Data-Driven Design-by-Analogy: State of the Art

Design-by-Analogy (DbA) is a design methodology that draws inspiration from a source domain to a target domain to generate new solutions to problems or designs, which can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. Herein, we survey the prior data-driven DbA studies and categorize and analyze individual study according to the data, methods and applications in four categories including analogy encoding, retrieval, mapping, and evaluation. Based on such structured literature analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field.

Data-Driven Design-by-Analogy: State of the Art and Future Directions

Design-by-Analogy (DbA) is a design methodology wherein new solutions, opportunities or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications in four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.

An Integrated GIS Methodology to Assess the Impact of Engineering Maintenance Activities: A Case Study of Dredging Projects

Engineering infrastructures require regular maintenance and/or repair activities that have important social, environmental, and economic impacts. Despite their growing importance, few studies have focused on fully integrated analyses. This work presents a general methodological approach to design databases of engineering maintenance activities for their assessment. This methodology was applied to the case of dredging projects in the ports managed by the Andalusian Regional Government (Spain). The resulting database contains 87 fields of information obtained from the analysis of 70 activities performed between 1993 and 2015. This database is free, public, and available to the scientific community, and it was implemented in PostgreSQL using the PostGIS extension for spatial data; therefore, it can be integrated in a GIS. The assessment of deviations from the initial projects and the comparison between locations enhanced our methodology, which represents a valuable tool not only for scientists and managers to improve the decision-making process when planning future strategies, but also to evaluate the environmental impacts.