A conceptual tool for environmentally benign design: development and evaluation of a “proof of concept”

Abstract

Design is a decision-making process for which knowledge is a prerequisite. Most decisions are taken at the conceptual stage and have pronounced influence on the final design. The literature, therefore, recommends the incorporation of sustainability criteria, such as environment, at this stage. Difficulty in performing life cycle assessment (LCA) due to low availability of information at the conceptual stage for evaluation and highly abstract nature of solutions, inadequate incorporation of DfE (Design for Environment) guidelines and LCA reports into the design process, and a lack of effective communication of the same to the designers for prompt decision-making are major motivations for the development of a support. This paper discusses a “conceptual Tool for environmentally benign design” – concepTe – that supports designers in decision-making during the conceptual design stage, by offering environmental impact (EI) estimates of abstract solutions with associated uncertainty, for evaluation and selection of the most environmentally benign solution as concept. The EI estimates are calculated by a module in the tool based on a proposed EI estimation method, which requires the support of a knowledge base to fetch appropriate LCA information corresponding to the design element being conceptualized. This knowledge base is grounded in the domain-agnostic SAPPhIRE model ontology, allows semantic operability of the knowledge, and offers the results to the designers in a familiar domain language to aid decision-making. A “proof of concept” of the tool is developed for application in design of building in the AEC (Architectural design, Engineering, and Construction) domain. Further, empirical studies are conducted to evaluate the effectiveness of the “proof of concept” to support decision-making and results are found favorable. The paper also discusses the future scope for further development of the tool into a holistic design decision-making platform.

Evaluating the feeling of control in virtual object translation on 2D interfaces

“…According to the literature, scholars have mentioned that in most cases, a sample size of 25 or 30 observations is adequate, which is fairly common across statistics (Chang et al , 2006, 2008). In the design research area, some studies have considered about 20 subjects in the phase of experimental design (Acharya and Chakrabarti, 2020; Cun et al , 2021; Fillingim et al , 2021; Kwon et al , 2022; Pejic and Pejic, 2022; Sun et al , 2022 c ). Half of the participants had prior experience in employing CAD software, and others had limited knowledge about CAD; meanwhile, all participants were familiar with operations on mouse- and touch-based interfaces, but few had prior experience manipulating virtual objects in simulated settings through the handheld AR interface.…”

Engineering and Design for Sustainable Construction: A Bibliometric Analysis of Current Status and Future Trends

“…The impact of climate change on construction techniques has initiated research on adaptive and climate-resilient designs, such as the impact of sustainable design on climate change, the environmental effects of using different construction codes, etc. [106,[110][111][112][113][114][115][116][117][118][119][120]. There will be a greater emphasis on climate-resilient design in the future, considering adaptive techniques to mitigate extreme weather events, sea-level rise, and urban heat islands.…”

Semantic Networks for Engineering Design: A Survey

“…He et al (2019) constructed a semantic network of concepts based on their co-occurrences in a set of one thousand idea descriptions from an online crowdsourcing campaign via Mechanical Turk for reuse to inspire design ideation. concepTe (Acharya and Chakrabarti, 2020) is a decision-making support tool during the conceptual design stage offering aids in the designer's familiar domain, of which the knowledge base is grounded in the domain-agnostic SAPPhIRE model ontology.…”

“…Triplets classification (Cheong et al, 2017) ConceptNet Knowledge retrieval and reasoning (Yuan and Hsieh, 2015) Concept association and expansion (Han et al, 2018a) Knowledge retrieval and reasoning (Han et al, 2018b) Query expansion; ideation aid (Chen and Krishnamurthy, 2020) Hand-built extracting thematic relations in domain-specific knowledge Knowledge retrieval and reasoning (Georgiev et al, 2017) Total technology space map (TSM) Knowledge retrieval and reasoning (Luo et al, 2018; B-Link Cross-domain knowledge associations (Chen et al, 2019) TechNet Prior art search, design knowledge discovery and representation, idea generation and evaluation (Sarica et al, 2019a;2019b;Han et al, 2020) SAPPhIRE model ontology Knowledge retrieval (Acharya and Chakrabarti, 2020)…”

“…TechNet has been utilized to augment patent search (Sarica et al, 2019a), technology forecasting (Sarica et al, 2019b), idea generation (Sarica et al, 2021) and evaluation (Han et al, 2020) Table 2 summarizes the engineering design studies using semantic networks, highlighting the semantic networks employed and their applications. The (Goel et al, 2012) WordNet Concept association and expansion (Linsey et al, 2012) Executing queries (Siddharth and Chakrabarti, 2018) WordNet and FBS (Function-Behaviour-Structure) knowledge cell library Knowledge retrieval (Hu et al, 2017) WordNet and word2vec Triplets classification (Cheong et al, 2017) ConceptNet Knowledge retrieval and reasoning (Yuan and Hsieh, 2015) Concept association and expansion (Han et al, 2018a) Knowledge retrieval and reasoning (Han et al, 2018b) Query expansion; ideation aid (Chen and Krishnamurthy, 2020) Hand-built extracting thematic relations in domain-specific knowledge Knowledge retrieval and reasoning (Georgiev et al, 2017) Total technology space map (TSM) Knowledge retrieval and reasoning (Luo et al, 2018; B-Link Cross-domain knowledge associations (Chen et al, 2019) TechNet Prior art search, design knowledge discovery and representation, idea generation and evaluation (Sarica et al, 2019a;2019b;Han et al, 2020) SAPPhIRE model ontology Knowledge retrieval (Acharya and Chakrabarti, 2020)…”