Supporting sustainability assessment of building element materials using a BIM-plug-in for multi-criteria decision-making

Journal of Building Engineering 97 (2024) 110818 Available online 29 September 2024 2352-7102/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Supporting sustainability assessment of building element materials using a BIM-plug-in for multi-criteria decision-making Bernardette Soust-Verdaguer a , * , Jos´ e Antonio Guti´ errez Moreno a , Daniel Cagigas b , Endrit Hoxha c , Carmen Llatas a a Instituto Universitario de Arquitectura y Ciencias de La Construcci´ on, University of Seville, Seville, Spain b Department of Computer Architecture and Technology, University of Seville, Seville, Spain c Department of the Built Environment, Aalborg University, Copenhagen, Denmark ARTICLE INFO Keywords: Sustainability Life cycle sustainability assessment Building information modelling Tool development Multi-criteria decision-making Plug-in Technique for order preference by similarity to ideal solution Building early design steps ABSTRACT The environmental crisis requires the immediate implementation of accurate and robust sustainable solutions throughout the building life cycle. Life Cycle Sustainability Assessment (LCSA) is a scientifically recognised method that integrates the triple dimensions of the life cycle approach, and thereby enabling the evaluation of the performance of mitigation strategies implemented in building projects. However, implementing the LCSA in buildings is limited by the weighting of environmental, economic, and social dimensions to select the best option regarding the numerous materials available. In order to fill this knowledge gap, the paper aims to present the developed Smart BIM3LCA tool, which supports multi-dimensional assessment during the project’s early design steps. Automatic integration of the LCSA, a multi-criteria decision-making tool such as TOPSIS, and building information modelling (BIM), was developed to support the selection of building materials. The BIM plug-in was then validated through its application to a multi-family residential building to select the most sustainable materials during the project’s early design stage. 1. Introduction The current climate change together with the depletion of biodiversity point towards a catastrophic future unless urgent actions are taken [1]. In this vein, given the impact of buildings and the built environment therein [2], initiatives such as the Green Deal [3] encourage decarbonisation and progressive reduction of carbon emissions. Achieving this goal requires measuring the CO 2 eq emissions generated throughout the life cycle of the building, as well as incorporating instruments and solutions to reduce them. The life cycle assessment (LCA) method is widely recommended for measuring and quantifying the building sector’s CO 2 eq emissions and decarbonisation solutions implemented [4]. Consequently, LCA is the most objective and widely used method [5,6] to assess the environmental impact of building projects. Nevertheless, sustainability assessment, based on the triple bottom-up approach including environmental, economic, and social dimensions throughout the life cycle of buildings, the so-called Life Cycle Sustainability Assessment (LCSA), provides a more comprehensive approach since it combines three methods based on the quantification of the life cycle impacts: (environmental) LCA, life cycle costing (LCC), and social life cycle assessment (S-LCA) [7,8]. Over recent decades, the use of the LCA, LCC, and S-LCA methods has been under development and focus on academia [9]. It has yet to be integrated into * Corresponding author. E-mail address: [email protected] (B. Soust-Verdaguer). Contents lists available at ScienceDirect Journal of Building Engineering journal homepage: www.elsevier.com/locate/jobe https://doi.org/10.1016/j.jobe.2024.110818 Received 29 March 2024; Received in revised form 2 August 2024; Accepted 19 September 2024 Journal of Building Engineering 97 (2024) 110818 2 environmental certification, such as the European sustainability assessment framework Level(s) [10], other sustainability assessment systems for buildings [11], and professional practice. Consequently, this fact represents an opportunity to include a broader holistic approach to enriching the environmental assessment. Moreover, existing literature [12] highlight the necessary to develop LCSA tools focus on the building processes and the built environment. Furthermore, using the BIM method to support sustainable construction and the implementation of LCA methods during the design process has been growing exponentially in recent years [13–19], providing clear evidence for automatic environmental impact assessment [20,21]. Nevertheless, its effective implementation in the building sector requires tools adapted to each context and integrated into the design workflow, which remains insufficiently mature [14,20,21]. Although BIM-based is considered a likely solution to overcome the integration of the LCSA in the building design process, existing studies [22–24] highlight challenges in the framework application, including the complexity in considering the triple dimensions in the life cycle inventory, the data interoperability, and the software integration. Given the rapid progress in developing design-assessment tools and life cycle inventory databases, LCSA analyses will be performed in the BIM environment without intermediary tools [22]. Recent scientific works [25] offer some of the first developments that propose Autodesk Revit Plug-ins and define the main steps to conduct real-time LCSA calculations in BIM. Nevertheless, a significant challenge is posed by the need for more consensus for establishing or adopting a clear methodology to link the three dimensions of sustainability [22]. Furthermore, the weighting methods for aggregating the pillars of LCSA remain an unanswered challenge [22]. On the one hand, studies [8,26] state that there is an equivalence in the three dimensions where one pillar could be balanced with the others. However, when the sustainability evaluation requires comparing and selecting different options, multi-criteria methods are used for that purpose [9]. Indeed, an existing study [27] shows that half of the LCSA implementations in the building sector have applied selective weighting. To this end, various methods are used in the literature. For example, Figueredo et al. [28] conducted an LCSA multi-dimensional assessment that used multi-criteria decision-making (MCDM), such as the Analytic Hierarchy Process (AHP), to support building material selection based on BIM models. The MCDA utilised for sustainability evaluation is a decision support approach appropriate for addressing not only complex problems, conflicting objectives, different forms of data and information, multiple interests and perspectives but also the accounting for complex and evolving biophysical and socio-economic systems [29]. Sanchez-Garrido et al. [30] conduct the sustainability assessment of building structures, including MCDM and the LCSA methods, focusing the study on a specific building system. However, integrating these methods in the digital design workflow remains one of the main limitations of existing literature, such as those of Figueredo et al. [28] and Sanchez-Garrido et al. [30]. In this vein, Hossaini et al. [31] propose a framework that, in addition to combining the AHP and LCSA, ranks different structural systems by considering 18 impact categories. These studies integrate the AHP technique, one of the most commonly used methods to rank and support the decision-making process in the LCSA field [32]. Other possible techniques used for similar purposes are the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and the Analytic Network Process (ANP) [29,32]. Therefore, multi-criteria evaluation systems based on methods such as TOPSIS [33] and AHP [28,31] are when applying LCSA in the construction sector [27]. The TOPSIS method is a mathematical framework used to measure the sustainability performance of different alternatives and rank them by coupling LCSA data with the criteria weights [34–37]. Backes et al. [27] demonstrate that the integration of the LCSA and TOPSIS methods applied in the construction sector has been explored less than AHP. The weighting of the different dimensions and indicators must be explained since they affect the results [38]. These assessments lead to the assignment of different mathematical weightings in impacts within the three areas and in impacts aggregated in the three pillars; these assessments eventually produce different overall scores, as Camana et al. [38] illustrate in their study. The study schematises several possible starting points and weighting variations and emphasises that sustainability is not defined as a system with three independent and equivalent columns; nevertheless, it is based on a different number of characteristics, potentially weighing differently. Furthermore, in particular cases, for example, during a state of emergency such as the COVID-19 pandemic, the social dimension and human health became more prevalent than other pillars, such as the economic dimension [38]. In the case of construction materials, solutions that generate the lowest environmental impacts are often not the cheapest from an economic point of view [39]. However, challenging objectives such as decarbonisation require prioritising the environmental dimension, affordability, and social benefits. Thus, a major aspect of applying the multi-criteria method to weigh different dimensions during the design phase involves the risks and limits of implementing the method to evaluate different systems in a building. In order to address these risks, existing studies [38] recommend including sensitivity analyses on the effects of change on indicator weight and the results using bottom-up or top-down approaches. Thus, given that recent contributions [25,39,40] enabling calculation of the embodied carbon (CO 2 eq emissions), together with cost and social impacts (working hours), using a Triple Bottom Line (TBL) database within the BIM environment, it should be highlighted that the multi-dimensional weighting of the different dimensions and impact categories assessed in real-time assessment has not been fully explored. Therefore, the multi-dimensional assessment using the TOPSIS technique is automatically implemented in the BIM workflow, and its risks and limitations are detected as a gap in the literature to be addressed by this study. Previous studies are based on the LCSA implementation in BIM [25,39–41]. This study explores the potential for building material selection based on multi-criteria decision-making techniques in real-time evaluations by investigating the risks and limitations of solution selected. 2. Goal of this study Existing studies [28,31,33] perform these evaluations manually and focus on manual processes of handling different tools, databases, and existing data sources. Previous studies [25,39,40], which are focused on the development of tools based on LCSA integrated into BIM, point to this issue as an opportunity to focus on new developments, implement multi-criteria evaluation, and identify the optimal design option for all the evaluated dimensions in a simple way. B. Soust-Verdaguer et al. Journal of Building Engineering 97 (2024) 110818 3 Therefore, unlike the previous studies, and to fulfill the detected gaps in knowledge, the present paper aims to answer the following Research Questions (RQ). RQ1: Is it possible to implement an automatic multi-criteria decision-making tool (that integrates environmental, economic, and social dimensions) using the TOPSIS technique to support the building material selection and to compare different solutions for the building envelope and structure within the BIM environment? RQ2: What risks and lessons learned can be identified in the multi-dimension assessment (environmental, economic, and social) and TOPSIS implementation in the plug-in, especially given the change in the weight of the indicators? To this end, the authors developed the Smart BIM3LCA tool, a BIM-based LCSA plug-in that supports the integration of environmental, economic, and social information into BIM objects and multi-criteria sustainability assessment. 3. Materials and methods The procedure followed to achieve the goals and develop the multi-criteria evaluation tool based on LCSA in BIM is shown in Fig. 1. The methodology was supported with previous developments [25,39,40], such as the plug-in for the implementation and real-time visualisation of LCSA results in the prototype phase (Dynamo Script) [25], the LCSA implementation limited to a building system developed in the C# programming language and integrated as an Autodesk Revit plug-in Ref. [40], as well as the development of a work methodology [23,41] that enables efforts to be reduced and errors to be minimised during the implementation of LCSA in early design phases, which has been enriched in this study by the simultaneous multi-dimension assessment during the design process. As shown in Fig. 1, the method began with the fusion of the previous developments focused on the LCSA of structure [40] and the envelope [39] systems. The novelty of this study is the enrichment of the BIM-TBL library BIM-object database [42], with new materials and products to automatically conduct the LCSA in BIM. This stage required the enrichment of the integrated data to include the building elements and materials of the structure, envelope, partitions, and finishing systems to evaluate and compare various constructive solutions. This version of the tool incorporates to the evaluation of the partitions and finishing systems to the envelope and structure developed in previous versions [25,39,40]. Additionally, the novelty of this study is the multi-criteria decision-making implementation using the TOPSIS method in an LCSA-BIM-based tool in real-time. It implies the normalisation and weighting of the different dimensions evaluated. The TOPSIS method was implemented in the plug-in integrated into Autodesk Revit [43]. Subsequently, the plug-in was applied to a case study to evaluate and compare the various alternative building elements of the structure, envelope, partitions, and finishing systems. Once the results were obtained, a sensitivity analysis was conducted to detect and analyse the factors that could influence the variability of the TOPSIS results in the triple-bottom-line sustainability assessment. Lastly, this study focused on the operational implementation of the approach from previous studies to validate the developed tool since the in-depth analysis of the LCSA results falls outside its scope. Fig. 1. Schema of the methodology developed in this work. B. Soust-Verdaguer et al. Journal of Building Engineering 97 (2024) 110818 4 3.1. Case study description The case study location is Seville (Spain), specifically in an area bordering the historic city centre. It is a new multi-family residential building, called “La María”, and promoted by the Municipal Company of Housing, Land and Equipment of Seville S.A. (EMVISESA [44]). The building consists of 16 dwellings, garages, and storage rooms, and has a Gross Floor Area (GFA) of 2247 m 2 distributed over four floors above ground level and one floor below ground (see Fig. 2). The top floor is designated in an attic style, complete with a walkable roof. There is an additional floor below ground level where the parking area and storage rooms are located. The case study has been utilised to validate the developed tool and analyse the variability of results in the LCSA and TOPSIS implementation. To this end, two alternative construction system options have been compared. Table 1 shows the materials and construction elements of each option. The study focuses on comparing two possible options for each building element. Starting with the structure, the method aims to choose the best solution for each building element through a step-by-step selection process. The supplementary data section provides a detailed description of the materials included in the compared solutions. The study focuses on two alternatives to show on a simply way the potential of the tool and the possible analysis derivative. The study has considered the design dimensions of structural elements in a pre-design stage. The concrete structure design that the study used was the one that has been prepared for the building process. For the design of the steel structure, an attempt was made to match the loads and strengths of the elements designed in concrete. 3.2. Smart BIM3LCA development 3.2.1. Upgrade from BIM3LCA structure and BIM3LCA envelope Smart BIM3LCA arises from the combination and enrichment of two previous software programs, BIM3LCA structure [45] (for assessing structural elements), and BIM3LCA envelope [46] (for assessing building envelope) and provides a more versatile tool. To this end, Smart BIM3LCA automatically groups different building components into structural and architectural categories, thereby facilitating the location of elements within the project and detecting potential modelling errors. This feature allows for detecting errors in the study project, such as incorrectly defined structural walls and foundations mistakenly defined as floors, among other improperly defined elements. New features have been added, thereby enhancing the previous versions by incorporating a new section “Covering Type”, which enables the assignment of IfcCoveringType to elements according to the buildingSMARTInternational [47] and updates the database with new elements: IFC Ceiling and IFC Covering. In Fig. 3, the differences between the previous version (BIM3LCA structure/BIM3LCA envelope) and the new release (Smart BIM3LCA) are marked in orange. 3.2.2. Automatic TOPSIS implementation in the LCSA Smart BIM3LCA incorporates TOPSIS multi-criteria evaluation methodology to obtain the best solutions from among the studied projects based on weights assigned to environmental, economic, and social impact indicators. The TOPSIS integration in the plug-in involves the automatic incorporation of the calculation formulae that the method proposed [34,48–50], where the decision-maker defines the weights to be utilised for the evaluation of different alternatives. The raw data in the decision matrix is normalised (Euclidean Normalised Matrix), whereby the raw data is first transformed into dimensionless values, which allows for a fair comparison across diverse criteria, and is then adjusted by multiplying each normalised value by its corresponding weight, thereby providing a comprehensive view of the significance of each criterion for each alternative. Fig. 2. Case study BIM model (external view). B. Soust-Verdaguer et al. Journal of Building Engineering 97 (2024) 110818 5 The ideal solution for each criterion is determined and represents the best possible value. In our case, the ideal solution is given by the minimum values for the environmental and economic impact and by the maximum value for the social impact. Simultaneously, the negative ideal solution is identified, representing each criterion’s worst possible value. Separation measures are calculated using a chosen distance metric, whereby the performance of each alternative is evaluated against these extremes (’ideal best’ and ’ideal worst’): Euclidean distance from the ideal best (Eq. (1)): S+ i=[∑ m j=1(Vij −V+ j)2]0.5 (Eq. 1) S− i=[∑ m j=1(Vij −V− j)2]0.5 (Eq. 2) Euclidean distance from the ideal worst (Eq. (2)).Where. •V ij represents the elements of the weighted normalised value for project i and impact j. •V j + is the ideal best value for impact j. •V j − is the ideal worst value for impact j. Table 1 Description of the main materials included in the design options. Building system/Building element Design Option 1 Main material Design Option 2 Main material Structure/Beams Reinforced Concrete H25 Steel Structure/Columns Reinforced Concrete H25 Steel Structure/Slabs Reinforced Concrete H25 Steel +Concrete Structure/Foundations Reinforced Concrete H25 Reinforced Concrete H25 Envelope/External walls ETICS Ventilated façade Envelope/Roofing Walkable Non-walkable Envelope/Windows Aluminium Wooden Envelope/Doors Aluminium Steel Partitions/Interior walls Brick Plasterboard Finishings/Ceilings Plasterboard Rock wool Finishings/Flooring Hydraulic Ceramic tile Fig. 3. Comparison of the BIM3LCA structure with Smart BIM3LCA Building User Interface. B. Soust-Verdaguer et al. Journal of Building Engineering 97 (2024) 110818 6 •S i + is the distance from the ideal best for project i. •S i − is the distance from the ideal worst for project i. The relative closeness of each alternative to the ideal solution is determined by comparing its distance to the positive ideal solution with its distance to the negative ideal solution, thereby quantifying the overall performance of each alternative and leading to the ranking of alternatives, where the preferred alternative, with the highest closeness value is then recommended as the optimal choice (Eq. (3)). Pi=S− i S+ i+S− i Eq. 3 where. •S i + is the distance from the ideal best for project i. •S i − is the distance from the ideal worst for project i. •P i is the relative closeness value for project i. Smart BIM3LCA enables a quick assessment of multiple projects. It allows for sensitivity analyses when adjusting impact weights efficiently without external tools. The projects must be uploaded for comparison, and the analysis is initiated by selecting the "Compare Projects" button. After clicking this button, the weight menu is displayed, and the weights applied to each impact in the TOPSIS methodology are specified, as shown in Fig. 4. After indicating the weights, either directly in the textboxes or by using the Weight Indicator Assistant, the relative score button is pressed to obtain the project result. Smart BIM3LCA assigns green as the best solution, while red indicates the worst. In the TOPSIS methodology, it is essential to consider that the closer the relative score value is to unity, the better the obtained solution will be. To facilitate weight evaluation, an assistant has been incorporated, and the Saaty scale [51] is implemented (See Fig. 5). This allows weights to be assigned naturally, using natural language. The assistant also detects inconsistencies when assigning importance to each impact and displays a warning pop-up window, as shown in Fig. 6. As mentioned earlier, Smart BIM3LCA provides a quick sensitivity analysis by conducting several tests while changing the weights and observing the reflected change in the relative score value. 3.3. Smart BIM3LCA plug-in application to the case study The procedure for applying LCSA to the case study is founded on previous studies developed by this team [23,25,40]. These studies specify the initial assumptions, life cycle scenarios, information organisation, sources, and databases employed in the work. The application of LCSA is based on the use of the BIM-TBL database [42], which is organised according to the IfcBuildingElements classes [47] and contains information on the complete life cycle of these elements (manufacture, construction, use, and end of life). The Fig. 4. Manual weighting assignment in Smart BIM3LCA. B. Soust-Verdaguer et al. Journal of Building Engineering 97 (2024) 110818 7 Fig. 5. Manual assignment of weighting in Smart BIM3LCA. Fig. 6. Warning pop-up window. B. Soust-Verdaguer et al. Journal of Building Engineering 97 (2024) 110818 8 BIM-TBL database [42] follows the systematic classification system BCCA [52] to organise the material, labour and machinery information about the building elements, also used in other holistic assessment for buildings at detail stages of design [53]. The application of LCA to building solutions is conducted according to ISO 21930-1 [54] and focuses on the following life cycle phases and information modules: Product phase (A1: Supply of raw materials, A2: Transport, A3: Manufacturing); Construction phase (A5: Construction); Use phase (B2: Maintenance, B3: Repair, B4: Replacement); and End-of-life phase (C1: Demolition/deconstruction). The impact categories and their indicators used in this work are as follows: global warming potential (GWP), indicator CO 2 eq emissions in kg (environmental dimension); economic cost, indicator euros ( € ) (economic dimension); and employment category, indicator working hours (h) of workers in each phase of the building’s life cycle (social dimension). To conduct the LCA, LCC, and S-LCA on previous studies are based, which basically consisted of multiply the bill of quantities for the building elements (extracted from BIM) with the factors of each dimension: environmental expressed in CO 2 eq emissions, extracted from ecoinvent v3.7.1 [55] database, economic expressed in cost in euros, extracted from BCCA database [52], and social expressed in working hours, extracted from BCCA database [52]. Fig. 7. Steps for the LCSA and TOPSIS implementation to the case study. B. Soust-Verdaguer et al. Journal of Building Engineering 97 (2024) 110818 9 The social indicator working hours has been used in previous works. This inventory indicator is referred in the UNEP. Guidelines for S-LCA of Products and Organisations [56] as the most frequently used activity variable, and it has been used in other S-LCA studies [57, 58]. In order to determine which of the construction options produces the lowest environmental impact and cost and generates the most local labour, a BIM model with a LOD [59] 200/300 of the selected building part is used as a starting point. The model includes the main elements that make up the building envelope (walls, windows, doors, roofs, floors) to obtain the quantities of materials used as accurately as possible. Before applying the plug-in, verification is made that all the elements that make up the model are correctly modelled, configured, and joined together. Subsequently, the Smart BIM3LCA plug-in is used to evaluate the material options to be compared (see Fig. 7). The procedure consists of assigning the chosen materials to each building element for evaluation by selecting them from the elements available in the BIM-TBL database [42] inserted in the Smart BIM3LCA plug-in. Fig. 3 shows the library material assignment procedure. This procedure is repeated several times to complete the material solutions for their subsequent comparison. Thus, the order in which the options are evaluated would not alter the results since the option with the lowest impacts would always be chosen. The method seeks not only to evaluate different material solutions for the elements that compose the building, but also to obtain the total result of the comparison of all the solutions. Once the projects are saved and loaded, they are compared. In the left-hand-side panel, the user interface (Fig. 3) shows the models to be compared/evaluated, while in the right-hand-side panel, all the elements of that type are broken down. To identify the range of variation in the results, the TOPSIS method follows a weighting variation in the possible combinations developed in previous studies [38], including 33 % environmental/33 % economic/33 % social; 50 % environmental/25 % economic/25 % social; 45 % environmental/45 % economic/10 % social; 5 % environmental/5 % economic/90 % social: and extreme combinations (of the weighting variation). The selected weighting variation aims to show the variation of the results if any of the dimensions is more important than the other two. A detailed description of the procedure and the assumptions, and the model consideration for the BIM-LCSA application are included in the Supplementary data section Tables 2.1–9.4 4. Results 4.1. Validation of the tool in the case study application The results obtained from applying the Smart BIM3LCA plug-in are shown in Fig. 8. It includes the environmental impact (GWP), economic impact (euros), and social impact (working hours) of the evaluated solutions. The colour code used in Fig. 8 indicate that the green-coloured option is the most favourable design option, and the red-coloured the least favourable design option. Impacts are organised according to the life cycle phases and reporting modules described in ISO 21931-1 [54]. The tool obtains the embodied impacts [25] for each building element and a multi-criteria evaluation of the different dimensions (environmental, economic, and social) and impact categories by weighting and normalising the results obtained in the LCSA. In this case, an equivalent weight is given to each of the three dimensions (33.3 %). Table 2 shows the optimal combination of construction solutions for the building with these weights. The supplementary data includes screenshots of all the steps followed to achieve the final results. Table 3 presents the outcomes and highlights optimal choices utilising a 0.33 weighting factor across three dimensions, where the ideal solutions for the economic and environmental dimension is that which obtains the lowest values. For the social dimension, the best solution is defined as that of the highest value. Notably, the reinforced concrete structure (TOPSIS relative score0.685) emerged as the top-ranking option, surpassing the economically and environmentally superior steel solution (24.2 % and 32.5 % respectively lower than the reinforced concrete solution). However, it is worth mentioning that the social dimension plays a pivotal role in determining this outcome, which is evident from its notably higher absolute or total values (74.5 % higher for the reinforced concrete than for the steel solution). Regarding roofing elements, the walkable option is the best choice (TOPSIS relative score 0.876), parallelling the structural scenario where the nonwalkable alternative excelled in economic and environmental aspects (1.98 and 4.3 % lower than the walkable solution, respectively). As for external wall solutions, although the ventilated façade ranks highest in the TOPSIS assessment, which does not lead across all dimensions (TOPSIS relative score0.57). The benefits for the social dimension are particularly pronounced in these cases, 17.8 % higher than the ETICS solution. However, the ETICS solution was 13.8 % lower in the economic dimension and 0.4 % lower in the environmental dimension. Although both solutions include similar thickness of the insulation material (EPS is used in the ETICS, and the mineral wool is used in the ventilated façade) and light clay bricks (external leaf), the ventilated façade requires more material for the auxiliary structure, which increases the cost and the CO 2 eq emissions, and consumes more working hours for the installation. For windows and doors, Fig. 8. Screenshot of the results obtained. B. Soust-Verdaguer et al. Journal of Building Engineering 97 (2024) 110818 16 best values in each of the dimensions, but that it is the solution with the greatest difference between the best and the worst values. Not only does this study show the relevance of obtaining the relative score itself, but it also visualises the magnitude of the difference from other options by obtaining a comprehensive examination of how each option affects different dimensions and aids in understanding the broader sustainability implications. For example, the solution for the external walls has a 0.4 % variation for the environmental dimension, representing a small amount that can be potentially improved. Moreover, the usefulness of employing both this method and the results of the relative score for TOPSIS method to assess a building’s sustainability can be translated into the issues laid out below. In cases where the relative score for TOPSIS method approaches a difference that is close to 1 between the evaluated options, then it is observed that there are very tight scenarios. Minimal variations in certain dimensions can lead to the ideal choice to switch between one material option or another. However, when that relative score approaches values around 0.5, it indicates that the differences are more significant in certain dimensions and raises the risk that modifying the weighting factors could alter the relative score (see Table 5, external wall selection). Therefore, it is crucial to delve into such solutions in detail. A major factor here is the contradiction within the ideal options of the results that depend on the dimension (economic, environmental, social), such as when the best option for the economic dimension is not aligned with the ideal option for the social dimension. When applying TOPSIS to the sustainability assessment, it is therefore, necessary to identify whether it is the best solution for the greatest number of dimensions, or the best is the one closest to the optimum in the most important dimension. Even if the TOPSIS method aims to incorporate the quantitative dimension into the evaluation, it remains necessary to analyse the results and their impact from a more general perspective and to bear the overall objectives in mind. Thus, it is essential to broaden the scope of the studies and encompass different sectors in case studies to verify and compare the sustainability of products and services [9]. The magnitude of the variation of the results in the other dimensions must be considered, as should how this decision can affect other dimensions. For example, as shown in Table 5, the best rank solution is commonly influenced by option 1 and option 2 variation in each dimension. This means that the higher the variation (as shown in the structure) in the result of one dimension, the more probable solution matches these higher values. 5.3. Limitations and perspectives The present study focuses on the automatic sustainability assessment of various building material solutions and the relative score of the best solutions using a triple-dimension approach. The study employs the BIM methodology to frame the evaluation and utilises a library of TBL-enriched BIM objects to enhance the building information developing the LCSA. To date, this information regarding building elements (BIM objects) has been manually created: future developments can focus on improving the automatic and semiautomatic creation of the dataset. This entails, for example, using expert systems [64] to create automatic rules of data organisation, which could significantly speed up the procedure and develop TBL libraries with a larger number of items to evaluate. This study basis the interaction with the BIM model in the extraction of the bill of quantities at the element level. The material decomposition of the building element is provided by predefined data sets that uses the plug-in to calculate the LCSA. Future studies can focus on developing the Life Cycle Inventory using the bill of quantities from the building on more disaggregated way. Moreover, the study uses a limited number of building materials: this can be enhanced by incorporating a diverse range of new materials and elements to meet the evolving demands of sustainable construction practices, as well as materials with of recycled components and specific environmental data about products, components and materials such as Environmental Product Declarations (EPDs) [65]. The current investigation confines its evaluation to specific phases: the product phase (A1: Supply of raw materials, A2: Transport, A3: Manufacturing); the Construction phase (A5: Construction); the Use phase (B2: Maintenance, B3: Repair, B4: Replacement); and the End-of-life phase (C1: Demolition/deconstruction). Ensuring comprehensive assessment requires expanding system boundaries by covering more life cycle modules, thereby offering a holistic view of sustainability considerations. The results provide evidence of the variability in the values obtained and of the influence of the definition of the weighting factors on the results. They also indicate that the best solution for the TOPSIS method may only sometimes be the one that obtains the best results for the greatest number of dimensions (economic, environmental, social). Hence, the enrichment of the assessment process, both by utilising stakeholder participation and by reviewing the literature and governmental objectives such as in Lindfors [66], could be beneficial in attaining a more robust and multi-faceted evaluation of sustainable building materials and their impacts. The present study could be improved through the reception of feedback from designers, policymakers, and other relevant stakeholders who could test the development in existing practices, which would enable adjustments in data visualisation definitions and the alignment of assessment frameworks with practical needs and industry standards, in addition to the identification of hotspots. Appropriated visualisation strategies of LCA during the design process can be the use of model-colour-based 3D code, heat maps and boxplot diagrams, which are indicated in Forth et al. [19]. Moreover, the employment of dynamic and scalable databases such as developed in R¨ ock et al. [67], can help to integrate a large number of indicators and analysing its variation over time. Broadening the range of options within a more intricate assessment matrix is essential to gaining a comprehensive understanding of the results. This expansion would facilitate announced comprehension of the intricate dynamics and implications inherent in assessments of sustainable materials. 6. Conclusions This paper showcases the feasible integration of multi-dimensional assessment methods into BIM-based LCSA implementation by incorporating functions, such as multi-criteria analysis, across building elements, such as envelope, structure, partitions, and finishing B. Soust-Verdaguer et al. Journal of Building Engineering 97 (2024) 110818 17 systems. The innovation of this development lies in its simultaneous application during the design phase, which offers visualised sustainability assessment results that encompass environmental, economic, and social dimensions and the relative score and weighting of the evaluated solutions. This approach emphasises developing and validating the Smart BIM3LCA, an automated tool that facilitates early-stage building LCSA. Moreover, it supports the analysis of various solution combinations, weighting factors, and building material selection. It also analyses various combinations of solutions, thereby contributing to LCSA and TOPSIS studies. The key findings are identified as the TOPSIS implementation in the LCSA of buildings. It was detected that the relative score for the TOPSIS method is affected by the weighting factor. Hence, the TOPSIS results should be accompanied by the total or normalised results per dimension and their differences in order not to lose sight of the order of magnitude of what is affected by choosing one or another design option and to visualise to what extent the idea option will negatively affect the dimensions evaluated. Future developments should focus on increasing the scope of the assessed solutions by incorporating more building systems (e.g., installations), more building elements, and more frequent materials, as well as by incorporating more information modules on the life cycle of buildings and by testing the plug-in in other building typologies. Funding This publication is part of the following projects: Grant TED2021-129542B-I00, funded by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR”; and Grant PID2022-137650OB-I00 funded by MCIN/AEI/10.13039/ 501100011033 CRediT authorship contribution statement Bernardette Soust-Verdaguer: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Jos´ e Antonio Guti´ errez Moreno: Writing – review & editing, Writing – original draft, Validation, Software, Investigation, Data curation. Daniel Cagigas: Writing – review & editing, Visualization, Validation, Supervision, Software, Formal analysis, Data curation. Endrit Hoxha: Writing – review & editing, Validation, Data curation. Carmen Llatas: Writing – review & editing, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Acknowledgements The authors thank the funding organisation MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR” for supporting the following projects: Grant TED2021-129542B-I00 and Grant PID2022-137650OB-I00. 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