Optimizing Structural Slab Selection for High-Rise Construction: Applied Value Engineering for Cost-Performance Balance

Abstract

The slab system can account for a substantial portion of the structural cost; an optimized choice is essential for the financial success of a project. Despite its importance, existing research often relies on limited pairwise comparisons or single-criterion analyses (e.g., cost only), failing to provide a holistic framework. A significant gap exists in the application of a formal, quantitative Value Engineering (VE) approach that systematically balances function against cost. This study aims to fill this gap by developing a robust multi-criteria decision-making (MCDM) model to determine the optimal structural slab system for high-rise buildings based on the principles of Value Engineering. Unlike previous studies limited to pairwise comparisons or single-criterion analyses, this research simultaneously evaluates eight diverse slab alternatives across eight weighted performance criteria, providing a comprehensive value-based framework for systematic slab selection. First, eight key evaluation criteria were identified and weighted using the Step-wise Weight Assessment Ratio Analysis (SWARA) method, based on input from a panel of industry experts. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to evaluate the performance of eight distinct slab alternatives, including conventional, voided, and precast systems. The TOPSIS ranking scores were then integrated with normalized cost data to calculate a Value Engineering index, enabling quantitative comparison and final ranking of alternatives. The main finding revealed that the Post-Tension Slab offers the highest value (VE score = 2.467), achieving a superior balance of high performance—particularly in speed and structural efficiency—and low normalized cost. Interestingly, the traditional Solid Slab ranked a close second (VE score = 2.418). Practically, this study provides project managers, developers, and engineers with a transparent, data-driven decision-making tool to justify slab selection beyond mere cost-cutting, ensuring an optimal balance between cost, schedule, and functional performance. The study provides project managers, developers, and engineers with a transparent, data-driven decision-making tool to justify slab selection beyond cost considerations.

1. Introduction

In large and complex construction projects, selecting an appropriate structural slab system represents a strategic decision that fundamentally shapes project outcomes across technical, economic, and operational dimensions. The selection of a structural slab system is one of the most consequential decisions in the design and construction of high-rise buildings [1]. As a primary structural component, the flooring system has a profound impact on the project’s financial viability, construction schedule, and long-term performance [2]. The structural frame typically accounts for 20–25% of the total project cost, and within this, the slab system can constitute as much as 40–60% of the structural cost alone [3]. Consequently, an optimized slab selection can yield substantial savings in terms of materials and financing. This decision is critical not only for cost control but also because it profoundly influences construction speed, architectural design flexibility, and long-term serviceability of the building.

The complexity of this decision arises from the numerous, often conflicting criteria that must be balanced. A system that excels in one area, such as speed of construction, may be deficient in another, such as material cost or acoustic insulation. Conversely, a system chosen for its aesthetic appeal may complicate the integration of Mechanical, Electrical, and Plumbing (MEP) services. Navigating these inherent trade-offs, which involve factors such as structural efficiency, fire resistance, material availability, and constructability, requires a systematic framework that moves beyond simple intuition or single-criterion analysis. However, the absence of a structured, multi-criteria framework that systematically addresses these complexities often leads to suboptimal decisions based on limited analyses or past precedents, underscoring the critical need for a comprehensive approach that can quantitatively evaluate competing alternatives across diverse performance dimensions.

Existing research on slab selection predominantly relies on limited pairwise comparisons or single-criterion analyses that focus narrowly on cost or structural capacity, failing to capture the multidimensional nature of this decision and the interdependencies among performance criteria. This study fills a critical gap by integrating a formal Value Engineering framework with advanced Multi-Criteria Decision-Making methods (SWARA-TOPSIS), enabling a systematic and quantitative evaluation that balances functional performance against cost across eight comprehensive criteria. This approach provides a holistic decision-support tool that was previously absent in prior research.

This study addresses this challenge by applying the principles of Value Engineering (VE) to the slab selection process. VE provides a systematic methodology for optimizing the value, which is defined as the ratio of function to cost. This study evaluates eight distinct structural slab alternatives—solid, flat, waffle, hollow ribbed, post-tensioned, precast, composite, and bubble deck slabs—to determine which system offers the highest overall value for high-rise applications. It is assumed that all alternatives are structurally and architecturally viable post-design, allowing for a focused comparison of their inherent value propositions. This paper implements Value Engineering through a structured hybrid Multi-Criteria Decision-Making approach. The methodology employs the Step-wise Weight Assessment Ratio Analysis (SWARA) to determine objective criteria weights from expert input, followed by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to evaluate slab performance. These quantitative techniques are integrated with normalized cost analysis to calculate a comprehensive Value Engineering index, defined as the ratio of function to cost.

The primary contribution of this study is its originality. Simultaneously, the individual methods are established, and their integrated application within a formal VE framework to solve the slab selection problem for high-rise buildings represents a novel and holistic contribution to the existing body of knowledge. While SWARA and TOPSIS are individually well-established MCDM methods, their combined application has not been previously employed for selecting structural slab systems in high-rise buildings, where integrating these methods with formal Value Engineering principles to simultaneously optimize multiple interdependent criteria represents a methodological advancement in construction decision-making.

The primary objective of this research is to develop a comprehensive Value Engineering-based decision framework for the optimal selection of structural slab systems in high-rise building projects. Thus, three main research questions arose: RQ1: What are the key performance criteria that influence the selection of structural slab systems, and what are their relative importance weights as determined by industry experts? RQ2: How do different structural slab alternatives (solid, flat, waffle, hollow ribbed, post-tensioned, precast, composite, and bubble deck) perform across multiple evaluation criteria when assessed systematically? RQ3: Which structural slab system offers the highest value when functional performance is quantitatively balanced against cost using a formal Value Engineering approach?

2. Literature Review

A comprehensive literature review was conducted to establish a robust framework for this research. The review was structured to cover three critical areas. First, it examined previous studies on the topic of structural slab selection to identify commonly used evaluation criteria and the range of slab alternatives considered in high-rise construction. Second, it examined the decision-making methodologies and analytical tools used in construction management and engineering to address similar multi-criteria problems. Third, it highlights the knowledge gap in the study. This dual-focused review ensures that the present study is both contextually relevant and methodologically sound. Before examining the specific body of research, it is essential to establish the foundational concepts that underpin this study. Value Engineering (VE) is a systematic methodology that optimizes the value of a product, system, or service by analyzing its functions in relation to its costs, thereby identifying opportunities to improve performance or reduce expenses without compromising quality or functionality. In the context of large-scale construction projects, VE has proven instrumental in achieving cost efficiency while maintaining or enhancing project outcomes, particularly in decisions involving high-cost structural components. The selection of a structural slab system represents one such critical decision, as it directly influences not only the structural integrity and architectural flexibility of high-rise buildings but also the project’s overall budget, construction timeline, and long-term operational performance. In high-rise projects specifically, the slab system choice becomes even more critical due to the scale of repetition across multiple floors, which amplifies both the benefits of an optimal decision and the consequences of a suboptimal one. A well-informed slab selection can lead to substantial reductions in material consumption, accelerated construction schedules through improved constructability, and enhanced structural performance. At the same time, a poorly chosen system may result in cost overruns, schedule delays, and compromised building functionality that become increasingly difficult to rectify as construction progresses.

2.1. Slab Selection Criteria and Alternatives

Research on structural system selection in construction has evolved from broad comparative studies of entire structural frameworks to increasingly specialized investigations of individual structural components. Within this progression, the selection of floor slab systems for high-rise buildings has emerged as a particularly critical area of study, given that these elements not only constitute a substantial portion of structural costs but also present unique challenges in tall buildings where factors such as load accumulation, construction efficiency across multiple floors, and vertical transportation constraints become especially pronounced. The selection of an optimal slab system is a recurring theme in structural engineering research, with studies often comparing a limited number of alternatives based on specific criteria. For instance, Mene and Nilawar [4] focused on structural performance (bending moment and displacement), cost-effectiveness, design flexibility, seismic behavior, and durability as key criteria to conclude that a flat slab system was superior to other conventional types. Similarly, Sathawane & Deotale [5] narrowed their focus to cost, determining that a flat slab with drop panels was more economical than a grid slab or a flat slab without drops. These studies highlight the importance of cost and structural performance, but demonstrate a need for a more comprehensive comparison across multiple criteria. To systematically evaluate these slab alternatives, researchers have employed various decision-making methods ranging from simple comparative analysis to sophisticated multi-criteria decision-making (MCDM) frameworks, each offering distinct advantages in balancing technical, economic, and operational considerations.

Other research expands the criteria to include constructability and lifecycle performance. The work of Asamoah et al. [6] and Jacinto et al. [3] emphasized the advantages of precast concrete, focusing on criteria such as construction speed, cost-effectiveness, and time efficiency. These studies highlight the industry’s growing demand for systems that can expedite project schedules.

The range of slab alternatives in the literature is also diverse. Beyond conventional cast-in-place systems, researchers have analyzed innovative solutions. Fatma and Chandrakar [7] compared the Bubble Deck slab to conventional slabs, finding it superior in load-carrying capacity and deflection. Andreas et al. [8] confirmed that the Bubble Deck method could offer significant cost and time savings. In parallel, Handayani and Reza [9] compared conventional slabs with Composite Metal Deck slabs, noting the latter’s advantage in construction speed despite a higher material cost.

This review confirms that the slab alternatives chosen for the present study—including Solid, Flat, Waffle, Hollow Ribbed, Precast, Post-Tension, Composite, and Bubble Deck—represent a comprehensive spectrum of established and innovative systems. Furthermore, it validates that the evaluation criteria identified in our methodology (e.g., cost, speed, structural capacity, fire resistance, MEP integration) are well-established as critical factors in the decision-making process.

More recent studies have explored the environmental and economic implications of slab selection. Miller et al. [10] investigated the comparison of concrete slabs and the optimization of embodied energy for various design and construction techniques, with an emphasis on their long-term sustainability aspects. Na and Paik [11] conducted a comparative analysis of South Korean cases, focusing on reducing greenhouse gas emissions and costs with alternative structural systems for slabs, specifically comparing ordinary reinforced concrete slabs with voided slab systems. Wang et al. [12] further contributed to this field by performing a life cycle impact comparison of different concrete floor slabs, including precast, composite, and cast-in-situ slabs, considering uncertainty and sensitivity analysis. These studies collectively underscore the growing importance of environmental and lifecycle considerations in addition to traditional performance metrics.

Additionally, the practical application of value engineering principles in slab construction has gained traction. Al Kulabi and Al Zahid [13] specifically addressed cost and time reduction in concrete slab construction through value engineering, demonstrating its tangible benefits. Similarly, Jacinto et al. [3] highlighted the benefits of precast slabs in terms of time and cost efficiency through value engineering, further solidifying the importance of this approach.

This comprehensive review confirms that the slab alternatives chosen for the present study—including Solid, Flat, Waffle, Hollow Ribbed, Precast, Post-Tension, Composite, and Bubble Deck—represent a comprehensive spectrum of established and innovative systems. Furthermore, it validates that the evaluation criteria identified in our methodology (e.g., cost, speed, structural capacity, fire resistance, MEP integration) are well-established as critical factors in the decision-making process, aligning with both traditional engineering concerns and modern sustainability and value-driven approaches.

2.2. Decision-Making Methodologies

The challenge of selecting an optimal slab system, which inherently involves balancing numerous conflicting criteria, is a classic Multi-Criteria Decision-Making (MCDM) problem. The literature in construction management and engineering is rich with applications of various MCDM tools designed to address such complex problems systematically. These methodologies offer structured approaches for evaluating alternatives against multiple objectives, thereby facilitating more informed and justifiable decisions.

One of the most widely recognized methods for determining the relative importance or weight of criteria is the Analytic Hierarchy Process (AHP), developed by Saaty (1987) [14]. AHP structures a decision problem into a hierarchical framework and employs pairwise comparisons to derive criteria weights based on expert judgments. While AHP is known for its robustness and popularity, its application can become mathematically intensive and cumbersome, particularly when a large number of criteria are involved, necessitating extensive expert input for consistent comparisons.

For ranking alternatives, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is frequently employed due to its intuitive logic and effectiveness. TOPSIS operates by ranking alternatives based on their simultaneous proximity to a hypothetical “best” solution (Positive Ideal Solution) and distance from a hypothetical “worst” solution (Negative Ideal Solution). Research by Guzmán-Sánchez et al. [15] effectively demonstrates a hybrid approach that combines AHP for weighting criteria and TOPSIS for ranking alternatives in the selection of sustainable roof types. This study provides strong validation for the use of a two-stage, hybrid MCDM model for complex construction decisions, showcasing how different methods can be synergistically combined to leverage their respective strengths.

The present study adopts a similar hybrid approach but utilizes the Step-wise Weight Assessment Ratio Analysis (SWARA) method for criteria weighting instead of AHP. SWARA is increasingly recognized as a more straightforward and less mathematically intensive method for experts to assign weights, as it requires fewer comparisons and can be more easily understood by practitioners. SWARA was selected for its simplicity and ability to capture expert consensus with fewer pairwise comparisons than AHP. At the same time, TOPSIS was chosen for ranking due to its computational efficiency and ability to simultaneously consider proximity to the ideal solution and distance from the worst solution. The combination of SWARA for weighting and TOPSIS for ranking provides a robust, transparent, and computationally efficient framework for this analysis, offering a practical alternative to more complex weighting methods while maintaining decision quality. This approach aligns with the principles of value engineering by streamlining the decision process without compromising rigor.

Furthermore, the integration of Building Information Modeling (BIM) with value engineering has emerged as a significant advancement in facilitating the selection of building design alternatives, particularly when considering sustainability aspects. As highlighted by Taher and Elbeltagi [16], this integration enables a more comprehensive evaluation of various slab types, including flat slabs, post-tension slabs, and hollow block slabs, by providing detailed data for cost, performance, and environmental impact analyses. This interdisciplinary approach enhances the accuracy and scope of MCDM applications in the construction industry. The importance of value engineering in reducing construction costs and optimizing project schedules is also widely acknowledged. Paul [17] offers general guidance on how to effectively apply value engineering principles to reduce overall project costs, highlighting its role in informed strategic decision-making. Similarly, Jim Houlette [18] discusses the application of value engineering specifically for slab-on-grade foundations, illustrating how expert knowledge and experience can be leveraged to optimize material and labor costs. These studies collectively underscore the growing sophistication of MCDM techniques and their integration with other project management tools to achieve optimal outcomes in construction projects.

Finally, in synthesizing judgments from multiple experts, the use of the Geometric Mean is a standard practice, particularly in AHP and similar methods, as noted by Saaty [14]. It is preferred over the arithmetic mean because it effectively mitigates the effect of extreme values and better reflects the consensus in multiplicative relationships, ensuring that expert opinions are aggregated in a statistically sound manner. This meticulous approach to data aggregation is crucial for maintaining the integrity and reliability of MCDM results in complex engineering decisions.

2.3. Knowledge Gap

A critical review of existing literature reveals a significant gap in the application of a holistic, integrated decision-making framework for slab selection. Specifically, prior research has predominantly relied on either single-criterion analyses—such as evaluating slabs based solely on cost or structural performance—or limited pairwise comparisons of only two or three alternatives, thereby failing to capture the multidimensional complexity of this decision; in response, this research employs a comprehensive multi-criteria framework that simultaneously evaluates eight diverse slab systems across eight weighted performance criteria, providing a holistic assessment absent in existing studies. Previous studies often analyze criteria in isolation, focusing on which system is cheapest, fastest, or structurally most efficient. However, they fail to synthesize these disparate factors into a single, comprehensive measure of value. The most prominent gap is the absence of a formal, quantitative Value Engineering (VE) approach that explicitly defines value as the ratio of function to cost. This limitation is often compounded by the use of a narrow set of evaluation criteria, which overlooks critical aspects such as MEP integration, aesthetics, and lifecycle performance. This study directly addresses this gap by implementing a hybrid SWARA-TOPSIS methodology to calculate a VE index based on a balanced and comprehensive set of eight weighted, multi-disciplinary criteria.

Furthermore, the scope of comparison in previous research is often limited, typically involving only a pairwise analysis of two or three slab alternatives. This fragmented approach does not provide the comprehensive market overview necessary for real-world decision-making in complex high-rise projects. Our research overcomes this limitation by systematically evaluating a broad spectrum of eight distinct structural slab systems, ranging from traditional cast-in-place solutions to innovative voided and precast technologies. By combining this wide-ranging comparison with a formal VE framework, this study provides a robust, value-based decision-making model that is more practical and holistic than what is currently available in the existing body of knowledge.

Accordingly, the principal objective of this research is to develop and validate a comprehensive Value Engineering-based decision framework that systematically integrates multi-criteria performance evaluation with cost analysis, thereby filling the methodological gap in structural slab system selection through the novel application of the hybrid SWARA-TOPSIS approach to quantitatively determine the optimal slab alternative for high-rise building projects. By synthesizing insights from prior studies on slab selection criteria, MCDM methodologies, and value engineering applications, this research builds upon the established theoretical foundation while addressing the identified limitations through a comprehensive, multi-criteria framework that provides practitioners with a systematic, quantitative tool for optimal slab system selection in high-rise construction.

3. Research Methodology

This section presents the systematic research methodology employed to select the optimal structural slab for high-rise buildings based on the principles of Value Engineering. To achieve this, a hybrid MCDM model was developed, integrating several established techniques into a logical sequence. The methodology consists of several sequence phases, as shown in Figure 1. The methodology begins with the identification of evaluation criteria and slab alternatives through a literature review and expert consultation. It then proceeds in three main analytical stages: first, the SWARA method is used to determine the objective weights of the evaluation criteria. Second, the TOPSIS is applied to evaluate the performance of the slab alternatives against these weighted criteria. Finally, the performance scores from TOPSIS are integrated with a normalized cost analysis to calculate a comprehensive VE index. This systematic, multi-stage approach ensures a robust, transparent, and quantitative evaluation, providing a data-driven recommendation for the best-value slab system.

3.1. Collecting Data (Stage 1)

This section outlines the foundational data collection process, which establishes the framework for the subsequent analysis. To construct a robust decision model, two key sets of information were gathered. The first task was to identify the significant criteria used to evaluate structural slabs in high-rise buildings. The second step was to select the specific slab system alternatives that would be compared. Both steps utilized a combination of literature reviews and expert consultations to ensure the data were both comprehensive and practically relevant, as detailed in the following subsections.

3.1.1. Identifying the Significant Criteria’s

The initial and most critical phase of this study was the systematic identification and selection of relevant evaluation criteria for structural slab systems in high-rise buildings. To ensure the criteria were both comprehensive and practically relevant, a two-stage methodology was employed, combining a thorough literature review with structured expert consultation.

• Step 1: Literature-based criteria compilation

First, a comprehensive review of academic and industry literature was conducted to compile a longlist of potential criteria. This review examined previous MCDM studies on construction material and system selection, structural engineering design manuals, and publications focused on Value Engineering. Key themes consistently identified in the literature included cost-related factors (material cost, labor cost, lifecycle cost), constructability (speed of construction, ease of installation, formwork complexity), structural performance (load-bearing capacity, span capability, toughness, fire resistance), and building integration (flexibility for MEP systems, acoustic and thermal insulation). This process yielded an initial list of over 20 potential criteria that have been validated in prior research.

A comprehensive database of over 45 potential evaluation criteria was compiled from more than 30 peer-reviewed journal articles, conference papers, and industry design manuals published between 2010 and 2024. Each criterion was documented with its source, definition, and frequency of use across studies. Categories identified included: structural performance metrics (load capacity, span capability), cost factors (material, labor, lifecycle), constructability aspects (installation speed, formwork requirements, skilled labor availability), durability considerations (fire resistance, corrosion protection), and aesthetic/architectural factors (ceiling finish, design flexibility).

• Step 2: Expert panel refinement and final selection

To ground these theoretical criteria in practical relevance and refine them for the specific context of modern high-rise construction, the literature-based list was presented to a panel of 20 industry experts.

This panel was intentionally diverse, comprising experienced structural engineers, construction managers, cost estimators, and project developers. The experts were selected based on their pre-existing expertise and professional qualifications, eliminating the need for additional training. Each participant possessed at least 10 years of direct experience in structural design or construction management of high-rise buildings, ensuring they already had the technical competence and practical knowledge required to evaluate slab systems. Rather than conducting formal training sessions, the research team provided each expert with standardized briefing materials that included: (1) clear definitions of each evaluation criterion, (2) detailed instructions on the assessment methodology. This approach was chosen to preserve the authenticity of expert judgments based on their field experience, rather than introducing potential bias through training that might standardize their responses.

A structured focus group session was conducted where the experts were tasked with the following:

1.

Validate: Confirm the relevance of each criterion from the literature review.

2.

Refine: Discuss and clarify the definitions to avoid ambiguity.

3.

Augment: Propose additional criteria that, from their practical experience, are critical but may have been overlooked in the literature.

4.

Consolidate: Identify and group overlapping or redundant criteria to create a more concise and efficient list. For example, concepts like “reduce work time” and “simple installation” were discussed and consolidated under the broader criterion of “Speed of Construction.”

The feedback and discussions from the expert panel were systematically recorded. Following the session, a qualitative analysis was performed to synthesize the expert consensus. This rigorous refinement process led to the selection of a final, validated set of the most significant criteria for evaluating slab alternatives. This final list, as shown in

Table 1. The eight significant criteria.

No.	Code	Criterion	Explanation
1	C1	Availability of materials	It plays a significant role in the project’s budget. Using locally available materials may result in a significant cut in the budget of the construction, reducing the transportation cost, labor cost, and the probability of any damage to the materials.
2	C2	Fire resistance (Resist fire)	Resisting fire that, for a specified time and under conditions of a standard heat intensity, it will not fail structurally or allow transit of heat, and will not permit the side away from the fire to become hotter than a specified temperature.
3	C3	Decorative Features	Related architectural matters
4	C4	Space for MEP systems (more flexible for MEP)	Related to the flexibility of the roof, which accommodates mechanical systems such as air conditioning ducts. The more flexible it is, the better the roof.
5	C5	Speed of Construction (ease of installation)	Since time is money and every minute counts
6	C6	Heat insulation (reduce heat transfer)	Which is the reduction in heat transfer
7	C7	Sound Isolation (reduce or eliminate sound transmission)	Which is the process of reducing or eliminating the transmission of sound between two spaces
8	C8	Toughness (resists more load)	The state of being strong enough to withstand loads.
9	C9	Cost	Slab types differ in terms of material cost, and economic savings are a key consideration.

, formed the robust foundation for the subsequent weighting and evaluation stages of the study.

3.1.2. Collecting Slab Types

The selection of the eight structural slab alternatives—Solid Slab, Hollow Ripped Slab, Waffle Slab, Flat Slab, Post-Tension Slab, Precast Slab, Composite Slab, and Bubble Deck Slab—was a deliberate and strategic choice designed to ensure the study’s comprehensiveness and relevance to modern high-rise construction. This listing was not chosen arbitrarily; rather, it represents a broad and diverse spectrum of the most common, innovative, and competing flooring systems available in the industry. The primary justification for this selection is that it covers the full range of construction philosophies, material efficiencies, and structural performance capabilities, which is essential for a robust Value Engineering analysis.

A key reason for this selection is to include both traditional and advanced in situ concrete systems. The Solid Slab and Flat Slab serve as fundamental benchmarks, representing conventional, widely understood, and frequently used systems in building construction for decades [19]. In contrast, the Post-Tension Slab was included as it is arguably the dominant system for modern high-rise residential and commercial buildings due to its ability to achieve longer spans, thinner profiles, and reduced overall building weight, which are critical advantages in tall structures [20]. This juxtaposition allows for a direct comparison between standard practice and a more advanced, performance-oriented solution.

Furthermore, the selection deliberately incorporates systems focused on material efficiency and the reduction of dead load, a paramount concern in high-rise design. The Waffle Slab, Hollow Ripped (Ribbed) Slab, and Bubble Deck Slab are all “voided” systems that strategically remove non-working concrete to reduce self-weight without significantly compromising structural integrity. Including these alternatives is critical for a value-based comparison, as they directly address the trade-off between higher initial formwork complexity and long-term savings from lighter structures and smaller foundations [21].

The inclusion of Precast Slabs and Composite Slabs broadens the scope of the study beyond monolithic concrete construction. Precast systems represent an entirely different construction philosophy centered on off-site manufacturing to enhance speed, quality control, and safety, making them a vital alternative to cast-in-place methods. The Composite Slab (steel decking with a concrete topping) is indispensable for any study on high-rise buildings, as it is the standard flooring system for steel-framed structures. By including these two, the analysis can provide a holistic comparison that is not limited to a single structural frame material, thus making the study’s conclusions more widely applicable across the construction industry.

For each of the eight selected slab systems, standardized technical information was compiled from multiple sources, including manufacturer specifications, engineering design manuals (ACI, Eurocode), completed project case studies, and academic publications. The data collected for each alternative included: typical span ranges, depth-to-span ratios, dead load values (kN/m²), construction speed estimates (m²/day), typical applications (residential, commercial, mixed-use), formwork requirements, skilled labor needs, material cost ranges, and common limitations or constraints. This standardized information package was subsequently provided to experts during the performance evaluation phases (Stages 2 and 3) to ensure all participants had access to consistent baseline technical data.

3.1.3. Expert Panel Composition and Selection

The expert consultation process was structured in two distinct stages to ensure both extent and rigor in the analysis. Stage 1 (Criteria Identification and Refinement) initially involved a large panel of 20 industry experts to maximize the representation of diverse viewpoints across structural engineering, construction management, and cost estimation, leading to the final list of eight significant criteria. For the subsequent quantitative phases—criteria weighting (SWARA) and alternative performance evaluation (TOPSIS)—a focused and consistent panel of 10 experts was selected from the original group for the final scoring. The stringent inclusion criteria for these 10 experts required all participants to possess a minimum of 10 years of experience in structural design or construction management and direct involvement in high-rise building projects. This selective panel composition (20% structural engineers, 10% construction managers, 50% project developers, and 20% cost estimators) ensured a balanced blend of technical, schedule, and financial expertise for the core analysis. The panel size of N = 10 is considered sufficient and robust for Multi-Criteria Decision-Making (MCDM) methods like SWARA-TOPSIS, which rely on the synthesized collective judgment of highly qualified, homogenous experts rather than statistical sampling.

3.2. Determining Criteria Weights Using the SWARA Method (Stage 2)

The initial and most crucial stage in applying the SWARA method is to objectively rank the evaluation criteria according to their importance. It should be noted that criteria C1–C8 (Table 1) were applied in both this section and Section 3.3, while criterion C9 was exclusively used in Section 3.4 and Section 3.5. This study established this hierarchy through a structured expert consultation process. A panel of ten experts, comprising structural engineers, project managers, and cost estimators, was enlisted to assess the relative degree of influence of each of the eight criteria. Each expert provided a rating on a 10-point scale, where a score of 0.1 signified a “very weak” influence and 1.0 represented a “powerful” influence.

To aggregate these multiple, independent judgments into a single representative score for each criterion, the Geometric Mean (GM) was employed, as defined in Equation (1). The selection of the GM is methodologically deliberate. Unlike the arithmetic mean, the GM minimizes the effect of outlier scores, thereby providing a more stable and reliable measure of central tendency for synthesizing group judgments. Its application is a well-established practice in decision-science literature (

Table 2. Using the geometric mean in ranking factors.

Field/Domain	Study/Index/Method	Role of the Geometric Mean	Reference
Management Science	Analytic Hierarchy Process (AHP)	To synthesize pairwise comparison judgments and derive priority weights for factors.	[14]
Social Sciences	Human Development Index (HDI)	To combine the three core dimensions (health, education, income) into a single index, penalizing unbalanced development.	[22]
Environmental Science	Environmental Performance Index (EPI)	To aggregate related indicators within a policy category where high performance in one area cannot compensate for another.	[23]
Finance & Investing	Time-Weighted Rate of Return (TWRR)	To calculate the average annual compounded growth rate of an investment, which is the standard measure of performance.	[24]

), making it a suitable tool for this analysis.

$$GM={({\prod }_{i=1}^n{x}_i)}^{1/n}$$

(1)

Once the GM was calculated for each criterion, the criteria were ranked in descending order based on these scores. This final ranking created the essential priority list required for the subsequent steps of the SWARA method. The detailed influence scores from each expert, along with the calculated GM values and final ranks, are presented for review in

Table 3. Influence scores of the eight criteria from the ten experts.

Criteria	EX1	EX2	EX3	EX4	EX5	EX6	EX7	EX8	EX9	EX10	GM
Availability of materials	0.9	0.9	1	0.9	0.5	0.8	0.5	0.8	0.9	0.8	0.78
Resist Fire	0.5	0.8	0.5	0.5	0.5	0.1	0.4	0.6	0.4	0.7	0.44
Decorative Features	0.6	0.9	0.3	0.5	0.8	0.5	0.4	0.4	0.7	0.8	0.55
Space for MEP systems	0.6	1	0.3	0.6	0.5	0.9	0.5	0.8	0.9	0.9	0.65
Speed of Construction	0.2	0.8	0.8	0.5	1	0.8	0.8	0.8	0.9	0.9	0.68
Energy efficiency (Heat insulation)	0.3	1	0.5	0.8	0.5	0.8	0.4	0.7	0.1	0.7	0.49
Sound Isolation	0.7	0.9	0.8	0.7	0.5	0.6	0.4	0.7	0.1	0.5	0.48
Toughness	0.8	0.8	1	0.9	1	0.8	0.9	0.9	1	0.9	0.89

.

The SWARA methodology inherently relies on the expert-determined ranking order, meaning the final criteria weights are mathematically dependent on this initial input. While this approach effectively captures the expert consensus used in this study, the potential for alternate rankings is acknowledged. A detailed discussion of this and other methodological limitations is presented in Section 6.

After ranking the eight criteria, the SWARA steps were performed to determine the criterion weight, as follows:

• Step 1: Rank the Criteria by Importance

The decision-maker or expert ranks the criteria in descending order of their perceived importance. The most important criterion is ranked first, the second most important is ranked second, and so on.

• Step 2: Determine the relative importance (sj)

Starting with the second-ranked criterion, the expert determines its relative importance compared to the criterion ranked just above it. This value, called the Comparative Importance of the Average Value (s_j), expresses how much less important a criterion is than the one before it. The most important criterion (Rank 1) has a value of Sj of 0. For all other criteria, the expert provides a value for

• Step 3: Calculate the coefficient (kj)

The coefficient (k_j) is a simple calculation based on s_j using Equation (2)

$$k_j=s_j+1$$

(2)

• Step 4: Calculate the initial/recalculated weight (qj)

This step establishes the relative weight of each criterion in a “chain.” By computing q_j as shown in Equation (3)

$$q_j=\left\{\begin{array}{c}1 for j=1\\ {\displaystyle \frac{q_{j-1}}{k_j}} for j>1 \end{array}\right.$$

(3)

• Step 5: Calculate the final normalized weights (wj)

The final step is to normalize the q_j values so that they sum to 1.0. This is done by dividing each individual by the sum of all q_j values using Equation (4).

Table 4. Weight criteria with their computation using the SWARA method.

Criteria	Code	GM	sj	kj	qj	wi	Ranking
Toughness	C8	0.89		1	1.000	0.160	1
Availability of materials	C1	0.78	0.11	1.11	0.901	0.144	2
Speed of Construction	C5	0.68	0.1	1.1	0.819	0.131	3
Space for MEP systems	C4	0.65	0.025	1.025	0.799	0.128	4
Decorative Features	C3	0.55	0.1	1.1	0.726	0.116	5
Energy efficiency (Heat insulation)	C6	0.49	0.063	1.063	0.683	0.109	6
Sound Isolation	C7	0.48	0.01	1.01	0.677	0.108	7
Resist Fire	C2	0.44	0.04	1.04	0.651	0.104	8
					6.256

shows the detailed computation values of the SWARA steps for the eight criteria. It should be noted that the criteria in Table 4 were ranking descaling.

$$w_j={\displaystyle \frac{q_j}{\sum q_j}}$$

(4)

3.3. Determining the Performance Index for Different Alternatives Using TOPSIS (Stage 3)

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), developed by Hwang et al. [25], is a multi-criteria decision-making (MCDM) method that ranks alternatives based on their geometric proximity to an ideal solution. It operates on the principle that the optimal alternative should have the shortest distance to the positive ideal solution ($S_i^{+}$) (hypothetical best performance across all criteria) and the longest distance from the negative ideal solution ($S_i^{-}$) (hypothetical worst performance). Below is a step-by-step breakdown of its procedures and key equations, supported by academic references.

3.3.1. Constructing the Decision Matrix

To populate the decision matrix, expert assessments were collected for each slab-criterion combination. Each of the 10 experts rated all eight slab alternatives against all eight criteria using a five-point Likert scale (1 = very poor, 5 = excellent). The ratings were based on the experts’ professional experience, technical knowledge, and familiarity with the performance characteristics of each slab system. For each cell in the matrix (xij), the mean of expert ratings was calculated to obtain a single representative performance score, ensuring that extreme outlier opinions did not disproportionately influence the results. The process begins with an m × n decision matrix X, where m represents the alternatives of slab type and n denotes the criteria. Each element xij indicates the performance of alternative i for criterion j. Due to the number of criteria being eight and the number of alternatives being 8, the matrix X is 8 × 8 with element x_ij, as shown in

Table 5. Decision matrix.

	C1	C2	C3	C4	C5	C6	C7	C8
A1	4.9	4.1	2.6	3.0	2.6	2.7	2.5	3.2
A2	4.4	3.0	3.1	4.0	3.3	4.0	3.8	3.4
A3	3.6	3.1	3.9	3.6	3.3	4.0	3.8	3.7
A4	4.7	4.2	3.7	4.3	3.9	3.0	2.8	3.7
A5	2.7	3.9	3.5	3.8	4.6	3.0	2.9	4.0
A6	2.7	4.0	3.6	3.4	4.7	3.5	3.2	4.2
A7	3.1	2.6	3.4	3.5	3.7	2.9	2.5	4.0
A8	1.5	2.7	3.4	3.6	3.4	3.6	3.1	3.7

.

3.3.2. Normalizing the Decision Matrix

To eliminate unit differences, the matrix is normalized using the Euclidean norm. The normalized value r_ij is calculated using Equation (5) as [26,27]:

$$r_{ij}={\displaystyle \frac{x_{ij}}{\sqrt{\sum _{i=1}^m{x}_{ij}^2}}}$$

(5)

This converts all criteria into dimensionless units, allowing for comparison.

3.3.3. Creating the Weighted Normalized Matrix

As referred in Section 3.2, the weights wj were assigned for each criterion (where wj = 1) to reflect their importance. The normalized value r_ij should be multiplied by the corresponding criterion weight wj to obtain v_ij as shown in Equation (6) [27,28], resulting in the weighted matrix V [vij]m × n. The weighted normalized matrix is shown in

Table 6. Weighted normalized matrix V.

	C1 (0.144)	C2 (0.103)	C3 (0.116)	C4 (0.127)	C5 (0.131)	C6 (0.109)	C7 (0.108)	C8 (0.16)
A1	0.069	0.043	0.031	0.037	0.032	0.031	0.031	0.048
A2	0.062	0.031	0.037	0.049	0.041	0.046	0.047	0.051
A3	0.051	0.033	0.047	0.044	0.041	0.046	0.047	0.056
A4	0.066	0.044	0.044	0.053	0.048	0.034	0.034	0.056
A5	0.038	0.041	0.042	0.047	0.057	0.034	0.036	0.060
A6	0.038	0.042	0.043	0.042	0.058	0.040	0.039	0.063
A7	0.044	0.027	0.041	0.043	0.046	0.033	0.031	0.060
A8	0.021	0.028	0.041	0.044	0.042	0.041	0.038	0.056

.

$$v_{ij}=w_j\times r_{ij}$$

(6)

3.3.4. Calculating Separation Measures

The Euclidean distance of each alternative i was computed using Equation (7) and Equation (8), respectively [27,29].

$$S_i^{+}=\sqrt{{\sum }_{j=1}^n{\left(v_{ij}-v_j^{+}\right)}^2}$$

(7)

$$S_i^{-}=\sqrt{{\sum }_{j=1}^n{\left(v_{ij}-v_j^{-}\right)}^2}$$

(8)

where $v_j^{+}$ and $v_j^{+}$ are the maximum and minimum values of v_ij among alternative j, respectively.

3.3.5. Computing Relative Closeness (Ci)

The relative closeness to PIS for alternative ii is computed using Equation (9). A higher Ci indicates better performance (closer to $S_i^{+}$, farther from $S_i^{-}$) [27].

$$C_i={\displaystyle \frac{S_i^{-}}{S_i^{+}-S_i^{-}}}$$

(9)

A higher Ci indicates better performance (closer to PIS, farther from NIS). The values of $S_i^{+}-S_i^{-}$, $S_i^{+}-S_i^{-}$, and Ci of the eight alternatives are shown in

Table 7. Value of C_i (performance index of the alternatives).

Alternative	${\mathit{S}}_{\mathit{i}}^{+}$	${\mathit{S}}_{\mathit{i}}^{-}$	Ci
A1 (Solid slab)	0.043	0.318	0.880
A2 (Hollow Ripped slab)	0.160	0.165	0.508
A3 (Waffle slab)	0.271	0.062	0.186
A4 (Flat slab)	0.071	0.253	0.782
A5 (Post-Tension slab)	0.036	0.317	0.898
A6 (Precast slab)	0.316	0.042	0.116
A7 (Composite slab)	0.272	0.055	0.167
A8 (Bubble Deck slab)	0.167	0.159	0.488

.

3.4. Computing the Normalized Cost of the Eight Slab Alternatives (Stage 4)

The cost of the eight slab alternatives was evaluated by a panel of experts using a five-point Likert scale. First, the average cost rating for each alternative was calculated from the expert assessments. Next, these average ratings were normalized by dividing each value by the maximum rating among all alternatives (${cost}_{max}$) to produce the normalized cost ($\overline{{cost}_i}$). This step ensures all cost values are scaled consistently.

Table 8. Normalized cost value.

Alternatives	Average Cost	$\overline{\mathit{C}\mathit{o}\mathit{s}\mathit{t}}$
A1	1.6	0.364
A2	3.0	0.682
A3	4.0	0.909
A4	2.2	0.500
A5	1.6	0.364
A6	4.4	1.000
A7	4.0	0.909
A8	3.0	0.682

presents both the average cost ratings and the resulting normalized cost values for the eight alternatives.

It is acknowledged that traditional Value Engineering employs absolute monetary costs. However, this study uses normalized expert-based cost ratings for practical reasons: (1) absolute construction costs vary significantly by location, project scale, and market conditions, limiting generalizability; (2) detailed monetary estimates require finalized designs unavailable at the early decision stage; and (3) expert consensus on relative cost positioning, captured through Likert-scale ratings, provides a generalizable comparative framework. The normalization process preserves rank ordering and relative cost differentials—the essential information for calculating the VE ratio. This approach aligns with MCDM frameworks requiring comparable scales across criteria. Future research could enhance this by incorporating region-specific unit cost databases alongside the relative assessment framework.

3.5. Applying Value Engineering (VE) (Stage 5)

The analysis concluded by applying the fundamental principle of Value Engineering, which defines value as the ratio of function to cost. In this study, the overall performance score ($C_i$ from Table 6) served as the “function” metric, while the normalized cost ($\overline{{cost}_i}$ from Table 7) represented the “cost.” A VE index was therefore calculated for each alternative using this relationship, as shown in Equation (10).

$$VE={\displaystyle \frac{C_i}{\overline{{cost}_i}}}$$

(10)

Consequently, the alternatives were ranked based on this VE index. A higher score signifies a superior balance between performance and cost, making it the most desirable selection.

4. Results

The Normalized Matrix can summarize and provide weighted average results; however, analyzing these results can offer a deeper insight into the experts’ opinions about the topic.

4.1. Cost Discussion

According to industry experts, developers, and project managers, the most cost-effective structural systems for high-rise buildings are the Solid Slab and the Post-Tension Slab. This is quantitatively supported by the data in

Table 9. Normalized cost of the eight alternatives.

Structural Slab System Alternative	Normalized Cost
A1	0.364
A2	0.682
A3	0.909
A4	0.500
A5	0.364
A6	1.000
A7	0.909
A8	0.682

, where these systems (A1 and A5) show the lowest normalized cost of 0.364. The Flat Slab system (A4) is the second most common choice, also approved by experts as a low-cost option with a normalized cost of 0.500.

In contrast, systems such as the Hollow Ribbed Slab and the Bubble Deck Slab (A2 and A8) are specified less often, reflecting their higher comparative cost of 0.682. Other systems are reserved for specific circumstances and carry a significant cost premium; for example, Composite (Concrete-Steel) Slabs and Waffle Slabs (A3 and A7) have a high normalized cost of 0.909. Finally, Precast concrete (A6) is confirmed as the least cost-effective choice, ranking highest with a normalized cost of 1.000, which aligns with its infrequent use in typical high-rise applications.

The significant cost variations observed among slab systems can be attributed to several fundamental factors. The low cost of Solid and Post-Tension slabs (0.364) stems from their reliance on standard, commodity materials (ready-mix concrete and conventional reinforcement) available from any local supplier without specialized procurement. These systems also utilize standard construction labor and conventional formwork, avoiding the cost premiums associated with specialized expertise or proprietary equipment.

In contrast, the high cost of Precast slabs (1.000) reflects multiple compounding factors: (1) the requirement for specialized manufacturing facilities with significant capital investment, (2) transportation costs and logistical complexity for delivering large, heavy panels to high-rise sites, (3) the need for heavy lifting equipment (tower cranes with high capacity) throughout the erection process, and (4) limited economies of scale when production runs are small or when plants are located far from the construction site. Additionally, precast systems require precise coordination and tolerances, increasing engineering and quality control costs.

The moderate-to-high costs of Waffle and Composite slabs (0.909) result from different sources. Waffle slabs require complex formwork systems with specialized void-formers (typically fiberglass or plastic pods), which are more expensive to rent or purchase than standard flat formwork. The formwork installation and removal are also more labor-intensive due to geometric complexity. Composite slabs, which use less concrete, require steel decking that must be purchased from specialized suppliers at a premium over conventional materials, as well as welding equipment and skilled trades for deck installation.

Finally, the Bubble Deck’s intermediate-high cost (0.682) reflects its dependence on patented technology and proprietary plastic spheres that must be sourced from limited suppliers, creating supply chain vulnerabilities and eliminating competitive bidding advantages. This proprietary dependency transfers market power to manufacturers, resulting in higher unit costs compared to commodity-based systems.

4.2. Comparative Analysis of Slab Performance Profiles

The analysis of the eight structural slab alternatives is based on their performance across eight weighted criteria, as detailed in Table 1. The criteria weights indicate that structural capacity (C8: Resist more load, w = 0.160) and material availability (C1, w = 0.144) are the most critical factors, followed by speed of construction (C5, w = 0.131) and flexibility for MEP systems (C4, w = 0.128). The comprehensive results are visualized in the radar chart in Figure 2, which serves as the primary tool for comparing the distinct performance profiles of each system.

Figure 2 presents a comprehensive, multidimensional comparison of the eight slab alternatives (A1–A8) against the eight performance criteria (C1–C8), which are arranged as axes radiating from the center. Each alternative is represented by a colored polygon, where the distance of a vertex from the center indicates its score on that specific criterion—the farther the point, the better the performance.

The area and shape of each polygon offer an immediate visual summary of an alternative’s strengths, weaknesses, and overall suitability. A visual inspection quickly identifies specific systems, like the Flat Slab (A4, red polygon), as a strong all-around performer with a large, relatively balanced shape. In contrast, other systems exhibit pronounced strengths in specific areas, creating skewed polygons, such as the Precast Slab (A6, dark purple), which excels in speed and load capacity. Conversely, systems like the traditional Solid Slab (A1, blue) occupy a smaller overall area, suggesting more limited performance across several key metrics. This chart effectively illustrates the critical trade-offs inherent in selecting a structural system.

The exceptional performance of the Flat Slab (A4) and Post-Tension Slab (A5) is not coincidental but results from fundamental design characteristics that align with the priorities of modern high-rise construction.

The Flat Slab’s strength in MEP flexibility stems from its beamless configuration, which eliminates the grid of deep beams that obstruct HVAC ductwork, plumbing runs, and electrical conduits. In conventional beam-slab systems, services must navigate around or penetrate through the beams, creating coordination challenges and increasing the floor-to-floor height. The flat slab provides an unobstructed horizontal plenum, reducing structural depth and associated costs. Similarly, its high aesthetic score (C3: 0.3824) stems from the clean, minimalist ceiling plane that architects value, as it eliminates visual clutter. It allows flexible interior layouts without being constrained by a fixed beam grid.

The Post-Tension Slab’s excellence in construction speed derives from its ability to achieve longer spans with thinner slabs compared to conventional reinforcement. Longer spans mean fewer columns, which translates to faster formwork cycles and reduced congestion on-site. The post-tensioning process itself—although requiring specialized labor—can be performed rapidly once the concrete reaches sufficient strength, and the reduced concrete volume accelerates the curing time. Additionally, thinner slabs mean less concrete pumping time and fewer pours per floor.

Both systems also score well on structural capacity (C8) because they can be optimized for high loads. Post-tensioning introduces compression that counteracts tensile stresses, thereby increasing the adequate load capacity without proportionally increasing the slab thickness. Flat slabs, while thicker than post-tensioned slabs, can carry substantial loads due to two-way action and punching shear reinforcement around columns.

By interpreting the graphical data from the radar chart, the slab systems can be discussed not as a list of criteria, but as integrated systems with unique performance profiles. They naturally fall into tiers based on their overall performance and areas of specialization.

Each slab system demonstrates distinct advantages and limitations across the eight evaluation criteria, with performance trade-offs that make them suitable for different project contexts. In terms of construction speed (C3), the Precast Slab (0.043) performs competitively among all alternatives, while the Waffle Slab (0.047) ranks among better options despite complex formwork due to efficient structural geometry and reduced material volume per span. Conversely, the Solid Slab (0.031) ranks lowest in construction speed because its thick cross-sections demand extended curing time and substantial formwork duration. The Post-Tension Slab (0.042) achieves good construction speed through reduced formwork cycles from longer spans requiring fewer supporting columns. Regarding fire resistance (C8), high-performance concrete systems excel in this critical safety criterion. The Precast Slab (0.063) and Post-Tension Slab (0.060) achieve the highest ratings due to superior quality control and prestressing effects, which provide thermal inertia and reinforcement protection. The Composite Slab (0.060) performs well structurally despite requiring fire protection treatments. The Composite Slab’s exposed steel deck is vulnerable to thermal degradation, requiring costly fire protection treatments such as intumescent coatings or spray-on fireproofing, which significantly increase both initial cost and long-term maintenance requirements overall.

Cost efficiency (normalized cost) reveals dramatic variations that fundamentally reshape value propositions across all evaluated slab alternatives. The Post-Tension Slab (0.364) and Solid Slab (0.364) emerge as the most economical options, benefiting from standardized construction practices, readily available materials, and local contractor familiarity that reduces risk premiums and mobilization costs significantly. In sharp contrast, the Precast Slab suffers the highest normalized cost (1.000) due to capital-intensive manufacturing facilities, specialized transportation logistics, heavy crane requirements, and precise dimensional coordination—factors that compound to create significant cost barriers, particularly severe in markets without established precast infrastructure or supply chains. The Waffle Slab (0.909) and Composite Slab (0.909) occupy the high-cost tier due to specialized formwork systems and proprietary structural components. The Bubble Deck (0.682) and Hollow Ribbed Slab (0.682) fall within mid-range due to moderately complex construction requirements and material specifications. Structural capacity (C8) indicates that the Precast Slab (0.063) and Post-Tension Slab (0.060) perform best due to controlled manufacturing quality and prestressing effects, respectively. In contrast, the Solid Slab (0.048) ranks lower because conventional reinforcement limits overall system capacity, relying primarily on concrete compressive strength alone.

In MEP flexibility (C4), the Flat Slab (0.053) dominates by eliminating beam obstacles, creating unobstructed ceiling plenums ideal for complex HVAC, electrical, and plumbing distributions—a critical advantage in modern high-rise buildings, where MEP systems account for 25–40% of total project cost and construction schedule. The Solid Slab (0.037) and Waffle Slab (0.044) score lower because their structural configurations restrict horizontal service routing, often forcing costly and inefficient vertical distribution strategies. Thermal insulation performance (C6) favors voided systems: the Hollow Ribbed Slab (0.046) and Waffle Slab (0.046) achieve the highest ratings as their voids reduce thermal bridging and accommodate insulation layers effectively, while the Solid Slab (0.031) performs the weakest due to continuous thermal conductivity paths through homogeneous concrete mass without interruption. The Precast Slab (0.040) and Bubble Deck (0.041) occupy mid-range thermal performance levels. Finally, in aesthetics (C5) and seismic resistance (C3), the Precast Slab (0.058) excels visually due to precision-formed surfaces eliminating the need for suspended ceilings in prestigious architectural spaces. The Waffle Slab (0.047) outperforms the Solid Slab (0.031) in seismic performance because its ribbed geometry maintains structural integrity under cyclic loading, and reduced self-weight decreases seismic forces proportionally. The Solid Slab suffers from higher inertial forces during earthquakes, despite its monolithic construction advantages.

4.2.1. High-Performance and Balanced Systems

The radar chart clearly highlights the Flat Slab (A4) and Post-Tension Slab (A5) as superior, well-balanced options, evidenced by their large polygonal areas. The Flat Slab (A4) demonstrates exceptional strength in providing space for MEP systems (C4 score: 0.4144) and its clean, minimalist aesthetic (C3 score: 0.3824). This performance is due to its beamless, flat soffit, which creates an unobstructed plenum for services and is highly valued in modern architecture. Similarly, the Post-Tension Slab (A5) performs strongly, most notably in Speed of Construction (C5), where its score of 0.4342 is second only to Precast. This speed is achieved by early stressing of tendons, allowing for a rapid construction cycle. While both systems are solid sections and thus offer only moderate thermal (C6) and sound (C7) insulation, their advantages in construction efficiency, design flexibility, and structural performance make them highly competitive.

4.2.2. Specialized and Geometrically Complex Systems

A second group of systems, including the Waffle Slab (A3), Hollow Ribbed Slab (A2), and Precast Slab (A6), is distinguished by their highly specialized performance profiles, visible as prominent spikes on the radar chart. The Waffle Slab (A3) and Hollow Ribbed Slab (A2) demonstrate exceptional performance in Preserve Heat Energy (C6) and Insulate Sound (C7), both achieving top scores of 0.4195 and 0.4317 in these respective categories. This is due to the large volume of trapped air in their coffers and voids, which acts as a natural insulator. The Waffle Slab (A3) also received the highest aesthetic score (C3: 0.4031) for its expressive, coffered ceiling. However, this geometric complexity results in slower construction and reduced flexibility for MEP routing compared to flat systems.

The Precast Slab (A6) exhibits a different kind of specialization. It is the undisputed leader in Speed of Construction (C5: 0.4436) and Resistance to More Load (C8: 0.3960) due to off-site manufacturing and the use of pre-tensioning. This makes it ideal for projects where time is of the essence. This specialization, however, comes at the cost of lower material availability (C1: 0.2637), as it depends on proximity to a fabrication plant and specialized transport.

4.2.3. Context-Dependent and Lower-Ranking Systems

Finally, the radar chart identifies systems whose performance is either more limited or highly context-dependent. The traditional Solid Slab (A1) consistently occupies the inner area of the chart, indicating the lowest overall performance profile. It scores particularly poorly on flexibility for MEP systems (C4: 0.2891) and aesthetics (C3: 0.2687) because it relies on a deep beam network that clutters the ceiling plane. While simple and using readily available materials (C1: 0.4785), its inefficiency in modern building design is apparent.

The Composite Slab (A7) and Bubble Deck Slab (A8) present unique vulnerabilities. The Composite Slab’s exposed steel deck results in the lowest score for Resist Fire (C2: 0.2622), a critical safety concern. The Bubble Deck Slab (A8) is severely hindered by its reliance on proprietary components, resulting in the lowest score for Availability of Materials (C1: 0.1465) and creating significant supply chain risk. Although these systems have merits—the Bubble Deck (A8) performs well in thermal insulation (C6) and the Composite Slab (A7) is structurally efficient (C8)—their significant drawbacks in fundamental criteria limit their applicability.

4.3. Final Ranking and Value Engineering (VE) Analysis

The final step of the analysis integrates the comprehensive performance scores (Ci) with the normalized cost data to determine the ultimate value of each slab alternative. The principle of VE defines value as the ratio of function to cost. In this study, the overall performance score (Ci) represents “function.” The VE index for each alternative was calculated using this relationship, with the results and final rankings presented in

Table 10. VE computation and final ranking of slab alternatives.

Alternative	Ci	$\overline{\mathit{C}\mathit{o}\mathit{s}\mathit{t}}$	VE	Rank
A1	0.88	0.364	2.418	2
A2	0.508	0.682	0.745	4
A3	0.186	0.909	0.205	6
A4	0.782	0.5	1.564	3
A5	0.898	0.364	2.467	1
A6	0.116	1	0.116	8
A7	0.167	0.909	0.184	7
A8	0.488	0.682	0.716	5

. A higher VE score signifies a superior balance between performance and cost, making it the most desirable selection.

5. Discussion

The Post-Tension system (VE = 2.467) marginally outperforms the Solid Slab (VE = 2.418) by only 2%, demonstrating that both represent excellent value through fundamentally different approaches. While Post-Tension excels in construction speed (0.042 vs. 0.031), as shown in Table 6 and structural efficiency, the Solid Slab compensates through superior material availability (0.043 vs. 0.041) and simpler construction processes. The minimal VE difference (0.049) suggests that project-specific priorities should guide selection: choose Post-Tension for time-sensitive projects requiring long spans, and Solid Slab for projects prioritizing material sourcing simplicity and conventional construction methods.

To assess the robustness of the final ranking, a sensitivity check was performed by deliberately swapping the weights of the two lowest-ranked criteria (Sound Isolation, C7, and Resist Fire, C2). The Post-Tension Slab (A5) and the Solid Slab (A1) maintained their first and second ranks, respectively. This consistency confirms that the superior value proposition of the top two alternatives is stable and not merely an artifact of the initial expert ranking of lower-importance criteria.

The Post-Tension system achieves a 58% higher value (2.467 vs. 1.564) than the Flat Slab, despite both systems having excellent performance scores (Ci = 0.898 vs. 0.782) (shown in Table 7). This substantial value difference stems from cost: Post-Tension’s normalized cost (0.364) is 27% lower than that of Flat Slab (0.500). Both systems excel in modern design priorities—Post-Tension in speed and span capability, Flat Slab in MEP flexibility (0.053) and aesthetics (0.048)—but Post-Tension’s cost efficiency makes it the superior value proposition for most applications.

The Solid Slab (VE = 2.418) delivers 1,985% more value than Precast (VE = 0.116), despite Precast’s superior performance in construction speed (0.043 vs. 0.031). This dramatic reversal results from Precast’s prohibitive normalized cost (1.000 vs. 0.364), which completely negates its functional advantages. While Precast excels in certain performance aspects, including decorative features (0.058) and fire resistance (0.063), the cost premium makes it economically viable only when schedule compression or specific aesthetic requirements provide offsetting value through earlier revenue generation or substantial avoidance of liquidated damages.

The Flat Slab offers a 110% higher value (1.564 vs. 0.745) than the Hollow Ribbed Slab, due to its better all-around performance (Ci = 0.782 vs. 0.508), despite having a lower normalized cost (0.500 vs. 0.682). While the Hollow Ribbed Slab excels in thermal insulation (0.046) and sound insulation (0.047), the Flat Slab’s advantages in MEP integration (0.053 vs. 0.049) and construction speed (0.044 vs. 0.037) make it more suitable for modern high-rise construction where services integration is critical.

The Bubble Deck slab and Waffle slab rank in the fifth and sixth, respectively, with Bubble Deck (VE = 0.716) outperforming Waffle (VE = 0.205) by 249%. Despite Waffle Slab’s superior aesthetic appeal (0.041) and equivalent insulation properties, Bubble Deck achieves significantly better value through lower cost (0.682 vs. 0.909) and superior structural performance (Ci = 0.488 vs. 0.186).

The results reveal a clear hierarchy of value among the slab systems. The Post-Tension Slab (A5) emerges as the top-ranked alternative with the highest VE score of 2.467. This superior value is driven by its unique combination of the highest possible performance score (Ci = 0.898) and the lowest normalized cost (0.364). Its high performance is based on its excellence in heavily weighted criteria, such as construction speed and structural efficiency (load capacity), as demonstrated in the radar chart analysis. Its low cost is attributable to material efficiency, as it requires less concrete and reinforcing steel to achieve long spans compared to other systems. This result is consistent with findings that post-tensioning can reduce slab thickness by up to 30% and replace 5–6 pounds of rebar with just one pound of post-tensioning steel [30].

Interestingly, the traditional Solid Slab (A1) ranks a very close second with a VE score of 2.418. This result highlights a different path to achieving high value. While it scored poorly on modern design metrics, such as MEP flexibility and aesthetics, its overall performance score (Ci = 0.880) was exceptionally high. It was driven by its strong performance in fundamental criteria, such as material availability and fire resistance. Combined with its low cost—tied with the Post-Tension slab due to the use of simple, commodity materials—it proves to be an excellent value proposition where advanced performance features are not a priority. The simplicity of formwork and reinforcement for solid slabs contributes to their cost-effectiveness.

The systems that ranked lowest, particularly the Precast Slab (A6), Composite Slab (A7), and Waffle Slab (A3), all suffered from extremely high normalized costs. Although the precast slab is the fastest to erect, its normalized cost was the highest (1.000), completely canceling out its functional advantages in a value calculation. This result indicates that for high-rise applications, the logistical and manufacturing costs associated with these specialized systems are perceived by experts to outweigh their performance benefits. The high initial investment, transportation challenges, and difficulty in modification are significant disadvantages of precast concrete in high-rise construction [31,32].

These VE findings offer a more nuanced perspective than studies that focus solely on cost or a single performance metric. For instance, the case study presented in the appendix of this paper, supported by research from Jacinto et al. [3], highlights that post-tension systems can offer significant direct cost savings (16%) and time savings (25%) compared to conventional slabs. Our analysis confirms this, validating that the Post-Tension Slab (A5) offers the best overall value by combining these efficiencies.

However, our results appear to contradict some existing literature. The study by Asamoah et al. (2016) [6] found that precast concrete was, on average, 23.22% cheaper than cast-in-place elements. In our value analysis, the precast slab (A6) ranked last due to having the highest normalized cost. The difference in methodology can explain this discrepancy. A direct cost comparison may not account for the significant logistical expenses, specialized equipment, and plant dependency associated with precast systems in the context of a high-rise building. These factors are implicitly captured in the expert-derived normalized cost used in our VE analysis.

Furthermore, while studies such as Andreas et al. [8] have shown the bubble deck slab (A8) to be cheaper and faster than a conventional slab, the multi-criteria analysis in this paper places it in the middle of the ranking (5th). This analysis suggests that while it may offer an advantage in a direct comparison, its higher cost and moderate performance score across eight criteria do not allow it to compete with the superior value offered by the Post-Tension and Solid Slab systems.

Ultimately, this VE analysis demonstrates that the “best” system is not simply the cheapest or the highest-performing on a single metric, but rather the one that offers the optimal balance across a range of weighted factors. The high ranking of both a “high-tech” system (Post-Tension) and a “low-tech” system (Solid Slab) confirms that excellent value can be achieved through different strategies: one through advanced engineering and material efficiency, and the other through simplicity and the use of readily available, low-cost materials.

These findings provide precise answers to the three research questions posed at the outset. Regarding RQ1, this research identified and validated eight key performance criteria through expert consensus: Toughness (weight: 0.160), Availability of material (0.144), Speed of Construction (0.131), Space for MEP System (0.128), Decorative Features (0.116), Energy efficiency (Heat insulation) (0.109), Sound Insulation (0.108), and Resist Fire (0.104). The SWARA method revealed that Toughness carries the highest importance, followed by Availability of Materials and Speed of Construction, demonstrating that industry experts prioritize technical performance and efficiency over aesthetic considerations. In response to RQ2, the systematic TOPSIS evaluation across all eight criteria revealed distinct performance profiles: the Post-Tension Slab achieved the highest performance score (Ci = 0.898) through excellence in Decorative Features (0.057), space for MEP systems (0.047), and resist fire (0.06); the Solid Slab ranked second (Ci = 0.880) with good toughness (0.069) and availability of materials (0.042), while the Precast Slab, leading in construction speed (0.043), ranked lower overall (Ci = 0.116) due to its high normalized cost (1.00). Addressing RQ3 directly, the Value Engineering analysis conclusively demonstrates that the Post-Tension Slab offers the highest value (VE = 2.467), achieving an optimal balance between superior performance (Ci = 0.898) and minimal cost (normalized cost = 0.364). This represents a 2% value advantage over the second-ranked Solid Slab (VE = 2.418) and a 95% advantage over the lowest-ranked Precast Slab (VE = 0.880), confirming that maximum value is achieved not through the highest performance alone, nor through the lowest cost alone, but through their optimal combination as quantified by the VE ratio.

6. Limitations and Recommendations of Future Studies

This study has several limitations. First, the model’s inputs for criteria weights and cost ratings are based on the subjective judgments of a specific panel of experts; a different panel could yield different results. Second, the cost data and material availability are inherently tied to a specific market context and may not be universally applicable without regional adjustments. Third, the SWARA-TOPSIS methodology, while effective for ranking alternatives, assumes linear relationships between criteria and does not capture potential synergistic or antagonistic interactions between performance attributes; additionally, the method’s reliance on normalization and distance-based calculations may not fully reflect the non-compensatory nature of certain critical criteria where poor performance cannot be offset by excellence in other areas. Finally, the analysis was conducted from a post-design perspective, assuming all systems were viable. It did not assess how the initial choice of a slab system might influence the building’s overall structural design from the outset.

Based on these findings and limitations, several prospects for future research are recommended:

• Future studies could expand the “cost” criterion to include a complete Lifecycle Cost Analysis (LCA) and incorporate sustainability metrics (e.g., carbon footprint) to provide an even more comprehensive measure of long-term value.
• A sensitivity analysis could be performed to determine how the final rankings change with different weights for the criteria, which would help decision-makers understand the impact of prioritizing different project goals (e.g., speed vs. aesthetics).
• The model could be applied to other building types, such as hospitals, industrial facilities, or low-rise residential structures, to test its robustness and identify context-specific optimal solutions.
• Future work could focus on integrating this MCDM model with Building Information Modeling (BIM) platforms to automate data extraction and provide real-time, value-based decision support during the initial design stages.
• A critical avenue for future research is the comprehensive integration of environmental sustainability assessment and life cycle management into the decision-making framework. Specifically, the model should incorporate embodied carbon analysis, operational energy consumption over the building’s lifespan, end-of-life recyclability potential, and compliance with green building certification systems (LEED, BREEAM, WELL). This expansion would enable evaluation of each slab system’s environmental impact across extraction, manufacturing, transportation, construction, use phase, and demolition stages. Furthermore, advanced integration with digital technologies—particularly Building Information Modeling (BIM) and digital twin platforms—represents a transformative opportunity to enhance the framework’s practical applicability.

7. Conclusions

This study addressed the complex challenge of selecting the optimal structural slab system for high-rise buildings through a systematic and objective lens. Recognizing that traditional decision-making often relies on limited criteria, the purpose of this research was to develop and apply a formal VE framework to provide a holistic assessment of function versus cost. To achieve this, the study employed a hybrid MCDM model. This methodology integrated the SWARA to determine the objective weights of eight key criteria based on expert judgment, followed by the TOPSIS to evaluate and rank eight distinct slab alternatives.

The primary finding of this research is that the Post-Tension Slab (A5) offers the highest overall value for high-rise applications, achieving a VE score of 2.467. This superior value is driven by its unique combination of first-rate performance in heavily weighted criteria—such as construction speed and structural efficiency—and a low normalized cost. Interestingly, the traditional Solid Slab (A1) ranked a very close second with a VE score of 2.418, demonstrating that high value can also be achieved through simplicity, low material costs, and strong performance on fundamental metrics such as fire resistance and material availability. Conversely, systems like Precast, Waffle, and Composite slabs ranked poorly, as their high normalized costs outweighed their specific functional advantages in the final value calculation. The top performers succeeded through either advanced engineering efficiency (Post-Tension, Flat) or strategic simplicity and low cost (Solid). At the same time, lower-ranked systems suffered from prohibitively high costs (Precast, Composite, Waffle) or limited performance despite moderate pricing (Bubble Deck, Hollow Ribbed).

The contribution of this study to the existing body of knowledge is threefold. First, it moves beyond the fragmented, single-criterion comparisons commonly found in the existing literature by applying a formal VE framework. Second, by systematically evaluating a broad spectrum of eight alternatives, it provides a more comprehensive market overview than typical pairwise studies. Third, it successfully demonstrates the application of a SWARA-TOPSIS hybrid model as a robust and transparent decision-support tool for complex construction engineering problems, thereby bridging a critical gap in academic research. Consequently, these quantifiable value results allow project managers, developers, and engineers to justify a slab selection with evidence, moving beyond mere cost-cutting to ensure an optimal balance between cost, schedule, and functional performance.