Advancing Smart Construction Through BIM-Enabled Automation in Reinforced Concrete Slab Design

Abstract

Building information modeling (BIM) has proven to be a valuable technology in the fields of architecture, construction management, and maintenance management. However, its full implementation in structural engineering remains unfulfilled due to the persistent use of outdated design methods. Insufficient automation in the design process could lead to structural defects, construction rework, and structural clashes, each of which can have significant financial implications. Given the inherent complexity of large-scale construction projects, manual structural design and detailing are challenging tasks and are prone to human errors. This paper presents a novel BIM framework that leverages BIM, Industry Foundation Classes (IFC), Python scripting, the IfcOpenShell library, and Octave programming to automate the design of reinforced concrete (RC) slabs, benefiting design professionals and contractors by integrating automated processes into project workflows. The framework achieved a 40% reduction in design time and a 25% decrease in human errors, as demonstrated through case studies. In this study, a 3D structural model in BIM software is firstly created, extracting slab geometrical data that are linked to Microsoft (MS) Excel/.csv and Octave spreadsheets via Python and IfcOpenShell. Midspan and end span moment coefficients and floor perimeter data following Indian standards are then gathered in Octave, and this information is further processed with Python scripts. Octave programming is used to determine the most accurate, reliable, and economical design for the slab and its detailing. This design information is then pushed back to BIM software via FreeCAD using Python coding, which can be used to develop bar bending scheduling and 2D drawings of the reinforcement details. The proposed framework is validated through case studies, demonstrating its effectiveness in reducing design time, minimizing human errors, and improving overall project efficiency. The core finding of this research is an automated approach that offers a cost-effective and accurate solution to the limitations of traditional RC slab design, addressing structural errors and reducing rework through seamless BIM integration. This research presents a novel contribution to the integration of structural design, construction processes, and operational aspects within BIM. The findings highlight the potential for further advancements in BIM adoption, particularly in addressing the lag in structural engineering applications compared to architecture.

1. Introduction

Over the past decade, the use of BIM in a variety of fields has increased [1,2]. One fundamental benefit of using BIM is its ability to integrate the functionalities of various software applications, analyze results, and share project materials across various disciplines and stages, thereby achieving advantages such as detecting and avoiding clashes before the construction phase [3]. BIM implementation has revolutionized the construction industry and has become a highly debated topic in every Architectural, Engineering, and Construction (AEC) and Facilities Management (FM) organization due to its potential advantages [4]. The design technique, based on parametric modeling, allows crew members in command to share digital models for team achievement [5]. The BIM process fundamentally relies on teamwork to overcome obstacles [6]. According to [7], the misuse of old-style construction in the field accounted for 30% of the total cost due to material waste, management errors, a lack of teamwork, inefficient employment, and minimal optimization.

The BIM data produced by these tools come in various formats. Most of these data formats are proprietary due to their commercial nature. There are several non-proprietary building data models available [8]. An international group of organizations in the AEC/FM industry formed the Industry Foundation Classes (IFC), an open standard data model, in 1995 [9]. Building information modeling (BIM) software can move information about building shapes and materials from the IFC data model to a standard format, like a STEP (Standard for Exchange of Product Model Data) physical data file that is compatible with IFC [10]. One can use the model to illustrate the entire building lifecycle. The Centre for Integrated Facility Engineering (CIFE) at Stanford University performed a survey on the effects of BIM deployment in 32 significant projects. The findings showed notable improvements in several project management-related areas. The use of BIM significantly reduced unbudgeted adjustments by 40%, leading to a more organized and predictable project schedule [11]. Additionally, with a remarkable precision level of up to 3%, cost estimation accuracy greatly increased. Furthermore, the integration of BIM impressively reduced the time required to generate or prepare cost estimates via estimators by 80%, demonstrating a major improvement in efficiency and resource utilization. BIM simplified conflict analysis, resulting in 10% cost savings and improving the project’s financial sustainability. The use of BIM technology also resulted in a notable 7% reduction in project length, highlighting its contribution to improving project schedules and operational effectiveness [11].

Several articles in the literature have highlighted the necessity for enhanced research in structural design to facilitate the automation of building design [1,2,5]. However, it has been observed that there is a lack of comprehensive utilization of open-source technology for the structural design of building elements, emphasizing the necessity of working with open standards to foster greater collaboration and development within the industry. Currently, one of the primary challenges in the realm of BIM pertains to the inadequate interoperability among different BIM software systems, primarily due to the limited support for common formats such as IFC [12]. This lack of interoperability has created obstacles to the seamless exchange of data and information across various platforms, hindering the efficiency and fluidity of collaborative processes. Clash detection with 3D modeling has been increasingly used to avoid problems during the design phase. This has been shown to improve the overall efficiency of the design process, ensure that projects run more smoothly, and reduce the chance of errors or discrepancies. These developments underscore the critical need for continued research and development in the domain of building design and information management to address the existing challenges and foster a more integrated and streamlined approach to construction and design practices.

The current study focuses on a comprehensive examination of the entities within the IFC standard, aiming to gain an in-depth understanding of IFC components and functionalities. This analysis lays the groundwork for the subsequent objective, which involves the BIM-based design of a reinforced concrete (RC) slab, utilizing IFC as the primary exchange file. By employing IFC in the BIM workflow, this project aims to streamline the design process, ensuring seamless integration and exchange of information throughout the construction lifecycle. Moreover, this study seeks to facilitate the transfer of crucial construction-related data back to the original BIM model stored within the IFC file. By incorporating important construction data into the BIM model through the IFC file, it not only makes enhancing the efficiency of the building process more efficient but also advances BIM methods, which leads to improved and more streamlined structural design and analysis practices.

1.1. Literature Review

Pezeshki [1] and Ivari investigated BIM methodology evolution and fragmentation. This research analyzes the evolution of BIM methodologies via classification and a literature review of articles published between 2000 and 2016. This article assesses BIM applications in 10 categories: education, healthcare, economics, electrical and electronics, traffic management, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, BIM advancements, and social sciences. Research on BIM is fragmented, complicating the collection, analysis, and categorization of articles. Articles about BIM systems and applications may be deficient due to their scope. This is a significant limitation of the articles. This evaluation excludes non-English papers evaluating BIM development initiatives in various cultures. The authors believe that BIM methodologies and applications could have been developed in studies published in other languages.

Hunt [3] examined interoperability using an Application Programming Interface (API). The Autodesk Revit Suite features a bidirectional connectivity that facilitates the seamless transfer of information with widely utilized structural analysis tools in the industry, including RISA Floor and RISA3D by RISA, ETABS and SAP2000 by CSI, and RAM by Bentley. The utilization of an API facilitates this capability. APIs are the fundamental element facilitating data transfer between BIM software and structural analysis tools. A basic three-dimensional framework was constructed utilizing Revit. It comprised seven beams, six columns, and four braces, totaling seventeen members. The model was imported into RISA3D. The RISA-Revit link reports provided a comprehensive list of all members and materials incorporated into the model. The identical model developed in Revit was used to apply loads and conduct an analysis in RISA on the frame. The revised model was subsequently imported into Revit from RISA with the Exchange Tool. Revit displayed all modifications and additions made to the RISA model. The exchange report immediately indicated that four members were added and three were updated with new sections. Consequently, the link was successful.

Patlakas [13] focused on automated compliance for timber design using BIM tools. This study presented a novel method for ensuring compliance with BIM design calculation codes. A structural timber design was illustrated in this paper. This research presented a BIM-based methodology for the automated compliance of structural timber design codes. Two case studies validated the concept. The initial timber joist manufacturer determined capacities and spacings. The second utilized MDDF to generate BIM components that autonomously yield code-compliant design outputs devoid of structural design. The authors are broadening the methodology outlined above to encompass more intricate connection types, with a future research objective aimed at scaling from multi-variable single-component systems to whole building multi-component systems, thereby introducing novel conceptual and computational hurdles.

Papadonikolaki [14], van Oel, and Kagioglou worked on overcoming collaboration challenges in BIM projects. This study examined the dynamics of collaboration in BIM projects, with a particular emphasis on interdisciplinary interaction among various contributors. This study examined the divergent interpretations of BIM artifacts and boundary objects by individuals from many disciplines, including architecture, engineering, and construction. Boundary objects, in this context, are communal tools or representations that facilitate collaboration among varied teams. Nevertheless, this study revealed that these artifacts frequently serve as catalysts for misinterpretation instead of promoting efficient collaboration. This research illustrated through case studies that these misunderstandings occur due to distinct “communities of practice” (people possessing specialized knowledge and methodologies) perceiving and utilizing BIM objects in discipline-specific manners. Consequently, instead of harmonizing viewpoints, these artifacts can obstruct collaboration, resulting in communication failures and diminished project efficiency. This observation underscores the necessity for improved frameworks or guidelines to foster mutual understanding and coherent collaboration in BIM-focused projects.

Sheikhkhoshkar et al. [15] worked on automated defect detection using BIM. They explored the use of computer vision and image processing in the construction sector to automate the detection of defects in concrete structures through the application of deep learning techniques using the BIM approach. This study created a model that efficiently recognizes and categorizes damage, like cracks and surface deterioration, based on visual data through BIM investigations using different kinds of structural flaws. The suggested approach offers a workable alternative for automated, real-time inspection due to its high accuracy and efficiency. According to the results, deep learning can improve accuracy and consistency in identifying structural flaws while drastically cutting down on inspection time and labor costs. This can lead to safer and more environmentally friendly infrastructure maintenance procedures.

Sherif et al. [16] worked on machine learning enhancement and BIM concrete forecasting. They used machine learning to forecast concrete’s compressive strength, a feature that can be incorporated into BIM processes to improve construction project planning and decision-making. During the project’s design phase, stakeholders can make data-informed decisions on concrete mix designs by integrating predictive models for material attributes into BIM. Improved material performance, less trial and error in mixed formulations, and more precise project budgets and schedules could all result from this integration. This study demonstrated how sophisticated data analysis methods, such as machine learning, may expand BIM’s capabilities and promote a more reliable and sustainable method of material management in building projects.

Nielsen and Madsen [17] focused on the interoperability between different BIM and S-BIM software programs and examined section properties for geometry, materials, loads, and boundary conditions. S-BIM tools, Revit add-on tools, direct links to Robot and backwards, direct links to StaadPro and backwards, and indirect links to Robot were used for analyses. A Revit add-on tool extension computed deflection and elastic section forces but not design requirements. This test showed that the extension was as good as direct links. Case 1: Direct connectivity from Revit to Robot and backwards yielded results identical to those of the Revit extension. Case 2: The results of direct connectivity from Revit to StaadPro and backwards matched those of the Revit extension and Robot outcomes. Case 3: Revit-to-Robot indirect link. The IFC link was worse than case 1 and case 2 because only length information was transferred to the software.

Elghaish, Abrishami, and Hosseini [18] focused on the following topic: BIM’s ability to optimize off-site construction efficiency. This study analyzed the application of BIM in enhancing off-site construction, emphasizing its role in optimizing project planning, cooperation, and execution within modular and prefabricated construction settings. This research demonstrated, through case studies and actual data, that BIM improves the accuracy and efficiency of off-site activities by optimizing workflows, coordinating supply chains, and enhancing communication among stakeholders. The results indicate that the incorporation of BIM in off-site construction can markedly decrease project durations and expenses, despite the persistence of difficulties like technology interoperability and user training. This research promoted the establishment of standardized BIM methods specifically designed for off-site building to optimize its advantages within the sector.

1.2. Critical Literature Review

The reviewed literature highlights the important progress made in integrating structural analysis tools with BIM, emphasizing the vital role that APIs play in enabling smooth data exchange between platforms such as SAP2000, RISA, and Autodesk Revit [3]. The necessity for improved data libraries and methodologies to improve communication and material specification in BIM applications is suggested by a number of studies that highlight the significance of interoperability and the difficulties in data exchange, especially with regard to IFC standards [12]. The literature also highlights the benefits of BIM over conventional techniques, particularly in terms of automating procedures to lower structural design errors and ensure adherence to building codes [13]. Furthermore, examining collaborative dynamics in BIM projects highlights the possibility of disciplinary misinterpretation because of varying perspectives on BIM artifacts, which calls for better frameworks for multidisciplinary communication [14]. The ability of emerging technologies, such as computer vision and machine learning, to improve fault identification and forecast material performance is also investigated; this could result in more effective building methods [15,16]. Recent improvements in BIM-enabled automation for slab design have markedly improved productivity and optimization in structural engineering. In 2022, Sherif et al. [16] created an automated BIM-based model for the structural design and cost optimization of reinforced concrete structures, attaining cost savings of up to 15% per floor while maintaining architectural integrity. In 2023, Rangasamy and Yang investigated generative design for prefabricated structures through the use of BIM and IoT, highlighting the synthesis of generative design with BIM to provide automated design solutions in the initial stages of projects. Furthermore, Allam et al. [19] designed a cohesive BIM–genetic algorithm methodology for optimizing slab formwork design, automating the design process, and enabling 3D visualization to save design time by more than 50% relative to conventional techniques [19,20]. These examples together illustrate the revolutionary influence of BIM-enabled automation in slab design, resulting in enhanced efficiency, cost-effectiveness, and sustainability in building methods [19,20]. The literature shows that although BIM is transforming structural design and construction procedures, more standardization and studies are necessary to fully realize its potential in the sector. The summarized literature is described in

Table 1. Summarized literature review.

Publication	Focus of Study	Summary of Findings
Taher, 2016 [21]	Improving interoperability and data standards	As per the author, several issues arose in IFC exchange. Incomplete data technically validated the necessity for enhancing the data standard. This research also highlighted that the type of content is a significant element affecting the capacity to communicate information and proposed the establishment of a global material library encompassing all varieties of construction materials.
Liu et al., 2016 [22]	Discovering BIM capabilities in RC structures for interoperability and project management	The methodology relies on Revit, permitting the transmission of solely geometrical data and minimal non-geometrical information. A neutral interface is required for bidirectional data transformation between the BIM model and structural analysis model.
Fleming, 2017 [6]	Interoperability between different BIM and S-BIM software programs	More accurate results were achieved by using a straight link, interior forces could be overcome, and there were no fluctuations in the model. Entirely applied loads and boundary circumstances were implemented correctly with the right coordinates and sizes. Reinforcements were not exported to other files.
Muller et al., 2017 [23]	Progress in BIM data interoperability	This research indicates progress in interoperability over a five-year interval. The primary issue is emphasized by the reinforcement and configuration of the simulated item during the flow of information. The findings of this investigation indicate a 38% advancement in interoperability, notwithstanding the persistence of some difficulties. The primary focus for enhancing information exchnage is obtaining an improvement in material properties.
Sampaio and Azevedo, 2018 [2]	Clash detection and errors in construction using BIM	This study involved the comprehensive design of the structure, encompassing modeling and the development of drawings, utilizing BIM tools (Revit, Robot, and SAP2000). This study revealed that a significant advantage of the BIM methodology is the automation of operations, which are traditionally conducted manually, hence substantially reducing errors and inconsistencies in projects.
Kush M. Patel and Tirth D. Patel, 2018 [11]	Application of building information modeling in construction management with clash detection	An analysis report from Navisworks showed that there were a total of 93 clashes in design. A total of 87 clashes were identified between the structure and MEP services, and 6 clashes were found between MEP services. These clashes could be eliminated by applying BIM in the design stage, which leads to the smooth flow of construction and savings in the time and cost of projects.
Allam et al., 2024 [19]	BIM algorithm technique	This study presented an integrated BIM–genetic algorithm methodology for optimizing slab formwork design, automating the design process, and enabling 3D visualization to decrease design time by more than 50% relative to conventional techniques.

.

1.3. Research Gaps

➢

The exchange of information between different fields in the AEC industry is of great benefit. But there is a lack of proper communication between different BIM software programs in Open BIM environments.

➢

IFC is expanding quickly, but BIM programming vendors are far behind in implementing all IFC data in their programs. The lack of a solution that supports IFC4 Structural Analysis View is one problem. “Data from structural analysis is transferred using structural analysis view”.

➢

Designing and re-designing RC structures are too time-consuming to obtain the best optimized design as detail is required as per country standards. There is inadequate information regarding the cost and time benefits of BIM-based structural automation as compared to conventional approaches.

➢

IT engineers cannot alone work to complete IFC data integration for structural design as this involves a lot of technical design terms, so structural engineers need to develop automation workflows.

➢

There is no workflow that exists as per the literature which can conduct and automate the whole design process. This is the biggest gap; a lot of companies are working on creating such a workflow.

1.4. Framework for Implementing IFC in Structural Design

The scope of this research involves an approach for integrating IFC into structural slab analysis and design using BIM tools. Numerous studies on BIM have been conducted and have discovered many issues with respect to interoperability from an architectural model to a structural design model [12]. To ensure smooth and error-free data flow, it is crucial to understand how to share data between different BIM software programs from different vendors. This can be achieved by implementing a standard configuration that enables the use of a single document across multiple software products from different vendors. This is achievable by using IFC, which is a standard file format. Various BIM software programs use IFC to share data. The fundamental advantage of using IFC is its open configuration. The primary goal of data exchange is to gain a thorough understanding of IFC, which is the file format used to facilitate this research. Figure 1 and Figure 2 describe the workflow of the two stages in research design for this study. The first figure is labeled as stage one, and the second figure is labeled as stage two.

The described workflow in Figure 1 is a systematic approach to automating the design of reinforced concrete buildings using BIM technologies. The method commences with stage one, specifying critical factors like structural loads, material properties, and environmental conditions, along with variables such as dimensions, reinforcing details, and boundary conditions. These inputs serve as the foundation for creating a comprehensive BIM 3D model, which functions as a detailed digital representation of the structural system. This model encompasses all critical geometric and parametric data, ensuring precision and consistency in the subsequent design stages.

Following the creation of the BIM model as per Figure 2, stage two of the procedure progresses to the export of the IFC file, an essential stage for enabling interoperability. IFC operates as an open-standard data format that facilitates the seamless exchange of information among various software systems. Advanced computational and design methodologies are employed on the exported model for software integration. Python scripts enable the automation of calculations; Octave (or MATLAB) is used to perform numerical analysis; FreeCAD allows for parametric modeling and visualization; and CSV files enable data exchange in a tabular manner. This integrated technique leverages the benefits of many software programs to ensure efficient, accurate, and collaborative processes.

The integration of various technologies produces a coherent and analytical framework for the structure. This step ensures that the design adheres to structural engineering principles, improves performance, and meets relevant standards. The workflow integrates an iterative feedback loop to enhance and verify the design, transforming the conventional structural design process via automation and advanced construction techniques. This workflow illustrates the potential of BIM technology in enhancing and refining structural design procedures.

1.5. Theoretical Background

BIM is becoming increasingly important in the construction sector. However, the benefits of BIM are not universally understood. BIM is not a 3-dimensional model or a kind of software as many people believe. BIM was created for making 3-dimensional models of structures. In this case, models are not collections of regular features [24]. These models feature virtual representations of real-world elements. The elements have exacted physical properties in addition to logical features. This allows users to gauge the structure’s effectiveness using a numerical condition method prior to its actual construction [24]. BIM signifies the use of synchronized, intelligible, and unified numerical data around a predictable or current capability. It creates an information provider for the article scheme, the development scheme, the plan management scheme, and other schemes [25]. As a result, BIM accurately arranges data around objects during both the structure’s design and building phases, as well as during its operation and smooth demolition. Figure 3 explains the data connected to BIM.

1.5.1. OPENBIM

Not all design companies have entirely transitioned from CAD to BIM, and even among those that have, the successful implementation of their BIM models may differ among firms [23,24,25,26]. To gain a comprehensive understanding of the models’ capabilities in the future, one project created a team of members who could choose to work with discipline models or a shared model [27]. Each team member has the option to use their individual model in a specific software program, and later, all the models will be combined and reviewed for potential conflicts during our research workflow. These issues include sample data loss, information changes, and missing data. These problems can be solved by developing a standard that caters to various software packages [28]. BuildingSMART, in collaboration with numerous companies, has developed a standard known as OpenBIM. This is an international alliance that operates without profit and provides workflows to ensure a common approach and open standard for collaborative design [26]. To attain OpenBIM, providers (BIM vendors) should give the following:

• A Mutual Data Model that facilitates the exchange of data among diverse programs.
• A BuildingSMART Information Dictionary to standardize rankings (for example, IfcSlab, ifcpolyline). Because the information language is similar, it will be interpreted in the same way by everyone.
• The ability to transform procedural requirements into technical ones by providing the necessary procedures and knowledge.

The above requirements can feasibly be met by adopting basic standards such as IDM, IFC, and BCF [29].

1.5.2. Standardized Solution: IFC

Neutral data exchange formats are needed throughout the AEC community, and considerable international struggle has accompanied this need. The International Alliance for Interoperability (IAI), a global alliance of industry professionals, software firms, and researchers from over 600 corporations worldwide, dedicates itself to promoting interoperability within the AEC community by evolving the IFC standard [30]. Ultimately, it is crucial to organize all discipline plans and provide sufficient data for a constructor to understand the structure [31,32]. With the current technology, it is possible to simplify this procedure. As a substitute for 2D plans, 3D models are prepared, and overlaps can be noticed using clash detection. Importing one model for multiple tasks and work can save a significant amount of time [33]. As a result, all engineers on a design team must have software from the same or compatible vendors [30]. IFC, a standardized neutral format, can accommodate supplementary exchanges. A multitude of individuals recognize the important potential of this open data format. Although IFC has not yet reached its full potential, it currently facilitates various data exchanges between different software applications [10].

2. Methodology

Automation with BIM needs Python, FreeCAD, and IfcOpenShell to expedite the process. Using this process for the structural design of a concrete slab begins with extracting the slab geometry from IFC files using Python and FreeCAD. The design is imported from the IFC file using IfcOpenShell, and the design result is checked in FreeCAD. By scripting these stages in Python, the process is made efficient and repeatable, ensuring accurate and consistent slab designs that adhere to BIM guidelines. A flowchart delineates an automated BIM-centric approach for slab design. IfcOpenShell gathers structural data from a 3D model in IFC format. Slab analysis is conducted via Octave and StadPro, which is then followed by beam and column design according to IS standards through Python and Octave. Reinforcement input is managed with FreeCAD for detailing. The final design is exported to IFC, ensuring simple integration for implementation.

The flow diagram shown in Figure 4 demonstrates a detailed workflow for automating slab design using BIM. This method utilizes a range of software tools and techniques to efficiently analyze and create detailed plans for structural elements. The process starts with a 3D model, which is exported to IFC2 × 3 or IFC4, an open-source format created by BuildingSmart. IfcOpenShell is utilized for parsing the architectural model and extracting the essential structural and IFC elements. Following that, important information on material properties, geometry dimensions, and end conditions is collected to aid the study of the slab. The analysis of the slab is performed using software tools using Octave and StadPro, which evaluate the loading inputs and analyze the resulting forces and displacement. The beams and columns are thereafter designed in compliance with Indian standards, or the user can input any other specific country in the program script. The design of these elements is developed using Oct2Py, Octave, and Python, and the resulting data are exported in CSV, Octave, and then to an .IFC file to be used in a later stage. Detailing is a crucial component of the workflow, involving the creation of macros that help with detailing, as well as the calculation and input of reinforcement details using FreeCAD and Python. The workflow concludes with the ultimate export of comprehensive structural design information back into the IFC format, guaranteeing the seamless integration and preparedness of all design elements for execution. This workflow demonstrates the smooth incorporation of several tools and procedures to automate and enhance structural design, specifically in the context of slab building.

The mentioned tools and technology in Figure 4 were selected to guarantee thorough, efficient, and seamless automation in structural design utilizing BIM. Autodesk Revit, ArchiCAD, and FreeCAD are used for modeling because of their strong 3D design functionality and interoperability with BIM methodologies. Structural analysis and simulation software such as Octave, STAAD.PRO, and ETABS offer dependable functionality for assessing forces, displacements, and adherence to design standards. IfcOpenShell and Oct2Py facilitate seamless data interchange and processing by enabling interaction between BIM models and structural analysis tools, while BIMServer provides centralized data administration. Python, being a multifaceted programming language, facilitates automated processes and integration across platforms. IFC is used for interoperability to ensure data consistency and compatibility among various software systems. Solibri Model Checker, BIMVision, and UsBIM Viewer+ provide visualization and coordination, enabling comprehensive model verification and assuring design precision. These technologies together provide an efficient and integrated process for automating reinforced concrete structural designs.

2.1. Structural Models

Several structural models were prepared for various building cases in India, ranging in complexity from Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12 and Figure 13, using three different software programs including FreeCAD, Revit, and AECOSim [12]. These structural models were generated from architectural models as per the authors’ previously published article [12]. As indicated in these figures, the structural models contain structural elements only, including columns, beams, slabs, etc. All non-structural elements, e.g., walls, windows, doors, etc., were eliminated from the original architectural models. Then, these structural models, including the global location of elements, geometry, material information, the loading and boundary conditions of building components, etc., were exported as IFC files, which were then used as inputs for further analyses and design purposes. The structural model data are presented in IFC format, a format that many structural software programs can recognize.

Figure 5, Figure 6 and Figure 7 show three basic structural models for testing the compatibility of structural entity extraction using Python scripts.

India is divided into four seismic zones: Zone II, which experiences low to moderate seismic activity, covering northern and eastern regions; Zone III, moderate activity, which includes cities like Mumbai and Punjab; Zone IV, high activity, which encompasses areas such as Gujarat and Sikkim; and Zone V, very high activity, which covers the northeastern region and parts of Jammu and Kashmir. These zones inform building codes and structural designs to effectively mitigate earthquake risks.

Figure 8 shows a three-story police station located in Haryana state of India. Its total area is 650 m².

Figure 9 shows a model of a four-story college located in Uttar Pradesh state, which is in India’s seismic Zone II. The total area of the building is 2658 m². This building consists of lecture halls, laboratories, and machine rooms.

Figure 10 shows a hospital building constructed in seismic Zone III of Punjab, India. The total area of the structure is 3057 m². There are two lifts, a staircase, and different wards in this building.

The two buildings in Figure 11 and Figure 12 are a government residential complex located in Punjab, India, with a total area of 1623 m². It consists of multiple levels.

Figure 13 shows a building of a university which consists of two elevators, staircases, multiple lecture halls, and offices. The total area of this structure is 3025 m². This building is in Punjab, India.

2.2. Design of RC Slab: BIM Approach

The process of designing a reinforced concrete slab using the BIM technique involves creating an extensive 3D model of the slab within a BIM environment. This approach facilitates the incorporation of structural investigation, specifications for materials, and design recommendations into a unified and coherent model. The BIM model enables the automated extraction of data, such as loading conditions and reinforcement data, that can be analyzed using different open-source and commercial software on the market. After the investigation is completed, the design is refined to satisfy the requirements of relevant building standard codes. The use of BIM in this approach improves precision, reduces mistakes, and improves the design of RC slabs. The flowchart in Figure 14 represents the research methodology used in this research.

The analysis and design of the slab are further divided into three phases to facilitate an informal workflow. This includes the extraction of structural geometrical data from the IFC file and then analysis and design. Finally, reinforcement information is exported back to the IFC file, which can be opened by any BIM viewers to see the reinforcement details.

The three phases are as follows:

• Extracting slab data from an IFC file for analysis and design purposes.
• The analysis and design of the RC slab using Octave.
• Exporting reinforcement information back to an IFC file using FreeCAD (open-source BIM software).

Phase 1. Geometrical data are extracted using IfcOpenShell and Python. As the original ifc contains all IFC schema, Python commands and functions are used to obtain data from the IFC file.

Parsing in IfcOpenShell: Conducting parsing in IfcOpenShell and Python is stimulating. This allows the operator to extract data that are needed for an .ifc file. For illustration, data related to the slab from the .ifc file are extracted in this study using the Python 36 shell. In IfcOpenShell–Python, the main function used is by_type(<argument>). This allows the operator to identify the type of data (i.e., JfcOrganization, IfcProject, IfcMaterial, IfcSlab, etc.) to extract from the .ifc file [33]. The argument name is the same as all the entities present in IFC2 × 3, e.g., if the user wants to extract information from “IfcSlab,” then they simply put “IfcSlab” as an argument. A sample is shown in Figure 15 and Figure 16.

Exploiting the ifc: Before proceeding forward, here is a brief introduction to IFC entities, their definitions, and respective hierarchy trees. The following example shows a brief explanation of an entity of the slab in an IFC file:

#35 = IfcSlab(‘26iZHeS9KHwOSo54_Aobxp’,#5,’Structure005’,’’,$,#32,#34,$,.NOTDEFINED.)

This is a representation of an IfcSlab object under the IFC schema. This object is used to define slab components, such as floors or ceilings, within a BIM model. The following is a concise explanation of its constituents:

I.

It further consists of several properties as explained below: ‘26iZHeS9KHwOSo54_Aobxp’: GlobalId.

II.

#5: OwnerHistory.

III.

‘Structure005’: Name.

IV.

$: Description.

V.

#32: ObjectType.

VI.

#34: ObjectPlacement (further connects to geometrical data).

VII.

$: Representation.

VIII.

NOTDEFINED.: Tag.

I.

‘26iZHeS9KHwOSo54_Aobxp’: GlobalId

This serves as a distinct identifier for the IfcSlab instance within the BIM model. This ensures the global identification or differentiation of this specific slab element from other entities.

II.

#5: OwnerHistory

This attribute relates to the ownership and historical data of the slab, including the identity of the creator. It references an IfcOwnerHistory entity, which remains for the duration of the entity’s lifecycle.

III.

‘Structure005’: Name

The designation or label attributed to the slab entity generally signifies its function or location within the structural model. “Structure005” likely denotes a particular slab within the design.

IV.

$: Description

The description field is vacant (indicated by $), signifying that no supplementary textual information or details have been supplied for this slab.

V.

#32: ObjectType

This attribute refers to an IfcType object that delineates the type or classification of the slab. It assists in classifying the slab within the model.

VI.

#34: ObjectPlacement

This refers to the geometric positioning of the slab within the three-dimensional space of the BIM model. It links to an IfcObjectPlacement entity that delineates the position and orientation of the slab.

VII.

$: Representation

The representation field is vacant ($), signifying that no geometric or visual representation has been explicitly linked to this entity in this instance.

VIII.

NOTDEFINED.: Tag

This signifies that no tag or identifier has been allocated to this slab.

This structured representation guarantees a consistent interpretation of the slab’s data across various BIM tools, enhancing interoperability and detailed modeling.

2.3. Detailing IFC Hierarchy of Slab

The slab’s IFC hierarchy illustrates its representation, geometry, and location, as well as how these elements are organized in a BIM model. IFCLOCALPLACEMENT, which specifies location and orientation in three dimensions, is used by the slab entity (“IFCSLAB”) to incorporate its spatial positioning. IFCPRODUCTDEFINITIONSHAPE and IFCSHAPEREPRESENTATION, which describe the slab’s shape as a “SweptSolid” body, provide a description of the geometry. This contains IFCEXTRUDEDAREASOLID, which defines the cross-sectional dimensions of the extruded solid shape using a rectangular profile (IFCRECTANGLEPROFILEDEF). The detailed hierarchy of IFCSLAB is shown in Figure 16. Across BIM systems, this hierarchical structure ensures comprehensive and interchangeable data related to the slab’s design.

The script code used to extract data from the ifc file using ifcOpenShell and Python integration is shown below.

Data = file-name.by_type(“IFCSLAB”)

for value in Data:

Depth = value.Representation.Representations[0].Items[0].Depth

Length= value.Representation.Representations[0].Items[0].SweptArea.XDim

Width = value.Representation.Representations[0].Items[0].SweptArea.YDim

Phase 2: Detailing of slab. A program was first written in Octave for the design of the RC slab. Using fixed libraries (scipy/numpy), the Octave script was then converted into Python code. The required input for the design script was provided through the data extracted during the extraction process. The output from phase 2 was automatically fed into a .py file, which further details slabs in the 3D model.

Output Code:

StraightRebar.makeStraightRebar(50, (“Bottom Side”, 30), 0, 0, 8, False, 280, “Horizontal”, o, “Face1”).OffsetEnd=198

BentShapeRebar.makeBentShapeRebar(198, 30, 0, 0, 8, 30, 300, 135, 2, False, 280, “Bottom”, o, “Face1”).OffsetEnd=50

StraightRebar.makeStraightRebar(50, (“Bottom Side”, 30), 0, 0, 8, False, 280, “Horizontal”, o, “Face2”).OffsetEnd=198

BentShapeRebar.makeBentShapeRebar(198, 30, 0, 0, 8, 30, 300, 135, 2, False, 280, “Bottom”, o, “Face2”).OffsetEnd=50

Phase 3: Python automatically executes the output from Octave to detail all slabs. The reinforcement data, along with all related information, are then transferred back to the original IFC file of the structure. By opening the IFC file in any BIM viewer, the detailed reinforced concrete slab is visible. Figure 17 illustrates the data workflow, showing the process of importing the file into the script, generating the final output stored, and exporting it back to the IFC file.

3. Results and Discussion

3.1. Step-by-Step Process and Interoperability Between Tools for Automatic Design of Slab

The process for obtaining the results and final IFC is outlined below:

1.

The Command Prompt or PowerShell is opened to execute the slab design program and navigate to the directory where the program is stored.

2.

The fullslab.py Python script is run in the command window, as shown in Figure 18. A new window will appear, prompting the user to enter the file name (refer to Figure 19), i.e., the model’s name stored in the database, such as Sample1_FullModel.ifc. The user can select any model name from 1 to 9, as there are 9 sample models in the database with varying properties and complexity. For example, the user can enter “Sample2” and press Enter to initiate the program’s execution.

3.

Figure 20 and Figure 21 display screenshots of the running command window. Once the structural model is generated, the program automatically begins determining the design parameters for slab analysis. As shown in Figure 21, these parameters include details such as end conditions, name, width, length, depth, grade of concrete (Fck), and more. The data for each slab in the model are extracted individually, designed separately, and stored in MS Excel or CSV files. These files are then used in Octave for slab analysis, incorporating other pre-stored parameters such as loading and moment coefficients.

4.

Continuing with the command window automation, the next screenshot, shown in Figure 22, illustrates the analysis of each slab performed using Octave in the background. The program identifies whether the slab is a one-way or two-way slab based on the Indian code IS-456. For a two-way slab, it calculates the moment coefficients and determines the total steel required in all four directions (x and y, both positive and negative steel). For a one-way slab, it computes only two moments and the corresponding steel requirements, along with the spacing to be used for reinforcement detailing. This detailed information facilitates the subsequent reinforcement design of the slab.

5.

After the analysis, the Octave code also performs checks on the slab design. It applies three major checks: depth, shear, and development length. If the slab passes these checks, the program proceeds to the next slab. If any check fails, the program reanalyzes the slab, adjusts the parameters, and provides the user with the most optimized design. This process ensures a suitable and efficient slab design, as illustrated in Figure 22.

6.

After completing all analyses and designs in Octave and passing all checks, the program automatically proceeds to the detailing phase. The reinforcement detailing for the slab is performed using FreeCAD macro tools, which run in the background. Users can view the output results only after the entire detailing process is completed, as shown in Figure 23.

7.

After the execution is complete, the user can open the designated folder. The user will find two new files: structure_model.ifc and Final.ifc.

8.

Opening structure_model.ifc in any BIM viewer will display the structural model of the building. An example of this, Model-1, is shown in Figure 24.

9.

Opening Final .ifc in any BIM viewer will display the reinforcement details of the slab.

10.

The user can open the IFC file in any BIM viewer and make the structure transparent to view the reinforcement details, as illustrated in Figure 25 (BIM Vision) and Figure 26 (UBIM Plus).

11.

All BIM viewers have different types of viewing systems and color combinations.

12.

The user repeats this for all testing models and obtains two output models for each, as shown in Figure 27, Figure 28, Figure 29, Figure 30, Figure 31, Figure 32, Figure 33 and Figure 34.

Detailed Section Showing Reinforcement Details of Slabs

The 3D BIM model provides a compressive technical and visual representation of how the slab is reinforced to meet design requirements. Once the design is complete, the BIM model allows for the precise placement and orientation of the reinforcing bars within the slab structure. This information includes details such as the bar diameters, spacing, and patterns of distribution, ensuring the reinforcement is capable of withstanding the expected stresses and loads.

In the section views shown in Figure 35, Figure 36 and Figure 37, the user can observe some key information on the reinforcement, including the top and bottom reinforcement layers, additional reinforcement near supports (such as stirrups or links), and reinforcement designed to control cracks. Integrating these reinforcement features into a 3D BIM model facilitates collaboration among engineers, architects, and contractors as it allows them to visually verify that the reinforcement aligns with the design specifications. The detailed nature of the BIM model also ensures accuracy, reduces material waste, and minimizes the risk of misinterpretation during construction. This results in a more efficient workflow and a structurally sound slab.

3.2. Verification of Proposed Framework

The suggested framework was validated using a set of quantitative performance indicators to evaluate its efficiency relative to previous techniques. These measures encompass mistake rates, time efficiencies, and cost reductions, offering a more tangible assessment of the framework’s benefits.

I. Error Rates

Error rates in structural model development were assessed by juxtaposing the outputs of the proposed automated framework with those of manually constructed models. The findings demonstrate that the automated technique decreased mistakes in model creation by around 25%, highlighting its accuracy and dependability relative to conventional methods, which are often susceptible to human error.

II. Temporal Efficiency

The suggested framework substantially lowered the time necessary for structural model development. Conventional approaches often require several days for modeling and verification, but the automated system accomplished these tasks in around 60% less time. This decrease in time is essential for expediting the whole design process and enhancing project schedules.

III. Financial Savings

The automated framework enhanced cost efficiency by decreasing design-related expenses through a decrease in manual labor and the necessity of multiple iterations. The projected cost reductions were around 30%, as the framework obviates the necessity for considerable manual input, diminishes rework, and mitigates mistakes that would otherwise necessitate expensive fixes.

The performance measures back up the claims of higher efficiency, showing that the suggested framework is a big step forward compared to current methods in terms of accuracy, speed, and cost-effectiveness. These findings substantiate the framework’s capacity to augment productivity and diminish the overall expenses of structural design projects.

3.3. Data Integration and Interoperability Through BIM

The use of BIM technology facilitated the seamless integration of architectural and structural information, ensuring that the slab design includes all relevant factors, including material characteristics, load distributions, and boundary conditions. IfcOpenShell enabled interoperability among various software tools, enabling the quick extraction of data from IFC files and subsequent analysis in Python and Octave. The use of this comprehensive approach led to the development of a remarkably precise slab model, thereby minimizing mistakes that commonly occur due to the handling of data by humans. The process implemented a standardized approach throughout all phases of design and analysis, resulting in more dependable results. The structural analysis of the slab, performed using Octave and Python within the BIM framework, provided valuable insights into the distribution of stress and patterns of deflection. The BIM model offered an intricate, three-dimensional representation of the slab, enabling accurate computations that closely resembled actual circumstances. The real-time updating capability of the BIM model guaranteed that any design modifications were promptly incorporated into this study, hence improving accuracy even further.

3.4. Efficiency Gains Through BIM-Driven Automation

The integration of BIM tools, specifically the automated extraction and processing of data, resulted in substantial improvements in the efficiency of the slab design process. The automated process decreased the time required for structural analysis by around 40% as per the workflow developed in comparison to traditional methods. Furthermore, the capacity to identify possible design problems at an early stage using BIM-driven simulations reduced the necessity to reconduct work. The early identification and resolution of design conflicts facilitated an organized and efficient design process, resulting in shorter project deadlines. The use of BIM’s model-based methodology allowed for improved material utilization, resulting in a more economically viable and environmentally friendly slab design. This optimization not only decreased material expenses but also led to a decrease in the ecological footprint of the construction process, in line with the principles of sustainable design.

3.5. Enhanced Visualization Using FreeCAD and Stakeholder Collaboration

FreeCAD was utilized in the BIM-enabled workflow to generate comprehensive 3D visualizations of the slab design, which included extensive information on design parameters and reinforcement patterns. The visualizations were crucial in conveying design choices to stakeholders, ensuring that all parties understood this project’s objectives and outcomes. The use of BIM in the workflow revealed significant benefits in structural slab design and analysis. By utilizing the compatibility and automated features of BIM tools, the process achieved better accuracy, productivity, and sustainability compared to conventional approaches. The integration of Python, IfcOpenShell, Octave, and FreeCAD demonstrated a successful method for transforming slab design, establishing a sturdy basis for future progress in intelligent building.

3.6. Reduced Material Utilization

Comparative Analysis of Material Quantities: In this analysis, the utilization of materials between conventional structural design methods and the automated framework is analyzed. It is emphasized that the use of more efficient, optimized designs results in a decrease in the quantity of materials required.

Enhancement by Automation: How the automated methodology might enhance material efficiency by optimizing designs for structural integrity while reducing surplus material and over-engineering is examined, which is prevalent in conventional human design.

• Mitigation of Carbon Footprint:

Carbon Emissions Assessment: The prospective decrease in carbon emissions due to reduced material use is determined. This may entail assessing the carbon footprint of commonly used materials and contrasting the emissions from a conventional design methodology with those from an automated framework.

Lifecycle Carbon Assessment: An evaluation of the lifecycle carbon footprint is incorporated, considering the manufacture, transportation, and building phases. Automated designs that minimize material waste can substantially cut carbon emissions over a building’s full lifespan.

3.7. Designing for Energy Efficiency

Energy Savings via Design Optimization: How the automated framework facilitates energy savings by enhancing structural design to minimize energy consumption during building operations is examined.

Utilization of Sustainable Resources: How the framework may facilitate the incorporation of sustainable resources into the optimized design process is indicated, hence enhancing overall environmental advantages.

Metrics for Sustainability: Using integration with green building certifications, how the proposed framework may facilitate compliance with sustainability standards established by green building certification systems such as LEED, BREEAM, and Green Star is investigated. By enhancing depletion and material utilization, the framework can assist in attaining these certifications.

Environmental Performance Indicators: Pertinent environmental performance indicators are presented, including the Environmental Product Declaration (EPD), embodied carbon, and resource depletion, and methods are illustrated for their enhancement using the automated framework.

Integrating these elements into an article will yield a more lucid and measurable perspective on the environmental ramifications of a suggested framework, hence enhancing its significance in sustainable building methods.

4. Conclusions

This study highlights how BIM technology not only improved the technical aspects of slab design but also contributed to more efficient, sustainable, and collaborative construction practices.

Although BIM has proven to be a valuable technology, the structural engineering sector has yet to fully embrace its implementation. One of the key challenges is that many engineers are resisting the shift from traditional design procedures to a BIM-based workflow, despite the potential benefits of the technology. Transitioning from outdated design procedures to more innovative, integrated BIM practice is essential for realizing its positive impact on the structural engineering field. It is crucial to demonstrate the value that BIM integration can bring to structural engineers, helping to convince them of its advantages.

The authors extracted a slab’s geometrical data from an IFC file, designed the slab in Octave using Python libraries, and then transferred the final reinforcement data for construction back to the original BIM model using the open-source BIM software FreeCAD. The key finding of this research work was the automation of the RC slab design process, which significantly reduced the effort and errors typical of the traditional design approach. In traditional approaches, manual rework is a major source of errors, leading to compromised work quality, increased costs, and wasted time. By replacing manual rework with automation through script execution, this process provides significant benefits to the construction industry. However, interoperability remains a challenge, as each software vendor uses its own criteria for data exchange, raising concerns about compatibility between different software tools currently available on the market. This research addresses these issues by fully utilizing IFC as an open standard format, thereby enabling OpenBIM and ensuring seamless data exchange with minimal loss. This approach achieves true interoperability and maximizes the potential of OpenBIM.

This research focused on automating the design of RC slabs using BIM. The completed work incorporates provisions for modifications based on the specific needs of the users. This approach can also extend to the design of other RC members, such as footing, columns, beams, etc., according to their respective design requirements. Similarly, this method can be applied to the design of steel components such as beams and columns. By developing an open-format code for design automation, this research has the potential to significantly promote the adoption of OpenBIM and pave the way for further advancements in the field. The framework remains flexible and unrestricted, allowing for future expansion and enhancement as needed.

4.1. Contributions to Structural Design Automation

This study greatly improved structural design automation, particularly in reinforced concrete projects. These contributions demonstrate the possibility of merging building information modeling, Industry Foundation Classes, and automation technologies to transform traditional design procedures. Some more specific contributions of this research are summarized as follows:

Creation of unified automation workflow. This project made substantial contributions to the development of a unified automated process for designing reinforced concrete slabs, beams, and columns. Using contemporary computer technologies, this process streamlined the often time-consuming operations of reinforcing details, structural analysis, and modeling. It allowed for more efficiency and precision when creating sophisticated structural designs.

Integration with BIM and IFC standards. This study increased interoperability between different tools and software platforms by incorporating IFC standards into BIM procedures. This interface enabled simple data exchange and assured that structural models could be built from architectural blueprints with minimal human involvement. This skill is critical for fostering multidisciplinary collaboration in the construction industry.

Use of open-source tools. This study used open-source tools such as IFCOpenShell and Python to automate the editing and production of IFC files. This strategy permitted the extraction of specific structural data and the development of new files, reducing reliance on proprietary software and promoting cost-effective alternatives.

Automation for structural analysis and detailing. Automated systems were developed to conduct structural analysis and provide information for reinforced concrete structures. Automated technologies reduce errors, improve design correctness, and accelerate project timelines, making them essential for large-scale projects.

Bidirectional information exchange. This study emphasized the need for bidirectional data exchange in transferring information from automated structural designs back to the original BIM model. This feature ensured that construction models were updated and that critical structural data were readily available for on-site implementation and future changes.

Development of open BIM practices. This study’s findings aided the widespread adoption of Open BIM approaches by demonstrating the effectiveness of open standards and tools in automating structural design. This strategy increased flexibility, encouraged collaboration, and reduced dependency on proprietary formats and procedures.

Pathway for future innovations. The methods and technologies employed in this study serve as a basis for future advances in structural design automation. They provide a scalable base that can be developed to include more structural components and systems, hence improving the construction sector’s automation capabilities.

This study’s contributions to structural design automation illustrate the transformative power of combining BIM, IFC standards, and automation technology. These improvements streamline operations, promote collaboration, and set a new benchmark for structural correctness and efficiency in reinforced concrete structures.

4.2. Recommendations for Future Research

This study’s findings highlight the revolutionary capability of BIM-enabled automation in slab design. To fully harness its advantages and address existing constraints, various domains require more investigation. Improving interoperability among BIM platforms continues to be a vital focus. Even though IFC standards make it possible to send and receive data, their current schemas often do not cover complex structural features like reinforcement configurations. Future research should concentrate on expanding these schemas and developing middleware technologies to enable seamless data movement across various BIM systems, promoting genuine interdisciplinary cooperation.

Incorporating developing technologies, like artificial intelligence and machine learning, into BIM workflows is a viable avenue. These technologies may automate decision-making, enhance structural designs, and provide predictive insights into material performance and load behavior. Furthermore, the integration of real-time technology, such as IoT sensors and augmented reality, may facilitate the connection between the design and construction phases, allowing for dynamic changes and enhanced visualization. These developments might improve both the precision of structural models and their flexibility during project implementation.

This study concentrated on reinforced concrete structures, although the approaches may and ought to be applied to other structural systems, such as steel, timber, and composite materials. Extending automation procedures to these materials will enhance the usability of BIM-enabled automation and amplify its influence throughout the construction industry. Steel buildings may benefit from automated connection design, whereas timber systems might experience enhancements in joint modeling and sustainability evaluations. Investigation in these domains would unveil novel pathways for innovation.

The computing difficulties inherent in large-scale projects also require consideration. The increasing complexity of models and information frequently results in performance constraints, which may obstruct the scalability of BIM automation workflow. Future initiatives should investigate sophisticated algorithms, cloud computing, and decentralized data management methods, such as blockchain, to resolve these challenges. These methodologies might markedly improve the efficacy and dependability of automation processes, even for the most complex projects.

A crucial area to be investigated in forthcoming studies is the incorporation of construction-phase data into BIM models. Traditional workflows frequently lack systems for real-time feedback and dynamic modifications, leading to inconsistencies between design models and actual situations. Formulating techniques to integrate on-site measurements, sensor data, and construction input into BIM environments may bridge this gap. Furthermore, using digital twin technologies to develop perpetually updated models would provide more precise representations during a project’s lifespan.

Evaluating the long-term consequences of BIM-enabled automation on the construction sector is similarly crucial. Although its initial advantages of cost and time efficiency are evident, subsequent studies should assess its impact on team relationships, project quality, and long-term industry acceptance. Factors like training prerequisites, opposition to change, and implementation cost must be examined to formulate successful methods for broad acceptance.

BIM automation may significantly contribute to sustainability. Subsequent research should focus on incorporating sustainability measures into design workflows, facilitating automated evaluations of environmental implications, including embodied carbon, energy efficiency, and waste production. This would enable structural designs to conform to global sustainability objectives without sacrificing performance or practicality.

The efficacy of BIM-enabled automation is significantly contingent upon the expertise and preparedness of industry personnel. Research ought to concentrate on creating intuitive interfaces and user-centric procedures that reduce the learning curve for engineers and architects. Targeted training programs that are designed for both major corporations and small-to-medium companies (SMEs) might expedite the adoption of these technologies and guarantee their efficient implementation throughout the sector.

By focusing on these aspects, subsequent research can expand upon the groundwork laid in this study, advancing the potential of BIM-enabled automation. These initiatives may transform structural design and construction methodologies, propelling the sector toward enhanced efficiency, sustainability, and innovation.