Optimizing Rebar Process and Supply Chain Management for Minimized Cutting Waste: A Building Information Modeling-Based Data-Driven Approach

Abstract

Rebar procurement inefficiencies, such as inaccurate quantity estimation and misaligned delivery schedules, often lead to excessive waste, supply shortages, and project delays. While existing optimization methods reduce cutting waste, their effectiveness diminishes without integration into supply chain management (SCM). This study presents an integrated framework to optimize rebar processing and supply chain management (SCM) by leveraging Building Information Modeling (BIM) and data-driven optimization strategies. A 24-floor case study validated the approach, optimizing continuous main rebars into special lengths and combining discontinuous lengths into cutting patterns based on special lengths. Rebar orders were organized into 12 batches, each meeting a 15-ton minimum and requiring order placement at least two months in advance. An activity database integrated rebar optimization with the construction schedule, facilitating SCM analysis. BIM automation streamlined procurement by generating Bar Bending Schedules (BBSs) and synchronizing rebar tracking with real-time updates, improving coordination, efficiency, and project outcomes, particularly in high-rise building projects.

1. Introduction

Rebar procurement relies on precise execution, accurate quantity estimation, and timely procurement. Failing to meet these requirements can lead to cost overruns from excessive waste, rebar shortages or surpluses, project delays, and diminished overall project performance [1]. Effective inventory management plays a crucial role in delivering the required rebar quantities to the site on schedule. A study [2] reveals that effective inventory management of construction materials can enhance productivity by 6%. Additionally, ensuring materials availability before work execution can further boost productivity by 8–10%, as it prevents delays caused by material shortages. Since building materials constitute 60–70% of a facility’s direct costs [3], with rebar contributing 15–20% of that [4,5], minimizing rebar waste, often exceeding 5% [6], requires a well-optimized supply chain and inventory management system.

Numerous studies have focused on optimizing rebar cutting waste through techniques such as special-length rebar algorithms. Widjaja and Kim’s two-stage optimization algorithm [6] reduced cutting waste to 0.93% for beams by optimizing lap splice positions and using special-length rebars. This approach dramatically decreased rebar usage by 12.31% from the total rebar quantity of the original state that used market-length rebars. A similar special-length priority optimization algorithm applied to column rebars [7] reduced cutting waste to 0.83%, saving 17.76% of rebar usage compared to the original design with lap splices on every floor.

Even with optimization methods, rebar work can fail if rebars are not ordered, purchased, procured, and delivered according to the project schedule. Such misalignment can result in installation delays and additional costs, compounded by challenges in managing excessive rebar inventory. Precise management and adherence to the procurement schedule are crucial in significantly reducing delays and material overages [8,9]. Effective inventory management of rebar data can streamline procurement processes and minimize waste by categorizing rebars based on their information and usage [9]. Moreover, special-length rebars must adhere to specific order requirements, such as minimum quantities and lead times. To meet these conditions, rebar for a particular construction phase must be ordered well in advance of the installation schedule to avoid delivery delays and procurement errors. Despite advancements in rebar optimization techniques, substantial limitations persist, particularly in integrating optimization methods into the broader rebar procurement and supply chain management processes. Most existing studies focus on optimizing rebar usage and cutting waste, with less emphasis on the complexities of rebar procurement and its synchronization with construction schedules. Optimization of rebar usage and cutting waste alone will be ineffective if not properly supported by a well-structured and precise rebar supply chain management on-site.

Given the above circumstances, a critical gap persists in integrating optimization methods with supply chain management processes to address the complexities of procurement timing, supply chain alignment, and real-time delivery scheduling, particularly in phased construction projects. Construction phases are typically determined by structural elements such as columns, beams, or floors. However, for rebar optimized in special lengths, the cutting and bending must follow optimization data to minimize waste, which requires phase determination to align with optimization results. Although previous research has highlighted the waste-reducing benefits of special-length rebar, challenges persist in synchronizing optimized rebar quantities with construction phases and ensuring timely delivery. While Building Information Modeling (BIM) has been primarily used for optimizing rebar design and layout, its role in rebar procurement and supply chain management has not been fully explored. BIM’s capacity to streamline and automate key processes, such as procurement, Bar Bending Schedule (BBS) generation, and shop drawing creation, holds promise for improving communication and coordination among project stakeholders. BIM offers the potential to address these limitations by enabling real-time updates and improving communication between stakeholders. This ensures that rebar quantities and delivery schedules align with project requirements, overcoming the gaps in previous BIM applications that focused primarily on design, material usage, and waste optimization.

This research aims to develop an inventory management system that integrates rebar optimization with supply chain management, ensuring accurate rebar quantities and on-time deliveries aligned with the construction schedule. The proposed system addresses critical gaps in current rebar procurement practices, delivering multifaceted improvements. This research integrates rebar optimization with supply chain management using a case study approach, incorporating real-world project data, procurement schedules, and rebar optimization. The findings demonstrate that the integration closely aligns with special-length priority optimization outcomes with a deviation of ≤1%, thereby enhancing procurement efficiency by minimizing ordering errors and supply delays. Furthermore, the system facilitates precise rebar orders tailored to specific construction phases, ensuring optimal material utilization. Additionally, automating supply chain processes through BIM improves documentation accuracy, enhances coordination between stakeholders, mitigates delays, reduces cost overruns, and minimizes management inefficiencies commonly associated with rebar logistics.

The key contributions of this research are twofold. First, it introduces the integration of BIM, which automates documentation and drawings, such as Bar Bending Schedules (BBS), while simultaneously enhancing supply chain management through improved data accuracy and streamlined process efficiency. Second, it offers a practical solution for optimizing rebar usage through the adoption of special-length priority optimization, which adheres to specific minimum requirements. This dual focus bridges the gap between advanced digital tools and practical implementation, contributing to more efficient and precise construction workflows. Furthermore, this research represents a significant contribution as one of the first to adopt this innovative approach, establishing a profound novelty in the field. It enriches the understanding of the special-length priority rebar concept while addressing critical gaps in existing research.

Nonetheless, this research is organized in the following manner. Section 1 presents the problem definitions, related studies, and the study’s gaps and objectives. Section 2 describes the literature review regarding the problems and their existing solutions. Section 3, Section 4 and Section 5 present research methodology and system development, a case study application to validate the proposed system, and an in-depth discussion of the results, potential, and current limitations that can be addressed in future endeavors, respectively.

2. Literature Review

2.1. Conventional Rebar Workflow

The conventional rebar work process is initiated by interpreting structural drawings, which reflect the results of the structural design analysis. Quantity Take-Off (QTO) is a fundamental step in rebar procurement. This includes measuring and calculating rebar usage required in a construction project [10]. The rebar arrangement in structural drawings is meticulously analyzed to categorize it into various lengths and quantities based on structural members. These parameters are optimized to reduce cutting waste and rebar usage. Consequently, a Bar Bending Schedule (BBS) is prepared, including detailed information needed before placing orders with the steel mill. Rebars may either be prefabricated in a plant or processed (cut and bent) on-site. While this conventional method relies on expertise to ensure accuracy in analyzing drawings and identifying, counting, and calculating rebar, it remains prone to errors due to its manual execution, especially when there are adjustments in the design and project. In addition, it is challenging to ensure on-time deliveries of rebar supplies due to the unpredictable conditions of the construction site in practice [11]. Interdependency between construction processes impacts rebar procurement, forming unnecessary waste and delays in the supply chain [12]. These common issues are caused by a lack of a systematic data management system for rebar procurement and supply chain coordination.

2.2. BIM-Based Rebar Workflow

Building Information Modeling (BIM) is a process where various teams can use various functions to streamline models and data information [13]. BIM has demonstrated significant potential to enhance the efficiency of rebar workflows by ensuring data accuracy and automating the calculation of rebar lengths and quantities. The study [10] compared the rebar QTO between the conventional method and the BIM approach, resulting in a 0.6742% difference with no structural elements limitation and lessened design revision time. By incorporating both graphical and mathematical data, BIM dynamically updates in response to modifications made in the model. This capability improves precision in rebar data while minimizing human errors inherent in conventional methods [14,15]. The study by Khosakitchalert et al. [16] proposed a method to improve the accuracy of the quantities of elements retrieved from incomplete BIM models by using BIM-based clash detection.

The BIM-based rebar workflow begins with data collection and rebar optimization before model creation. Structural design and analysis provide critical data and specifications, which serve as a primary source for rebar optimization. A BIM model is then created from scratch, starting with the structural frame construction and followed by the placement of rebars according to optimization results. BIM software, in this context, Autodesk Revit 2024 version retains all component data within the model, enabling efficient documentation generation, such as BBS. Research has shown that a BIM-based BBS preparation algorithm that integrates rebar specifications and optimizes the rebar work process can reduce cutting waste by up to 33% while enhancing productivity and minimizing error [8].

2.3. Conventional and Special-Length Priority Optimizations

The rebar cutting waste issue has been a focus in the construction industry for decades, prompting the development of various optimization methods to enhance efficiency. Despite these efforts, achieving an acceptable rebar waste threshold of 5% remains challenging. For instance, Khalifa et al. [17] reported a cutting waste rate of 5.15% using stock-length rebar, while Khondoker [18] optimized cutting patterns for reinforced concrete (RC) frames, achieving a reduced waste rate of 2.69%. Similarly, Zheng et al. [19] examined slab reinforcement but recorded a significantly higher waste rate of 14.49%. Other studies have attempted to optimize lap splice positions and lapping patterns [20,21], yet they still struggled to consistently keep waste below the 5% threshold.

Recent advancements in Building Information Modeling (BIM) have facilitated more effective approaches to rebar optimization. Studies [6,7] have explored the integration of special-length priority optimization with BIM to generate accurate and optimal rebar quantities for rebar procurement. These algorithms assess the total continuous rebar length for structural members, reducing lap splices by prioritizing special lengths. The remaining rebars are combined into optimal cutting patterns, further prioritizing special lengths. Additionally, bend deductions are incorporated within BIM for precise rebar length adjustment of post-bending by employing shape codes [22]. This approach ensures accurate rebar consumption, reducing rebar waste and costs. This integrated approach has proven effective, significantly reducing rebar waste to as low as 0.83–0.93% [6,7]. However, while special-length priority optimization has demonstrated substantial waste reduction, its practical implementation must account for various order constraints. These include minimum and maximum rebar lengths, batch quantity requirements, and preorder lead times, all of which are crucial to ensuring timely and efficient rebar delivery for construction activities.

2.4. Supply Chain Management for Rebar Work

Despite the advancements in optimization methods, rebar work can fail if rebar ordering, procurement, and delivery are not aligned with the project schedule. Misalignment may lead to installation delays, increased costs, and challenges in managing excessive inventory. Supply chain management (SCM) plays a crucial role in rebar procurement, ensuring the correct quantity of rebar is ordered well in advance and delivered before the installation phase. Construction phases must be carefully planned, taking into account on-site activities such as the preparation of temporary support systems and formwork assembly prior to the rebar layout. The working schedule must align with the sequential order of construction works to enable the preorder and timely delivery of subsequent batches of rebar. Key constraints must be defined to ensure proper rebar procurement, including the construction schedule, BBS, and minimum order requirements for special-length rebars. Establishing these parameters reduces supply lead time, prevents rebar shortages or excess usage, and ensures that rebar specifications meet project requirements with accuracy. A significant gap exists in integrating optimization methods with supply chain management to address procurement timing, supply chain alignment, and delivery scheduling, particularly in phased construction. Synchronizing rebar quantities with construction phases and leveraging BIM for supply chain optimization remain underexplored in the context of the special-length rebar approach. Hence, further research is warranted to track accurate rebar types, diameters, and quantities for the relevant construction phase, integrating rebar procurement management and BIM.

3. Methodology

The methodology establishes a systematic approach to managing rebar quantities within the supply chain of construction projects through BIM. Figure 1 demonstrates the concept diagram of the SCM-based rebar work process. Using the results of rebar special-length priority optimization, the structural model was constructed with BIM tools, and rebars were arranged accordingly. The BIM model stores the data and dynamically updates to reflect any modifications made throughout the project. The integration with BIM also enables semi-automatic or automatic generation of 2D drawings and BBS, which enhances efficiency while maintaining adaptability to project changes. The BBS is generated from 2D drawings derived from the BIM model aligning with optimization results, while the construction schedule is separately prepared. By leveraging BIM’s data consistency, an activity database is developed that integrates various rebar work trades and relevant quantities derived from the optimization results and the construction schedule. This database facilitates the calculation of purchased rebar quantities for each construction phase, ensuring an optimized and systematic procurement strategy. Additionally, the SCM process is reflected in the BIM model whenever any modification is made, ensuring that all the related data remain current. This approach enhances the SCM rebar work, aligning rebar orders with the construction schedule while maintaining flexibility to adapt to changes.

Conventionally, rebars are optimized floor by floor based on the construction phase. However, in the proposed methodology, special length is prioritized for rebar optimization, which generates special length covering more than two floors in column continuous rebars. In this case, the scheduling phase for SCM is determined based on the generated special lengths for column members. Consequently, structural members within the identified special-length range undergo optimization to determine rebar usage for each construction phase. In the conventional method, rebar quantities are calculated from 2D drawings and rebar schedules for rebar orders, which can be error-prone and require expertise. By diagnosing inefficiencies in conventional methods, this methodology introduces an innovative approach to rebar supply chain optimization, contributing to efficient rebar detection for quantity calculation.

The workflow of the methodology illustrated in Figure 2 includes four main steps, as follows:

• Retrieving information: The activity database generated from the BIM-based BBS and the construction schedule retrieves information to determine rebar order phases based on optimization results. This information involves the sequence of activities and their earliest start date (ESD), latest finish date (LFD), and duration, as well as optimization results associated with the schedule.
• Selecting relevant rebar data: Rebars in the activity database are sorted by diameters and special lengths, facilitating quick identification of the rebar order phase. The relevant rebars are selected to meet special-length order requirements for each schedule phase.
• Computing the optimal rebar quantity for the order phase: After selecting relevant rebar data, the order phases can be investigated to conform to the minimum quantity and lead time for the rebar order. Rebar quantities of each activity and each phase are calculated and optimized, satisfying the quantity and lead-time constraints.
• Ordering rebar and conducting subsequent works: Rebars are ordered based on a phase-wise schedule, ensuring alignment with planned construction activities.

Following information retrieval and the selection of relevant rebar data, the process advances to determining the optimal rebar quantity and corresponding order timing that satisfies the requirements for special-length rebar, denoted as Step 3, as illustrated in Figure 2. This step is crucial for organizing and aligning the optimized rebar into order batches while considering construction site conditions to ensure efficient procurement and installation. The following equations are applied to calculate the rebar quantity of a particular activity and that of SCM according to the construction phase identified for special-length order.

Equation (1) demonstrates the quantity of activity by combining quantities of variable rebar diameters within it.

$$q_{ac{t}_i}={\displaystyle \sum }_{d=1}^n{q}_d$$

(1)

where

$q_{ac{t}_i}$: Quantity of special-length rebar required for each activity within the schedule

$q_d$: Rebar quantity of diameter d

$n$: Number of rebar diameters

Then, the rebar quantity for the supply chain can be calculated by summing up all the activities within the corresponding construction phase based on the schedule, as in Equation (2).

$$Q_{SCM}={\displaystyle \sum }_{i=1}^l{q}_{ac{t}_i}$$

(2)

where

$Q_{SCM}$: Rebar quantity of diameter d to be procured based on SCM

$q_{ac{t}_i}$: Quantity of special-length rebar required for each activity within the schedule

$l$: Number of activities in the schedule phase/each floor of diameter d

Additionally, Equations (3) and (4) are the conditions that must be conformed to satisfy the special-length order requirements.

$$Q_{SCM}\ge Q_{sp\_order}$$

(3)

$$T_{order}=T_{schedule}-\Delta t$$

(4)

where

$Q_{sp\_order}$: Minimum order quantity of special length

$T_{schedule}$: Time for rebar installation according to the schedule

$\Delta t$: Lead time for special length

4. Case Study Application

To verify the developed method of rebar process and the SCM optimization algorithm, the case study should have more than 10 floors with variable column and beam members, yet it should not be too complex to conduct the comprehensive application of the method. A high-rise building with a reinforced concrete structure is selected, comprising 24 floors—7 floors underground and 17 above ground—and the structural members (columns and beams) are normalized to clarify the application process. Three types of columns (C1, C1A, and C2A) are selected for the simplified column layout and four types of beams (G1, G2, G3, and B1) for the beam layouts, as illustrated in Figure 3. The column layout comprises 24 columns, and the size of the continuous columns decreases from the bottom to the top of the building. Additionally, rebar size is denoted by ‘D’, 29 mm diameter rebars are employed in columns, and 22 mm diameter rebars in beam members.

Table 1. Variable sizes of structural column and beam members.

Members	Rebar Diameter (mm)	Dimension (mm × mm)
Column, C1	D29	C1-1(B7F-F5)	C1-2(F5-F7)	C1-3(F7-F9)	C1-4(F9-F13)	C1-5(F13-RF)
		1000 × 1400	1000 × 1200	900 × 1200	900 × 900	700 × 700
Column, C1A	D29	C1A-1(B7F-F5)	C1A-2(F5-F9)	C1A-3(F9-F11)	C1A(F11-RF)
		900 × 1400	900 × 1200	800 × 800	700 × 700
Column, C2A	D29	C2A-1(B7F-F5)	C2A-2(F5-RF)
		900 × 900	700 × 700
Beam, G1	D22	G1(B7F-RF)
		600 × 600
Beam, G2	D22	G2-1(B7F-F8)	G2-2(F9-RF)
		1400 × 600	600 × 600
Beam, G3	D22	G3-1(B7F-F8)	G3-2(F9-RF)
		1400 × 600	600 × 600
Beam, B1	D22	B1(B7F-RF)
		600 × 600

summarizes the different sizes of structural columns and beams as they decrease from the bottom to the top of the building.

Special-length priority optimization of column rebars is initially executed on the continuous rebars that extend from the foundation to the top slab. The optimization procedure is as follows: (1) the total length of column rebars from the foundation to the top floor slab is calculated, (2) the number of special lengths is calculated by dividing with the optimal reference length 12,000 mm, reducing the number of lap splices, (3) the total length is re-calculated with reduced lap splices, and (4) special length is generated by dividing the new total length with the number of special-length rebars. The detailed workflow of this procedure can be observed in the study in [6]. The obtained special length of the first group from the bottom to the top is employed in the remaining groups of column rebars, and the remaining length of each group is calculated.

Figure 4 demonstrates the groups of continuous rebars in the continuous column, C1A. The total continuous length from the foundation to RF was calculated as 140,472 mm, divided by 12,000 mm optimal length, generating 12 special-length rebars. This reduced the number of lap splices by 12 from the original design. The new total length was revised with the reduced number of lap splices, obtaining a special length of 10.2 m, which was employed to optimize rebar groups 2 to 6, and their remaining lengths were calculated.

The same calculation process was conducted on columns C1 and C2A. The rebar optimization results for the continuous columns C1, C1A, and C2A are summarized in

Table 2. Special-length rebar optimization of continuous columns.

Description		Rebar Diameter (mm)	${\mathit{L}}_{\mathit{t}\mathit{o}\mathit{t}\mathit{a}\mathit{l}}$ (mm)	${\mathit{n}}_{\mathit{s}\mathit{p}}$	$\mathbf{Reduced}{\mathit{n}}_{\mathit{l}\mathit{a}\mathit{p}}$	$\mathbf{New}{\mathit{L}}_{\mathit{t}\mathit{o}\mathit{t}\mathit{a}\mathit{l}}$ (mm)	${\mathit{L}}_{\mathit{s}\mathit{p}}$ (m)	${\mathit{L}}_{\mathit{r}\mathit{e}\mathit{m}\mathit{a}\mathit{i}\mathit{n}}$ (m)
C1	Foundation-RF	D29	140,472	12	12	122,112	10.2	-
	Foundation-F17	D29	134,361	12	11	117,531	10.2	5.34
	Foundation-F11	D29	101,781	9	8	89,541	10.2	7.95
	F1-F11	D29	58,870	5	5	51,220	10.2	0.22
	F1-F9	D29	48,010	5	3	43,420	10.2	2.62
	F1-F7	D29	37,150	4	2	34,090	10.2	3.49
	F1-F5	D29	26,290	3	1	24,760	10.2	4.36
C1A	Foundation-RF	D29	140,472	12	12	122,112	10.2	-
	Foundation-F17	D29	134,361	12	11	117,531	10.2	5.34
	Foundation-F9	D29	90,921	8	7	80,211	10.2	8.82
	F1-F9	D29	48,010	5	3	43,420	10.2	2.62
	F1-F7	D29	37,150	4	2	34,090	10.2	3.49
	F1-F5	D29	26,290	3	1	24,760	10.2	4.36
C2A	Foundation-RF	D29	140,472	12	12	122,112	10.2	-
	Foundation-F17	D29	134,361	12	11	117,531	10.2	5.34
	Foundation-F5	D29	69,201	6	5	61,551	10.2	0.36
	F1-F5	D29	26,290	3	1	24,760	10.2	4.36

.

The remaining lengths generated from the optimization of continuous column rebars were combined into special-length cutting patterns to minimize cutting waste. The 8.8 m special length covered the cutting pattern of the remaining lengths of C1: 5.34 m, 7.95 m, 0.22 m, 2.62 m, 3.49 m, and 4.36 m. The 8.9 m cutting pattern included 5.34 m, 8.82 m, 2.62 m, 3.49 m, and 4.36 m, the remaining lengths of C1A. Also, the 9.7 m cutting pattern provided a combination of 5.34 m, 0.36 m, and 4.36 m.

Figure 5 demonstrates the arrangement of beam rebars. The beam members G1, G2, G3, and B1 were used for the beam layouts of the entire building, in which their sizes decreased on higher floor levels. By adopting the beam rebar optimization algorithm from the study [5], top and bottom beam continuous rebars were optimized in special lengths, depending on the beam spans of X-direction and Y-direction. It is worth noting that the total lengths of beam continuous rebars were varied bit by bit due to the change in column sizes within the beam spans. This optimization generated 10.4 m and 10.5 m for the X-direction spans and 8.8 m, 8.9 m, and 9 m for the Y-direction spans. Consequently, the discontinuous top and bottom rebars were calculated and combined in optimal cutting patterns, generating special-length cutting patterns of 9.3 m, 9.4 m, 11.4 m, and 11.5 m.

The special-length optimization resulted in an order quantity of 496.44 tons of rebar for column members and 523.79 tons for beam members, with rebar waste of 0.43% and 2.49%, respectively, for D29 and D22 rebars. Although the target of near-zero waste (less than 1%) was not achieved, the total rebar quantity for the case study building amounted to 1020.23 tons, with a rebar cutting waste of 1.49% from the actual rebar quantity. This is still lower than the typical waste rate of 3–5% [6].

Once the special-length optimization was completed, the optimization results were integrated into a BIM platform to transform them into a 3D model. This process is illustrated in Figure 6, which outlines the procedure of activity database preparation for the case study. The BBS data were retrieved from the 3D model and organized into a bill of quantity (BOQ), in which the BOQ code provides information on the type of structure, type of member, floor, member, rebar, and serial number of the corresponding rebar within the project.

To streamline construction planning and SCM, the BOQ was further integrated with the project’s construction schedule. A summary of the construction schedule is demonstrated in Figure 7, which indicates the duration, start date, and end date of the construction stages. Subsequently, an activity database was set up by linking the BOQ and the construction schedule. This database served as a central repository for managing the correlation between rebar quantity requirements and scheduling milestones. Uniformat [23] is employed for the code preparation for the documentation. The activity code indicates the structure type, floor level, structural member, and rebar. By linking the rebar data from the BIM with information from the construction schedule, this approach significantly enhanced project coordination. Furthermore, this integration provided rebar tracking while facilitating real-time updates on rebar usage and work.

Table 3. Activity data of C1A columns.

Activity Code	Activity Name	Duration (days)	Early Start Date	Late Finish Date	Predecessors	Quantity (ton)	BOQ Code
B1010-B7-C1A-R	Column C1A main rebar	14	12/17/24	1/3/25	5, 7	17.273	B1010-B7-C1A-R-01A0
B1010-B5-C1A-R	Column C1A main rebar	14	4/29/25	5/16/25	19, 21	17.273	B1010-B5-C1A-R-01A0
B1010-B3-C1A-R	Column C1A main rebar	14	8/7/25	8/26/25	33, 35	17.273	B1010-B3-C1A-R-01A0
B1010-B1-C1A-R	Column C1A main rebar	14	11/13/25	12/1/25	47, 49	17.273	B1010-B1-C1A-R-01A0
B1010-01-C1A-R	Column C1A main rebar	14	1/1/26	1/20/26	54, 56	43.183	B1010-01-C1A-R-01A0
B1010-03-C1A-R	Column C1A main rebar	14	4/9/26	4/27/26	68, 70	43.183	B1010-03-C1A-R-01A0
B1010-05-C1A-R	Column C1A main rebar	14	7/16/26	8/4/26	82, 84	23.442	B1010-05-C1A-R-01A0
B1010-07-C1A-R	Column C1A main rebar	14	10/22/26	11/9/26	96, 98	14.806	B1010-07-C1A-R-01A0
B1010-09-C1A-R	Column C1A main rebar	14	1/27/27	2/15/27	110, 112	12.338	B1010-09-C1A-R-01A0
B1010-12-C1A-R	Column C1A main rebar	14	6/24/27	7/13/27	131, 133	12.338	B1010-12-C1A-R-01A0
B1010-14-C1A-R	Column C1A main rebar	14	9/30/27	10/19/27	145, 147	9.870	B1010-14-C1A-R-01A0
B1010-16-C1A-R	Column C1A main rebar	14	1/5/28	1/24/28	159, 161	16.148	B1010-16-C1A-R-01A0

illustrates a part of the activity database, providing the code, name, duration, early start date, late finish date, predecessors, quantity, and BOQ code of C1A columns at every floor level. Predecessors show the construction works that must be completed before a particular activity, as identified in the construction schedule.

The BOQ code directs to the BBS, where specific rebar information such as diameter, number of rebars, number of structural members, rebar shape, bar length, total length, and quantity can be tracked efficiently.

Table 4. BBS rebar information of column C1A main rebars.

Building name: 000 Structure: Column Floor: B7 Member: C1A BOQ code: B1010-B7-C1A-R-01A0 Sheet title: C1A rebar
No	BBS Code	Diameter (mm)	No of Rebars	No of Members	Shape	Length (mm)				Bar Length (m)	Total Length (m)	Weight (ton)
						A	B	C	D
1	M01	D29	16	12	12	350	9850			10.2	163.2	10.575
2	M02	D29	4	12	12	350	9850			10.2	40.8	2.644
3	M03	D29	8	12	12	350	9850			10.2	81.6	5.288
18.507

demonstrates the BBS information tracked for the main column rebars of column C1A on the basement, B7F.

Once the activity database was completed, the rebar order phase was determined based on the results of column optimization to align with SCM. As summarized in Table 2, the rebar optimization of column members resulted in a special length of 10.2 m, which indicates that 10.2 m column rebars will be initially installed and fabricated. Therefore, rebars of the structural members located within the range of 10.2 m column rebars were considered in the same order batch. Based on the generated positions of the columns’ main rebars in the BIM model, a total of 12 order batches were determined, and the activities included in each batch are shown in

Table 5. Determining rebar order batches.

Order Batch	Floors	Activities
		Columns (D29 Rebars)	Beams (D22 Rebars)
1	BF7, BF6	B1010-B7-C2A-R, B1010-B7-C1A-R, B1010-B7-C1-R	B1010-B6-G1-R, B1010-B6-G2-R, B1010-B6-G3-R, B1010-B6-B1-R, B1010-20-B6-DR
2	BF5, BF4	B1010-B5-C2A-R, B1010-B5-C1A-R, B1010-B5-C1-R	B1010-B5-G1-R, B1010-B5-G2-R, B1010-B5-G3-R, B1010-B5-B1-R, B1010-B5-DR, B1010-B4-G1-R, B1010-B4-G2-R, B1010-B4-G3-R, B1010-B4-B1-R, B1010-B4-DR
3	BF3, BF2	B1010-B3-C2A-R, B1010-B3-C1A-R, B1010-B3-C1-R	B1010-B3-G1-R, B1010-B3-G2-R, B1010-B3-G3-R, B1010-B3-B1-R, B1010-B3F-DR, B1010-B2-G1-R, B1010-B2-G2-R, B1010-B2-G3-R, B1010-B2-B1-R, B1010-B2-DR
4	BF1	B1010-B1-C2A-R, B1010-B1-C1A-R, B1010-B1-C1-R	B1010-B1-G1-R, B1010-B1-G2-R, B1010-B1-G3-R, B1010-B1-B1-R, B1010-B1-DR
5	F1, F2	B1010-01-C2A-R, B1010-01-C1A-R, B1010-01-C1-R	B1010-01-G1-R, B1010-01-G2-R, B1010-01-G3-R, B1010-01-B1-R, B1010-01-DR, B1010-02-G1-R, B1010-02-G2-R, B1010-02-G3-R, B1010-02-B1-R, B1010-02-DR
6	F3, F4	B1010-03-C2A-R, B1010-03-C1A-R, B1010-03-C1-R	B1010-03-G1-R, B1010-03-G2-R, B1010-03-G3-R, B1010-03-B1-R, B1010-03-DR, B1010-04-G1-R, B1010-04-G2-R, B1010-04-G3-R, B1010-04-B1-R, B1010-03-DR
7	F5, F6	B1010-05-C2A-R, B1010-05-C1A-R, B1010-05-C1-R	B1010-05-G1-R, B1010-05-G2-R, B1010-05-G3-R, B1010-05-B1-R, B1010-05-DR, B1010-06-G1-R, B1010-06-G2-R, B1010-06-G3-R, B1010-06-B1-R, B1010-06-DR
8	F7, F8	B1010-07-C2A-R, B1010-07-C1A-R, B1010-07-C1-R	B1010-07-G1-R, B1010-07-G2-R, B1010-07-G3-R, B1010-07-B1-R, B1010-07-DR, B1010-08-G1-R, B1010-08-G2-R, B1010-08-G3-R, B1010-08-B1-R, B1010-08-DR
9	F9, F10, F11	B1010-09-C2A-R, B1010-09-C1A-R, B1010-09-C1-R	B1010-09-G1-R, B1010-09-G2-R, B1010-09-G3-R, B1010-09-B1-R, B1010-09-DR, B1010-10-G1-R, B1010-10-G2-R, B1010-10-G3-R, B1010-10-B1-R, B1010-10F-DR, B1010-11-G1-R, B1010-11-G2-R, B1010-11-G3-R, B1010-11-B1-R, B1010-11-DR
10	F12, F13	B1010-12-C2A-R, B1010-12-C1A-R, B1010-12-C1-R	B1010-12-G1-R, B1010-12-G2-R, B1010-12-G3-R, B1010-12-B1-R, B1010-12-DR, B1010-13-G1-R, B1010-13-G2-R, B1010-13-G3-R, B1010-13-B1-R, B1010-13-DR
11	F14, F15	B1010-14-C2A-R, B1010-14-C1A-R, B1010-14-C1-R	B1010-14-G1-R, B1010-14-G2-R, B1010-14-G3-R, B1010-14-B1-R, B1010-14-DR, B1010-15-G1-R, B1010-15-G2-R, B1010-15-G3-R, B1010-15-B1-R, B1010-15-DR
12	F16, F17, RF	B1010-16-C2A-R, B1010-16-C1A-R, B1010-16-C1-R	B1010-16-G1-R, B1010-16-G2-R, B1010-16-G3-R, B1010-16-B1-R, B1010-16-DR, B1010-17-G1-R, B1010-17-G2-R, B1010-17-G3-R, B1010-17-B1-R, B1010-17-DR, B1010-RF-G1-R, B1010-RF-G2-R, B1010-RF-G3-R, B1010-RF-B1-R, B1010-RF-DR

.

In this study, hoops for columns and stirrups for beams were considered to be prefabricated; therefore, the calculation of rebar quantities for SCM was focused on the main rebars of structural members that underwent special-length optimization. Once the order batch was identified from column rebar optimization results, the relevant activities mentioned in Table 5 were tracked in the activity database, as illustrated in Table 4. Subsequently, the quantities of each activity and rebar SCM were calculated by employing Equations (1) and (2), which are mentioned in the methodology section.

For the special-length order constraints, the minimum order quantity for the special length of each diameter rebar ($Q_{sp\_order}$) was considered as 15 tons for each order batch. Appendix A 

Table A1. Calculations of SCM rebar quantities.

Order Batch		1	2	3	4	5	6	7	8	9	10	11	12	Total
Columns (D29 rebars)	$q_{ac{t}_i}$ (ton)	7.403	7.403	7.403	7.403	12.338	12.338	9.102	6.169	6.169	6.169	6.169	4.935
		17.273	17.273	17.273	17.273	43.183	43.183	39.590	14.806	12.338	12.338	12.338	9.870
		8.637	8.637	8.637	8.637	24.676	24.676	23.938	12.995	8.637	6.169	6.169	4.935
	$Q_{SCM}$ (ton)	33.313	33.313	33.313	33.313	80.197	80.197	72.630	33.970	27.144	24.676	24.676	19.740	496.482
Beams (D22 rebars)	$q_{ac{t}_i}$ (ton)	2.656	2.656	2.656	2.656	2.656	2.656	2.656	2.681	2.681	2.681	2.681	2.681
		7.082	7.082	7.082	7.082	7.082	7.082	7.082	7.150	7.150	5.363	5.363	5.363
		2.568	2.568	2.568	2.568	2.568	2.568	2.568	2.568	1.948	1.948	1.970	1.970
		4.045	4.045	4.045	4.045	4.045	4.045	3.543	4.035	7.511	7.511	7.496	7.496
		4.482	4.482	4.482	4.482	4.482	4.482	3.967	4.651	8.638	8.638	8.799	8.799
			2.656	2.656		2.656	2.656	2.656	2.681	2.681	2.681	2.681	2.681
			7.082	7.082		7.082	7.082	7.082	7.150	7.150	5.363	5.363	5.363
			2.568	2.568		2.568	2.568	2.568	2.568	2.568	1.970	1.970	1.970
			4.045	4.045		4.045	4.045	3.978	4.035	7.511	7.496	7.496	7.496
			4.482	4.482		4.482	4.482	4.560	4.651	8.638	8.799	8.799	8.799
										2.681			2.681
										5.363			5.363
										1.948			1.970
										4.925			4.925
										7.511			7.496
	$Q_{SCM}$ (ton)	21.166	42.332	42.332	21.166	42.332	42.332	41.763	42.498	71.878	44.863	44.870	67.305	523.817
$Q_{SCM}$ (ton)/batch		54.479	75.645	75.645	54.479	122.529	122.529	114.393	76.468	99.022	69.539	69.546	87.045	1020.299

summarizes the calculation of rebar SCM quantities: D29 rebars for columns and D22 for beams, indicating that the rebar SCM quantity of each order batch aligned with the special-length order requirements by exceeding 15 tons. The final rebar SCM quantities were calculated as 496.48 tons for column members and 523.82 tons for beam members. A total of 1020.3 tons was generated for rebar quantities supplied on-site by employing a systematic approach for accurate quantities for specific phases of the construction schedule.

Table 6. Comparison of rebar quantities after rebar optimization and SCM optimization.

Description	Rebar Order Quantity After Optimization (ton)	Rebar Order Quantity After SCM Optimization (ton)	Difference
			ton	%
Column	496.440	496.482	0.042	0.0085
Beam	523.799	523.817	0.018	0.0034
Total	1020.239	1020.299	0.060	0.0059

compares the rebar quantities between rebar optimization and SCM optimization. For columns, the rebar quantity increased by 0.042 tons (0.0085%), while the beam rebar quantity increased by 0.018 tons (0.0034%). Overall, the total rebar quantity of SCM optimization increased by only 0.060 tons (0.0059%), demonstrating that the proposed method ensures accurate ordered quantities aligning with the construction schedule.

The lead time for special length ($\Delta t$) was regarded as two months in this study so that the calculated rebar SCM quantity of order batches could be preordered sequentially to satisfy the delivery time in the schedule.

Table 7. Estimation of rebar order time.

Order Batch	Floors	Early Start Date	Late Finish Date	$\mathbf{Lead}\mathbf{Time}\Delta \mathit{t}$	$\mathbf{Order}\mathbf{Date}{\mathit{T}}_{\mathit{o}\mathit{r}\mathit{d}\mathit{e}\mathit{r}}$
1	BF7, BF6	12/17/24	1/3/25	2 months	10/17/24
2	BF5, BF4	4/29/25	5/16/25	2 months	2/29/25
3	BF3, BF2	8/7/25	8/26/25	2 months	6/7/25
4	BF1	11/13/25	12/1/25	2 months	9/13/25
5	F1, F2	1/1/26	1/20/26	2 months	11/1/25
6	F3, F4	4/9/26	4/27/26	2 months	2/9/26
7	F5, F6	7/16/26	8/4/26	2 months	5/16/26
8	F7, F8	10/22/26	11/9/26	2 months	8/22/26
9	F9, F10, F11	1/27/27	2/15/27	2 months	11/27/26
10	F12, F13	6/24/27	7/13/27	2 months	4/24/27
11	F14, F15	9/11/27	9/29/27	2 months	7/11/27
12	F16, F17, RF	1/5/2028	1/24/2028	2 months	11/5/2027

summarizes the estimation of rebar order time, considering a minimum lead time of two months. Rebars of relevant batches can be ordered on the generated order date to avoid delivery delays. Note that in practice, logistics and temporary rebar storage on site also need to be considered to determine the rebar preorder time ($T_{order}$).

5. Discussion

Previous studies [6,7] introduced special-length priority optimization, where customized rebar lengths were prioritized over standard stock lengths available in the market. These studies demonstrated measurable reductions in rebar waste, 0.93% for beams and 0.83% for columns, by optimizing lap splice positions of main rebars. The total rebar length was optimized in special lengths, reducing lap splices, while the remaining rebars were arranged into cutting patterns that prioritized special lengths. Further advancing this approach, a study [8] explored the integration of special-length optimization with BIM in a large-scale infrastructural project involving diaphragm walls. This study not only optimized rebar usage but also automated Bar Bending Schedule generation within the BIM model, improving data accuracy and workflow efficiency. While these studies demonstrate the effectiveness of optimization methods, they do not establish a direct link between rebar optimization and supply chain management (SCM), as highlighted in the introduction. Without a well-structured SCM framework, the practical benefits of optimization remain limited. Given these findings, there is an opportunity to integrate optimization methods with BIM-driven SCM strategies, creating a systematic framework for improving data accuracy, material efficiency, and procurement planning.

This study proposed an innovative BIM-based approach to optimizing the rebar work process and SCM with a data-driven methodology to streamline rebar procurement. The activity database with its activity codes was constructed from special-length optimization results and BIM-driven data, facilitating the easy and fast tracking of accurate rebar data and improving both rebar optimization and supply chain processes. The contributions of this study highlight the integration of BIM to automate BBS data retrieval and streamline SCM of the rebar process, improving data precision and process efficiency. These can be widely generalized to large-scale construction projects that involve extensive rebar usage with challenging rebar supply and lead time. In addition, the study provides a practical solution for rebar usage optimization by using special length, ensuring compliance with specific requirements.

The adopted special-length priority optimization algorithm effectively reduces cutting waste to 1.49%. In comparison, existing studies report an average cutting waste exceeding 5%, with an acceptable threshold of 5%, highlighting the superior efficiency of the proposed approach. This reduction directly decreases rebar consumption, leading to both economic and environmental benefits. Previous studies [6,7] have demonstrated potential cost savings and CO₂ emission reductions ranging from 12.31% to 17.75%. The findings of this study align with those results, suggesting that similar benefits can be expected.

The proposed method integrated rebar optimization with a supply chain management system by leveraging BIM, automating the BBS generation, and coordinating with the construction schedule to develop an activity database. This integration addressed critical issues in conventional workflows, such as procurement errors and delivery delays. The developed activity database ensures that procurement timelines are aligned with rebar requirements, enabling real-time updates and tracking of rebar quantities. The contributions of this study extend beyond optimization and SCM alignment since the BIM integration introduces automation, minimizes manual errors, and enhances data accuracy. This can serve as a foundation for future research in automating construction workflows and improving efficiency in construction management.

As this study offers a structured approach to minimizing rebar supply chain misalignment, contractors, engineers, and procurement managers can leverage this approach to reduce rebar waste and mitigate delays related to unpredictable site conditions. Furthermore, this approach can help practitioners achieve greater accuracy in rebar estimation and improve coordination between project stakeholders. Moreover, encouraging the adoption of BIM and SCM optimization strategies through standardization guidelines and mandatory integration into public infrastructure projects could enhance the construction industry’s efficiency.

Despite the promising results, there are certain limitations to this research. The proposed method was tested on a simplified structural case project, and further studies are needed to assess its applicability to more complex geometries and configurations. The current case focuses solely on the primary structural frame, including columns and beams, without considering secondary elements such as slabs and walls. Future research should extend the proposed method to more complex structures with irregular geometries, where similar benefits to those observed in the simplified case are expected. Additionally, logistical and inventory management aspects, such as temporary on-site rebar storage, handling, and transportation constraints, were not comprehensively addressed and require further investigation to fully assess and enhance the real-world applicability of the approach. Since this pilot study primarily relied on manual implementation, future work should focus on programming and automating the proposed framework to enhance its practicality and applicability. Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning algorithms, offer promising solutions for addressing these challenges, as they have proven effective in a wide range of engineering applications. Moreover, data interoperability issues, software constraints, and the requirement for skilled personnel pose additional challenges to the proposed method. Addressing these concerns necessitates comprehensive and intensive personnel training and regular software updates to ensure effective implementation.

6. Conclusions

This research introduces an innovative methodology for enhancing the rebar work process and SCM by integrating BIM with a data-driven optimization approach. The outcomes demonstrated a notable improvement in rebar usage efficiency, reaching 98.51% compared to the planned 95%, while also streamlining procurement processes, thereby validating the effectiveness of the proposed method in a simplified high-rise building case study. The key findings of this study are summarized as follows:

• Employing special-length priority optimization effectively reduced cutting waste to 1.49% by reducing lap splices and combining rebars into efficient cutting patterns. The initial rebar optimization resulted in ordered quantities of 496.44 tons for D29 diameter column rebars and 523.79 tons for D22 beam rebars, totaling 1020.23 tons. These quantities were optimized by employing special lengths for main continuous rebars and special-length cutting patterns for discontinuous rebars. It is important to note that these ordered rebar quantities should be supplied for on-site usage.
• Integrating BIM with SCM processes automated key documentation, such as BBS/BOQ and the activity tracking database. This alignment addressed typical challenges regarding procurement errors and delivery delays, facilitating accurate forecasting and timely rebar delivery.
• This systematic approach enabled real-time updates and effective rebar tracking, ensuring smooth application on-site.
• The proposed method was validated in a simplified project that included 24 floors—17 floors above the ground and 7 underground floors. The case study demonstrated that optimizing column rebars led to 12 procurement batches. Each batch aligned the minimum order requirement of 15 tons of the same rebar diameter and met the two-month lead-time requirement.
• After implementing the proposed method of SCM optimization, the column rebar quantity was calculated as 496.482 tons and the beam rebar quantity as 523.817 tons, summing up to 1020.299 tons. Therefore, the proposed method increased an insignificant amount of 0.06 ton (0.0059%) in the total ordered quantity compared to the special-length rebar optimization while facilitating phase-wise optimized rebar usage.

Through the optimization results, rebars were grouped into order batches, demonstrating scalability and practical applicability to large-scale construction projects. In conclusion, this study provides a robust framework for optimizing the rebar process and aligning the construction schedule, leveraging BIM capabilities. Future research can expand upon this foundation by addressing logistical challenges and exploring broader applications in diverse construction scenarios, in addition to exploring its applicability to more complex structures with irregular geometries, which would expand the method to incorporate secondary elements. Additionally, future work should focus on automating the proposed framework to enhance its practicality and real-world applicability, with the potential integration of advanced AI techniques such as machine learning and deep learning.