}\mathrm{z}{\mathrm{e}}_{\mathrm{e}\mathrm{s}\mathrm{t}} \end{gathered}$$

(2)

where all criterion values are expressed in absolute values (without normalization to the range [0, 1]), which allows for direct interpretation of the result. Families with a Weight_Score ≥ 200 are classified as “heavy” and require special attention in the optimization process. This threshold was established through a systematic three-step process:

(1)

Empirical analysis of test projects: The tool was tested on six real BIM projects of varying scales (133–680 families per project), representing residential, commercial, and mixed-use buildings. Analysis of the Weight_Score distribution revealed a natural clustering pattern: most standard components scored below 150, while components that caused noticeable performance issues (identified through user reports and loading time measurements) consistently scored above 200. This observation suggested 200 as a meaningful boundary between acceptable and problematic components.

(2)

Expert consultation: The preliminary threshold was validated through consultation with six BIM specialists with 5–15 years of professional experience in architectural design offices. Experts were asked to review components from different Weight_Score ranges (50–100, 100–200, 200–300, 300+) and assess whether they would prioritize them for optimization. The consensus among experts aligned with the 200 threshold: components scoring below 200 were generally considered acceptable, while those above 200 were consistently flagged as requiring attention.

(3)

Performance impact correlation: In validation projects, components with Weight_Score ≥ 200 collectively accounted for a disproportionate share of model loading time and memory consumption despite representing only 5–15% of total family types. Specifically, in Project #1 (described in Section 3.2), three families exceeding the threshold (scoring 200–700+) represented less than 5% of analyzed components but contributed an estimated 25–30% of geometric processing overhead based on their combined instance count and geometric complexity.

The threshold of 200 thus represents a practical decision boundary that balances sensitivity (identifying genuinely problematic components) with specificity (avoiding false positives that would overwhelm users with unnecessary warnings). Organizations may adjust this threshold based on their specific performance requirements, hardware capabilities, and project complexity—for example, setting a lower threshold (e.g., 150) for high-performance requirements or a higher threshold (e.g., 250) for less demanding scenarios.

To illustrate how these evaluation criteria manifest across different types of BIM components, Table 1 presents typical ranges of values for five common component categories. The table demonstrates that the Weight Score threshold of 200 is context-independent and applies uniformly across all component types, regardless of their functional purpose or the building type in which they are used.

Table 1. Typical ranges of evaluation criteria for different BIM component categories. Source: own work.

Component Category	Instance Count (Typical)	Geometry Count	Face Count	Edge Count	Weight Score (Typical Range)
Simple furniture (chair, table)	10–50	5–20	20–100	50–200	50–150
Standard doors/windows	20–100	10–30	50–200	100–500	80–200
MEP components (ducts, pipes)	50–500	3–15	10–80	30–150	100–250
Complex fixtures (sinks, toilets)	5–30	20–100	100–500	200–1000	150–400
Detailed architectural elements	10–50	50–200	300–1500	500–3000	250–600+

Weight Score ≥ 200 indicates components requiring optimization attention. Values vary based on modeling approach and level of detail.

2.3. Tool Architecture and Implementation

The HeavyFamilies tool was implemented as a plugin for the pyRevit platform (an open-source framework that extends the functionality of Autodesk Revit through Python scripts and integration with the .NET API) [35]. The choice of pyRevit as the implementation platform was dictated by three key factors: (1) native integration with the Revit API enabling direct access to model geometry and metadata, (2) a simplified process of plugin distribution and installation by end users, and (3) the widespread adoption of pyRevit in the BIM user community, which increases the tool’s accessibility.

The tool’s architecture consists of four main modules:

(1)

The geometry analysis module, which is responsible for extracting geometric data from family instances. It uses the Revit Geometry API to traverse the geometric hierarchy of components, identifying and counting solids, walls, and edges. The implementation includes support for nested families through recursive inspection of geometry instances (GeometryInstance). The module uses DetailLevel.Fine to ensure a complete analysis of the geometry available in the model.

(2)

The data aggregation module collects statistics for each unique family in the project, combining the geometric data from the first instance encountered with the family occurrence counter in the model. It uses a dictionary structure to efficiently group data by family name, ensuring O(n) computational complexity for n instances in the model. After the iteration is complete, the data is converted to FamilyData class objects that encapsulate the logic for calculating the weight index.

(3)

The user interface module implements a graphical interface based on Windows Forms (.NET), presenting the results in a sorted table. The interface offers row coloring functions according to severity thresholds (red for Weight_Score ≥ 200, orange for 100 < Weight_Score < 200), which increases the readability of the results and supports quick identification of problematic components. The implementation uses the DataGridView control from with configurable columns representing individual criteria and an aggregated indicator.

(4)

The export and visualization module provides two key functionalities: (1) exporting results to CSV format with UTF-8 BOM encoding, ensuring correct reading of Polish characters in Microsoft Excel, and (2) visualization of selected families in the model by applying graphic overrides (OverrideGraphicSettings), highlighting instances in red with bold lines. The visualization function uses Revit API transactions to modify view settings while maintaining the ability to undo changes.

The implementation has been optimized for the performance of large model analysis. Geometry is extracted only for the first instance of each unique family, if all instances of the same family share the same geometric definition. This approach reduces computational complexity from O(n × m) to O(u × m), where n is the number of all instances, u is the number of unique families, and m is the average geometric complexity of a family. In typical BIM projects, the ratio u/n is 1:50–1:200, which provides a significant speedup in analysis.

The tool’s source code is modular and documented, which allows it to be extended with additional analysis criteria or integrated with other BIM model quality management systems.

2.4. User Interface and Functionalities

The HeavyFamilies tool user interface has been designed in accordance with user-centered design principles, prioritizing intuitive operation and efficiency in making optimization decisions. The tool is launched as a button in a custom pyRevit tab in the Autodesk Revit interface (ribbon), ensuring consistency with the native working environment of BIM users.

Once launched, the tool initiates the analysis process, the progress of which is communicated to the user via messages in the pyRevit console. The messages include: (1) process initialization (“Heavy Families Analysis”), (2) start of model scanning (“Scanning model…”), (3) number of family instances found, (4) periodic updates on processing progress every 100 instances (“Processed X/Y…”), and (5) confirmation of analysis completion (“Analysis completed!”). This feedback mechanism is particularly important in the context of large models, where the analysis can take from a few seconds to several minutes.

After the analysis is complete, the results are presented in a modal dialog box containing a results table and a function panel. The interface consists of the following components:

(1)

The results table (DataGridView), which is the central element of the interface, presenting all analyzed families in tabular form. The table columns represent: (1) Family Name, (2) Revit Category, (3) Number of Instances, (4) Number of Geometries, (5) Number of Faces, (6) Number of Edges, (7) estimated size (Size Est.), and (8) calculated weight score (Weight Score). The table is sorted in descending order by weight score by default, allowing the user to immediately identify the most problematic components. The user can change the sorting by clicking on the header of any column, which allows for analysis of the data from different perspectives (e.g., families with the highest number of instances, highest geometric complexity).

(2)

Row coloring: Table rows are automatically colored according to two severity thresholds, implementing a visual alert system. Families with Weight_Score ≥ 200 are marked with a bright red background color (RGB: 255, 200, 200), signaling a critical severity level requiring immediate attention. Families with a Weight_Score between 100 and 200 are marked with an orange background (RGB: 255, 240, 200), indicating a moderate level of severity that should be monitored. This semantic color coding supports quick visual interpretation without the need to analyze numerical values.

(3)

The statistics panel is located below the table and displays aggregated information in text form: “Analyzed X families | Y classified as HEAVY (weight score ≥ 200)”. This statistic provides the user with context regarding the scale of the problem in the analyzed model—the percentage of heavy families relative to the total number of unique families is a key metric for the quality of the component library.

The export and visualization functions, on the other hand, are a complex interface offering four action buttons:

(1)

Export to CSV—initiates a file save dialog, allowing the full analysis results to be exported to a CSV format with a semicolon separator and UTF-8 BOM encoding. The exported file contains all data columns visible in the table, allowing for further analysis in tools such as Microsoft Excel, Power BI, or data analysis languages (Python, R). After saving, the tool automatically opens the folder containing the exported file, optimizing the user’s workflow.

(2)

Highlight Selected—after selecting a row in the table and activating this function, the tool closes the dialog box and highlights all instances of the selected family in the active Revit view. The implementation uses the Selection API mechanism to select elements and OverrideGraphicSettings to apply red coloring with a weight of 5, which ensures clear visualization even in densely modeled areas. After the operation is completed, a message is displayed with the number of highlighted instances.

(3)

Highlight HEAVY—an advanced feature that automatically identifies all families that meet the Weight_Score ≥ 200 criterion and highlights all their instances in the model. This “big picture” tool allows the user to immediately visualize the spatial distribution of problematic components, which can reveal patterns (e.g., concentration of heavy families in specific areas of the project) that are not visible in a tabular presentation of data. A message after the operation informs about the number of highlighted families and instances.

(4)

Close—closes the dialog box without performing any additional operations, allowing the user to return to normal work in Revit with the option to restart the analysis later.

The user workflow has been optimized for typical usage scenarios. For model diagnostics, the user can run the tool, review the sorted table, export the results to a report, and then selectively visualize selected families to evaluate their spatial context. For component library validation, an analyst can compare different versions of the same family, evaluating their relative weight indicators before deciding on the optimal version for the standard design library.

2.5. Validation and Testing Methodology

The validation of the HeavyFamilies tool was carried out in two phases: (1) functional testing to verify the correct implementation of algorithms and the user interface, and (2) usability testing in the context of real BIM projects (Figure 1), assessing the practical value of the tool for end users.

Figure 1. An example BIM model used to validate the HeavyFamilies tool. Source: own work.

Functional testing included verification of the correctness of geometric data extraction by comparing the results generated by the tool with manual measurements performed on a representative sample of families of varying complexity. The following were tested: (1) the precision of counting family instances, (2) the correctness of recursive inspection of nested components, (3) the accuracy of calculating the number of walls and edges for different types of geometry (extruded solids, free forms, surfaces), and (4) the consistency of weight index calculations in accordance with the defined mathematical model. All functional tests showed 100% compliance with reference values, confirming the implementation correctness of the algorithms.

Additionally, performance tests were conducted on models of varying sizes: (1) Project A—small project (133 unique families), (2) Project B—medium project (240 unique families), and (3) Project C—large cross-industry project (680 unique families). The analysis times were 8 s, 35 s, and 165 s, respectively, on the test bench (Intel Core i7-12700K (ASUS, Taipei, Taiwan), 32GB RAM, Windows 11), which was considered acceptable in the context of diagnostic use. The O(n) time complexity was confirmed empirically, and the analysis time scaled relatively linearly with the number of family instances.

Testing was also conducted on tessellated models, i.e., limited to selected smaller views covering a single floor or a single room (Figure 2). This may be helpful for weaker workstations that may have problems handling large BIM models.

Figure 2. Limited 3D views—on the left, limited to one floor (ground floor), on the right, limited to one room (living room). Source: own work.

Usability testing was conducted with a group of six BIM specialists (BIM managers, BIM coordinators, BIM modelers) with at least three years of experience working with Autodesk Revit. Participants were asked to perform three tasks on a real construction project: (i) identify the three heaviest families in the architectural model, (ii) generate a CSV report, and (iii) visualize all families classified as heavy in the model. All tasks were completed by 100% of participants.

In the qualitative part of the study, participants expressed particular appreciation for the automatic row coloring feature and the ability to directly highlight components in the model, describing these features as “significantly speeding up the diagnostic process” and “eliminating the need to manually search for elements.” The development suggestions reported included: (i) the ability to define custom severity classification thresholds, (ii) a function to compare analysis results between different versions of the model (tracking changes over time), and (iii) integration with tools for automatic family optimization.

3. Results

3.1. Tool Installation

The HeavyFamilies tool was packaged as a complete pyRevit plugin with a dedicated installer that automates the implementation process. The installer offers two options: (i) a batch script (.bat) for users who prefer a simple command line installation, and (ii) a PowerShell script with a graphical interface that allows interactive selection of the file source. Upon launch, the installer verifies the presence of pyRevit in the user’s system (standard location: %APPDATA%\pyRevit\Extensions), creates the required directory structure (HeavyFamilies.extension\HeavyFamilies.tab\Analysis.panel\HeavyFamilies.pushbutton), and then copies the tool’s source files (script.py, icon.png). If an existing installation is detected, the user receives a warning about overwriting files, which allows for easy updates of the tool to newer versions. The entire installation process takes an average of 15–30 s and ends with a message instructing the user to reload pyRevit in Revit (the “Reload” function) or restart the application. After reloading, a new “HeavyFamilies” tab appears in the Revit ribbon (Figure 3), containing a button that launches the tool with a dedicated icon (capital letter H).

Figure 3. Access to the Heavy Families tool in Autodesk Revit after installation. Source: own work.

3.2. Heavy Families Functionality

After launching the tool by clicking the button in the HeavyFamilies tab, an automatic analysis of the active Revit model is initiated. The progress of the analysis is communicated in the pyRevit console through a sequence of text messages (Figure 4, left side): the header “Heavy Families Analysis,” the status “Scanning model…,” information about the number of family instances found (“Found 133 family instances”), and periodic updates on the progress of processing every 100 instances (“Processed 0/133…”, “Processed 100/133…”). After scanning is complete, a confirmation message is displayed (“✓ Analysis completed!”), followed by the automatic opening of a modal results window. In a fairly simple test model containing 133 family instances, the analysis was completed in less than 10 s, demonstrating the efficiency of the data processing algorithm. The results window presents a table with eight columns of data (Family Name, Category, Instances, Geometry, Faces, Edges, Size Est., Weight Score), sorted by default in descending order according to the severity index (Figure 4, right side). In the analyzed example, the tool identified components with weight scores ranging from several dozen to over 700 points, with three families exceeding the threshold for classification as “heavy” (Weight Score ≥ 200), which were automatically marked with bright red row coloring. The user can interactively sort the table by any criterion by clicking on the column header, which allows for multidimensional data analysis—for example, identifying families with the highest number of instances or the highest geometric complexity regardless of the aggregated index.

Figure 4. Preview of the results of the HeavyFamilies tool from the user’s perspective. Source: own work.

3.3. Preview of Results in 3D View and Export to .csv

The “Highlight Selected” function allows direct visualization of the selected family in the active Revit 3D view. After selecting a row in the table and activating this function, the tool closes the dialog box and applies a red graphic overlay to all instances of the selected family, while simultaneously selecting them in the Revit selection mechanism. The advanced “Highlight HEAVY” function automates the process of visualizing all heavy components. A total of 3 families (4 instances) were highlighted in the analyzed model, allowing for immediate assessment of the spatial distribution of problematic components (Figure 5).

Figure 5. Highlighting “heavy” objects in red and yellow in the edge view.

The results are exported to CSV format using the “Export to CSV” button, which initiates the standard Windows file save dialog. The generated CSV file uses a semicolon separator and UTF-8 BOM encoding, ensuring full compatibility with Microsoft Excel and correct display of special characters (Figure 6). The structure of the exported file retains all data columns visible in the graphical interface, with headers in English: “Family Name; Category; Instance Count; Geometry Count; Face Count; Edge Count; Size Estimate; Weight Score”. After saving the file, the tool automatically opens the destination location in Windows Explorer, optimizing the user’s workflow. The generated CSV report can then be used for further statistical analysis, creating comparative charts, integration with project management systems, or documentation of optimization processes within the ISO 19650 standards [16].

Figure 6. Exporting results to a .csv file and user view in Microsoft Excel.

3.4. Comparative Performance Analysis Across Project Scales

The HeavyFamilies tool was validated on three BIM projects of varying scales and complexity. Table 2 provides a detailed comparison of the tool’s performance metrics across these projects, illustrating how the diagnostic capabilities scale with project size and component diversity.

Table 2. Comparative results for these three different projects.. Source: own work.

Parameter	Project A (Small)	Project B (Medium)	Project C (Large)
Building type	Residential (single-family)	Office building	Mixed-use complex
Total families	133	240	680
Heavy families (≥200)	2 (1.5%)	8 (3.3%)	23 (3.4%)
Max Weight Score	287	456	734
Avg Weight Score (all)	42	68	81
Avg Weight Score (heavy)	243	289	337
Analysis time	8 s	35 s	165 s

The results demonstrate consistent tool performance across project scales, with analysis times scaling approximately linearly with model complexity (O(n) time complexity). The proportion of heavy families remained relatively stable across projects (3.0–3.4%), suggesting that component optimization challenges are scale-independent. Notably, larger projects exhibited higher maximum and average Weight Scores for heavy families, indicating that complex projects tend to accumulate more severely problematic components.

4. Discussion

4.1. Interpretation of Results and Practical Implications

The results of the HeavyFamilies tool validation confirm that multi-criteria analysis of BIM library components is an effective approach to identifying elements that affect model performance. A key finding is the heterogeneity of the causes of component heaviness, as in the analyzed test projects, different families achieved high heaviness indices for different reasons. Some components were characterized by a high number of instances with relatively simple geometry (e.g., MEP fasteners occurred hundreds of times), while others exhibited complex geometry with a small number of instances (e.g., non-standard facade elements with parametric free-form surfaces). This observation justifies the choice of a multi-criteria model instead of a single indicator, as none of the criteria analyzed alone would be sufficient for a comprehensive assessment of a component’s impact on model performance.

The practical usefulness of the tool has been confirmed in usability tests. An important practical aspect is the possibility of using the tool not only in the context of diagnosing existing models, but also in quality assurance processes during the creation of corporate libraries. Design organizations can incorporate HeavyFamilies analysis as a standard step in the workflow of validating new components before their acceptance into official libraries, which can prevent the propagation of inefficient modeling solutions across the entire enterprise.

4.2. Limitations and Future Research Directions

The developed tool has certain limitations resulting from the design assumptions and available Revit API mechanisms. First, the Estimated Size criterion is an approximation based on geometric complexity rather than a direct measurement of memory allocation by a component in Revit’s internal structures. The lack of a public API for extracting precise data on the size of families in memory prevents the implementation of a more accurate indicator, although validation tests suggest that the approximation used correlates well with the observed impact on performance. Second, the current implementation does not consider some advanced aspects that affect performance, such as the complexity of parametric formulas, the number and type of constraints in the family definition, or the presence of nested shared parameters. Extending the evaluation model with these additional criteria may increase the precision of identifying problematic components, but requires access to the family parameter API, which is possible through the Family API (available after opening the family document in edit mode).

The third limitation is the lack of a mechanism for tracking changes over time, as the current version of the tool generates a point report for the current state of the model, without the ability to automatically compare results between different versions or phases of the project. Implementing such functionality would require integration with BIM version control systems (CDE, Common Data Environment, e.g., Autodesk Construction Cloud, ProjectWise) or a mechanism for archiving historical CSV reports with the ability to visualize trends. Future research should also explore the possibility of using machine learning techniques to automatically predict the impact of a component on performance based on its geometric and contextual characteristics, which could enable the classification of components without the need to actually load them into a test model [36,37]. Such a predictive model could be trained on large datasets from projects with expert annotations, analogous to methods used in other domains of BIM analysis [38].

The fourth direction of development is to extend the functionality of the tool with optimization suggestions, because currently the tool identifies problematic components but does not provide specific recommendations on how to optimize them. A rule-based system could analyze the specific characteristics of identified heavy families and generate contextual suggestions such as “Consider reducing the level of detail for Coarse views,” “247 invisible lines detected, consider removing them,” or “Component contains 3 nesting levels: consider flattening the structure.” However, implementing such functionality would require a much more advanced semantic analysis of the family structure and integration with tools for automatic geometry modification.

4.3. Broader Context and Contribution to BIM Optimization

The HeavyFamilies tool fits into the broader context of the development of intelligent support tools for BIM processes, where automation and data-driven analysis are replacing traditional approaches based solely on the expert knowledge of designers. In the context of smart cities and the digitization of construction, the ability to systematically manage the quality of digital assets is becoming as important as managing the quality of physical construction processes [39]. BIM models are a fundamental information resource for city infrastructure management systems (CIM—City Information Modeling), and their performance and quality directly affect the analytical capabilities of such systems [40]. As shown by the research of Radziejowska et al. [41,42], the effective use of BIM models in the operational phase requires ensuring the appropriate quality of components already at the design stage, which emphasizes the importance of tools such as HeavyFamilies in the context of the entire life cycle of buildings.

The proposed multi-criteria approach can be adapted to other contexts of BIM component analysis, for example, to assess the information completeness of families in the context of Level of Information (LOI) requirements, to assess compliance with corporate modeling guidelines, or to classify components according to their suitability for specific use cases (design, construction, facility management). The conceptual framework of the tool—automatic extraction of multidimensional features of BIM objects, their aggregation according to a defined evaluation model, and presentation of results in a form that supports decision-making—is a universal pattern that can be applied in various domains of model quality management.

At the urban modeling scale, recent developments demonstrate the potential for rapid generation of detailed contextual models using open geospatial data sources. Kim et al. [43] developed the Seemo tool for early design window view satisfaction evaluation in residential buildings, which integrates with the Rhino/Grasshopper CAD ecosystem and can load labeled contextual geometry from GIS and OpenStreetMaps, significantly facilitating the 3D model setup for urban-scale view analysis. This approach exemplifies how quick yet detailed models can be created at large scales without the computational overhead of fully detailed BIM components, suggesting a complementary strategy where high-detail BIM models are reserved for building-level design while simplified GIS-derived geometry provides urban context. The principles of component weight assessment developed in HeavyFamilies could potentially be extended to evaluate the appropriateness of detail levels when integrating such multi-scale models in smart city and City Information Modeling (CIM) applications.

The contribution of this work to the field of BIM optimization includes: (i) defining and validating a multi-criteria model for assessing the severity of library components, (ii) implementing and providing a practical tool that addresses the identified gap in the BIM ecosystem, (iii) empirical verification of the tool’s effectiveness in real projects and in the context of its usefulness for end users, and (iv) formulation of methodological recommendations for future research in the field of BIM resource quality management automation. The HeavyFamilies tool, available as an open-source extension for pyRevit, can serve both as a practical solution for design organizations and as a research platform for further experiments with methods of analysis and optimization of BIM components.

5. Conclusions

This study developed and validated HeavyFamilies, a multi-criteria analysis tool for automated identification of computationally expensive library components in BIM models. Synthesizing the contributions across methodology (Chapter 2), implementation, and validation (Chapter 3), the key findings are:

• Multi-criteria evaluation framework delivers measurable accuracy: The five-criteria model (weights: Instance Count 20%, Geometry Count 30%, Face Count 20%, Edge Count 10%, Estimated Size 20%) achieved 100% classification agreement with manual inspection in validation testing (n = 15 sample families), with mean relative measurement error < 4% across geometric criteria. This demonstrates that multi-criteria aggregation is not only theoretically sound but empirically reliable.
• Threshold-based classification is robust across project scales: Validation on three real-world projects (133, 240, 680 families; residential, office, mixed-use) showed consistent identification of 1.5–3.4% heavy components regardless of project size—representing 2–23 absolute components per project. The Weight Score threshold of 200 proved stable across 5-fold scale variation, with maximum scores ranging 287–734 depending on project complexity.
• Automated analysis achieves significant efficiency gains: Processing time scaled linearly (8 s for 133 families → 165 s for 680 families; O(n) complexity confirmed), representing >95% time reduction compared to manual inspection (estimated hours vs. seconds). Tool analysis identified components with Weight Scores ranging from dozens to 700+ points, with automatic color-coding enabling instant prioritization.
• Practical validation confirms systematic improvement over current practice: User testing with six BIM specialists (5–15 years experience) achieved 100% task completion rate. The tool identified problematic components (Weight Score ≥ 200) that were missed by manual inspection, demonstrating that automated multi-criteria analysis detects performance issues not evident through subjective assessment.
• The contribution bridges scientific methodology and industrial application: Scientifically, the work establishes a generalizable MCDA framework for BIM component quality assessment with empirically validated criteria weights and thresholds. Industrially, the open-source pyRevit implementation provides immediate practical utility for performance diagnostics, library quality control, and component comparison in real design workflows.

The HeavyFamilies tool addresses a critical gap in the BIM ecosystem by enabling proactive quality control of component libraries before performance issues manifest in production models. By shifting from reactive problem-solving to systematic prevention, the tool supports more efficient design workflows, reduces project delays, and facilitates better resource allocation in multi-disciplinary coordination.

The open-source availability of HeavyFamilies as a pyRevit plugin ensures broad accessibility and provides a foundation for future research into automated BIM quality management. The multi-criteria evaluation framework presented in this work is generalizable beyond performance assessment and can be adapted to evaluate other quality dimensions such as information completeness, modeling guideline compliance, or suitability for specific lifecycle phases.