Design for Adaptability (DfA)—Frameworks and Assessment Models for Enhanced Circularity in Buildings
Abstract
:1. Introduction
Circular Buildings and Design for Adaptability (DfA)
2. Materials and Methods
- What are the objectives and criteria of existing adaptability frameworks/tools? Which of these criteria are empirically proved to be most efficient in facilitating adaptations?
- What is the current role of DfA in existing circularity frameworks in buildings?
- How can the role of DfA be objectified in circularity frameworks, and what are the requirements that ensure an inclusive implementation of circularity in buildings?
- Models for circular materials and products
- Design-support tools for whole buildings
- Circularity integration in sustainability frameworks
- MFA and LCA-based tools
- Circularity criteria and guidelines
3. Results
3.1. Adaptability Assessment Tools and Models
3.1.1. Adaptive Reuse Potential Model (ARP), 2007
3.1.2. The Adaptable Building Design (ABD) Framework, 2010
3.1.3. IconCUR, 2012
- Low condition and low utilization—reconstruct or dispose
- High condition and high utilization—retain or extend
- Low condition and high utilization—renovate or preserve
- High condition and low utilization—reuse or adapt
3.1.4. AdaptSTAR Model, 2013
3.1.5. Preliminary Assessment Adaptation Model (PAAM), 2014
3.1.6. Flex 4.0, 2016
3.1.7. The SAGA Method, 2019
3.1.8. Adaptive Reuse Assessment Model (ARAM), 2021
3.2. Circularity Assessment Frameworks
3.2.1. Models for Circular Materials and Products
- 1.
- The Circularity Calculator. Tha circularity calculater was developed by IDEAL and CO Explore BV [50]. The calculator uses the bill of materials of a product to compare the impacts of different circularity scenarios (e.g., reuse, refurbishment, remanufacture, and recycling) and consequently choose the best design options and business models relying on experimental trade-offs between circularity and value capture. The calculator provides four quantitative key performance indicators (KPIs): circularity indicator, value capture indicator, recycled content indicator, and reuse index, which help companies to determine the economic viability of potential design ideas for products [51].
- 2.
- Material Circularity Indicator (MCI). MCI was developed by the Ellen McArthur Foundation (EMF) and Granta Design [13] to assess the ability of material flows of a product or company to be restorative. The calculation of the MCI for a product relies on a detailed bill of materials and three types of inputs:
- Amount of virgin, recycled, or reused content V;
- Mass of unrecoverable waste W;
- Product utility factor X.
- 3.
- The Material Reutilization Score (MRS). MRS was proposed by the Environmental Protection Encouragement Agency through the Cradle-to-Cradle certification scheme to address the recycling value of materials. A material reutilization score is estimated based on the end-of-life strategy considering the performance of input materials and reutilization options. MRS is calculated using two variables: the intrinsic recyclability (IR) and the recycled content (RC), according to the following formula:MRS = (2 · IR + RC)/3
- 4.
- Longevity and resource duration. Longevity is a value-based performance indicator that relies on the length of time a resource is used in a product system, which can be measured in time units [52,53]. The idea is that the higher the material retention, the slower the loop and consequently, the higher the contribution to a circular economy [53]. According to the authors, the indicator can be measured using three parameters that are the three drivers for longevity: 1. the initial lifetime, 2. lifetime after refurbishment, 3. lifetime after recycling. These parameters fell short in considering the number of times a resource can be utilized, which is essential to measure resource efficiency and eventually close the loop. Moreover, the indicators do not address the complexities of refurbishment and recycling processes and assume that a resource is refurbished, recycled, and reused through a similar product which is not always the case [52]. Still, the longevity indicator provides a simple tool companies can use to determine the value chain of a product.
3.2.2. Design-Support Tools for Whole Buildings
- 1.
- The CE Meter, 2015
- 2.
- Building Circularity Indicator (BCI) frameworks, (2016, 2018, 2019, 2020).]
- 3.
- Circular Building Assessment Prototype (CBA)
- 4.
- Disassembly and Deconstruction Analytics System (D-DAS), 2019
- 5.
- Design criteria for circular buildings, 2021
- 6.
- Circular Construction Evaluation Framework (CCEF), 2021
3.2.3. Circularity Integration in Sustainability Frameworks
- 1.
- BREEAM
- 2.
- LEED
- 3.
- Level(s) framework
- Scenario 1: Building and elemental service life planning.
- Scenario 2: Design for adaptability and refurbishment.
- Scenario 3: Design for deconstruction, reuse, and recyclability.
3.2.4. MFA- and LCA-Based Tools
3.2.5. Circularity Criteria and Guidelines
- 1.
- The Circular Design Guide
- 2.
- The reversible building protocols
- Trans-functional: switching between four functional scenarios: housing, office, education, and public buildings.
- Adaptable mono-functional block: when the main use persists while the habitants’ requirements change as the family members increase or grow older.
- Transportable from one location to another.
- Transformable: when it has the capacity to perform all of the three previous changes.
4. Discussion
4.1. Adaptability Assessment Frameworks—Scope and Objectives
4.2. Circularity Assessment Frameworks—Scopes and Objectives
4.3. DfA Role in Circularity Frameworks
4.4. Outline of Requirements for Inclusive Circularity Assessment
5. Conclusions
- There is a lack of quantitative models for adaptability assessment as well as evidence on the effectiveness of their indicators and criteria on the different typologies of buildings.
- There is a lack of multi-criteria approaches supporting design that allow to deal with conflicting situations, as well as assess design options and advise better circularity scenarios at whatever point throughout a building’s lifecycle. More automated indicators are essential to evaluate design options and facilitate necessary trade-offs for enhanced circularity.
- Future research should tackle the practicality questions of some of the application of the prominent tools on real-life case studies and identify further obstacles and rooms for innovation that on-desk research methods could not identify.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Tool Designation | Lifecycle Intervention | Criteria/Indicators | Tool Purpose/Outcomes | ||
---|---|---|---|---|---|
Categories | Prioritized Criteria/Indicators | Weighting Methods | |||
ARP | Use stage | 1. Physical 2. Economic 3. Functional 4. Technological 5. Social 6. Legal | Equally weighted criteria | User subjective decision | ARP index: a percentage of a building potential for adaptive reuse |
ABD | Design stage and later incremental adaptations for full lifecycle | 1. Market conditions 2. Performance & technology 3. Policies and standards 4. Climate conditions | Case-sensitive | Case-sensitive predictive uncertainty modeling | Lifecycle planning |
IconCUR | Use stage | 1. Condition 2. Utilization 3. Reward | Not well-established | -Weighted matrix -Pairwise comparison for assigning weights to each sub-criterion -Risk sensitivity analysis | Conceptual decision-making support |
AdaptSTAR | Design stage | 1. Physical 2. Technological 3. Economic 4. Functional 5. Social 6. Legal 7. Political | Physical criteria: 1. Structural integrity and foundations 2. Material durability and workmanship 3. Maintainability | Practitioner survey using a five-point Likert scale | Star-rating scheme for design evaluation |
PAAM | Use stage | 1. Physical 2. Social 3. Economic 4. Environmental 5. Legal 6. Technological | 1st Physical and size 2nd Land 3rd Social | Principal Component Analysis | Assessment of minor adaptation actions’ potential ranging from very low to very high |
FLEX 4.0 | All lifecycle stages | Brand’s layers: 1. Site 2. Structure 3. Skin 4. Facilities 5. Space plan | Two possibilities: 1. default: Structure | Author subjective relative weighting | Classification according to flexibility score |
2. Case sensitive | |||||
SAGA | All lifecycle stages | Space Plan layer | Spatial configuration | Plan graph permeability using a combination of unweighted and weighted graphs | Evaluation of buildings’ Spatial configuration of layouts |
ARAM | Use stage | 1. Environmental 2. Social 3. Economic 4. Legal 5. Political 6. Physical 7. Locational 8. Technical | 1st Political and social factors (heritage significance and authenticity) 2nd Economic 3rd Technical 4th Legal 5th Physical | Revision and adaptations of previous models | Conceptual decision-making support |
Tool Designation | Domain | Indicators/Criteria | Methodology | |||||
---|---|---|---|---|---|---|---|---|
New Buildings | Existing Buildings | Typology | Qualitative | Quantitative | Mixed | Weighting System | Validation | |
ARP | x | General | x | Mathematical algorithm | Case studies | |||
ABD | x | Office commercial | x | ROA and Monte Carlo simulations | One case study | |||
IconCUR | x | General | x | Multi-Criteria Decision Analysis Adaptive Management | Case study Inter-model comparison | |||
AdaptSTAR | x | General | x | Weighted checklist of 26 design criteria | Case study Inter-model comparison | |||
PAAM | x | Office commercial | x | Weighted checklist of 12 indicators | Illustrative case study | |||
FLEX 4.0 | x | General Office School | x | x | The sum of indicators values after multiplying by assigned weights | Case study | ||
SAGA | x | x | Residential | x | No final score Indicators are individually calculated using BIM software | Case study of 6 representative layouts | ||
ARAM | x | Heritage | x | A sequence of conditions needs to be met for a building to be considered suitable for adaptation | Case study Inter-model comparison |
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Askar, R.; Bragança, L.; Gervásio, H. Design for Adaptability (DfA)—Frameworks and Assessment Models for Enhanced Circularity in Buildings. Appl. Syst. Innov. 2022, 5, 24. https://doi.org/10.3390/asi5010024
Askar R, Bragança L, Gervásio H. Design for Adaptability (DfA)—Frameworks and Assessment Models for Enhanced Circularity in Buildings. Applied System Innovation. 2022; 5(1):24. https://doi.org/10.3390/asi5010024
Chicago/Turabian StyleAskar, Rand, Luís Bragança, and Helena Gervásio. 2022. "Design for Adaptability (DfA)—Frameworks and Assessment Models for Enhanced Circularity in Buildings" Applied System Innovation 5, no. 1: 24. https://doi.org/10.3390/asi5010024
APA StyleAskar, R., Bragança, L., & Gervásio, H. (2022). Design for Adaptability (DfA)—Frameworks and Assessment Models for Enhanced Circularity in Buildings. Applied System Innovation, 5(1), 24. https://doi.org/10.3390/asi5010024