Content area

Abstract

Translate

The integration of Building Information Modeling (BIM) and Digital Twin (DT) technologies offers new opportunities for enhancing reinforcement design and on-site constructability. This study addresses a current gap in DT applications by introducing an intelligent framework that simultaneously automates rebar layout generation and reduces rebar cutting waste (RCW), two challenges often overlooked during the construction execution phase. The system employs heuristic algorithms to generate constructability-aware rebar configurations and leverages Industry Foundation Classes (IFC) schema-based data models for interoperability. The framework is implemented using Autodesk Revit and Dynamo for rebar modeling and layout generation, Microsoft Project for schedule integration, and Autodesk Navisworks for clash detection. Real-time scheduling synchronization is achieved through IFC schema-based BIM models linked to construction timelines, while embedded clash detection and constructability feedback loops allow for iterative refinement and improved installation feasibility. A case study on a high-rise commercial building demonstrates substantial material savings, improved constructability, and reduced layout time, validating the practical advantages of BIM–DT integration for RC construction.

Full text

Turn on search term navigation
Translate

1. Introduction

The construction industry relies on the coordinated efforts of numerous stakeholders to successfully deliver projects. In reinforced concrete (RC) structure projects in particular, success is heavily dependent on effective collaboration among designers (architects), structural engineers, contractors, construction management teams, and other relevant stakeholders. Among various construction tasks, steel reinforcement (rebar) work stands out as one of the most complex, involving both on-site installation and prefabrication [1]. Consequently, the planning, scheduling, and execution (PSE) of rebar activities are critical for ensuring smooth project delivery [1].

Despite its importance, current approaches to rebar layout planning and optimization often involve excessive manual processes and result in suboptimal layout and cutting strategies, leading to inefficient material use and increased waste. Given that RC structures constitute a substantial share of global construction activity, the need for precise and resource-efficient rebar planning is increasingly evident. These contrasts highlight the need for an intelligent and accurate system capable of automating rebar arrangements, optimizing material usage, minimizing waste, and minimizing spatial conflicts.

As the construction industry undergoes a transformative shift toward digitalization, driven by demands for greater efficiency, precision, and sustainability, Building Information Modeling (BIM) has emerged as a key technology. BIM enables the visualization, coordination, and simulation of building components [2], offering a semantically rich data environment within a common data environment (CDE) that supports information repository, sharing, and integration [3]. Despite its advancement, BIM physical and functionality remains largely manual in nature, which increases the risk of human error and data inconsistency [4]. Furthermore, most BIM-based rebar solutions are geometry-centric and lack meaningful connection and interaction between the digital and physical worlds [5,6], limiting their responsiveness to dynamic construction sequences.

Construction sites are inherently dynamic, subject frequent changes in design, facilities, equipment, materials, personnel, and other elements across different project phases [1,5,6,7,8]. This ever-changing environment intensifies the need for real-time coordination and collaboration. The digital twin (DT) paradigm, defined as a dynamic virtual representation of a physical asset that continuously evolves in response to real-time data, has shown promise in addressing these challenges [1,3,4,5,6,7,8,9,10]. Specifically, DT offers a powerful approach for enhancing the management and execution of complex tasks such as rebar work by enabling seamless integration between physical construction activities and their digital counterparts.

This study bridges the gap between BIM and DT technologies to address the dual challenges of automating rebar layout design and cutting waste reduction. Specifically, it introduces a novel framework that integrates a special-length prioritization strategy [11,12,13,14] into a heuristic layout optimization algorithm to minimize rebar cutting waste (RCW), while ensuring compliance with structural and code requirements. The proposed system leverages Industry Foundation Classes (IFC) schema-based data models [1,15] linked with real-time scheduling information to enable dynamic, constructability-aware rebar placement. Additionally, it embeds interference detection mechanisms and constructability feedback loops to iteratively refine the layout and enhance on-site applicability. In doing so, this study also addresses a current gap in DT applications, which have thus far focused primarily on the design and operational phases of buildings, with limited attention to execution-stage tasks such as rebar installation.

The main objectives of this research are:

To develop a BIM-based automated rebar layout generation system that incorporates special-length prioritization.

To integrate digital twin functionalities for real-time rebar placement in alignment with the construction schedule.

To minimize rebar cutting waste without compromising constructability or regulatory compliance.

To validate the proposed approach through a case study on a high-rise reinforced concrete structure.

By integrating intelligent optimization, digital representation, and real-time feedback, this research advances sustainable, data-driven practices in RC construction. The remainder of this paper is structured as follows: the Introduction outlines the research background, objectives, and contributions; followed by the Literature Review; Framework Development; Case Study Application; and finally, Discussion and Conclusion.

2. Literature Review

2.1. BIM and Its Application in Rebar Modeling

Emerging Building Information Modeling (BIM) tools and technologies have gradually transformed the way information about the built environment is created, managed, and exchanged among stakeholders to enhance collaboration during the design and construction phases [16]. BIM is consensually defined as a comprehensive digital representation of a facility’s physical and functional characteristics, serving as a shared information resource throughout its entire lifecycle. It incorporates object-oriented, CAD-based modeling techniques that represent both geometric and non-geometric attributes of building components, as well as their interrelationships [17]. Moreover, BIM integrates modeling technologies with collaborative workflows, enabling digital prototyping of construction elements and processes for early validation of both design and fabrication aspects [18]. A core emphasis of BIM lies in the embedding of rich information within the design model (specifications, material types, installation methods, scheduling, and cost data) and in ensuring interoperability across project stakeholders in the architecture, engineering, construction, and facility management (AEC/FM) sectors [17]. This embedded information supports multi-dimensional (nD) BIM, such as 3D (geometry), 4D (time), and 5D (cost), which introduces new degrees of complexity due to the need to correlate diverse datasets with the BIM model across different project phases over time [16]. While BIM maturity continues to progress, it is important to highlight that the majority of current applications remain at Level 2. This level is characterized by collaborative modeling based on standardized (IFC) file exchanges among project stakeholders. In contrast, Level 3 BIM represents a more advanced stage, involving fully integrated collaboration through a shared model stored in a centralized, cloud-based repository [17]. The apex of BIM implementation is reflected in Level 3 environments that enable seamless stakeholder collaboration using 4D/5D models, centralized data management, and high-volume information generation throughout the building lifecycle [16].

While BIM implementation continues to mature across various project phases, most practical applications remain concentrated in the design and coordination stages. In the context of rebar work, BIM usage is still predominantly design-oriented, such as layout automation, with limited extension into construction-phase like waste optimization and real-time coordination. For instance, a study [19] developed a BIM-integrated genetic algorithm framework for reinforced concrete (RC) frames, while some studies [20,21] employed artificial potential fields and decomposed particle swarm optimization to automate rebar layout generation. Another study [22] contributed to the integration of BIM-based programming techniques for reinforcement modeling tools. Additional solutions include the application of evolutionary algorithms for rebar detailing [23] and the development of specialized rebar BIM plugins [24]. Although BIM has proven effective for accurate rebar modeling, quantity estimation, and clash detection, its potential for advanced, construction-stage applications remains underutilized. Recent approaches [14,15] have begun to address this limitation by incorporating construction-aware strategies, for example, leveraging special-length rebar prioritization and Dynamo-based BIM modeling to enhance constructability and reduce rebar waste across both the design and construction phases. However, those studies fall short in response to the dynamic construction site’s nature that require and generate vast amounts of data.

2.2. Digital Twins (DT)

Digital Twin (DT) originated in the manufacturing industry, emerging from traditional simulation-based approaches and advancing through the integration of sensor networks and the digitalization of physical systems [16,17,18]. While some experts, practitioners, and academia view DT as equivalent to or a direct extension of BIM [5,18], others define it more specifically as the integration of Internet of Things (IoT) technologies, such as sensors with BIM [8], to enable real-time data exchange. DT is widely understood as a digital or virtual representation of physical assets with dynamic data links enabling real-time building analysis and monitoring [1,3,4,5,6,7,8,9,10,16,17,18]. Its architecture typically comprises three core components: the physical asset, its virtual or digital counterpart, and the data connection that binds the two [5,16,18].

Although BIM is fundamental and serves as a core technology enabling DT [18], the two differ significantly in purpose, data requirements, and functional capabilities. According to Khajavi et al. [17], BIM was originally designed to improve the efficiency of the design and construction phases of a building’s lifecycle. It remains widely used for planning, documentation, and coordination during these early project stages. In contrast, a DT is intended for use during the operational phase of a building, focusing on real-time monitoring, performance optimization, and predictive maintenance. Unlike BIM, which relies on static data and is not inherently designed to process real-time inputs, DT systems continuously receive and analyze sensor-fed data to simulate and manage the physical asset dynamically. Moreover, the types of data each system integrates reflect their respective objectives: BIM typically incorporates cost estimations and scheduling information to support construction management, whereas DT systems integrate real-time environmental and structural data to improve a building’s interaction with its environment and users. In construction contexts, DT further extends these capabilities by enabling simulation of site operations, monitoring of structural health, and optimization of resource deployment throughout the asset’s lifecycle.

While BIM has significantly advanced information management in design and construction, it falls short in capturing the dynamic interactions between the physical and digital realms during active construction, as evident in existing solutions. DT technologies, in contrast, address this shortcoming by enabling real-time monitoring, simulation, and decision-making. However, their potential remains largely untapped in material-intensive domains such as rebar installation, an area where DT integration could lead to substantial gains in efficiency, accuracy, and constructability.

2.3. Optimization Approaches for Rebar Cutting Waste (RCW)

Rebar cutting waste (RCW) remains a significant cost and environmental burden in reinforced concrete (RC) construction, primarily due to the mismatch between available stock lengths and the required bar lengths. Conventional cutting strategies typically rely on fixed stock-length rules, commonly using 8, 9, or 10-m bars, while overlooking the potential of special-length prioritization, which enables near-optimal cutting solutions in 0.1-m increments within a defined range.

Numerous studies [12,13,25] have addressed this challenge, often focusing on specific structural members. These approaches are generally divided into two stages: continuous and discontinuous rebar arrangement optimization, depending on reinforcement characteristics. The special-length prioritization concept, which involves selecting preferred bar lengths during the cutting process to maximize material utilization and minimize offcuts, has demonstrated potential for improving cutting efficiency. However, most existing solutions are either disconnected from constructability considerations or not integrated into broader construction planning workflows, as evidenced in existing BIM-rebar layout optimization solutions [19,20,21,22,23,24]. Although some recent studies [14,15] have started to incorporate these aspects, their applicability in real-world scenarios remains limited.

2.4. Constructability Assessment and Clash Detection

Clash management, encompassing both detection and resolution, is critical for design coordination, as it helps prevent serious design errors and costly rework during construction phase [26]. A core function of BIM is the early identification and resolution of clashes during the design phase, which is essential for preventing major project issues such as reduced construction quality, costly rework, missed schedule milestones, and increased overall project costs and duration [26,27,28,29]. While BIM-based platforms like Navisworks facilitate automated clash detection, resolving these clashes largely requires manual intervention. Compounding the issue, many detected clashes are irrelevant or trivial that may not require resolution, leading to wasted time and reduced productivity during coordination efforts [26,27,28,29].

In the context of rebar installation, constructability, the ease and efficiency with which structures are built, is especially critical. Congested reinforcement zones, such as column-beam joints and bar overlaps, frequently result in rebar-specific clashes and construction delays. Rebar clashes are typically classified as hard and soft. Hard clashes involve physical intersections that obstruct constructability and may compromise structural integrity. Soft clashes, while not causing direct interference, indicate insufficient clearances or tolerances, affecting installation ease and compliance with construction standards [14]. BIM-based platforms like Navisworks enable early detection and visualization of such clashes during the design stage. However, most existing solutions [19,20,21,22,23] focus solely on design phase and lack integration with construction-phase data.

This is where DT technologies become valuable, by enabling real-time data integration, DT extends clash detection and resolution capabilities across both design and construction phases. When combined with iterative feedback mechanisms, these tools can support rebar layout adjustments that enhance constructability while preserving code compliance.

2.5. Summary

The advancement of BIM has transformed conventional practices into modern, data-driven workflows aimed at improving performance, efficiency, and reliability. However, for complex and labor-intensive tasks such as rebar work, current BIM implementations remain limited. In dynamic construction environments, where frequent design or execution changes occur, the fragmentation between the physical and digital representations of a project often leads to inefficiencies and data inconsistencies. Moreover, existing BIM solutions tend to overlook constructability, as seen in high material usage and cutting waste due to continued reliance on fixed stock-length rebar. These issues frequently result in rebar-related constructability problems, such as clashes and congestion due to the increased lap splicing. The DT paradigm offers a promising solution by enabling real-time linkage between the physical and digital models, enhancing consistency and adaptability across the project lifecycle. Yet, DT applications have largely focused on design and operation phases, leaving the construction execution stage, particularly rebar installation, untapped and underrepresented in existing literature. To overcome these limitations, this study proposes an integrated BIM–DT framework for automating rebar layout generation and minimizing cutting waste across project stages. The framework combines a heuristic layout algorithm with a special-length prioritization strategy to reduce material waste while maintaining constructability and code compliance. Leveraging IFC schema-based BIM models linked to real-time scheduling data, the framework incorporates clash detection and spatial clearance assessment. This ensures the digital model evolves with actual site conditions, enabling informed, real-time decision-making and adaptive layout adjustments.

This proposed framework responds to the growing industry demand for innovation by reengineering existing technologies into a new framework architecture that reshapes the way rebar work is designed and executed. Although the individual components are not entirely novel, their integration into a responsive and constructability-aware framework represents a significant departure from the traditional rebar work practices. This innovation not only fills the current gap in DT applications for construction-phase tasks but also sets new directions for intelligent, automated, and efficient rebar work.

3. System Framework Development

This section presents the development of the proposed system framework for automated rebar layout generation and cutting waste optimization, built upon the integration of BIM and DT technologies to achieve the objective listed in the previous section. The framework incorporates heuristic layout generation algorithms, a special-length prioritization mechanism, IFC schema-based data modeling that supports real-time construction scheduling, and constructability feedback via clash detection.

3.1. Overview of the Integrated Digital Twin—BIM System

The proposed system architecture integrates the modeling capabilities of Building Information Modeling (BIM) with the real-time data exchange and simulation features of Digital Twins (DT). As illustrated in Figure 1, the architecture is structured into four sequential stages: (1) Data preparation, where structural design data are compiled from structural analysis reports, applicable building codes, and the construction schedule; (2) Optimization and generation, involving rebar layout optimization through a two-stage special-length prioritization framework, followed by layout generation using rule-based heuristic algorithms; (3) Digital integration, in which the generated layout is embedded within a BIM environment (e.g., using tools like Dynamo) and dynamically updated via real-time schedule data through a DT platform to reflect sequencing changes and field conditions; and (4) Constructability assessment, where rebar clash detection and resolution are conducted to validate feasibility and improve installation efficiency. Feedback loops between the BIM environment, the DT platform, and the clash resolution module enable iterative refinement of the rebar layout, enhancing automation, coordination, and on-site constructability. By utilizing this integrated approach, the system can not only simulate reinforcement layouts but also continuously update and optimize them based on actual site progress, material availability, and constructability evaluations.

3.2. System Development Procedure

The development of the proposed integrated system followed a structured, four sequential stages introduced in Section 3.1 and later illustrated in Figure 1. The key steps are outlined as follows:

Data Acquisition and Preparation

Structural analysis results, design reports, and applicable building codes (e.g., ACI 318, BS 8110, and JGC 15) were compiled to acquire rebar requirements and detailing constraints. Construction schedule data were obtained in daily timeframe and formatted to align with BIM object identifiers. It is assumed that the design (architectural, structural, MEP, etc.) and input data is well coordinated, complete, up-to-date, and BIM friendly. Applicable codes are consistently used across design stages.

2.. Rebar Optimization and Layout Generation

A two-stage special-length prioritization algorithm [12,13] was applied to handle continuous and discontinuous rebar arrangements. The output was organized into summary sheets for automated layout generation. A heuristic layout algorithm was adopted from previous study [30], emphasizing on constructability-aware bar placement. It is assumed that the identified special-length can be procured with feasible layout.

3.. Digital System Integration

Rebar layouts were generated in Revit BIM model using Dynamo scripts aligned with the heuristic algorithm mentioned previously that interpreted the optimization output. IFC schema (IfcReinforcingBar) was used to structure the data, while IFC property sets supported subsequent fabrication and scheduling integration. Real-time synchronization with the project schedule was enabled through API linkage to schedule software, such as Microsoft Project. Software environments are assumed to support API interaction and model updates, with users having sufficient proficiency in using these tools.

4.. Constructability Assessment

Clash detection and clearance validation were conducted using Navisworks. Identified issues were used to refine bar placement, and layout adjustments were reflected in the Revit model. This feedback loop ensured the layout remained feasible under site conditions and compliant with minimum clearance regulations, thereby assured the concrete constructability. Clearance regulations are sufficient to ensure concrete workability and structural integrity. Meanwhile, feedback is digitally traceable and synchronized.

3.3. Data Preparation

In this stage, input data are collected from structural analysis results, applicable building codes and regulations related to rebar detailing, and the construction schedule. Structural analysis outputs typically provide both the geometric properties of structural members and the required reinforcement specifications. Building codes and regulations govern rebar detailing rules such as concrete cover, anchorage length, hook anchorage, lap splice length, bending deductions, and the minimum clear spacing between adjacent rebars. Widely global adopted standards, such as ACI 318, BS 8110, and JGC 15, provide specifications that may differ between vertical (e.g., columns) and horizontal (e.g., beams) members. For example, ACI 318-19 [31] requires the minimum spacing between vertical reinforcement bars to be at least 1.5 times the bar diameter or four-thirds the maximum coarse aggregate size, whichever is greater. BS 8110-97 [32] stipulates that the minimum horizontal spacing between bars must equal the maximum aggregate size plus 5 mm, while vertical spacing must be no less than two-thirds of the maximum aggregate size. Similarly, JGC 15–2007 [33] mandates a minimum clear spacing of 40 mm or the greater of 1.5 times the bar diameter and four-thirds of the maximum aggregate size.

To ensure site feasibility, these global codes were interpreted in conjunction with local construction practices. For instance, the adjustments to minimum bar spacing were made based on commonly available coarse aggregate sizes used by the local supplier and formwork constraints. The construction schedule provides all time-related and progress-tracking data, including real-time updates. This schedule is typically detailed on a daily basis, offering time-window data that reflects the actual progress on-site. These regulatory inputs are jointly interpreted to reflect actual site practice, ensuring both compliance and constructability.

3.4. Rebar Optimization and Layout Generation

The input data collected have been used to perform a two-stage special-length priority optimization process for both continuous and discontinuous rebar arrangements, as established in previous studies for specific structural members [12,13]. In this framework, the first stage addresses continuous rebar configurations with the objective of minimizing both cutting waste and material usage by reducing the number of lap splices through strategic splice position adjustments. Notably, it has been found that placing lap splices beyond the code-mandated splice zones can still maintain structural performance comparable to placements within the designated zones. This finding is consistent with common on-site practices, where strict adherence to mandated splice zones is often relaxed due to practical challenges and labor demands. In particular, vertical members like columns are typically spliced at each floor level to align with construction sequencing, regardless of the potential to reduce splice frequency. However, this study intentionally departs from that convention to reduce the number of lap splices and optimize material efficiency. The second stage focuses on discontinuous or shorter rebar segments, identifying near-optimal special-length at fine increments (e.g., 0.1 m) within the allowable rebar range, that meet length demands while minimizing offcuts and overall waste.

Following the rebar optimization process, the resulting data serves as input for the automated rebar layout generation. A heuristic algorithm lies at the core of this stage, designed to produce feasible and efficient reinforcement configurations. In this context, the heuristic algorithm is defined as a computational procedure that applies practical rules, design codes, geometric constraints, bar spacing requirements, and constructability considerations to determine high-quality rebar arrangements. Given the complexity of engineering constraints and variable interdependencies, identifying a mathematically optimal layout is computationally intractable. Therefore, the heuristic approach is adopted to rapidly generate practical solutions that satisfy code compliance and constructability requirements, prioritizing usability over theoretical global optimality.

This study adopts the heuristic algorithm previously developed by [33], which focuses on rebar placement in column and beam elements based on their geometric attributes. The detailed equations and procedures underlying this heuristic approach can be found in [33].

3.5. Digital System Integration

The digital integration stage embeds the generated rebar layout within a BIM environment (e.g., using tools like Dynamo), allowing dynamic updates through real-time scheduling data enabled by a DT platform. This integration ensures the layout reflects sequencing changes and field conditions. Previous studies [14,15] have developed Dynamo-based tools to automate rebar layout generation within BIM models, guided by heuristic algorithms [33]. Dynamo expands accessibility by enabling engineers with limited programming experience to efficiently utilize the system. These studies also introduced standardized summary sheet formats, which facilitate the implementation of Dynamo scripts for automatic layout generation.

The BIM model is structured using the IFC schema, promoting data consistency and interoperability, especially for downstream processes such as off-site rebar fabrication. Each rebar instance is represented as an IFC element containing attributes for bar type, length, anchorage type, bending shape, and placement sequence. Additionally, process-specific data such as planned installation dates, sequencing priorities, and clash detection statuses are embedded within custom IFC property sets. This approach supports seamless integration with construction management software and ensures model updates accurately reflect real-site project progress.

Regarding schedule integration, the system synchronizes the rebar layout model with the project schedule through API connections to scheduling tools such as Primavera P6 or Microsoft Project. When schedule deviations occur, such as changes in floor sequencing or material delivery delays, the DT adjusts the rebar layout, accordingly, maintaining alignment with the special-length prioritization optimization strategy and constructability constraints by creating a bidirectional connection. However, certain contextual limitations may hinder system implementation. The availability of BIM environments and IFC-compatible platforms varies across regions and project scales, complicating standardized implementation. Moreover, the real-time schedule data from used scheduling tools may not be consistently maintained or synchronized, limiting the effectiveness of the DT part. In many practical settings, construction sites may also lack the digital infrastructure or technology maturity required to support bidirectional data exchange between both physical and digital models.

3.6. Constructability Assessment

Constructability assessment is performed through rebar clash detection and resolution, with iterative feedback loops between the BIM environment, DT platform, and clash resolution module enabling automated refinement for improved layout feasibility, coordination, and on-site installation efficiency. This ensures continuous refinement of rebar layouts for improved feasibility, coordination, and installation efficiency.

As previously discussed, rebar clashes are typically categorized as either hard clashes, which involve physical interference between reinforcement bars occupying the same space, or soft clashes, which pertain to insufficient clearance between adjacent bars. Early identification of these issues during the design phase is critical to reducing costly delays, minimizing rework, and improving overall construction quality by decreasing the number of Requests for Information (RFIs) [14]. Adequate clearance between rebars is essential to ensure the proper flow of concrete during placement, as mandated by various building codes [14]. This clearance ensures complete concrete filling around the reinforcement, resulting in a dense and cohesive composite structure, one of the critical factors in achieving structural integrity. The regulatory requirements for minimum bar clearance have been outlined in Section 2.4.

In this study, Navisworks is utilized to perform clash detection and ensure compliance with clearance and constructability requirements. Nonetheless, contextual limitations remain. On many construction sites, including some large-scale projects, this workflow is not implemented, often due to the licensing costs for specialized software like Navisworks or insufficient expertise. In projects with lower levels of digital maturity, the resolution of clashes is often done manually on-site without documentation, diminishing the value of digital validation. Additionally, site deviations (e.g., misaligned formwork, on-site adjustments) can introduce new conflicts that may not be reflected in the system.

4. Implementation and Case Study

The proposed system was applied to generate the reinforcement layout for a continuous column in a high-rise commercial building located in Korea. The structure extends from the foundation level to the roof and features rebar layout variations in every certain floor, presenting challenges in both constructability and cutting waste minimization. The project was selected due to its complex rebar detailing requirements and the limited use of BIM tools during the design stage. Consequently, it exhibited minimal adoption of DT functionalities and thus provided an opportunity for implementing and validating the proposed system aimed at improving layout efficiency and digital responsiveness.

4.1. Data Preparation

The building comprises 22 floors, including two basement levels and 20 floors above ground. Column heights vary by floor, with the shortest and tallest levels measuring 3.7 m and 6.0 m, respectively, and a standard floor height of 3.8 m. Table 1 summarizes the overall column information. The minimum clear spacing between adjacent bars adopted in this study is presented in Table 2. Due to the gradual reduction in column dimensions at higher levels, multiple rebar layout configurations were observed along the column from B2 to the roof floor. Table 3 outlines the specific details of each rebar arrangement. In this study, the framework is implemented using Autodesk Revit and Dynamo for rebar modeling and layout generation, Microsoft Project for schedule integration, and Autodesk Navisworks for clash detection

Table 3 provides the rebar arrangements for a typical high-rise column, including section dimensions, compressive strength, main bars, and hoop detailing across different floor levels. These arrangements align with widely accepted building design codes such as ACI 318, BS 8110, and JGC 15, making them broadly applicable with only minor regional adjustments. The standardized information, such as bar sizes, spacing, and dimensions facilitates straightforward interpretation by industry professionals. Furthermore, the provided layout and cross-sectional details are sufficient for BIM representation, supporting parametric modeling, automated drawing generation, and seamless integration into digital framework. The rebar arrangements summarized in Table 3 are further illustrated throughout the full height of the column in Figure 2. As the column dimensions taper at higher floors, the reinforcement configurations are correspondingly adjusted to reflect changes in structural demand and dimension constraints.

4.2. Rebar Optimization and Layout Generation

Analysis of the rebar layout configuration reveals that while some reinforcement bars extend continuously from the foundation to the roof, others terminate at intermediate levels within the column. The proposed algorithm utilizes this characteristic by grouping rebars with similar lengths, thereby classifying column reinforcement into distinct categories based on shared length ranges. For instance, rebars spanning the entire column height, from the foundation to the roof, are assigned to the first rebar group, whereas those terminating at the 13th floor are classified as the second group. This grouping process is iteratively applied throughout the column, resulting in a total of seven distinct rebar groups. The number of continuous rebars in each group is as follows: 14, 2, 6, 12, 2, 2, and 4 continuous rebars, from the first to the seventh group, respectively.

This classification serves as input for the special-length rebar optimization implemented in this study. The optimization approach primarily treats reinforcement as a continuous element rather than discrete segments. A special-length rebar was first identified and optimized for the first rebar group and then prioritized across the remaining groups. The identified optimal special length was 10.327 m; therefore, a 10.4 m special-length rebar was purchased for practical application. Consequently, residual lengths are generated for the subsequent rebar groups. These residual lengths were then combined to identify the optimal special-length rebar that yields a minimum waste. A summary of the optimization results for the continuous rebar arrangement is presented in Table 4 and Table 5, while Table 6 outlines the results for the residual bars. The combined outcome of both arrangements is summarized in Table 7. To construct the column, 14,815 kg of rebar was required, whereas 14,939 kg needed to be purchased, resulting in a cutting waste rate of 0.83%. By doing this, significant reductions in both rebar usage and cutting waste were achieved.

This optimization results were then used to generate the rebar layout automatically in BIM model through Dynamo tool [14,15] which is guided by heuristic layout algorithms [33].

4.3. Digital System Integration

As previously mentioned, the BIM model used for generating the Dynamo script is structured based on the IFC schema to ensure data consistency and interoperability, particularly for reinforcement data. Users are required to utilize the IfcReinforcingBarType, which includes key attributes such as PredefinedType, NominalDiameter, CrossSectionArea, BarLength, BarSurface, BendingShapeCode, and BendingParameters [15]. Furthermore, since the system is intended for downstream use in rebar fabrication workflows, additional data fields, such as custom IFC Property Sets, must be incorporated [15]. These shared parameters can be sourced from the GitHub repository under the file name IFC Shared Parameters-RevitIFCBuiltIn-Type_ALL.txt.

Before implementing the Dynamo script, a summary sheet must be prepared to compile the essential information derived from the optimization results. Table 8 provides an example of the standardized rebar summary sheet of longitudinal rebar of the column spanning F4–F6. The results of the rebar generated are shown in Figure 3. Additionally, Figure 4 displays the assigned IFC class and property sets for the selected case within the BIM model.

The application of the Digital Twin (DT) paradigm involves establishing a bidirectional connection between the time-based construction schedule and the digital representation of physical assets. This integration facilitates real-time monitoring, proactive decision-making, and adaptive control of construction activities. To enable this linkage, Autodesk Platform Services are utilized to provide custom API functions that connect BIM elements with scheduling data. Each BIM object, such as rebar groups, column elements, or beam segments, is associated with a specific schedule or task ID embedded in the scheduling software, which in this case is Microsoft Project. Additionally, custom IFC property sets are employed to store relevant time-based attributes, including PlannedStartDate, PlannedEndDate, ActualStartDate, ActualEndDate, and ProgressPercentage. Figure 5 illustrates the project schedule developed using Microsoft Project.

4.4. Constructability Assessment and Feedback

In addition to the schedule linkage, a clash detection module assessing the constructability of structural members is embedded in the DT system. As previously discussed, clashes can be classified as either hard or soft. Hard clashes, involving physical intersections between bars occupying the same space, are relatively straightforward to detect and resolve. In contrast, soft clashes, typically related to insufficient clearance, pose a greater challenge. According to ACI 318-19 [30], the minimum required clearance between reinforcement bars depends on the maximum coarse aggregate size used in the concrete. Section 26.4.2.1(a)(5) specifies that the nominal maximum size of coarse aggregate shall not exceed the smallest of the following: one-fifth of the narrowest dimension between the sides of the formwork, one-third of the slab depth, or three-fourths of the minimum clear spacing between individual bars. For columns, only the first and second criteria are applicable.

Based on Table 1, the narrowest dimension between formwork sides ranges from 800 to 1000 mm, and the typical slab depth is 150 mm. These values yield a maximum coarse aggregate size of 50 mm. However, considering concrete workability in RC construction, a more practical maximum aggregate size of 10–40 mm is generally preferred [14]. Applying the ACI 318-19 provisions, the calculated minimum bar clearance for the column is approximately 54 mm.

Examples of soft clashes are shown in Figure 6. In Navisworks, soft clashes may or may not require resolution depending on their context. Figure 6a illustrates a soft clash resulting from an intentional rebar overlap, which does not necessitate correction. In contrast, Figure 6b highlights a case where resolution is required to maintain the minimum clearance between bars, as the bars are not meant to overlap. This clearance information can be linked to the DT system using sensor data. When corrective actions are taken on-site, these changes can also be reflected in the digital model, thereby enhancing model consistency and supporting accurate documentation.

5. Discussion, Limitation, and Future Direction

The implementation of the proposed BIM–Digital Twin framework for rebar layout generation and cutting waste optimization demonstrated substantial improvements across several performance metrics. The baseline approach, which relied on manual stock-length (8 and 10 m) rebar optimization, resulted in a purchase of 18,164 kg (18.164 tons) rebar and cutting waste rate of 12.93%. In contrast, the optimized layout employing special-length prioritization reduced waste to just 0.83%, representing a 93.58% reduction. Assuming a unit rebar price of USD 908 per ton [34], the baseline approach incurred a material cost of USD 16,493, whereas the proposed framework reduced this to USD 13,565, a saving of USD 2928 or 17.75%. These results demonstrate the framework’s potential to promote sustainable and efficient rebar usage without compromising structural integrity or regulatory compliance.

In terms of constructability, the system enables easier rebar clash detection and resolution within the digital environment, enabling more practical solutions through traceable and documented modifications. In contrast, the manual approach requires on-site resolution followed by separate documentation efforts. Additionally, layout automation also leads to measurable time savings. For instance, conventional rebar layout and detailing for a 20-story building typically required 8 months and 16 man-months of effort to complete structural analysis, design, and shop drawings [35]. The proposed system is estimated to reduce this workload by 3 to 6 man-months [14]. Although a direct comparison of rebar-related errors between traditional and proposed methods would enhance the analysis, such data are unavailable due to the lack of systematic error documentation in conventional site practices. Most errors are resolved informally during construction and are not recorded. Consequently, this study emphasizes quantifiable metrics as presented above.

From a practical perspective, the proposed frameworks offer several advantages that enhance their applicability in real-world construction projects:

Interoperability: Build on open standards (IFC schema), the system is compatible with widely adopted tools such as Revit, Dynamo, Navisworks, various scheduling software and other BIM-based platforms.

Responsiveness: Integrated DT platforms enable real-time synchronization with construction progress and site conditions. This data-driven approach improves transparency in rebar planning and supports the mitigation of constructability issues and schedule delays.

Adaptability: The framework accommodates various structural members and is applicable across project phases.

In real-world construction projects, particularly for vertical members like columns, rebar is commonly spliced at each floor level to align with construction sequencing. While this practice simplifies on-site installation, it often leads to excessive lap splicing and increased material waste. These site-level decisions reflect practical constraints such as labor availability, handling ease, and sequencing demands. The proposed framework addresses this gap by incorporating adjustable splice positioning through a special-length rebar prioritization strategy, allowing for layout adjustments that balance constructability with material efficiency.

Despite the advantages offered by the proposed system, several practical limitations and challenges must be acknowledged:

Initial Setup: The system requires accurate structural data and consistent BIM modeling practices, which may not always be available.

Infrastructure Gaps: Digital infrastructure readiness varies across projects and regions. Many small and medium enterprises (SME) may lack access to robust BIM or DT platforms.

Low Digital Maturity: Many sites have limited or low digital maturity. Digital applications such as clash detection often remain minimal due to software licensing costs or lack of expertise.

Learning Curve: The operators or engineers should have proficient and sufficient knowledge in scripting Dynamo environments and interpreting IFC-based models.

Constructability Adjustment: Excessively strict thresholds may lead to suboptimal layouts if not well-aligned with applicable building codes, regulations, and site practices.

Site deviations: On-site deviations such as misaligned formwork or site changes, may not always be captured or fed back into the digital model unless linked with advanced or automated site sensing, such as LiDAR.

To overcome these issues, a phased implementation strategy is recommended as the site needs to evolve from a perhaps low expertise and maturity level. Initially, the system can be deployed on structural members that exhibit complex rebar arrangements. Integration into digital workflows should be facilitated using standardized templates and Dynamo scripts. BIM engineers should lead customization and training efforts, ensuring alignment with both design standards and local practices. Domain knowledge in civil engineering remains crucial for meaningful application and interpretation. Over time, the system can evolve to integrate sensor-based feedback loops and artificial intelligence (AI)-based learning models that improve rebar layout using project-specific data.

Furthermore, the proposed framework is flexibly designed, making it applicable in both high and lower digital maturity environments. The limited knowledge or use of DT platforms, for example, can have the core layout optimization part functioned based on spreadsheet input or output linked to the used BIM tools. Currently, field feedback integration remains partially manual. Future enhancements should consider the adoption of fully automated sensor technologies, such as LiDAR-based verification, to improve responsiveness and accuracy. Furthermore, ongoing research could explore the incorporation of machine learning models to refine heuristic layout strategies based on historical project data. Additionally, the automation of feedback loops may be advanced through computer vision systems or augmented reality (AR)-assisted inspection technologies, improving on-site validation and layout adjustments.

In summary, the proposed framework introduces a practical and scalable innovation that bridges BIM and DT for rebar work across different project stages. It enhances automation, constructability, and coordination, aligns with real-site practices, and accommodates various levels of digital adoption. Nonetheless, careful attention must be paid to infrastructure, training, and data readiness to fully realize its benefits.

6. Conclusions

This study proposed and validated an integrated BIM–Digital Twin (DT) system for automated rebar layout generation and optimization in RC structures, with a specific emphasis on minimizing cutting waste through a special-length prioritization strategy. By combining heuristic algorithms, Dynamo, IFC-based data modeling, real-time schedule synchronization, and constructability assessment, the system addresses longstanding challenges in rebar detailing, including inefficiency, layout interference, and limited adaptability to field conditions.

Applied to a high-rise commercial building case study, the framework demonstrated substantial improvements in material efficiency, achieving 93.58% reduction in rebar cutting waste (RCW), 17.75% reduction in material cost, along with enhanced constructability and reduced layout time across the entire workflow, from structural analysis and design to shop drawing generation. These findings highlight the system’s potential to improve not only construction productivity but also sustainability by reducing material waste and rework.

Key contributions of the research include:

Introduction of a heuristic rebar layout algorithm which guides Dynamo integrated with digital twin feedback.

Implementation of a special-length prioritization strategy to minimize rebar cutting waste.

Integration of constructability assessment and automated clash resolution within the BIM workflow.

Synchronization of rebar layout and construction sequencing using an IFC schema and schedule-linked modeling approach.

The proposed framework demonstrates strong applicability in rebar-intensive construction projects through its use of open standards (IFC) and compatibility with widely adopted BIM tools such as Revit and Navisworks. Its integration of heuristic layout algorithms, special-length prioritization, and digital twin enhances constructability, reduces material waste, and supports adaptive, data-driven planning. However, limitations remain, including the need for accurate structural input, varying levels of digital maturity across sites, and a learning curve associated with scripting and model interpretation. Constructability settings must also be calibrated to reflect both regulations and local practices, and real-time feedback integration is currently limited. A phased implementation approach is recommended, starting with complex members and supported by standardized templates and training. Future work should focus on integrating AI-based layout prediction, automated field sensing (e.g., LiDAR, AR), and broader validation across diverse project types to further strengthen its real-world applicability. In conclusion, the proposed framework represents a significant step toward digitalizing rebar planning and embodies the construction industry’s broader shift toward intelligent, adaptive, and resource-efficient workflows enabled by BIM and digital twin technologies.

Author Contributions

Conceptualization, S.K.; methodology, D.D.W., J.L. and S.K.; validation, D.D.W., J.L. and S.K. and S.K.; formal analysis, D.D.W. and J.L.; investigation, D.D.W. and J.L.; resources, S.K.; data curation, S.K.; writing—original draft preparation, D.D.W. and J.L.; writing—review and editing, D.D.W., J.L. and S.K.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.

 Data Availability Statement

Data sharing is applicable upon reasonable request.

 Conflicts of Interest

Author Sunkuk Kim is employed by the company Earth Turbine Co., Ltd. The remaining authors declare the research was conducted in the absence of any commercial or financial relationships that could be interpreted as a potential conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1 The architecture of the proposed integrated system.

Figure 2 Enlarged rebar arrangements of a tapered column with floor-level dimension changes.

Figure 3 Dynamo applied BIM model on selected column (Reprint with permission [15]; Copyright 2024, Kyung Hee University).

Figure 4 IFC class and property sets assigned to the selected column (Reprint with permission [15]; Copyright 2024, Kyung Hee University).

Figure 5 Construction schedule as real-time data representation.

Figure 6 Rebar soft clashes example: (a) no corrective action required, (b) corrective action is required (modified from [14]).

Detail information of the selected column (adapted from [26]).

Description Contents
Foundation depth (Df) 600 mm
Foundation concrete cover (Cf) 50 mm
Basement level (B2–B1) height 8.3 m
Upper ground level (F1–roof) height 87.4 m
Total floor height (∑Hfloor) 95.7 m
Girder depth (Dgirder) 700 mm
Rebar yield strength (fy), diameter (d), unit weight UHD600 D29, 5.04 kg/m
Concrete compressive strength (fc) B2-F20: 35 MPa
Girder depth (Dgirder) 700 mm
Splice length (Lsplice) 1.5 m
Anchorage length (Lanchor+hook) 1.4 m
Dowel bar length (Ldowel) 2.35 m
Bend deduction (Bmargin) 79 mm

The minimum rebar clearance (adapted from [14]).

Component(s) Contents
Concrete (fc= 35 MPa) Maximum coarse aggregate size (dagg) = 40 mm
Steel rebar (fy= 600 MPa) Minimum allowable clearance = 54 mm

Rebar arrangement across sections along the structure (adapted from [14,26]).

Floors B2–B1 F1 F2–F3 F4–F6
Cross section [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.]
Compressive strength, fc (MPa) 35 35 35 35
Dimension (mm) 1400 × 1100 1200 × 1100 1200 × 1000 1200 × 800
Main rebar 42–UHD29 38–UHD29 36–UHD29 34–UHD29
Hoops Both ends HD10@300 HD10@150 HD10@150 HD10@150
Center HD10@300 HD10@300 HD10@300 HD10@300
Floors F7–F8 F9–F12 F13–F20
Cross section [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.]
Compressive strength, fc (MPa) 35 35 35
Dimension (mm) 1000 × 800 1000 × 800 1000 × 800
Main rebar 22–UHD29 16–UHD29 14–UHD29
Hoops Both ends HD10@150 HD10@150 HD10@150
Center HD10@300 HD10@300 HD10@300

Special-Length Priority Optimization for Continuous Rebar Arrangement on the Selected Case Study.

Rebar Group Total Length (m) Special-Length (m) Number of Special-Length Bars (pcs) Residual Bar Length (m)
1st (B2F-Roof) 130.092 10.4 11 -
2nd (B2-F13) 85.871 10.4 8 2.68
3rd (B2-F9) 64.671 10.4 6 2.28
4th (B2-F7) 54.071 10.4 5 2.08
5th (B2-F4) 32.371 10.4 3 1.18
6th (B2-F2) 18.171 10.4 1 7.78
7th (B2-F1) 12.071 10.4 1 1.68

Continuous Rebar Quantity of the Selected Case Study.

Rebar Group Number of Rebar in Each Group Number of Special-Length Bars Each Group (pcs) Total Number of Special-Length Bars (pcs) Required Quantity (kg) Purchased Quantity (kg)
1st (B2F-Roof) 14 10.4 154 8015 8072
2nd (B2-F13) 2 10.4 16 833 839
3rd (B2-F9) 6 10.4 36 1874 1887
4th (B2-F7) 12 10.4 60 3123 3145
5th (B2-F4) 2 10.4 6 312 314
6th (B2-F2) 2 10.4 2 104 105
7th (B2-F1) 4 10.4 4 208 210
Total 14,469 14,572

Residual Rebar Quantity of the Selected Case Study.

Length (m) Number Required (pcs) Required Quantity (kg) Purchased Quantity (kg)
10.4 7 346 367

Overall Rebar Quantity and Cutting Waste Analysis.

Description Required Quantity (kg) Purchased Quantity (kg) Waste (kg) Waste Rate (%)
Continuous 14,469 14,572 103 0.71
Residual 346 367 21 5.71
Overall 14,815 14,939 124 0.83

A rebar summary sheet for Dynamo script implementation (adapted from [14]).

Structural column data Column ID 428002 (F4–F5) & 428600 (F5–F6)
Dimension 800 × 1200 × 5600
Position F4–F6
Concrete cover 50 mm
Longitudinal rebar data Number of rebars in b-direction 8
Number of rebars in l-direction 11
Bar type 29
Rebar shape BS8666
Leg length 0
First and last bar No (for rebar in b-direction)
Yes (for rebar in l-direction)
End hook orientation Right
Curve From the starting point to the ending point
Lap splice length -
Special-length bar 10.4 m

1. Jiang, Y.; Li, M.; Li, M.; Liu, X.; Zhong, R.Y.; Pan, W.; Huang, G.Q. Digital twin-enabled real-time synchronization for planning, scheduling, and execution in precast on-site assembly. Autom. Constr.; 2022; 141, 104397. [DOI: https://dx.doi.org/10.1016/j.autcon.2022.104397]

2. Eastman, C.; Teicholz, P.; Sacks, R.; Liston, K. BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors; 3rd ed. John Wiley & Sons: Hoboken, NJ, USA, 2018; pp. 1-622.

3. Pan, Y.; Zhang, L. A BIM-data mining integrated digital twin framework for advanced project management. Autom. Constr.; 2021; 124, 103564. [DOI: https://dx.doi.org/10.1016/j.autcon.2021.103564]

4. Jiang, Y.; Li, M.; Guo, D.; Wu, W.; Zhong, R.Y.; Huang, G.Q. Digital twin-enabled smart modular integrated construction system for on-site assembly. Comput. Ind.; 2022; 136, 103594. [DOI: https://dx.doi.org/10.1016/j.compind.2021.103594]

5. Nguyen, T.D.; Adhikari, S. The role of BIM in integrating digital twin in building construction: A literature review. Sustainability; 2023; 15, 10462. [DOI: https://dx.doi.org/10.3390/su151310462]

6. Zhang, J.; Cheng, J.C.; Chen, W.; Chen, K. Digital twins for construction sites: Concepts, LoD definition, and applications. J. Manag. Eng.; 2022; 38, 04021094. [DOI: https://dx.doi.org/10.1061/(ASCE)ME.1943-5479.0000948]

7. Salem, T.; Dragomir, M. Options for and challenges of employing digital twins in construction management. Appl. Sci.; 2022; 12, 2928. [DOI: https://dx.doi.org/10.3390/app12062928]

8. Ammar, A.; Nassereddine, H.; AbdulBaky, N.; AbouKansour, A.; Tannoury, J.; Urban, H.; Schranz, C. Digital twins in the construction industry: A perspective of practitioners and building authority. Front. Built Environ.; 2022; 8, 834671. [DOI: https://dx.doi.org/10.3389/fbuil.2022.834671]

9. Nour El-Din, M.; Pereira, P.F.; Poças Martins, J.; Ramos, N.M. Digital twins for construction assets using BIM standard specifications. Buildings; 2022; 12, 2155. [DOI: https://dx.doi.org/10.3390/buildings12122155]

10. Tuhaise, V.V.; Tah, J.H.M.; Abanda, F.H. Technologies for digital twin applications in construction. Autom. Constr.; 2023; 152, 104931. [DOI: https://dx.doi.org/10.1016/j.autcon.2023.104931]

11. Lee, D.; Son, S.; Kim, D.; Kim, S. Special-length-priority algorithm to minimize reinforcing bar-cutting waste for sustainable construction. Sustainability; 2020; 12, 5950. [DOI: https://dx.doi.org/10.3390/su12155950]

12. Widjaja, D.D.; Kim, S. Reducing rebar cutting waste and rebar usage of beams: A two-stage optimization algorithm. Buildings; 2023; 13, 2279. [DOI: https://dx.doi.org/10.3390/buildings13092279]

13. Widjaja, D.D.; Rachmawati, T.S.N.; Kim, S.; Lee, S. An Algorithm to Minimize Near-Zero Rebar-Cutting Waste and Rebar Usage of Columns. Sustainability; 2024; 16, 308. [DOI: https://dx.doi.org/10.3390/su16010308]

14. Widjaja, D.D.; Rachmawati, T.S.N.; Kim, S. A BIM-based intelligent approach to rebar layout optimization for reinforced concrete columns. J. Build. Eng.; 2025; 99, 111604. [DOI: https://dx.doi.org/10.1016/j.jobe.2024.111604]

15. Rachmawati, T.S.N. BIM-based Optimization Model to Achieve Near-Zero Rebar Cutting Waste. Ph.D. Dissertation; Kyung Hee University: Seoul, Republic of Korea, 2024.

16. Boje, C.; Guerriero, A.; Kubicki, S.; Rezgui, Y. Towards a semantic Construction Digital Twin: Directions for future research. Autom. Constr.; 2020; 114, 103179. [DOI: https://dx.doi.org/10.1016/j.autcon.2020.103179]

17. Khajavi, S.H.; Motlagh, N.H.; Jaribion, A.; Werner, L.C.; Holmström, J. Digital twin: Vision, benefits, boundaries, and creation for buildings. IEEE Access; 2019; 7, pp. 147406-147419. [DOI: https://dx.doi.org/10.1109/ACCESS.2019.2946515]

18. Sacks, R.; Brilakis, I.; Pikas, E.; Xie, H.S.; Girolami, M. Construction with digital twin information systems. Data-Centric Eng.; 2020; 1, e14. [DOI: https://dx.doi.org/10.1017/dce.2020.16]

19. Mangal, M.; Cheng, J.C. Automated optimization of steel reinforcement in RC building frames using building information modeling and hybrid genetic algorithm. Autom. Constr.; 2018; 90, pp. 39-57. [DOI: https://dx.doi.org/10.1016/j.autcon.2018.01.013]

20. Liu, J.; Xu, C.; Wu, Z.; Chen, Y.F. Intelligent rebar layout in RC building frames using artificial potential field. Autom. Constr.; 2020; 114, 103172. [DOI: https://dx.doi.org/10.1016/j.autcon.2020.103172]

21. Liu, J.; Li, S.; Xu, C.; Wu, Z.; Ao, N.; Chen, Y.F. Automatic and optimal rebar layout in reinforced concrete structure by decomposed optimization algorithms. Autom. Constr.; 2021; 126, 103655. [DOI: https://dx.doi.org/10.1016/j.autcon.2021.103655]

22. Wang, D.; Hu, Y. Research on the Intelligent Construction of the Rebar Project Based on BIM. Appl. Sci.; 2022; 12, 5596. [DOI: https://dx.doi.org/10.3390/app12115596]

23. Xu, C.; Liu, J.; Wu, Z.; Chen, Y.F. Automated steel reinforcement detailing in reinforced concrete frames using evolutionary optimization and artificial potential field. Autom. Constr.; 2022; 138, 104224. [DOI: https://dx.doi.org/10.1016/j.autcon.2022.104224]

24. Li, S.; Shi, Y.; Hu, J.; Li, S.; Li, H.; Chen, A.; Xie, W. Application of BIM to Rebar Modeling of a Variable Section Column. Buildings; 2023; 13, 1234. [DOI: https://dx.doi.org/10.3390/buildings13051234]

25. Rachmawati, T.S.N.; Khant, L.P.; Lim, J.; Lee, J.; Kim, S. Optimization of lap splice positions for near-zero rebar cutting waste in diaphragm walls using special-length-priority algorithms. J. Asian Archit. Build. Eng.; 2024; 23, pp. 1933-1950. [DOI: https://dx.doi.org/10.1080/13467581.2023.2278881]

26. Hu, Y.; Xia, C.; Chen, J.; Gao, X. Clash context representation and change component prediction based on graph convolutional network in MEP disciplines. Adv. Eng. Inform.; 2023; 55, 101896. [DOI: https://dx.doi.org/10.1016/j.aei.2023.101896]

27. Hasannejad, A.; Sardrud, J.M.; Shirzadi Javid, A.A. BIM-based clash detection improvement automatically. Int. J. Constr. Manag.; 2023; 23, pp. 2431-2437. [DOI: https://dx.doi.org/10.1080/15623599.2022.2063014]

28. Kim, S.; Lee, W.; Yu, Y.; Jeon, H.; Koo, B. Employing Ontology and Machine Learning for Automatic Clash Detection and Classification in Multi-discipline BIM Models. Proceedings of the International Conference on Construction Engineering and Project Management; Sapporo, Japan, 29 July–1 August 2024.

29. Bitaraf, I.; Salimpour, A.; Elmi, P.; Shirzadi Javid, A.A. Improved Building Information Modeling Based Method for Prioritizing Clash Detection in the Building Construction Design Phase. Buildings; 2024; 14, 3611. [DOI: https://dx.doi.org/10.3390/buildings14113611]

30. Widjaja, D.D.; Khant, L.P.; Rachmawati, T.S.N.; Kim, S. Development of automatic rebar layout algorithms considering design characteristics of reinforced concrete members. Ain Shams Eng. J.; 2025; 16, 103367. [DOI: https://dx.doi.org/10.1016/j.asej.2025.103367]

31.ACI 318-19 Building Code Requirements for Structural Concrete: Commentary on Building Code Requirements for Structural Concrete (ACI 318R-19); American Concrete Institute: Farmington Hills, MI, USA, 2019.

32.BS 8110 Standard Structural Use of Concrete: Code of Practice for Design and Construction, Part 1; 2nd ed British Standard Institute: London, UK, 1997.

33. Japan Society of Civil Engineers. Standard Specifications for Concrete Structures—2007 “Design”; Japan Society of Civil Engineers (JSCE): Tokyo, Japan, 2010.

34. Widjaja, D.D.; Khant, L.P.; Kim, S.; Kim, K.-Y. Optimization of Rebar Usage and Sustainability Based on Special-Length Priority: A Case Study of Mechanical Couplers in Diaphragm Walls. Sustainability; 2024; 16, 1213. [DOI: https://dx.doi.org/10.3390/su16031213]

35. Lee, H. Development of Integrated Project Delivery Algorithms for Structural Work of Buildings (IPDSW). Ph.D. Dissertation; Kyung Hee University: Seoul, Republic of Korea, 2020.

© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.