The site selection for this research was guided by specific criteria to ensure a comprehensive and meaningful analysis. Considerations included the diversity of project locations, variation in project scales, and the extent of BIM implementation. Multiple case studies were chosen to provide a broad perspective on BIM clash detection, spanning different contexts to enhance the applicability of the findings. The projects were selected not only for their diversity but also for their well-documented BIM implementation, which enabled a detailed comparison of construction processes before and after the adoption of clash detection technology. Through this analysis, the study evaluates BIM’s role in enhancing operational efficiency, reducing errors, and optimizing costs, thereby reinforcing its exploratory and explanatory foundations.

This study employs a mixed-methods approach, integrating both quantitative and qualitative data collection techniques to evaluate the impact of BIM clash detection on construction efficiency, cost reduction, and project outcomes. The combination of numerical data and qualitative insights provides a comprehensive understanding of BIM’s effectiveness in minimizing rework and related expenses.

To assess the financial impact of BIM, cost data will be collected to compare BIM-integrated projects with non-BIM projects. This analysis will include both direct costs—such as labor, materials, and equipment associated with rework—and indirect costs, including delays, disruptions, and project inefficiencies. A comparative cost analysis will highlight the economic benefits of proactive clash detection in reducing rework and optimizing construction budgets.

In parallel, semi-structured interviews will be conducted with project managers, engineers, on-site workers, and BIM specialists. These interviews will capture practical insights into the effectiveness, challenges, and real-world implementation of BIM clash detection. Their open-ended format allows for an in-depth exploration of how BIM influences workflow efficiency, collaboration, and decision-making. Participants will also be encouraged to share their perspectives on the benefits of BIM and identify potential areas for improvement.

Additionally, a document review will be undertaken to analyze project reports, BIM clash detection logs, and rework records. This step will help validate the interview findings by cross-referencing qualitative perspectives with quantitative evidence. By examining documented clashes, resolutions, and modifications, the study will establish an evidence-based understanding of BIM’s effect on project outcomes. Combining cost analysis, interviews, and document review ensures a robust and triangulated research design, thereby enhancing the validity and reliability of the findings.

Descriptive statistical analysis will be employed for the quantitative data to quantify BIM's contributions to clash detection and rework reduction. Key statistical measures, including mean, median, standard deviation, and frequency distributions, will be used to identify trends in clash detection efficiency and cost reduction. Additionally, regression analysis will be applied to control for confounding variables such as project size, complexity, and duration, ensuring that BIM's direct impact on rework costs is accurately measured. By isolating BIM's effect on construction cost savings, the study aims to establish a clear link between clash detection efficiency and project financial performance.

A cost analysis will also be conducted to compare pre-and post-BIM implementation expenses, focusing on both direct costs, including labor, materials, and equipment, and indirect costs, such as project delays and inefficiencies. This financial assessment will provide tangible evidence of BIM's potential to enhance project efficiency, minimize waste, and optimize resource allocation.

The qualitative data from interviews and document reviews will be analyzed using thematic content analysis to identify recurring themes related to BIM adoption. This method will highlight key aspects such as BIM's impact on workflow efficiency, collaboration, communication, and project quality. Thematic coding will be applied to categorize data into meaningful insights that reveal BIM implementation's perceived benefits and challenges of BIM implementation. The qualitative findings will be integrated with the quantitative results to provide a holistic understanding of how BIM affects construction projects from an operational and financial perspective.

Descriptive statistical analysis will be employed to quantify BIM’s contribution to clash detection and rework reduction. Key measures such as the mean, median, standard deviation, and frequency distributions will be used to identify patterns in clash detection efficiency and cost savings. In addition, regression analysis will be applied to control for confounding variables such as project size, complexity, and duration, ensuring that BIM’s direct impact on rework costs is accurately isolated. By doing so, the study seeks to establish a clear link between clash detection efficiency and overall financial performance.

A cost analysis will also be carried out to compare expenses before and after BIM implementation. This assessment will consider both direct costs—labor, materials, and equipment—and indirect costs such as project delays and inefficiencies. The results will provide tangible evidence of BIM’s capacity to improve efficiency, minimize waste, and optimize resource allocation.

The qualitative data gathered from interviews and document reviews will be analyzed using thematic content analysis. This approach will identify recurring themes related to BIM adoption, with particular attention to workflow efficiency, collaboration, communication, and project quality. Thematic coding will be applied to organize responses into meaningful categories, highlighting both the perceived benefits and the challenges of BIM implementation. Finally, the qualitative findings will be integrated with the quantitative results to provide a comprehensive understanding of BIM’s impact on construction projects from both an operational and financial perspective.

Different construction methodologies will be compared based on cost, time, and quality to determine the most effective clash detection and rework strategies. A simulation-based approach will model and analyze rework strategies, reflecting the collected case studies' scale, complexity, and timeframe. This will provide insights into cost savings, efficiency improvements, and quality enhancements associated with each strategy.

BIM clash detection will also support identifying alternative rework strategies, such as substituting materials, adopting advanced construction technologies, or modifying construction sequences to reduce conflicts between structural and MEP systems. Each strategy will be assessed for feasibility, cost-effectiveness, and overall impact, ensuring optimal solutions are adopted.

In this context, A cost-efficiency analysis will evaluate the financial impact of various rework strategies, considering short- and long-term savings from reduced rework, increased efficiency, and improved project quality. This will assess each alternative under different conditions, visualizing potential risks and outcomes. This study will identify the most financially viable strategies for the collected case studies by systematically analyzing cost efficiency, ensuring that BIM implementation delivers maximum cost reduction and project efficiency benefits.

Return on Investment (ROI) will be calculated (see Equation 1) by comparing the total implementation costs with the quantified savings resulting from reduced rework and enhanced project performance to assess the financial feasibility of BIM implementation. The analysis will incorporate costs related to BIM software acquisition, personnel training, and maintenance while accounting for savings derived from fewer rework instances, improved scheduling, and increased construction accuracy.

ROI= (Total Benefits−Total Costs)/Total Costs ×100 (1)

Afterwards, a sensitivity analysis will be conducted to evaluate the impact of key cost variables on the overall financial feasibility of BIM. This analysis will assess variations in BIM software costs, training expenses, rework reduction rates, labor costs, material costs, and project timelines. To account for uncertainties, three scenarios will be examined: 1) The best-case scenario assumes maximum cost savings with minimal training expenses and a high rate of rework reduction. 2) The worst-case scenario considers higher-than-expected BIM implementation costs, limited efficiency improvements, and unexpected delays, and 3) The most likely scenario presents a balanced estimate based on industry standards and expected project conditions. To account for the time value of money, future costs and benefits will be discounted using the Net Present Value (NPV) (Equation 2). In equation 2, Bt represents benefit during year t, Ct refers to cost during year t, r stands for discount rate, and t means year. Additionally, the Benefit-Cost Ratio (BCR) will be used to determine economic feasibility (Equation 3), where a BCR greater than 1 signifies that benefits outweigh costs.

NPV=∑ B_t / (1+r)^t​​ −∑ C_t​​ / (1+r)^t (2)

BCR=∑B_t​ (1+r)^t / ∑C_t​/(1+r)^t (3)

The initial step in the framework implementation is developing the federated BIM models. This process starts with importing Revit models into the Navisworks environment to incorporate the structural, HVAC, and firefighting models. The step entails integrating structural, HVAC, and firefighting models to provide a holistic view of the whole building system. The model alignment is performed using the software's visualization capabilities to ensure that no element is missing or placed in the wrong position in the complex model. The integrated models give the various parties in the project a global picture of the overall building design and layout, as all components can be seen at once in their proper 3D position, as shown in Figure 2.

The implementation of clash detection using Autodesk Navisworks has been successfully executed in the selected case studies, ensuring effective coordination between structural components, HVAC ducts, piping, and fire protection systems. The process commenced with the setup of clash tests using the Clash Detective feature to identify potential conflicts. These tests were configured to detect hard clashes, where elements physically intersect, and clearance clashes, which indicate insufficient spatial separation. Once established, Navisworks analyzed the integrated Building Information Modeling (BIM) model, generating detailed clash reports highlighting conflicts with precise metadata, including element details, clash type, and location, facilitating a systematic approach to issue resolution.

Following detection, clashes were filtered based on discipline, severity, and location, prioritizing those risks to structural integrity and MEP coordination over minor interferences. The classification distinguished interdiscipline clashes between different systems from interdiscipline clashes, which arise within the same system. Minor clashes, such as a duct slightly intersecting a column, were resolved through small design adjustments, whereas major clashes, such as a pipe colliding with a slab or another MEP component, required substantial modifications, including rerouting systems or redesigning layouts.

To facilitate efficient resolution, clashes were systematically grouped and assigned to relevant engineers or designers, ensuring a coordinated approach to problem-solving. Navisworks enabled the categorization of clashes by type and discipline, allowing project teams to focus on critical issues first. Additionally, documentation and reporting tools were utilized to generate detailed records, images, descriptions, and status updates for each clash, enhancing transparency and communication among project stakeholders (see Figure 3).

The authors gathered various alternatives for opening, repairing, and reinforcing duct openings in concrete walls or slabs to determine the most cost-effective and high-quality approach. These methods were analyzed based on technical feasibility, material requirements, execution complexity, and overall impact on cost and quality to ensure efficiency and structural integrity in construction.

Core drilling was evaluated for precision and minimal structural impact for creating duct openings, while wall sawing provided deep, straight cuts for larger openings. Hammer drilling and chiselling offered a low-cost, portable solution but required additional finishing. Concrete cutting chainsaws were considered for versatile plunge cutting, though they required high maintenance. Hydraulic splitters were assessed for low-vibration concrete breaking, making them suitable for reinforced structures, while wire sawing allowed for precise, vibration-free cuts in large-scale openings.

After openings were created, different repair techniques were examined. Patching with concrete was durable and cost-effective, while epoxy injection provided strong bonding for crack repairs. Fibre-reinforced polymer (FRP) wraps improved tensile capacity, making them suitable for additional reinforcement. Grout injection was considered for void filling and structural stabilization, while self-leveling mortar ensured a smooth surface finish. Spray-applied concrete (shotcrete) allowed for fast, uniform repairs, and polyurethane foam injection offered quick expansion for sealing irregular gaps.

For cases requiring both opening and reinforcement, steel frame installation was analyzed for load-bearing enhancement and crack prevention. Prefabricated duct sleeves provided easy installation and a standardized finish, while cast-in-place concrete liners added rigidity and strength. Expanding cement mortar ensured tight void filling with strong adhesion, and precast concrete inserts minimized on-site curing time while maintaining quality. Through this systematic evaluation, the authors aimed to select the most efficient and cost-effective technique for duct openings, ensuring durability, ease of implementation, and overall project optimization in the selected case studies.

The cost analysis charts ( Figure 4) provide insights into the most cost-effective and high-quality methods for duct opening, repair, and reinforcement, and the implementation of BIM clash detection. For duct opening, Hammer Drilling & Chiseling (36.00 per sq. m) are the most affordable, while Wire Sawing (56.50 per sq. m) and Wall Sawing ($78.50 per sq. m) offer a balance between cost and efficiency.

For duct repair, Patching with Concrete (33.50 per sq. m) are cost-effective solutions, while FRP Wraps (105.00 per sq. m) provide greater reinforcement at a higher cost. For opening and reinforcing simultaneously, Prefabricated Duct Sleeves (80.00 per sq. m) and Cast-in-Place Concrete Liners ($82.50 per sq. m) offer higher structural durability. In this regard, the BIM clash detection system requires an initial investment of $5,305.00 but reduces rework and error costs, making it a valuable long-term investment.

Consequently, professional interviews were conducted to identify best practices for duct opening, repair, and reinforcement in the Egyptian construction industry, focusing on cost-effectiveness and structural integrity. The study involved at least ten experienced practitioners, including site managers, structural engineers, contractors, and equipment operators, all with a minimum of five years of experience. Interviews were conducted through face-to-face meetings, phone calls, and video conferencing, using a structured yet conversational guide to gather insights on various concrete cutting and repair techniques.

Participants rated their familiarity with different methods on a scale from 1 to 5, revealing high familiarity with core drilling, wall sawing, wire sawing, and concrete cutting chainsaws, while hydraulic splitters and steel frame installation showed mixed awareness. Responses were recorded, as revealed in Figure 5, analyzed, and compared to identify the most efficient and cost-effective solutions. The findings led to the selection of concrete sawing for its precision and versatility, grout filling for structural stability, and steel frame installation for long-term reinforcement. These choices were based on the practical experiences of industry professionals, ensuring their suitability for real-world applications.

The cost analysis (Figure 6) highlights the major contributors to duct opening, rework, and delays, emphasizing areas where cost reductions can be achieved. High labor expenses drive the actual cost per square meter (60) and Grout Injection ($45), along with material and tool costs. Concrete Cutting Chainsaws, while requiring a 8). The total area of duct and pipe openings amounts to 471.57 m², with the majority attributed to larger duct openings of 1.0 m × 0.5 m (35.9%) and 1.2 m × 0.5 m (22.2%), which substantially increase overall project costs. In contrast, smaller pipe openings with a diameter of 0.1 m contribute only 0.3% of the total opening area, reinforcing the observation that larger penetrations are the primary drivers of rework-related expenses. The cost of project delays is estimated at $3,350 per day, with labor costs and contractual delay penalties each accounting for 600 per day), equipment usage (250 per day). Cumulatively, the rework cost reaches $414,205.23 over 90 days. These findings underscore the critical importance of proactive clash detection through Building Information Modeling (BIM) and the implementation of improved coordination and planning strategies to reduce design errors, mitigate rework, and minimize avoidable delays and costs.

The cost-benefit analysis evaluates the financial feasibility of implementing BIM by comparing the investment cost against projected savings from reduced rework. BIM implementation costs $5,305, while the estimated savings from reduced rework over five years amount to $414,205.23. An inflation rate of 35.7% and a discount rate of 18.75% are applied to account for economic conditions.

The annual savings, assuming a constant reduction in rework costs, amount to $82,841.05 annually. However, after adjusting for inflation, the real value of yearly benefits decreases over time, starting at $61,056.71 in the first year and reducing to $17,997.47 in the fifth year. After discounting future benefits, these savings' Net Present Value (NPV) totals $123,020.59. Subtracting the initial BIM investment, the final NPV is $117,715.59, indicating a highly favourable return.

The BCR calculated by comparing total discounted benefits to costs results in 23.18, demonstrating that every $1 invested in BIM generates $23.18 in savings. Additionally, the Return on Investment (ROI) is exceptionally high at 7,704.23%, confirming that BIM implementation significantly reduces costs associated with construction rework and delivers substantial financial benefits.

The analysis demonstrates that the initial investment in BIM technology is recovered and generates substantial financial gains, making it a highly viable investment. The positive NPV of $117,715.59 from the first year indicates that BIM adds considerable value to the project, exceeding its costs and proving its financial soundness. Despite a high discount rate of 18.75%, the strong returns on investment confirm BIM's ability to provide long-term financial benefits while considering opportunity costs. Additionally, by accounting for a high inflation rate (35.7%), the analysis ensures a realistic and conservative projection of future cash flows, preventing overestimating benefits.

With a BCR of 23.18, the benefits of BIM are 23 times higher than its costs, reinforcing its financial feasibility. This high ratio also indicates significant cost savings due to reduced rework and improved project coordination, making BIM an efficient tool for large-scale construction projects. A higher BCR implies lower financial risk, as the extensive benefits buffer against potential cost overruns or delays. Similarly, the exceptionally high ROI of 7,704.23% highlights the disproportionate financial returns compared to the setup costs, proving that BIM implementation is cost-effective and highly profitable.

From a strategic perspective, BIM adoption provides a competitive advantage, as construction projects typically operate on slim profit margins. The financial superiority of BIM-supported projects establishes an industry benchmark, encouraging widespread adoption. This financial viability is crucial for decision-makers, justifying the required resources for BIM implementation. Furthermore, BIM enhances operational efficiency by reducing design clashes, rework, and resource wastage, leading to better labor and material management. Including high inflation and discount rates strengthens the robustness of the financial analysis, demonstrating that BIM remains beneficial even in adverse economic conditions.

For stakeholders, this data-driven financial assessment fosters confidence in BIM adoption. A well-documented ROI and cost-benefit analysis solidify BIM's role as a strategic investment, enhancing the project's attractiveness to investors and business partners. The demonstrated financial and operational gains position BIM as a transformative tool in construction, ensuring long-term profitability and efficiency in large-scale projects.

The sensitivity analysis confirms the financial viability of BIM implementation by evaluating the effects of discount rate, inflation rate, and rework reduction percentage on key financial metrics. As the discount rate increases from 10% to 30%, NPV declines but remains positive, indicating strong financial returns even at higher opportunity costs. At 10%, NPV is significantly high, while at 30%, it remains positive, proving project feasibility. BCR decreases with increasing discount rates but stays above 1, ensuring benefits outweigh costs, as rendered in Figure 7. Inflation impacts NPV by reducing the real value of future savings, with 20% inflation yielding a high NPV and BCR, while 50% inflation results in lower but viable financial outcomes. ROI remains constant across all inflation scenarios, based on nominal values (see Figure 8). As the extent of rework reduction increases from 50% to 100%, all financial indicators improve. Even at a 50% reduction, NPV remains positive, proving BIM's efficiency at moderate performance levels. With an 80%-100% reduction, NPV and BCR rise sharply, indicating substantial cost savings and stronger financial returns, as seen in Figure 9. The findings reinforce that, despite economic fluctuations, BIM remains a highly beneficial investment, providing cost efficiency, reduced rework, and financial predictability, with an NPV of $117,715.59, BCR of 23.18, and ROI of 7,704.2.

The outcomes of this study yield critical implications for both the theoretical advancement of construction informatics and the practical optimization of project delivery processes. Empirically establishing a quantitative link between unresolved spatial clashes and their associated financial ramifications reframes BIM clash detection from a predominantly technical exercise to a strategically essential function for risk mitigation and cost control. The disproportionate financial burden imposed by large-scale duct clashes, as demonstrated in the analysis, offers a predictive metric for prioritizing clash resolution based on dimensional and typological parameters—enabling more targeted, cost-effective interventions during preconstruction planning. Furthermore, the integration of BIM-based clash detection with delay cost modeling contributes a novel evaluative framework that can be operationalized by contractors, engineers, and project managers to inform decision-making regarding resource allocation, contingency planning, and digital coordination protocols.

On a broader scale, the findings substantiate the economic justification for early investment in BIM technologies and inter-disciplinary collaboration during the design phase, particularly in contexts where rework costs and schedule penalties constitute a significant portion of project risk. For academic and industry researchers, the study introduces a methodological foundation for further exploration of BIM’s intersection with construction economics, including the integration of real-time cost feedback loops, digital twin simulations, and AI-enhanced clash resolution mechanisms. As such, the research contributes not only to the refinement of current BIM practices but also to the evolving discourse on data-driven construction management and digital transformation in the AEC industry.

All research activities were conducted in full compliance with international and national ethical standards governing scientific research. The study followed the principles outlined in the Declaration of Helsinki (Association, 2013), the (Council for International Organizations of Medical Sciences (CIOMS, 2016), and the Ethics of Scientific Research in Egypt: Current Status and Future Outlook (Scientific Research & Technology, 2022). These frameworks emphasize the core principles of respect for human dignity, beneficence, justice, transparency, and informed consent, which were strictly upheld throughout the research process. Ethical approval for this study was obtained from the Research Ethics Committee of The British University in Egypt (BUE) under approval code BUE-REC-2025-014. The Committee operates in accordance with the national ethical governance structure established by Egypt’s Academy of Scientific Research and Technology (ASRT) and its National Committee for Bioethics.

Before data collection, all participants were provided with clear and comprehensive information about the study’s purpose, procedures, potential risks, and benefits. Participants were assured of the confidentiality and anonymity of their responses, and all data were securely stored and used solely for academic and scientific purposes. The ethical framework of this study fully aligns with Egypt’s national code of ethics for research, which integrates international standards with Egyptian cultural and legal contexts, as detailed in Ethics of Scientific Research in Egypt: Current Status and Future Outlook (ASRT, 2022, pp. 68–71, 109–124, 562–597)

The author(s) declare that there is no competing interest.