Advancing building design decisions through integrating multidisciplinary knowledge from existing buildings: a review

The architecture, engineering, construction and facilities management (AEC/FM) industry continues to face persistent challenges in achieving high-performing buildings, particularly during the operational phase (Che-Ghani et al., 2023; Mohamad et al., 2018). As modern buildings become complex with sophisticated building systems, the potential for various operation and maintenance (O&M) issues has risen, impacting building performance in terms of energy use, occupancy safety and overall functionality (Teo et al., 2022; Han et al., 2022). Previous studies have consistently identified poor or inadequately informed design decisions as a key contributor to this situation (Akanmu et al., 2020; Nazeer et al., 2025). For example, multiple problems such as difficulty accessing areas for maintenance, safety hazards, budget overruns, inefficient energy usage and spaces that do not serve their intended purpose may arise, impacting the functionality of various building components/systems (Akanmu et al., 2020). Building design, therefore, represents a critical opportunity to anticipate and mitigate downstream O&M problems by making more informed and performance-oriented decisions (Attobrah et al., 2021).

In conventional practice, design decisions are primarily driven by design assumptions and theoretical underpinnings, with limited iterative refinement based on real operational performance data (Davoodi et al., 2021; Breadsell et al., 2020). As a result, there are often buildings with excellent architectural designs but problematic situations in achieving high performance. Building performance considerations during design have been explored in recent studies through various approaches like performance-based design (PBD), building performance modelling (BPM), building information modelling (BIM), artificial intelligence (AI) and other computational tools (Tian et al., 2021; Petrova et al., 2018; Astarini et al., 2022; Akanmu et al., 2020). While these methods provide useful performance estimations during design decision-making, they are often grounded in simulated or data-based assumptions rather than real-time operational data (Yu et al., 2022; Breadsell et al., 2020). As a result, they rarely reflect actual building operations, overlooking the complexity, variability and evolving nature of actual building use, leading to discrepancies between predicted and real-world performance. Also, while a large amount of building performance data is captured through various techniques like building management systems (BMS) and post-occupancy evaluations (POE), the systematic application of these data for future building designs remains incomplete and underutilised due to different challenges (Petrova et al., 2018; Tian et al., 2021). Some of the significant examples of these challenges include limited integration of multi-source data into a centralised, well-designed data model, limited access to building performance data, a lack of building performance data disclosure laws, limited availability of high-quality datasets, financial limitations, interoperability issues between different data management platforms and the complexity of analysing large datasets (Astarini et al., 2022; Tian et al., 2021; Fang and Cho, 2019). Therefore, integrating multidisciplinary knowledge from existing buildings into new building designs is crucial for supporting current design activities, enabling better-informed design decisions (Petrova et al., 2018; Laovisutthichai and Lu, 2022).

This study introduces a novel design approach named “experiential design decision-making (EDDM)” at a conceptual level, which facilitates the structured use of knowledge derived from existing buildings and applying this knowledge to improve future building designs (Dasandara et al., 2025b). Rather than relying solely on predictive models, design assumptions or conceptual ideas, EDDM emphasises learning from how buildings actually operate in practice to improve current design decision-making. Facilitating approach like EDDM within existing design practices requires a clear understanding of the most critical knowledge categories for effective design decision-making (Dasandara et al., 2025a). Such understanding allows stakeholders involved in the design practices to apply the most appropriate, innovative and novel data and information when making design decisions. It also allows the use of emerging technological and computational tools to uncover novel knowledge from existing buildings, supporting better-informed decisions for future projects. Past studies have attempted to formalise various knowledge categories for design decision-making in many ways. However, those studies have explored the subject in a fragmented manner with various overlaps and distinctions across studies, yet these knowledge categories have not been consolidated into a unified classification. With the rapid increase in data generation within the AEC/FM industry and the growing potential to extract new knowledge from this data (Wang and Leite, 2016; Petrova et al., 2018), the range of knowledge categories for design decision-making is expected to expand substantially. Existing knowledge frameworks/classifications, therefore, require modifications to align with contemporary design contexts and to enable effective knowledge and information exchange, supporting EDDM implementation.

Following this context, this study intends to answer the following research question: “What are the key knowledge categories necessary for effective building design decision-making?” by proposing a modified set of knowledge categories that integrates design, construction and O&M-based knowledge categories identified through a systematic review of existing literature. The remainder of this article is organised as follows: Section 2 presents the theoretical background and introduces the concept of EDDM. Section 3 outlines the research methodology, including the search strategy and analysis procedures. Section 4 provides an overview of the selected articles, and Section 5 presents the refined set of knowledge categories. Section 6 discusses the implications, limitations and future research directions, while Section 7 concludes the article.

Building design decisions can significantly influence positive outcomes and cost reductions during building operations (Attobrah et al., 2021; Abadi et al., 2025). The “MacLeamy curve” proposed by Patrick MacLeamy in 2004 (Construction Users Roundtable, 2004) (see Figure 1) distinctly demonstrates the importance and influence of design decisions on later building lifecycle stages (Nguyen, 2022).

The MacLeamy curve highlights that any change during the early stage of a project is more effective in achieving cost reductions and project success with minimum effort than changes that occur later. Therefore, the building design is the most strategic stage within the project lifecycle for generating valuable knowledge that benefit subsequent stages (Akanmu et al., 2020).

An essential yet often overlooked aspect of improving conventional design decision-making is incorporating building performance feedback from existing buildings into future design decisions (Breadsell et al., 2020). Building performance data is vital for future design decisions to ensure continuous building performance improvement with modern complexities and demands (Goçer et al., 2015; Petrova et al., 2018). In the absence of a structured feedback loop from existing buildings, performance gaps frequently emerge, where buildings may excel in architectural expression but underperform in usability, adaptability and operational efficiency (Breadsell et al., 2020). Recognising this necessity to bridge the gap between building design and operations, this article introduces experiential design decision-making (EDDM) at a conceptual level: a feedback-oriented approach that integrates operational data from existing buildings into future design decisions.

Experiential or experience-based learning, most developed in education, views knowledge as created through the transformation of experience (Kolb, 1984). It can positively influence an individual's ability to comprehend and apply new knowledge effectively to gain more success in their future activities. Beyond the field of education, experiential learning has strong potential for advancing practices in the building industry (Yap and Shavarebi, 2019). Learning is taking place throughout the whole life of a project (Emmitt, 2014), however, its application in the building design process is particularly crucial, as building design can greatly influence positive outcomes in subsequent stages (Attobrah et al., 2021; Terim Cavka et al., 2023).

The cyclic model of experience-based learning, developed by David Kolb, remains the most influential study on experiential learning. David Kolb identified learning as a continuous process grounded in experience, dividing the experiential learning model into four interrelated stages (Kolb, 1984).

1. Concrete Experience (CE)

2. Reflective Observation (RO)

3. Abstract Conceptualisation (AC)

4. Active Experimentation (AE)

Based on this model, Kim (1997) proposed a more expanded and comprehensive experiential learning model named observe, assessment, design and implementation (OADI) cycle (see Figure 2).

According to the OADI cycle, the learner first observes the current context to gain experience, which is then evaluated during the assessment phase. This evaluation generates new knowledge that builds upon recently learned knowledge. Thereafter, actions are taken through the implementation of new knowledge. This process is iterative and dynamic, where the results of the implementation can feed back as new experiences.

Based on the logic of OADI, experience-based learning can be applied within the building design process as “Experiential Design Decision-Making (EDDM)”. Drawing on this concept, EDDM enables learning from existing buildings, creating new knowledge from that experience and applying this new knowledge to inform design decisions in future building projects in an evidence-based manner (see Table 1).

This adaptation transforms a general learning model into a practical decision-making approach in the building industry, tailored to the complex and multidisciplinary nature of building projects. EDDM supports the improvement of contemporary design practices, ensuring that both conceptual and experience-based knowledge are effectively integrated into future design decisions (Dasandara et al., 2025b). Further, EDDM enables stakeholders to identify inefficiencies in existing buildings and address them in future designs while also benefiting from new opportunities. This article introduces the EDDM conceptually to ground the discussion of knowledge categories required for effective design decision-making.

Existing buildings provide design, construction and O&M feedback data that can inform future design decisions in an evidence-based manner (Dasandara et al., 2025a). As new buildings are constructed, the cycle perpetuates, enabling a data-driven approach that improves design outcomes and overall building performance. Through this process, knowledge can be dynamically applied without loss, fostering continuous learning, new knowledge generation and the uptake of emerging innovations and opportunities.

To enable EDDM in future design practices, an in-depth understanding of key knowledge categories for effective design decision-making is essential (Wang et al., 2016; Guo et al., 2013). Such understanding enables design decision-makers to identify the critical data and information necessary to make more informed and evidence-based design decisions. In its absence, valuable knowledge from existing buildings may be overlooked in new building designs, leading to inefficiencies during O&M. Therefore, this study aims to identify key knowledge categories necessary for effective design decision-making by adopting the EDDM concept as a guiding theoretical lens. Although not yet formalised as a theory, EDDM provides a useful basis for identifying knowledge categories derived from existing buildings and positioning them as fundamental inputs for better-informed, evidence-based design decision-making.

This study employs a systematic search and review methodology following the typologies identified by Grant and Booth (2009) to explore existing literature on essential knowledge categories for building design decision-making. Such reviews combine the strengths of a critical review with the methodological rigour of a systematic search, enabling a comprehensive synthesis of the best available evidence within the subject matter (Grant and Booth, 2009). The review process is guided by the five-step approach proposed by Denyer and Tranfield (2009), as presented in Figure 3, to systematically search, analyse and interpret findings, achieving the research aim. This approach has been widely adopted in recent studies due to its structured and rigorous methodology, making it particularly suitable for the current study (Wang et al., 2019; Toorajipour et al., 2021; Rad et al., 2022).

Clearly defining the research question is essential to guide the selection of relevant studies and the data extraction process, as emphasised by Denyer and Tranfield (2009). Following an in-depth background study that identified key research gaps in the subject area, the following research question was formulated in this study: (1) What are the key knowledge categories necessary for effective building design decision-making?

The selection of keywords was informed by the research aim of identifying knowledge categories necessary for effective building design decision-making. The keywords included building design, design decisions, design decision-making, knowledge, knowledge requirements, knowledge categories, knowledge management, knowledge aspects and knowledge areas, capturing studies dealing explicitly with knowledge identification and categorisation for design decision-making. Boolean operators, “OR” and “AND” were used to combine the keywords and enable a more comprehensive and precise article search (Kraus et al., 2022). Since using multiple search engines reduces bias and enhances the breadth of publications retrieved (Ali et al., 2017), the selected keywords were searched in two databases, Scopus and Web of Science. These two databases are recognised as two of the largest and most reputable databases that facilitate access to high-quality publications (Ali et al., 2017).

Given the substantial volume of records retrieved through the search, several exclusion criteria were applied as; publication year (1999–2025), document type (journal articles and conference papers), language (English) and research area (engineering, building and construction). Studies based solely on literature reviews were excluded from the analysis to avoid duplication of previously analysed data. Articles that did not directly focus on the subject area and that addressed unrelated fields like manufacturing and healthcare were also excluded from the analysis. Only articles meeting these criteria were included in the detailed analysis. Since the search attempt yielded irrelevant articles, a multi-stage screening was necessary to select the most suitable articles. First, title screening was undertaken to remove the articles that were not consistent with the subject area. Second, abstract screening was conducted based on the irrelevance of the article content to the building industry context. Through this systematic search and screening process, 22 articles were selected for analysis. In addition, 06 articles were identified through cross-referencing and citation tracking, as such supplementary methods enhance the clarity and comprehensiveness of the review (Cooper et al., 2018).

Citation tracking refers to a method that directly or indirectly gathers other related literature sources based on “seed references” (Hirt et al., 2020). It allows to identify the most related and impactful articles on the subject area. The citation tracking method consists of two main approaches: the forward and backward approaches. The backward approach involves reviewing the citations and reference lists in seed articles to identify additional literature sources. In contrast, the forward approach identifies literature sources that cite the seed article (Hirt et al., 2020; Cribbin, 2014). Figure 4 distinctly presents the citation tracking methodology.

Additional articles related to the knowledge categories for building design decision-making were identified through a methodical process of tracking references of the selected articles. In total, 28 articles were identified through the systematic search and supplementary searches, allowing for detailed analysis. The overall article search process is clearly presented in Figure 5.

This study involved a comparative analysis technique to synthesise key insights from the selected articles. The initial stage involved examining the primary focus of each study to determine whether it explicitly addressed knowledge categories related to building design. Based on this assessment, the studies were grouped into those that identified relevant knowledge aspects and those that did not. The identified key knowledge categories from the selected articles were then tailored based on the definitions and categorisations provided by comparing their similarities and differences. Knowledge categories with similar meanings identified across different studies were consolidated into a single knowledge category. In contrast, broader categories were divided into multiple, more specific elements to ensure accuracy and conceptual clarity. Through this process, all identified knowledge categories were refined and updated based on their overlaps and distinctions, resulting in a precise and comprehensive synthesis of the findings.

Based on the results derived from the comparative analysis, a modified set of knowledge categories was developed. These modified knowledge categories were subsequently mapped across the building lifecycle to highlight the stages at which each knowledge category can be most effectively fulfilled using existing building data. This approach enabled a clearer understanding of the available evidence on knowledge categories for the effective building design process and facilitated the development of a modified set of knowledge categories that enables EDDM in building design practices. As the proposed modified knowledge categories are entirely derived from existing literature, they will be further refined and validated through industry perspectives for more clarity and applicability within today's industry. The limitations of the study were highlighted and recommendations for future research directions were proposed at the end of the article.

The overall research design framework is presented in Figure 6.

Table 2 presents an overview of the selected articles, categorising them according to whether they explicitly address and identify knowledge categories for design decision-making. The table distinguishes between studies that comprehensively identify these knowledge categories and those that provide indirect insights or focus on broader aspects of knowledge management within building design.

According to Table 2, most studies primarily focused on developing broader knowledge management frameworks or tools rather than explicitly identifying specific knowledge categories necessary for design decision-making. For instance, Schneider-Marin et al. (2022) developed a knowledge base for material decision-making in building design, concentrating on knowledge related to different materials. This work emphasises a single aspect of knowledge requirements rather than addressing the full scope needed for comprehensive design decision-making. Similarly, Kaldheim et al. (2023) developed a knowledge-based engineering system for integrating and re-using building knowledge in design, while Hsu et al. (2020) developed a knowledge-based system for design clash resolution using BIM and AI technologies. Khudhair et al. (2023) developed a knowledge-based OpenBIM data exchange framework that enhances knowledge transfer across the building lifecycle. Collectively, these studies demonstrate the growing use of advanced technologies to support knowledge integration in building design, but fall short of systematically identifying the broader range of knowledge categories required for effective design decision-making.

Despite valuable contributions from prior studies, only 12 articles explicitly identified and formalised knowledge categories for design decision-making. This limited number likely reflects the field's predominant emphasis on developing broad knowledge management frameworks and digital tools (e.g. BIM or AI-driven approaches), rather than directly identifying the essential knowledge categories for design decision-making. Over time, research attention has shifted towards technological enablers, with comparatively less focus on systematically identifying and structuring the fundamental knowledge categories necessary for design decisions within complex and evolving project environments. This trend demonstrates that while technological solutions continue to advance, the foundational understanding of “what knowledge is required for design decision-making within the modern complexities and changes” remains underdeveloped. At the same time, most of the selected 12 studies were conducted several years ago, indicating a stagnation in recent research on this critical aspect. This lack of recent, focused investigations highlights an overlooked yet essential dimension of design decision-making, which is inherently complex and multidisciplinary in the modern building industry (Khudhair et al., 2023; Kaldheim et al., 2023).

Effective building design requires integrating diverse knowledge categories across multiple disciplines, with explicit priorities and design considerations established for each building system and component (Wang et al., 2016; Guo et al., 2013). These knowledge categories need to be continuously informed by feedback data from existing buildings, alongside theoretical underpinnings or design assumptions, to enable data-driven, modern concepts like EDDM by bridging the gap between building design and O&M. Table 1 provides the current research landscape on this domain, emphasising the limited number of studies that directly address this critical aspect of design decision-making. Consequently, the necessity for developing a modified set of knowledge categories for building design decision-making was highlighted to enable EDDM in today's building design practices. The next section presents the comparative analysis conducted to extract key insights from the identified 12 articles and develop the modified knowledge categories for building design decision-making.

When exploring key knowledge categories for design decision-making, most studies have concentrated on specific design domains such as mechanical, electrical and plumbing (MEP) systems, sustainable design or clash detection, while others have focused on building design more generally. Among these, Korman et al. (2003) made an influential contribution by examining the knowledge aspects related to MEP design. It builds upon and extends the work by Tatum and Korman (1999) that identifies different design, construction and O&M knowledge categories for MEP design. By extending the breadth and depth of the above frameworks, Tabesh and Staub-French (2006) developed a three-dimensional knowledge framework for MEP design that incorporated additional knowledge categories to be considered during the MEP design process. However, they noted the need for further research to expand and structure these knowledge areas more systematically for better design decisions. As a result, many other research efforts have emerged in this area in later stages. For example, Rocha (2011) identified knowledge categories for building design by incorporating building maintenance considerations into the building design process. Guo et al. (2013) presented knowledge aspects for building design, focusing on interface integration for MEP design, while Wang and Leite (2016) presented formalised knowledge representation that can be used to identify conflicts and spatial problems during MEP design. Similarly, Wang et al. (2016) identified key knowledge aspects for building design through a BIM-based MEP layout integration framework. More recently, Petrova et al. (2018) proposed a performance-oriented design decision support system that identifies and integrates knowledge from O&M of existing buildings into the building design process.

Further, the analysis revealed that the knowledge required for effective design decision-making spans multiple stages of the building lifecycle. The knowledge categories for design decision-making identified across various studies can be categorised into three main types: design-based, construction-based and O&M-based, reflecting the key lifecycle stages that inform design decisions.

1. Design-based knowledge categories: Considerations during the early phase of the building lifecycle that ensure the expected performance of different building components/systems (Tatum and Korman, 1999, 2000).

2. Construction-based knowledge categories: Considerations during the building construction process that ensure the feasibility of the construction process with increased construction efficiency (Korman et al., 2003; Tatum and Korman, 1999).

3. O&M-based knowledge categories: Considerations during the O&M stage (post-occupancy stage) that ensure the improved operational performance of building systems with minimum disturbances (Korman et al., 2003; Tatum and Korman, 2000).

Table 3 summarises the knowledge categories for design decision-making under the categories above, drawing from the identified 12 key articles that focus on the subject matter.

Table 3 illustrates how design decision-making relies on knowledge originating from multiple stages of the building lifecycle. While each category, design-based, construction-based and O&M-based, addresses distinct considerations, their integration is essential to ensure holistic and informed design outcomes. It is also evident that, although many studies explicitly group these knowledge categories into design, construction and O&M-based knowledge, several studies do not apply such a lifecycle-based structure and instead present the knowledge areas as unclassified. Altogether, this synthesis forms the foundation for developing a modified set of knowledge categories that better support EDDM in contemporary practice, as discussed in the following section.

By reviewing the above findings, the authors critically compared and analysed similarities and differences in how knowledge areas were defined and categorised by various studies. Thereby, several refinements were made to develop the modified set of knowledge categories as presented in Table 4 below. Table 4 outlines the modifications made to refine the existing knowledge categories by comparing their definitions, identifying conceptual overlaps and distinctions, and consolidating or separating them where necessary. The table further presents the rationale for each modification and the respective modified knowledge categories proposed in this study.

The modifications presented in Table 4 are further elaborated below.

1. The knowledge area - “design performance” identified by Tabesh and Staub-French (2006)refers to criteria that, if not satisfied, may lead to performance issues in a component/system. For example, rainwater drainpipes require a minimum slope of 1% for optimal performance. Guo et al. (2013) classified such criteria under “functionality” and “efficiency” while Korman et al. (2003) classified them within “system function/performance” criteria. Drawing from these classifications, the current study explicitly identifies this function-oriented knowledge as a distinct category termed “functional requirements”. Based on definitions and examples from Hu and Castro-Lacouture (2018), Korman et al. (2003) and Tabesh and Staub-French (2006), these requirements primarily relate to the geometric attributes of building components/systems, such as component type, component geometry, slope and location, that directly influence their performance. Knowledge aspects like slope, component geometry and component type, identified by Wang and Leite (2016), are also considered under functional requirements.

2. The knowledge area - “system requirements” identified by Wang and Leite (2016) primarily refers to the systems to which different building components belong. A similar concept was described by Korman et al. (2003) as “system function/performance” in their study. Also, the knowledge aspect of “connection”, identified by Tatum and Korman (2000), highlights the details necessary for connecting building components and systems. Building on these perspectives, the current study consolidates these related knowledge aspects into a unified category referred to as “system requirements”.

3. The knowledge area - “clearance” has been identified as a key consideration in building design by Wang and Leite (2013), Hu and Castro-Lacouture (2018) and Korman et al. (2003). It primarily refers to the clearance spacing provided around building components for various purposes such as mitigating heat exchange, controlling vibration and minimising spatial conflicts. This knowledge aspect is closely related to component placements. For example, a minimum clearance needs to be maintained between two building components to avoid clashes (Wang and Leite, 2016). Previous research often combined clearance requirements with insulation requirements, which focused on designated insulation thickness and insulation types for building components (Tabesh and Staub-French, 2006; Tatum and Korman, 2000; Korman et al., 2003). However, the current study distinguishes these two aspects as separate knowledge categories for more clarity, recognising that clearance requirements apply not only to the insulation of the building systems but also to other building components.

4. Many studies have identified “Space requirements” as a key knowledge area to be considered for design decision-making. It refers to the physical spaces necessary to accommodate different building systems and components (Korman et al., 2003). For example, these space requirements may include spaces for building component installations, material handling and material storage. Therefore, “installation space”, as identified by Tabesh and Staub-French (2006), Hu and Castro-Lacouture (2018) and Wang and Leite (2013), can be generally considered as space requirements. Previous studies also note that space requirements vary across different stages of the building lifecycle. For example, space requirements can be considered during the construction stage for the installation of building components as well as during the O&M stage for the operational activities of building systems.

5. The knowledge area – “accessibility requirements” has been identified by many studies as a key consideration in building design, referring to the space required to provide access to building components for operation and maintenance activities (Tatum and Korman, 2000). Related criteria, such as access frequency and access space, identified by Wang and Leite (2016) and Wang and Leite (2013), access for operation and access for maintenance, identified by Tatum and Korman (1999), can be generally classified under accessibility requirements. Similar to space requirements, accessibility considerations may vary across different stages of the building lifecycle.

6. The knowledge areas – “adaptability requirements” and “expandability” identified by Rocha (2011) and Guo et al. (2013), respectively, refer to the flexibility of building systems and components to accommodate future adjustments or spatial modifications. Building on this perspective, the current study consolidates these aspects under the broader category named “expandability/retrofit requirements”, as identified by Tatum and Korman (2000). Tatum and Korman (2000) defined this knowledge aspect as the future expansion requirements and potential retrofit options for building systems and components.

7. According to Guo et al. (2013), maintenance space and operation routes need to be taken into consideration during design decision-making under the “maintainability” knowledge area. However, in the current study, these knowledge aspects are disaggregated and classified under “space requirements” and “accessibility requirements” to provide conceptual clarity and specificity.

8. The knowledge aspect – “coordination with civil works” identified by Guo et al. (2013), refers to the process of installing building components in coordination with other civil works. For example, electrical pipes from the slab need to be installed before the slab is grouted. Building on this perspective, the current study classifies this aspect under the broader term of “sequencing considerations”, which refers to the systematic arrangement of construction and installation activities to maximise efficiency and minimise conflicts (Korman et al., 2003).

9. The knowledge area – “economy requirements” identified by Rocha (2011) primarily concerns cost-related factors that need to be considered during the design stage. In this study, these are aligned with the broader classification of “cost requirements” as identified by Wang and Leite (2013) and Wang and Leite (2016).

10. The “lighting requirements” and “indoor air quality requirements”, identified by Wang and Leite (2013) and Wang and Leite (2016), and “indoor environment conditions” identified by Petrova et al. (2018) can be collectively classified as “comfort requirements”. These encompass the habitability conditions that ensure a healthy and comfortable indoor environment for building occupants (Rocha, 2011).

11. The knowledge aspect of “constructability”, as identified by Guo et al. (2013), represents the factors affecting the sequence of building component installation. In contrast, Wang et al. (2013) and Wang et al. (2016) identified constructability as a combination of different construction requirements, such as safety, access requirements and configuration. Given its broad scope, the current study disaggregates these elements into distinct criteria, such as safety considerations, access requirements and configuration, for greater clarity.

Through this refinement process, redundant or conceptually similar knowledge areas were consolidated, while broader categories were separated where necessary to enhance precision and applicability. The resulting modified set of knowledge categories provides a conceptual foundation for applying EDDM in building design practices, supporting the development of more informed and evidence-based design decisions. The revised knowledge categories following the modifications are summarised in Table 5 for the reader's better understanding.

In total, 21 key knowledge categories were defined through this evaluation. These modified knowledge categories can be further grouped as design-based, construction-based and O&M-based knowledge requirements, as previously described. Figure 7 provides a visual representation of these knowledge categories, illustrating that each relates to different stages of the building lifecycle.

This diagram illustrates the multidisciplinary knowledge aspects that need to be captured from existing building projects to inform future design decisions. It illustrates how different types of knowledge can be derived from various stages of the building lifecycle and highlights areas of overlap where certain knowledge categories can be fulfilled using data from multiple stages. For instance, space requirements and cost requirements span all stages, indicating the significance of informing these knowledge categories using data/information drawn from each lifecycle stage. Access requirements and safety considerations can be derived from both the construction and O&M phases of the current projects to inform new designs in the future. Overall, this diagram highlights the importance of systematically integrating diverse knowledge areas from different lifecycle stages to inform new design decisions and reinforce the concept of EDDM in building design. However, as the proposed framework is based primarily on a literature review, further refinement and validation are necessary to ensure its accuracy and applicability to contemporary industry practices.

This study extensively reviewed key knowledge categories for building design decision-making to enable EDDM in current building design practices. Despite considerable attention to exploring knowledge categories for building design decision-making, previous studies have approached this subject in a fragmented manner, lacking a cohesive structure to reflect the interconnected nature of these knowledge categories. For instance, some researchers have focused specifically on knowledge categories related to MEP design, clash detection and sustainability aspects of design, while others have addressed building design more broadly. Additionally, notable variations exist in how different authors define and classify these knowledge categories. This fragmentation led to the development of a more unified and comprehensive set of knowledge categories for building design by integrating these diverse perspectives through a systematic evaluation. It provides valuable insights into both the theory and practice of building design within contemporary settings. More importantly, the evaluation reflects a holistic understanding of knowledge categories for design decision-making, enabling EDDM by addressing the complexities and demands of modern building design practices while mitigating valuable knowledge loss throughout the building lifecycle.

In the building industry, the idea of knowledge transfer from existing buildings to new building designs is far from new (Jensen, 2011). Practices like post-occupancy evaluations, lessons learned exercises and post-project reviews have long been conducted to capture valuable knowledge from completed buildings. However, the application of this knowledge to inform future building designs remains incomplete and underutilised (Petrova et al., 2018). The failure to incorporate learnings from existing buildings often results in the loss of valuable knowledge that could enhance future design outcomes (Emmitt, 2014). Such knowledge loss contributes to unnecessary rework, project delays and financial inefficiencies across the building process (Paulson, 1976; Hsu et al., 2020).

To address this issue, the findings of this study support the implementation of EDDM in future building projects by consolidating and refining the dispersed knowledge aspects identified in previous research into a modified set of knowledge categories. The identified knowledge categories need to be informed by feedback data/information from existing buildings to guide evidence-based design decision-making. Unlike earlier research efforts, which often used overlapping terminologies or addressed knowledge needs in isolation, this study integrates the modified knowledge categories across the building lifecycle to highlight when and how each category can be most effectively fulfilled using building performance data from existing buildings. Such mapping of modified knowledge categories offers a more coherent and conceptual foundation for understanding how existing building performance data can be mobilised to reduce knowledge loss and strengthen design processes in the future.

The implications of the findings of this study are primarily conceptual at this stage. It positions EDDM as a guiding lens for structuring and unifying knowledge categories. Highlighting the need for collaborative and data-driven approaches in building design (Dasandara et al., 2025b) to address the major challenges associated with knowledge sharing and integration in today's building industry (Soltani et al., 2023; Carrara et al., 2009). Recent developments in BIM and computational approaches have accelerated the shift towards more data-driven design decision-making, presenting new opportunities to operationalise knowledge management in practice (Ahmadpanah et al., 2023; Khudhair et al., 2023). A multi-dimensional (n-D) BIM concept (Eg: 4D for integrating time/schedule aspects, 5D for integrating cost aspects, 6D for integrating sustainability and energy performance aspects) facilitates the digital representation of data/information management throughout the building lifecycle, from design to O&M (Shehzad et al., 2021; Chen and Tang, 2019). While primarily conceptual at this stage, the refined knowledge categories in this study offer a structured reference that can support the integration of knowledge management into emerging BIM dimensions and other computational approaches.

In this context, this study provides a coherent foundation for linking building performance data with design decision-making across the lifecycle to bridge the knowledge gap between design and O&M. By consolidating and refining previously fragmented knowledge areas, the findings clarify which knowledge areas are most important and identify where in the lifecycle they can be effectively fulfilled using existing building data. Following industry validation and further refinement, the modified set of knowledge categories has the potential to guide industry practitioners in systematically operationalising experiential knowledge within data-driven design environments, thereby reducing knowledge loss and strengthening design–operation integration.

Since the proposed set of knowledge categories is entirely derived from existing literature, it has several limitations that necessitate further refinement and validation from industry practitioners to enhance its clarity and applicability within today's building industry. The key limitations are outlined below.

1. The EDDM concept primarily intends to leverage knowledge from existing buildings to inform future design decisions in an evidence-based manner. This study identifies various knowledge categories for design decision-making that can be derived from different stages of the building lifecycle. However, it does not explicitly prioritise or differentiate knowledge that can be derived from empirical building performance data and that is primarily informed by design assumptions, personal expertise or theoretical understanding. For example, certain aspects, such as aesthetic considerations, may rely more on subjective judgment than on practical insights from the existing buildings. This can raise concerns about the practical applicability of proposed knowledge categories enabling EDDM, as not all knowledge categories contribute equally to a data-driven approach. Therefore, a critical industry evaluation is required to prioritise the most influential knowledge categories that are grounded in performance data to ensure that EDDM can be more effectively integrated into future design practices.

2. While this study maps knowledge categories according to their derivation from different stages of the building lifecycle, it does not fully capture the dynamic and data-intensive nature of contemporary building practices. With the increased data generation and circulation in modern building projects, many knowledge categories can be informed by multiple phases. The current mapping in Figure 7 does not adequately reflect these interdependencies, limiting its applicability in practice. Further refinement is therefore essential to ensure that the mapping accurately represents the fluid knowledge exchange across lifecycle stages, enabling EDDM to be effectively implemented within the current practices.

3. The proposed modified set of knowledge categories may not encompass all the knowledge aspects that can be derived from existing buildings to inform future design decisions. Since industry practitioners often consider project-specific challenges, evolving regulatory and technological trends, and changing market conditions, additional knowledge categories may emerge from the empirical industry insights. Therefore, further refinement of the proposed set of knowledge categories is required through real-world case studies or expert interviews to ensure its accuracy, completeness and relevance to contemporary practices.

Future research to address these limitations must be undertaken to ensure that the proposed set of knowledge categories provides a robust theoretical basis for implementing the concept of EDDM to support design activities towards optimised design outcomes.

The future research directions that can be built upon the findings of this study are discussed below.

1. Empirical validation and industry-driven refinement of the findings

To strengthen the implementation of EDDM, prioritisation of knowledge categories that can be effectively informed by existing building knowledge is crucial. Future research can focus on differentiating and prioritising knowledge categories that are most influenced by empirical building performance data. Methodologies such as expert interviews, surveys, focus group discussions and industry-driven evaluations can be conducted to determine which knowledge categories have the greatest impact on improving future design decisions when informed by real-world building performance data. Further, future research could involve empirical validation of the proposed set of knowledge categories through industry collaboration to ensure that it aligns with industry needs and captures all relevant knowledge areas. At the same time, longitudinal studies that focus on how these knowledge categories evolve over time would further strengthen the contribution of this subject area for continuous improvements in building design decision-making.

1. Evaluating the real-world applicability of the proposed knowledge categories

The applicability of the proposed set of knowledge categories in real-world building projects across different contexts and its impact towards project efficiency, building performance and sustainable outcomes can be explored through case studies. Different measurable indicators can also be developed under each knowledge area to quantify how the EDDM approach improves future design decisions, thereby enhancing building performance and contributing to long-term sustainability and efficiency. Further, different building typologies and geographic contexts can be compared to determine how knowledge categories and their applicability vary based on project complexity, climate conditions, regulatory environments and stakeholder involvement.

1. Expanding the scope of the literature review

Since the current study is limited to a few exclusion and inclusion criteria during article search, future research can expand upon the current selection process by incorporating more recent articles and more databases. This will ensure the continued relevance and applicability of the proposed knowledge categories to enable EDDM in evolving building design practices.

The introduced EDDM concept integrates multidisciplinary knowledge from existing buildings into future design practices to enable more informed and evidence-based design decision-making. However, the inconsistent and limited reuse of existing building performance data for future designs remains a significant challenge in today's building industry, resulting in the loss of valuable knowledge throughout the building lifecycle. Therefore, systematically integrating knowledge from existing buildings into future design practices is essential for minimising this knowledge loss, improving design decisions and ensuring enhanced building performance. This study comprehensively identified the essential knowledge categories for design decision-making and proposed a modified set of knowledge categories through a systematic review of existing literature. By consolidating fragmented knowledge aspects from previous studies into a cohesive structure, the current study provides a structured and conceptual knowledge foundation for enabling EDDM in building design practices. By utilising feedback data from existing buildings to fulfil the identified knowledge categories, EDDM enables the dynamic application of experiential knowledge across building projects, promoting continuous learning and improvement in design outcomes.

Since the proposed set of knowledge categories is entirely derived from existing literature, future research is recommended to further validate and refine the modified knowledge classification through the industry practitioners' perspectives to enhance its clarity and practical applicability. Also, the relevance and completeness of the modified knowledge classification across diverse project types, contexts and geographical locations require further investigation. While primarily conceptual at this stage, this study provides a valuable contribution to the field by providing a consolidated and modernised reference for both academic and industry practitioners to improve building design decision-making in future projects. Furthermore, after critical refinement and validation in the future, the proposed knowledge categories can serve as a baseline for capturing the most valuable data and information from existing buildings and generating multidisciplinary knowledge that informs future design decisions in an evidence-based manner. The modified knowledge classification provides a foundation for organisations to establish internal knowledge repositories that support EDDM implementation within design practices, thereby enabling data-informed activities like the development of new design briefs and the continuous updating of existing ones with timely operational requirements and performance insights. In this sense, the findings represent a crucial first step towards further developing and validating the EDDM approach within the industry, as the first author's ongoing Doctoral research.

This study is a literature review and did not involve any human participants or animals. Therefore, no ethical approval was required.

During the preparation of this work, the authors used GPT-4 and Grammarly for proofreading and improving the clarity of the writing. After using these tools, the authors reviewed and edited the content as needed.

This research is supported by Monash University, Australia, under the Monash Graduate Scholarship, MADA (Monash Art, Design and Architecture) Tuition Fee Scholarship and Building 4.0 Cooperative Research Centre (CRC) Top-up scholarship. The support of the Commonwealth of Australia through the Cooperative Research Centre Programme is acknowledged.