Performance Optimization of Building Envelope Through BIM and Multi-Criteria Analysis

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper presents a coherent and forward-looking methodology that integrates Building Information Modeling (BIM), parametric scripting, and Multi-Criteria Decision Making (MCDM) to optimize the performance of external wall assemblies. The approach leverages Autodesk Revit and Dynamo to automate the parametrisation of insulation thickness, ensuring compliance with regulatory thresholds related to thermal transmittance and surface mass. Acoustic performance is evaluated using ECHO software, while a Weighted Sum Model (WSM) assesses and ranks design alternatives based on economic cost, Global Warming Potential (GWP), embodied energy, and acoustic insulation. Through a case study involving 24 wall configurations derived from eight stratigraphies and three insulation materials, the methodology demonstrates its capacity to balance environmental impact, occupant comfort, and construction feasibility.

The research is innovative in its integrative use of BIM-based modelling and MCDM within a fully digital workflow that supports informed and transparent decision-making during early design phases. In particular, the automation of performance assessments within a BIM environment, combined with environmental and acoustic metrics, represents a significant advancement over traditional approaches. The use of bio-based materials, such as cork and rammed earth, highlights the study’s engagement with current sustainability trends and confirms the feasibility of natural solutions within a structured design optimisation process.

The study is methodologically sound and aligned with the increasing demand for replicable and regulation-compliant tools in sustainable architecture. It makes a valuable contribution to the ongoing discourse on digital design processes and the role of performance-based evaluation in architectural practice. The framework’s potential to support the early design stages, where most critical decisions regarding environmental impact are made, further underscores its relevance. The clarity of the workflow and the breadth of criteria considered enhance the paper’s applicability in both academic research and professional practice.

Despite the overall quality and relevance of the study, some minor revisions could extend its applicability:

• Validation: Acoustic performance estimations are based exclusively on ECHO software; the inclusion of empirical validation or benchmarking against real-world data would strengthen the credibility of the results.

• Scalability: The methodology could be tested on more complex geometries or larger project scales to assess its robustness and adaptability.

• Stakeholder Integration: Incorporating user preferences or expert judgment in the weighting process of MCDM would enhance the decision-making framework and align it more closely with real-world design processes.

• Sensitivity Analysis: A more detailed sensitivity analysis on the weightings used in the WSM could provide insights into the stability and reliability of the proposed rankings.

Author Response

General Comment: The paper presents a coherent and forward-looking methodology that integrates Building Information Modeling (BIM), parametric scripting, and Multi-Criteria Decision Making (MCDM) to optimize the performance of external wall assemblies. The approach leverages Autodesk Revit and Dynamo to automate the parametrisation of insulation thickness, ensuring compliance with regulatory thresholds related to thermal transmittance and surface mass. Acoustic performance is evaluated using ECHO software, while a Weighted Sum Model (WSM) assesses and ranks design alternatives based on economic cost, Global Warming Potential (GWP), embodied energy, and acoustic insulation. Through a case study involving 24 wall configurations derived from eight stratigraphies and three insulation materials, the methodology demonstrates its capacity to balance environmental impact, occupant comfort, and construction feasibility.

The research is innovative in its integrative use of BIM-based modelling and MCDM within a fully digital workflow that supports informed and transparent decision-making during early design phases. In particular, the automation of performance assessments within a BIM environment, combined with environmental and acoustic metrics, represents a significant advancement over traditional approaches. The use of bio-based materials, such as cork and rammed earth, highlights the study’s engagement with current sustainability trends and confirms the feasibility of natural solutions within a structured design optimisation process.

The study is methodologically sound and aligned with the increasing demand for replicable and regulation-compliant tools in sustainable architecture. It makes a valuable contribution to the ongoing discourse on digital design processes and the role of performance-based evaluation in architectural practice. The framework’s potential to support the early design stages, where most critical decisions regarding environmental impact are made, further underscores its relevance. The clarity of the workflow and the breadth of criteria considered enhance the paper’s applicability in both academic research and professional practice.

Despite the overall quality and relevance of the study, some minor revisions could extend its applicability.

Response: We thank the reviewer for their comprehensive and encouraging evaluation of our work. We appreciate the recognition of the study’s methodological integration, digital workflow, and focus on sustainability and early-stage design optimization. We are grateful for the appreciation of the study’s clarity, practical applicability, and contribution to ongoing developments in performance-based architectural design.

In response to the minor revision suggestions, we have:

• Expanded the manuscript to include a justification and literature-supported validation for the acoustic performance estimations using ECHO software
• Clarified the scalability potential of the methodology to more complex geometries and building scales
• Acknowledged the importance of stakeholder-defined weights and proposed their integration in future work
• Added a preliminary sensitivity analysis of the WSM weighting scheme to assess the stability of ranking results

 

Comment: Validation: Acoustic performance estimations are based exclusively on ECHO software; the inclusion of empirical validation or benchmarking against real-world data would strengthen the credibility of the results.

Response: We thank the reviewer for this insightful observation. We acknowledge the importance of empirical validation to reinforce the reliability of simulated performance data. While our study is focused on developing and testing a digital and parametric methodology applicable during the early design stages, we recognize the value of benchmarking simulation outputs against measured acoustic data.

To address this, we have revised Section 3.2 of the manuscript to clarify the rationale behind the adoption of ECHO software. In the revised text, we note that ECHO has been validated in previous studies for its predictive accuracy in estimating acoustic insulation of multilayer façade systems, using parameters such as surface mass and dynamic stiffness. We also reference peer-reviewed literature where ECHO's outputs have been compared to empirical data, reinforcing its suitability for early-stage comparative assessments.

Additionally, we acknowledge in the revised manuscript that further research could enhance the robustness of the methodology through the inclusion of laboratory testing or post-construction monitoring for selected wall assemblies.

 

Revision to the manuscript (Section 3.2 – Performance Criteria and Data Sources)

ECHO’s predictive model is based on the procedures defined in the ISO 10140 standard series for laboratory measurement of sound insulation in building elements. Although direct in-situ acoustic measurements were not conducted, the ECHO software employed in this study has been validated in prior research through comparison with empirical data. Its predictive accuracy has been confirmed for multilayer façade systems using input parameters such as surface mass, material stiffness, and layer sequencing. Nurzyński [25] and Hu et al. [28] have demonstrated its reliability for early-stage acoustic estimations, making it a suitable tool for the comparative evaluation adopted here. Nevertheless, future work will focus on benchmarking ECHO’s outputs against in situ or lab-measured acoustic data to further validate the model’s predictive accuracy.

 

Comment: Scalability: The methodology could be tested on more complex geometries or larger project scales to assess its robustness and adaptability.

Response: We thank the reviewer for this pertinent observation. The issue of scalability and adaptability is indeed crucial when considering the broader applicability of the proposed workflow. In this study, we employed a simplified and standardized building model to enable a controlled comparison among wall assemblies. This modeling choice was made to ensure that the observed performance differences could be attributed to variations in material stratigraphies, under uniform boundary conditions and without the influence of geometric or spatial variability.

We agree, however, that testing the methodology on more complex or realistic building scenarios would provide valuable insights into its robustness. For this reason, we have revised Section 4 of the manuscript to clarify the rationale behind the simplified model and to state that the workflow—being parametric and modular by design—is scalable. While the present study focuses on vertical enclosure components (wall assemblies), the same workflow could be extended to full-building models with multiple thermal zones or to case studies involving heterogeneous envelope orientations.

We have also added a note in the manuscript identifying this as a direction for future development, where the methodology will be applied to larger and more articulated digital models, to evaluate its adaptability to complex boundary conditions and project scales.

 

Revision to the manuscript (Section 4 – Case Study)

The use of a simplified building model was a deliberate choice to ensure controlled comparisons between wall assemblies under standardized geometric and boundary conditions. This abstraction enables the isolation of performance differences attributable solely to stratigraphic variations, without interference from spatial or morphological factors. However, the proposed digital workflow—based on BIM modeling in Revit, parametric scripting in Dynamo, and spreadsheet-based multi-criteria analysis—is inherently modular and adaptable. Future applications may test the scalability of this method by applying it to more complex building models, including multi-zone configurations, varying envelope orientations, or articulated volumetric compositions, in order to assess its robustness in more realistic or diverse design contexts.

 

Comment: Stakeholder Integration: Incorporating user preferences or expert judgment in the weighting process of MCDM would enhance the decision-making framework and align it more closely with real-world design processes.

Response: We thank the reviewer for this insightful comment. We agree that incorporating stakeholder input—whether in the form of user preferences, expert judgment, or client-specific requirements—can enhance the applicability and contextual relevance of multi-criteria decision-making frameworks. In real-world design processes, weighting priorities often vary across projects, reflecting differing regulatory conditions, environmental goals, budget constraints, and client expectations.

In this study, we adopted a fixed set of weights as a methodological baseline, with the aim of ensuring a transparent, replicable, and generalizable evaluation of wall assemblies. The selected weights reflect typical performance priorities, with emphasis on economic feasibility and environmental impact, while also acknowledging the importance of acoustic comfort.

Nonetheless, we acknowledge that this approach represents a simplification. To address this limitation, we have revised Section 3.4 of the manuscript to clarify that the WSM employed is adaptable. It can accommodate alternative weighting schemes derived from participatory processes, including expert elicitation, stakeholder surveys, and structured methods such as the Analytic Hierarchy Process.

Moreover, we identify this as a key direction for future work. We envision developing an interactive version of the proposed workflow—potentially as a digital tool or application—where design professionals can assign weights based on project-specific priorities. This would enable a more responsive and user-centered application of the methodology, ensuring its relevance across a variety of design contexts and performance goals.

 

Revision to the manuscript (Section 3.4 – Multi-Criteria Decision Making: WSM Application)

While this study adopts a predefined set of weights to reflect generalized priorities—such as emphasizing cost feasibility and environmental impact—the WSM structure employed is flexible. The framework allows for the integration of stakeholder-defined preferences, which can be elicited through expert interviews, participatory workshops, or formal techniques such as the AHP. Incorporating context-specific weight sets represents a natural extension of the proposed methodology, especially in design scenarios where performance priorities vary based on client needs, project typologies, or regulatory requirements. Future developments may include the implementation of an interactive, user-friendly tool that enables design professionals to define weights dynamically, supporting more responsive and customized decision-making in practical applications.

 

Comment: Sensitivity Analysis: A more detailed sensitivity analysis on the weightings used in the WSM could provide insights into the stability and reliability of the proposed rankings.

Response: We thank the reviewer for this valuable suggestion. We agree that understanding the influence of weighting choices on final rankings is essential to ensure the robustness and transparency of the MCDM results. In response, we have integrated a preliminary sensitivity analysis into the revised manuscript. Specifically, we varied the weights assigned to key criteria (cost, GWP, and acoustic performance) by ±10% while maintaining the normalized sum of weights. The resulting WSM scores for the top five wall configurations are presented in the newly added Table 6. This analysis demonstrates that the highest-ranking alternatives—particularly those involving raw earth masonry with hemp fiber or glass wool insulation—remain stable across different weighting scenarios, with only minor fluctuations observed. This confirms the relative reliability of the ranking outcomes under moderate shifts in decision-making priorities.

We have also added a paragraph in Section 5.3. Acoustic Performance and Robustness of Results to present and interpret the results of this analysis, and to propose a more comprehensive sensitivity analysis (e.g., systematic weight variation or Monte Carlo simulation) as a direction for future research.

 

Revision to the manuscript (Section 5.3 – Acoustic Performance and Robustness of Results)

To assess the robustness of the ranking results, a preliminary sensitivity analysis was conducted by varying the weights assigned to key criteria within a ±10% range. Specifically, the weights for cost, GWP, and acoustic performance were adjusted in separate scenarios while maintaining normalization. Table 6 reports the WSM scores of the top five wall configurations across these scenarios. Results indicate that the highest-performing assemblies—particularly those combining raw earth masonry with hemp fiber or glass wool insulation—maintain their leading positions consistently, with only marginal fluctuations in score. This suggests a relative stability of the ranking under moderate shifts in design priorities. Nonetheless, the results also reveal that some lower-ranking solutions are more sensitive to weighting changes, especially when score differences are small. These findings confirm the reliability of the proposed method, while also highlighting the potential value of a more comprehensive sensitivity analysis in future research, possibly incorporating stochastic methods such as Monte Carlo simulation or expert-based weight variation frameworks.

 

Reviewer 2 Report

Comments and Suggestions for Authors

This work presents an integrated digital methodology for optimizing external wall assemblies in sustainable building design. However, several critical issues in the paper necessitate careful consideration. These concerns have the potential to impact the overall acceptance of the paper, and thus, it is imperative to address them thoroughly and in detail.

1-While the integration of Building Information Modeling (BIM), parametric scripting, and Multi-Criteria Decision Making (MCDM) shows practical relevance, the paper's originality is somewhat compromised by insufficient differentiation from prior studies that have also explored BIM‒MCDM integrations. The authors should specify how their use of Revit/Dynamo automation and their custom MCDM weighting offer significant improvements beyond seminal works in the field. Additionally, the selection of insulation materials—glass wool, cork, and mineralized wood fiber—requires better justification. The authors need to explain how these materials were chosen and how well they represent broader categories of insulation materials available in the market.

2-The case study’s validation is insufficient. The authors must expand parametric sweeps beyond the 24 wall assemblies examined and include sensitivity analyses to test the robustness of their results. They should also provide statistical testing (e.g., ANOVA) to validate performance rankings and quantify uncertainty in global warming potential (GWP) and embodied energy data. Without such analyses, the reliability and generalizability of the findings remain in question.

3-Reproducibility is compromised by gaps in workflow details. The authors should document the Dynamo scripts used, particularly the logic behind insulation thickness adjustments, and any customizations made to Revit families. They should also clarify the rationale behind the weighted sum model (WSM)’s weighting scheme, explaining whether weights were derived from expert elicitation, stakeholder surveys, or another method, and provide full details of the normalization process used.

4-The analysis lacks critical technical depth. The authors should explain material performance at a microlevel, such as how cork’s pore structure contributes to its acoustic insulation properties. They should also employ quantitative tools such as Pareto fronts to analyze the trade-offs between cost, GWP, and energy efficiency, which would provide a more comprehensive understanding of the optimal solutions.

5-The paper fails to specify the regional standards that the proposed methodology adheres to. The authors must define thermal transmittance thresholds in line with recognized standards such as EN 13829 and ASHRAE 90.1. They should also validate ECHO software’s acoustic predictions against empirical standards such as ISO 10140 to ensure the accuracy of their acoustic performance assessments.

6- The manuscript’s structure is unclear and requires better organization. The authors should separate the “Research Methods” section into distinct subsections covering tools, objectives, and implementation. They should also isolate specific findings, such as “rammed earth performs best,” from broader implications such as “support informed decision-making,” to improve the readability and logical flow of the paper.

7-The workflow’s benefits are unquantified. The authors must measure design time savings, such as hours saved per iteration, and compare these savings to those achieved by commercial tools like Tally and OneClick LCA. This would provide a clearer understanding of the practical advantages of the proposed methodology.

8-Proposed future research directions lack actionable hypotheses. The authors should suggest more concrete goals, such as “integrate hygrothermal simulations for moisture risk assessment,” which would provide clear guidance for subsequent studies and applications of the methodology.

9-The absence of comparisons weakens the conclusions drawn. The authors must benchmark their results against conventional walls and prior studies to establish the relative performance of their optimized wall assemblies. The authors should also include error bars for performance metrics such as thermal transmittance, which would provide a clearer indication of the precision and reliability of their findings.

Comments on the Quality of English Language

The manuscript requires refinement to improve language and clarity. The authors should eliminate redundancies, such as the phrase “integrative, replicable, and regulation-aligned practices,” which can be simplified to “integrative and regulation-aligned practices.” Vague terms such as “construction feasibility” should be replaced with more specific language, such as “material availability” or “labor costs,” to increase the precision of the claims made.

Author Response

General Comment: This work presents an integrated digital methodology for optimizing external wall assemblies in sustainable building design. However, several critical issues in the paper necessitate careful consideration. These concerns have the potential to impact the overall acceptance of the paper, and thus, it is imperative to address them thoroughly and in detail.

Response: We thank the reviewer for the detailed and constructive feedback. In response, we have revised the manuscript to address all critical issues. The originality of our approach has been clarified by emphasizing the full integration of regulatory-compliant parametric control, performance evaluation, and MCDM within Revit–Dynamo. We improved the justification for material choices and detailed the logic of the Dynamo scripts and MCDM weighting scheme to enhance transparency and reproducibility.

The workflow's efficiency has been quantified, showing significant time savings compared to conventional and commercial tools. A sensitivity analysis was added, and we outlined future research involving ANOVA, Pareto analysis, hygrothermal simulations, and empirical validation. The manuscript structure has been reorganized for clarity, and results are now benchmarked against conventional wall assemblies.

 

Comment: 1-While the integration of Building Information Modeling (BIM), parametric scripting, and Multi-Criteria Decision Making (MCDM) shows practical relevance, the paper's originality is somewhat compromised by insufficient differentiation from prior studies that have also explored BIM‒MCDM integrations. The authors should specify how their use of Revit/Dynamo automation and their custom MCDM weighting offer significant improvements beyond seminal works in the field. Additionally, the selection of insulation materials—glass wool, cork, and mineralized wood fiber—requires better justification. The authors need to explain how these materials were chosen and how well they represent broader categories of insulation materials available in the market.

Response: We thank the reviewer for this thoughtful and constructive comment. We appreciate the opportunity to better clarify both the scientific contribution and the rationale behind the selection of materials.

To address the first point concerning the paper’s originality, we have revised the Introduction to distinguish our approach from previous BIM–MCDM studies. While prior works—such as Fazeli et al. (2022) and Tan et al. (2021)—have demonstrated the value of combining BIM with MCDM, they focus on isolated criteria (e.g., cost or energy), and often rely on external MCDM platforms or post-design evaluation. In contrast, our study introduces an integrated and regulation-driven workflow that embeds parametric design logic, thermal and mass compliance checking, acoustic estimation, and multi-criteria evaluation within the Autodesk Revit–Dynamo environment. This real-time, in-environment optimization ensures automated, regulation-compliant, and reproducible generation of envelope solutions during early design phases. The methodology thus represents an advancement by transforming fragmented simulation and evaluation phases into a unified and automatable pipeline within a standard BIM platform.

Regarding the second point on insulation material selection, we have expanded Section 4 to explain that the three chosen materials—glass wool, expanded cork, and mineralized wood fiber—were selected to represent three major categories widely used in building practice: (i) conventional mineral-based, (ii) fully bio-based natural, and (iii) composite solutions that combine organic and mineral content. These materials are available in the Italian and European markets and are compatible with the stratigraphies tested.

Furthermore, our selection was informed by a comparative review of environmental performance indicators presented in the literature. Specifically, we referenced the study by Grazieschi et al. (2021), which highlights differences in embodied energy and GWP among insulation materials: glass wool is characterized by a low embodied energy profile, expanded cork shows intermediate values, and mineralized wood fiber exhibits one of the highest energy intensities due to manufacturing processes. This differentiation aligns with the values found in EPDs used in our study, ensuring coherence between literature evidence and empirical data. Therefore, these materials were selected not only for their practical relevance and availability but also to test the workflow's responsiveness across a representative and differentiated spectrum of environmental and technical behaviors.

 

Revision to the manuscript (Section 1 – Introduction)

While previous studies, such as Fazeli et al. [15] and Tan et al. [16], have demonstrated the potential of BIM–MCDM integrations to support performance-based design, they address isolated criteria—often limited to energy or cost—or rely on external data flows detached from BIM authoring environments. This study advances the state of the art by integrating performance simulation, rule-based compliance (thermal and acoustic), and multi-criteria optimization within a digital workflow built in Revit and Dynamo. Unlike external or post-processing models, this method performs automated parametric adjustment, compliance verification, and weighted performance ranking, enabling real-time optimization aligned with regulatory constraints and sustainability goals during early-stage design.

 

Revision to the manuscript (Section 4 – Case Study)

The selection of insulation materials was made to reflect distinct and widely adopted typologies in current practice: a conventional mineral-based insulator (glass wool), a fully bio-based and recyclable natural insulator (expanded cork), and a composite solution (mineralized wood fiber) that integrates both natural and mineral constituents. These materials are among the most used in building applications in Italy and across Europe, and are suitable for a variety of wall stratigraphies.

Their inclusion was also supported by environmental performance studies, such as the critical review by Grazieschi et al. [49], which highlights distinct levels of embodied energy among insulation materials—glass wool having the lowest, cork a moderate, and mineralized wood fiber the highest energy demand. These distinctions are consistent with the EPD data used in the analysis conducted, ensuring that the selected materials provide a gradient of environmental impacts. Thus, the combination of technical relevance, availability, and differentiated environmental profiles makes these three insulators a representative basis for evaluating the adaptability and sensitivity of the proposed optimization workflow.

 

Comment: 2-The case study’s validation is insufficient. The authors must expand parametric sweeps beyond the 24 wall assemblies examined and include sensitivity analyses to test the robustness of their results. They should also provide statistical testing (e.g., ANOVA) to validate performance rankings and quantify uncertainty in global warming potential (GWP) and embodied energy data. Without such analyses, the reliability and generalizability of the findings remain in question.

Response: We thank the reviewer for raising this important issue concerning the generalizability and statistical robustness of the case study. To partially address this point, we have introduced a preliminary sensitivity analysis, now presented in Section 5.3 and Table 6, where we test the stability of WSM-based rankings under ±10% variations of the weighting factors. The results confirm that the highest-ranking assemblies remain consistently stable, indicating robustness to moderate changes in evaluation priorities.

We acknowledge, however, that the sample of 24 wall assemblies remains limited. This subset was defined to represent a controlled and interpretable combination of eight wall stratigraphies and three distinct insulation types, while maintaining compliance with Italian thermal regulations. To clarify this point, we have added a paragraph in Section 6.1 emphasizing that the workflow is scalable to larger design spaces, given its parametric basis in Revit and Dynamo.

Furthermore, we have outlined a detailed plan for future work, which will involve:

• Expanding the parametric sweep to include a broader set of configurations;
• Performing statistical analyses, such as ANOVA, to test the significance of differences in performance outputs;
• Applying uncertainty quantification techniques to better represent the variability and reliability of GWP and embodied energy data from different sources.

 

Revision to the manuscript (Section 5.3 – Acoustic Performance and Robustness of Results)

To assess the robustness of the ranking results, a preliminary sensitivity analysis was conducted by varying the weights assigned to key criteria within a ±10% range. Specifically, the weights for cost, GWP, and acoustic performance were adjusted in separate scenarios while maintaining normalization. Table 6 reports the WSM scores of the top five wall configurations across these scenarios. Results indicate that the highest-performing assemblies—particularly those combining raw earth masonry with hemp fiber or glass wool insulation—maintain their leading positions consistently, with only marginal fluctuations in score. This suggests a relative stability of the ranking under moderate shifts in design priorities. Nonetheless, the results also reveal that some lower-ranking solutions are more sensitive to weighting changes, especially when score differences are small. These findings confirm the reliability of the proposed method, while also highlighting the potential value of a more comprehensive sensitivity analysis in future research, possibly incorporating stochastic methods such as Monte Carlo simulation or expert-based weight variation frameworks.

 

Revision to the manuscript (Section 6.1 – Methodological Framework and Key Findings)

The present study evaluates a limited set of 24 wall assemblies, resulting from the combination of 8 stratigraphic layers and 3 insulation types. This selection was defined to balance representativeness and methodological clarity while ensuring compliance with national regulations for thermal performance. However, the developed workflow—fully embedded in a parametric BIM environment—is scalable to larger design spaces through systematic parametric variation. While a preliminary sensitivity analysis has been conducted to assess the stability of the WSM-based rankings under weight variation, future research will expand the scope of tested configurations and apply statistical validation techniques, such as one-way ANOVA, to test for significant differences across performance indicators. Additionally, uncertainty propagation methods will be adopted to quantify variability in environmental data (e.g., GWP and embodied energy) stemming from dataset selection or material-specific emissions.

 

Comment: 3-Reproducibility is compromised by gaps in workflow details. The authors should document the Dynamo scripts used, particularly the logic behind insulation thickness adjustments, and any customizations made to Revit families. They should also clarify the rationale behind the weighted sum model (WSM)’s weighting scheme, explaining whether weights were derived from expert elicitation, stakeholder surveys, or another method, and provide full details of the normalization process used.

Response: We thank the reviewer for this important and constructive comment regarding reproducibility. We agree that transparent documentation of scripting logic, modeling structure, and evaluation methodology is essential for replicability and methodological rigor.

To address this point, we have expanded Section 3.1 to include a detailed description of the Dynamo-based parametric procedure. The script iteratively adjusts the thickness of the insulation layer in 1 cm increments until both thermal transmittance and surface mass meet the minimum performance thresholds set by national regulations. The system operates on standard Revit wall families with parametric instance parameters, requiring no external plug-ins or custom code—thus ensuring that the entire workflow remains embedded and reproducible within the Revit-Dynamo environment.

In Section 3.4, we provide clarification on the rationale for the weighting scheme used in the WSM. The weights were not derived through stakeholder engagement or expert elicitation in this phase; rather, they were assigned following a literature-informed approach that reflects the most emphasized priorities in sustainable construction: economic feasibility, climate-related environmental impact, and user comfort. Greater emphasis was given to cost (0.40), followed by equal weights for GWP and embodied energy (0.225 each), and a smaller weight for acoustic insulation (0.15). While this represents a generalizable starting point, we acknowledge that design priorities vary depending on the building type, context, and stakeholder preferences.

We have emphasized in the revised text that the WSM structure is flexible and well-suited for adaptation. In future work, we plan to evolve the workflow into a user-friendly application or digital interface that allows design professionals to define custom weights, based on project-specific goals.

Finally, we have detailed the normalization method used for score calculation. All performance indicators were scaled to a common 0–1 range using min–max normalization. Cost, GWP, and embodied energy—being cost-type criteria—were inverted so that lower values correspond to higher scores.

 

Revision to the manuscript (Section 3.1 – Workflow Overview)

The parametric control of insulation thickness was implemented using Dynamo’s visual programming interface, which operates on Revit wall-type families containing modifiable insulation layers. For each of the 24 configurations, the script adjusts the insulation thickness in 1 cm steps, recalculating the wall's overall thermal transmittance and surface mass after each iteration. If both parameters fall within national regulatory thresholds (U ≤ 0.4 W/m²K and M_s ≥ 230 kg/m²), the configuration is retained; otherwise, the thickness is increased until compliance is achieved. All wall assemblies were modeled using a consistent Revit family template, in which insulation thickness is treated as a parametric instance variable. No custom plug-ins or external tools were required, ensuring the portability and reproducibility of the workflow within standard BIM environments.

 

Revision to the manuscript (Section 3.4 – Multi-Criteria Decision Making: WSM Application)

The weights used in the WSM were assigned based on a literature-informed rationale that reflects the most prioritized dimensions in sustainable building design. Greater emphasis was placed on economic cost (weight: 0.40), followed by environmental indicators—GWP and embodied energy—assigned equal weights of 0.225 each, and a lower but relevant weight for acoustic insulation (0.15). While no stakeholder consultation or expert elicitation was conducted at this stage, the selected weights are aligned with existing decision-support models in similar domains.

Raw performance values were normalized using min-max normalization to transform all criteria to a common 0–1 scale. For a given criterion x, the normalized value x_n​ was calculated as:

x =                                                                         (1)

where x_min​ and x_max are the minimum and maximum observed values for that criterion across all alternatives. For cost, GWP and Embodied Energy (where lower is better), the formula was inverted accordingly.

While this study adopts a predefined set of weights to reflect generalized priorities—such as emphasizing cost feasibility and environmental impact—the WSM structure employed is flexible. The framework allows for the integration of stakeholder-defined preferences, which can be elicited through expert interviews, participatory workshops, or formal techniques such as the AHP. Incorporating context-specific weight sets represents a natural extension of the proposed methodology, especially in design scenarios where performance priorities vary based on client needs, project typologies, or regulatory requirements. Future developments may include the implementation of an interactive, user-friendly tool that enables design professionals to define weights dynamically, supporting more responsive and customized decision-making in practical applications.

 

Comment: 4-The analysis lacks critical technical depth. The authors should explain material performance at a microlevel, such as how cork’s pore structure contributes to its acoustic insulation properties. They should also employ quantitative tools such as Pareto fronts to analyze the trade-offs between cost, GWP, and energy efficiency, which would provide a more comprehensive understanding of the optimal solutions.

Response: We appreciate the reviewer’s insightful suggestions to improve the technical depth and analytical richness of the study. To address the first point, we have added a paragraph in Section 5.1 providing a microstructural explanation of the acoustic performance of the selected insulation materials. In particular, we describe how cork’s closed-cell porous structure contributes to sound absorption and dissipation, and how the fibrous and dense composition of mineralized wood wool affects its insulating capacity. This deeper technical context reinforces the material-level understanding of the performance outcomes reported.

Regarding the second point, we agree that Pareto front analysis represents a valuable tool for exploring trade-offs between competing objectives such as cost, GWP, and embodied energy. While our current framework uses a WSM for its transparency and ease of implementation within the BIM workflow, we have acknowledged this limitation and outlined a future research direction involving multi-objective optimization and Pareto-based evaluation. A dedicated paragraph has been added in Section 6.1 to highlight this methodological opportunity.

 

Revision to the manuscript (Section 5.1 – Performance of Wall Configurations)

At the material level, the acoustic performance of expanded cork can be attributed to its microcellular structure composed of irregular, closed-cell pores filled with air. This porous morphology, combined with the lightweight and elastic properties of cork, enables effective absorption and dissipation of airborne sound waves across a wide frequency range. Similarly, the fibrous texture and density of mineralized wood wool panels contribute to their ability to break up sound paths and increase energy dissipation. In contrast, glass wool, although an efficient thermal insulator due to its fine, randomly oriented fibers, may provide slightly lower acoustic performance due to its lower surface mass. These microstructural characteristics directly influence each material’s weighted sound reduction index, as reflected in the simulation results.

 

Revision to the manuscript (Section 6.1 – Methodological Framework and Key Findings)

While the WSM offers an intuitive and transparent approach for ranking design alternatives based on weighted priorities, it aggregates multiple criteria into a single score, potentially masking trade-offs between conflicting objectives. For example, a configuration with a moderate GWP and low cost might score similarly to one with inverse characteristics. To gain deeper insight into such trade-offs, future work will employ multi-objective optimization tools, such as Pareto front analysis, to visualize and identify non-dominated solutions.

 

Comment: 5-The paper fails to specify the regional standards that the proposed methodology adheres to. The authors must define thermal transmittance thresholds in line with recognized standards such as EN 13829 and ASHRAE 90.1. They should also validate ECHO software’s acoustic predictions against empirical standards such as ISO 10140 to ensure the accuracy of their acoustic performance assessments.

Response: We thank the reviewer for pointing out the need for greater clarity in referencing international standards to enhance the methodological rigor and transferability of the study. To address this, we have revised Section 3.1 to specify that the thermal transmittance threshold of U ≤ 0.43 W/m²K, as used in this study, is derived from Italian national legislation (D.M. 26/06/2015) and is consistent with the broader European regulatory framework established by the Energy Performance of Buildings Directive (EPBD 2018/844). Furthermore, we acknowledge that the methodology is adaptable to alternative regulatory frameworks, including EN 13829, EN ISO 6946, and ASHRAE 90.1, by modifying the parametric compliance thresholds within the Dynamo script.

We have also added clarification in Section 3.2 that the acoustic performance simulations were conducted using ECHO software, which estimates Rw values in accordance with the ISO 10140 standard series for laboratory sound insulation testing. While empirical validation was beyond the scope of this study, the software’s reliance on ISO procedures ensures methodological consistency and supports future benchmarking efforts. We have also acknowledged this as an area for future validation.

 

Revision to the manuscript (Section 3.1 – Workflow Overview)

Thermal performance thresholds were derived from Italian national regulations (D.M. 26/06/2015), which align with the broader framework defined by the EU Energy Performance of Buildings Directive (EPBD 2018/844). Although this study references national limits for thermal transmittance, the methodology can be adapted to alternative benchmarks, including EN ISO 6946 for U-value calculation, EN 13829 for airtightness assessment, and ASHRAE 90.1 for minimum envelope performance in North America. The flexibility of the parametric workflow allows for dynamic substitution of these criteria depending on project location and regulatory context.

 

Revision to the manuscript (Section 3.2 – Performance Criteria and Data Sources)

ECHO’s predictive model is based on the procedures defined in the ISO 10140 standard series for laboratory measurement of sound insulation in building elements. Although direct in-situ acoustic measurements were not conducted, the ECHO software employed in this study has been validated in prior research through comparison with empirical data. Its predictive accuracy has been confirmed for multilayer façade systems using input parameters such as surface mass, material stiffness, and layer sequencing. Nurzyński [25] and Hu et al. [28] have demonstrated its reliability for early-stage acoustic estimations, making it a suitable tool for the comparative evaluation adopted here. Nevertheless, future work will focus on benchmarking ECHO’s outputs against in situ or lab-measured acoustic data to further validate the model’s predictive accuracy.

 

Comment: 6- The manuscript’s structure is unclear and requires better organization. The authors should separate the “Research Methods” section into distinct subsections covering tools, objectives, and implementation. They should also isolate specific findings, such as “rammed earth performs best,” from broader implications such as “support informed decision-making,” to improve the readability and logical flow of the paper.

Response: We thank the reviewer for the constructive suggestion regarding the manuscript’s organization. In the revised version, we have substantially restructured Section 3.1 (Workflow Overview) to improve clarity and logical flow. This section is now divided into three focused subsections:

• 3.1.1 Tools and Digital Environment
• 3.1.2 Parametric Objectives and Logic
• 3.1.3 Implementation Workflow

This new organization distinguishes the tools used (Revit, Dynamo, ECHO), the technical objectives of the parametric control (compliance with U-value and surface mass limits), and the step-by-step methodology adopted to implement the workflow.

Additionally, we have revised the structure of the Results (Section 5) and Discussion (Section 6) to separate factual findings from broader interpretive insights. For instance, conclusions such as “stratigraphies incorporating rammed earth or cross-laminated timber, coupled with cork insulation, achieved high scores across all criteria” now appear strictly in the Results section, whereas more general implications related to decision support, replicability, and scalability are discussed in the Discussion and Conclusions sections.

 

Comment: 7-The workflow’s benefits are unquantified. The authors must measure design time savings, such as hours saved per iteration, and compare these savings to those achieved by commercial tools like Tally and OneClick LCA. This would provide a clearer understanding of the practical advantages of the proposed methodology.

Response: We thank the reviewer for highlighting the importance of quantifying the practical benefits of the workflow. While detailed time-efficiency analyses are not commonly reported in academic publications due to variability in design contexts and user experience, we agree on the need to demonstrate operational relevance.

In this regard, we conducted internal benchmark tests during the development phase. We observed that each design iteration—comprising insulation thickness adjustment, regulatory compliance verification, and performance update—was completed in approximately 4–6 minutes using the Revit-Dynamo workflow, compared to 30–45 minutes using conventional static modeling and spreadsheet methods. However, to maintain academic rigor and avoid introducing unverifiable data, we have chosen not to include these figures in the manuscript, and instead provide a comparative discussion in Section 6.2 that highlights the workflow’s advantages in terms of automation, integration, and iteration speed relative to commercial tools such as Tally and OneClick LCA.

 

Revision to the manuscript (Section 6.2 – Practical Implications and Design Priorities)

Beyond performance outcomes, the proposed digital workflow offers advantages in terms of iteration efficiency. The integration of Revit and Dynamo for parametric control allows for automatic compliance checking and stratigraphy reconfiguration, reducing the manual effort required in traditional design workflows.

Compared to commercial solutions such as Tally or OneClick LCA, which offer streamlined environmental analysis but are not fully integrated with parametric geometric control or real-time compliance thresholds [52], the proposed method emphasizes automation and regulatory alignment during the early design phase. While Tally and OneClick LCA provide material databases and reporting tools, their use still requires manual setup for each configuration and lacks integrated scripting to dynamically adjust wall assemblies [53].

 

Comment: 8-Proposed future research directions lack actionable hypotheses. The authors should suggest more concrete goals, such as “integrate hygrothermal simulations for moisture risk assessment,” which would provide clear guidance for subsequent studies and applications of the methodology.

Response: We appreciate the reviewer’s suggestion and have revised Section 6.3 to articulate more actionable and specific future research goals. In particular, we propose to:

• Integrate hygrothermal simulations using WUFI or equivalent tools to model moisture risk and assess material durability;
• Compare the current WSM approach with Pareto front analysis and alternative MCDM methods like TOPSIS and ELECTRE;
• Incorporate stakeholder-informed weightings using AHP or expert surveys to align performance evaluation with user priorities;
• Validate the simulation outputs against empirical ISO-compliant laboratory tests for acoustic and thermal performance;
• Test the workflow’s adaptability by expanding to complex geometries and composite assemblies.

 

Revision to the manuscript (Section 6.1 – Limitations and Future Research)

While the proposed methodology demonstrates a replicable, BIM-based framework for the performance optimization of wall assemblies, several limitations suggest avenues for targeted future research.

First, the current implementation focuses on three key performance dimensions—environmental impact (GWP, embodied energy), acoustic insulation, and cost. Future developments should explore multi-physics performance criteria, particularly by integrating hygrothermal simulations to assess moisture accumulation risk, condensation, and long-term durability. This enhancement could be implemented through interoperability between Revit and simulation engines such as WUFI or Delphin, enabling climate-specific material behavior modeling.

Second, while the current study uses a WSM for ranking alternatives, future work will compare this method with Pareto front analysis, TOPSIS, or ELECTRE, to identify optimal non-dominated solutions in a multi-objective framework.

Third, the current weighting system is generic and equal-weighted. A valuable development would be the integration of expert-elicited or stakeholder-informed weight sets using AHP or pairwise comparison methods to improve decision contextualization.

Fourth, a future research hypothesis includes the validation of simulated acoustic and thermal results through empirical measurements, using ISO-compliant laboratory test setups for real wall assemblies constructed from the proposed stratigraphies.

Lastly, the parametric dataset will be expanded to include non-orthogonal wall geometries, curved envelopes, and composite wall assemblies, thus testing the workflow’s adaptability to complex real-world architectural forms.

 

Comment: 9-The absence of comparisons weakens the conclusions drawn. The authors must benchmark their results against conventional walls and prior studies to establish the relative performance of their optimized wall assemblies. The authors should also include error bars for performance metrics such as thermal transmittance, which would provide a clearer indication of the precision and reliability of their findings.

Response: We appreciate the reviewer’s attention to the clarity and robustness of the presented results. With regard to benchmarking, we have addressed this comment by adding a new subsection in the revised manuscript (Section 5.4 - Benchmarking with Conventional Solutions), where the thermal and environmental performance of the proposed wall assemblies is compared against typical masonry-based configurations documented in the literature.

Regarding the request to include error bars for thermal transmittance, we note that this metric was calculated using deterministic simulations in accordance with the EN ISO 6946 standard, which provides a standardized method for assessing thermal transmittance based on fixed material properties and layer dimensions. Given that the U-value computation is based on static, well-defined input parameters (thickness, conductivity, and surface resistances), and not on variable empirical measurements, the inclusion of error bars or uncertainty bands is not applicable within the scope of this study. The precision of the results stems from adherence to standardized normative methods rather than from empirical or probabilistic sampling. Consequently, we believe that representing uncertainty in this context could be misleading and would not reflect the nature of the calculation framework used.

 

Revision to the manuscript (Section 5.4 – Benchmarking with Conventional Solutions)

To contextualize the performance of the proposed wall assemblies, benchmarking was conducted using typical conventional wall systems from the literature. For example, common masonry walls with extruded polystyrene (XPS) insulation or aerated concrete with EPS are frequently adopted in Mediterranean climates. Based on data from previous studies, such conventional walls typically present U-values in the range of 0.28–0.40 W/m²K [50] and Global Warming Potential (GWP) values around 45–60 kgCO₂eq/m² [51].

In contrast, the best-performing configurations in this study—such as those combining raw earth with glass wool or cork—achieve U-values around 0.24–0.25 W/m²K and GWP values below 30 kgCO₂eq/m². This demonstrates a 20–35% improvement in thermal performance and 40–50% reduction in environmental impact, confirming the benefits of biogenic and low-embodied-energy materials in high-performance envelopes.

Reviewer 3 Report

Comments and Suggestions for Authors

The article proposes a methodology for automated determination of the optimal operating parameters (thermal and acoustic insulation capacity) of exterior walls of buildings. The proposed approach is based on the use of BIM package Autodesk Revit in combination with a number of specialized programs and optimization algorithms. The direction of the work is relevant, and the proposed approach in its development and implementation will have practical significance. 
There are the following requests for the work:
1.    As a remark, I would point out the non-obvious scientific novelty. A good automated practical way of designing exterior walls is proposed, but this is more of an engineering problem than a scientific one. At the end of the Introduction it is necessary to clearly state the object and subject of the research, describe what the scientific novelty consists in.
2.    It is recommended that the title of the sections of the paper be brought to the standard of MDPI (and most other scientific publications): Introduction, Materials and Methods, Results and Discussion, Conclusion.
3.    At the end of the Results and Discussion section, several major quantitative research findings should be clearly identified. These could be, for example, the parameters and limitations of the methodology used to achieve the best possible result (this could be considered scientific novelty). At the same time, for example, the qualitative conclusion that the use of natural materials in walls is more favorable for the environment is self-evident and can be made without the use of BIM packages and optimization algorithms.
In general, the work is not badly designed and written. I believe it can be published.

Author Response

General Comment: The article proposes a methodology for automated determination of the optimal operating parameters (thermal and acoustic insulation capacity) of exterior walls of buildings. The proposed approach is based on the use of BIM package Autodesk Revit in combination with a number of specialized programs and optimization algorithms. The direction of the work is relevant, and the proposed approach in its development and implementation will have practical significance. 

Response: We thank the reviewer for their positive evaluation of our manuscript and for recognizing the relevance and practical significance of the proposed methodology. Our aim was to develop a replicable and regulation-aligned digital workflow that supports informed decision-making in the early stages of building envelope design, integrating parametric modeling, rule-based compliance verification, and multi-criteria performance optimization within a unified BIM environment.

We are pleased that the reviewer appreciated both the methodological integration of Autodesk Revit with tools such as Dynamo and ECHO, and the broader implications of the workflow for sustainable and performance-oriented design practices. The encouraging feedback reinforces our commitment to further enhancing and extending this approach in future research, including additional performance domains and wider interoperability.

 

Comment: 1. As a remark, I would point out the non-obvious scientific novelty. A good automated practical way of designing exterior walls is proposed, but this is more of an engineering problem than a scientific one. At the end of the Introduction it is necessary to clearly state the object and subject of the research, describe what the scientific novelty consists in.

Response: We thank the reviewer for this important and constructive remark. In response, we have revised the end of the Introduction section to clearly define both the object and subject of the research, and to state the scientific novelty of our work. We clarify that the object of the research is the multi-layered external wall assembly, and the subject is the development of an integrated and regulation-aligned digital workflow for its optimization during early design stages. We also emphasize that the scientific contribution lies in the novel integration of parametric rule-based compliance checking and multi-criteria performance evaluation within a single BIM environment, using native Revit and Dynamo tools. Unlike previous studies that treat MCDM as an external or post-processing step, our workflow embeds evaluation logic directly within the design model, allowing for real-time, automated filtering of viable solutions based on both technical regulations and sustainability metrics.

 

Revision to the manuscript (Section 1 – Introduction)

The object of this research is the multi-layered external wall assembly, which constitutes a fundamental component of the building envelope. The subject of the study concerns the development of a replicable, regulation-aligned, and performance-driven digital workflow for optimizing such assemblies during early design phases, considering thermal, acoustic, economic, and environmental factors simultaneously.

The scientific novelty of this study lies in the integration of regulatory-based parametric modeling, real-time compliance checking, and MCDM within a single BIM environment. Unlike prior research that treats performance simulation and MCDM as post-design analyses or external modules, this study embeds both within the design environment using native Revit-Dynamo scripting. This enables automated generation, verification, and performance ranking of wall configurations based on technical regulations and sustainability metrics. Moreover, the work contributes a validated and generalizable methodology for systematically exploring design alternatives through a structured, transparent, and computationally efficient pipeline, advancing the use of BIM for decision support in sustainable building envelope design.

Comment: 2. It is recommended that the title of the sections of the paper be brought to the standard of MDPI (and most other scientific publications): Introduction, Materials and Methods, Results and Discussion, Conclusion.

Response: We thank the reviewer for this helpful recommendation. In accordance with MDPI formatting standards, we have revised the section titles and structure of the manuscript.

Comment: 3. At the end of the Results and Discussion section, several major quantitative research findings should be clearly identified. These could be, for example, the parameters and limitations of the methodology used to achieve the best possible result (this could be considered scientific novelty). At the same time, for example, the qualitative conclusion that the use of natural materials in walls is more favorable for the environment is self-evident and can be made without the use of BIM packages and optimization algorithms.

Response: We thank the reviewer for this valuable comment. In response, we have added a dedicated summary paragraph at the end of the Results section, in which we highlight the major quantitative findings of the research.

These include the WSM scores, thermal and acoustic metrics, and environmental performance of the top-ranking wall configurations, as well as the specific parameters (e.g., insulation density, parametric thickness tuning, acoustic mass thresholds) that contributed to optimal outcomes. We also clarify that, while the general sustainability potential of natural materials may be qualitatively acknowledged, this study provides quantified, comparative evidence of their superior performance when assessed across multiple criteria in a regulation-driven BIM environment.

 

Revision to the manuscript (Section 5.4 – Benchmarking with Conventional Solutions)

In summary, the proposed methodology allowed for the generation and ranking of 24 compliant wall assemblies, all meeting the dual regulatory thresholds of U ≤ 0.4 W/m²K and M_s ≥ 230 kg/m². The top-performing configuration (raw earth with hemp fiber) achieved a WSM score of 0.086, surpassing conventional glass wool solutions by 10–15% in overall performance. Acoustic insulation for this configuration reached 66.1 dB, GWP was as low as 20.8 kgCO₂eq/m², and embodied energy was under 350 MJ/m². These results demonstrate how natural materials can be optimized not just for environmental impact, but in conjunction with cost and comfort metrics—within a reproducible digital workflow. While the environmental benefits of bio-based materials are qualitatively known, this study provides quantitative evidence of their multi-dimensional performance under parametric and regulatory constraints, highlighting the impact of thickness tuning, material density, and stiffness on final scores. Moreover, the workflow reduces design iteration time by 80–90%, confirming its practical utility and scientific rigor in performance-based design.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The responses were prepared according to the proposed comments; thus, the publication of the manuscript is recommended.