Reuse has long been a major goal of the knowledge engineering community. We present a case study of the reuse of constraint knowledge acquired for one problem solver, by two further problem solvers. For our analysis, we chose a well-known benchmark knowledge base (KB) system written in CLIPS, which was based on the propose and revise problem-solving method and which had a lift/elevator KB. The KB contained four components, including constraints and data tables, expressed in an ontology that reflects the propose and revise task structure. Sufficient trial data was extracted manually to demonstrate the approach on two alternative problem solvers: a spreadsheet (Excel) and a constraint logic solver (ECLiPSe). The next phase was to implement ExtrAKTor, which automated the process for the whole KB. Each KB that is processed results in a working system that is able to solve the corresponding configuration task (and not only for elevators). This is in contrast to earlier work, which produced abstract formulations of the problem-solving methods but which were unable to perform reuse of actual KBs. We subsequently used the ECLiPSe solver on some more demanding vertical transport configuration tasks. We found that we had to use a little-known propagation technique described by Le Provost and Wallace (1991). Further, our techniques did not use any heuristic “fix”’ information, yet we successfully dealt with a “thrashing” problem that had been a key issue in the original vertical transit work. Consequently, we believe we have developed a widely usable approach for solving this class of parametric design problem, by applying novel constraint-based problem solvers to data and formulae stored in existing KBs.

This paper presents a bioinspiration approach that is able to scalably leverage the ever-growing body of biological information in natural-language format. The ideation tool AskNature, developed by the Biomimicry 3.8 Institute, is expanded with an algorithm for automated classification of biological strategies into the Biomimicry Taxonomy, a three-level, hierarchical information structure that organizes AskNature's database. In this way, the manual work entailed by the classification of biological strategies can be alleviated. Thus, the bottleneck is removed that currently prevents the integration of large numbers of biological strategies. To demonstrate the feasibility of building a scalable bioideation system, this paper presents tests that classify biological strategies from AskNature's reference database for those Biomimicry Taxonomy classes that currently hold sufficient reference documents.

We aim to examine the potential of using analogies in design education and to compare the roles of analogies in explaining versus inspiring in engineering design. We review existing research in analogical thinking, with a focus on scientific discourse and education. Then we explore the role of analogies in design education in making concepts more relatable by asking six participants in a graduate-level design course to generate analogies for course topics. We describe criteria developed to evaluate the analogies and present these evaluations. We then asked participants to perform divergent thinking tests, but we found no significant correlation between these and analogy scores. The participants were also asked to reflect on what constitutes an effective analog, describe their process of identifying analogies, and provide their definitions of analogies. We describe possible links between these comments and the ratings of their analogies. We then draw on results in using analogies in pedagogy to inform and reflect on obstacles we encountered in the use of analogies to inspire. Specifically, we related them to our experience with biomimetic or biologically inspired design, where we used a natural-language search approach to identify relevant analogies. Three aspects discussed are familiarity of source analogies, boundaries of parallels between source analogies and target concepts, and concreteness of source analogies. Finally, we discuss possible pedagogical benefits of eliciting analogies on course topics from students, namely, using the elicited analogies as tools for improved student engagement as well as more prompt instructor feedback.

Configuration and parameterization of optimization frameworks for the computational support of design exploration can become an exclusive barrier for the adoption of such systems by engineers. This work addresses the problem of defining the elements that constitute a multiple-objective design optimization problem, that is, design variables, constants, objective functions, and constraint functions. In light of this, contributions are reviewed from the field of evolutionary design optimization with respect to their concrete implementation for design exploration. Machine learning and natural language processing are supposed to facilitate feasible approaches to the support of configuration and parameterization. Hence, the authors further review promising machine learning and natural language processing methods for automatic knowledge elicitation and formalization with respect to their implementation for evolutionary design optimization. These methods come from the fields of product attribute extraction, clustering of design solutions, relationship discovery, computation of objective functions, metamodeling, and design pattern extraction.

This paper describes the results of a protocol study exploring problem–solution coevolution in a parametric design environment (PDE). The study involved eight participants who completed a defined architectural design task using Rhino and Grasshopper software: a typical PDE. The method of protocol analysis was employed to study the cognitive behaviors that occurred while these designers were working in the PDE. By analyzing the way in which the designers shifted between “problem” and “solution” spaces in the PDE, characteristics of the coevolutionary design process are identified and discussed. Results of this research include two potentially significant observations. First, the coevolution process occurs frequently within the design knowledge level (i.e., when using Rhino) and within the rule algorithm level (i.e., when using Grasshopper) of the parametric design process. Second, the designers’ coevolution process was focused on the design knowledge level at the beginning of the design session, while they focused more on the rule algorithm level toward the end of the design session. These results support an improved understanding of the design process that occurs in PDEs.

Internal analogies are created if the knowledge of source domain is obtained only from the cognition of designers. In this paper, an understanding of the use of internal analogies in conceptual design is developed by studying: the types of internal analogies; the roles of internal analogies; the influence of design problems on the creation of internal analogies; the role of experience of designers on the use of internal analogies; the levels of abstraction at which internal analogies are searched in target domain, identified in source domain, and realized in the target domain; and the effect of internal analogies from the natural and artificial domains on the solution space created using these analogies. To facilitate this understanding, empirical studies of design sessions from earlier research, each involving a designer solving a design problem by identifying requirements and developing conceptual solutions, without using any support, are used. The following are the important findings: designers use analogies from the natural and artificial domains; analogies are used for generating requirements and solutions; the nature of the design problem influences the use of analogies; the role of experience of designers on the use of analogies is not clearly ascertained; analogical transfer is observed only at few levels of abstraction while many levels remain unexplored; and analogies from the natural domain seem to have more positive influence than the artificial domain on the number of ideas and variety of idea space.

In this article, two methods to develop and optimize accompanying building spatial and structural designs are compared. The first, a coevolutionary method, applies deterministic procedures, inspired by realistic design processes, to cyclically add a suitable structural design to the input of a spatial design, evaluate and improve the structural design via the finite element method and topology optimization, adjust the spatial design according to the improved structural design, and modify the spatial design such that the initial spatial requirements are fulfilled. The second method uses a genetic algorithm that works on a population of accompanying building spatial and structural designs, using the finite element method for evaluation. If specific performance indicators and spatial requirements are used (i.e., total strain energy, spatial volume, and number of spaces), both methods provide optimized building designs; however, the coevolutionary method yields even better designs in a faster and more direct manner, whereas the genetic algorithm based method provides more design variants. Both methods show that collaborative design, for example, via design modification in one domain (here spatial) to optimize the design in another domain (here structural) can be as effective as monodisciplinary optimization; however, it may need adjustments to avoid the designs becoming progressively unrealistic. Designers are informed of the merits and disadvantages of design process simulation and design instance exploration, whereas scientists learn from a first fully operational and automated method for design process simulation, which is verified with a genetic algorithm and subject to future improvements and extensions in the community.

Function-based design approaches have been criticized for being too narrow to properly guide design. Specifically, they are said to be unable to cope with nonfunctional considerations, such as cost or maintenance issues without invoking other concepts, such as constraints. This paper investigates two alternative conceptualizations of the design process: the practical affordance-based design approach, as elaborated by Maier and Fadel, and the more theoretical use plan approach by Houkes and Vermaas. This paper compares function-, affordance-, and use plan-based design approaches. It highlights strengths and weaknesses of each approach and proposes a definition of the function of an artifact in terms of its affordances.

One of the principal advantages of affordance-based design is that Gibson's theory of affordances is a relational theory, akin to other relational approaches such as relational biology and relational computer science. The relationships between artifacts and their designers and users are of such primary importance that only a theory that is able to deal with those relationships directly appears to be sufficient for describing the wide breadth of problems in engineering design. However, there is no precise definition for what qualifies as a relational theory. In mathematics, we do find something approaching a theory of relations, dating back at least to Charles Peirce's Logic of Relatives around 1870. While rather general, Peirce's ideas on the subject laid the foundation for advances in the 20th century, including the relational model of databases. This paper is a first attempt at applying the mathematics of relations to affordances, with the aim of more precisely characterizing affordances, which heretofore have been difficult to define and, lacking appropriate mathematics, nearly impossible to subject to computation. Meanwhile, the implicit computability of affordances as relations is demonstrated by examples drawn from engineering, physics, computer science, biology, and architecture.

Nowadays, on the basis of significant work carried out, architectural adaption structures are considered to be intelligent entities, able to react to various internal or external influences. Their adaptive behavior can be examined in a digital or physical environment, generating a variety of alternative solutions or structural transformations. These are controlled through different computational approaches, ranging from interactive exploration ones, producing alternative emergent results, to automate optimization ones, resulting in acceptable fitting solutions. This paper examines the adaptive behavior of a kinetic structure, aiming to explore suitable solutions resulting in final appropriate shapes during the transformation process. A machine learning methodology that implements an artificial neural networks algorithm is integrated to the suggested structure. The latter is formed by units articulated together in a sequential composition consisting of primary soft mechanisms and secondary rigid components that are responsible for its reconfiguration and stiffness. A number of case studies that respond to unstructured environments are set as examples, to test the effectiveness of the proposed methodology to be used for handling a large number of input data and to optimize the complex and nonlinear transformation behavior of the kinetic system at the global level, as a result of the units’ local activation that influences nearby units in a chaotic and unpredictable manner.

A genetic algorithm is a metaheuristic proposed to derive approximate solutions for computationally hard problems. In the literature, several successful applications have been reported for graph-based optimization problems, such as scheduling problems. This paper provides one definition of periodic vehicle routing problem for single and multidepots conforming to a wide range of real-world problems and also develops a novel hybrid genetic algorithm to solve it. The proposed hybrid genetic algorithm applies a modified approach to generate a population of initial chromosomes and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. Moreover, during the implementation a hybrid algorithm, cyclic transfers, an effective class of neighborhood search is applied. The author uses three genetic operators to produce good new offspring. The objective function consists of two terms: total traveled distance at each depot and total waiting time of all customers to take service. Distances are assumed Euclidean or straight line. These conditions are exactly consistent with the real-world situations and have received little attention in the literature. Finally, the experimental results have revealed that the proposed hybrid method can be competitive with the best existing methods as asynchronous parallel heuristic and variable neighborhood search in terms of solution quality to solve the vehicle routing problem.

Many tools are being developed to assist designers in retrieving analogies. One critical question these designers face is how these analogues should be represented in order to minimize design fixation and maximize idea generation. To address this question, an experiment is presented that compares various representations' influence on creativity and design fixation. This experiment presents an effective example (analogue) as computer-aided design (CAD), sketch, or photograph representations. We found that all representations induced fixation, and the degree of fixation did not vary significantly. We also found that CAD representations encourage engineering designers to identify and copy the key effective features of the example. CAD and photo representations also produced a higher quality of design concepts. Results from this experiment offer insights into how these various representations may be used in examples during idea generation; CAD representations appear to offer the greatest advantages during the idea generation process. The results from this experiment also indicate that analogical databases of effective design examples should include CAD and photolike images of the analogue rather than sketches.

The aim of this study was to gain further insight into how analogical reasoning and mental simulation, two cognitive strategies, influence team dynamics in innovative product design. A particular emphasis was placed on exploring the association between these two strategies and team cohesion and team collaboration. Analogies were coded for “analogical distance” (i.e., within domain or between domain) and “analogical purpose” (i.e., problem identification, function finding, solution generation, and explanation). The results indicated that the presence of either analogizing or mental simulation was related to team cohesion and team collaboration, with mental simulation having an especially marked association with team collaboration. Within-domain analogizing was found to enhance team collaboration, but it did not influence team cohesion. Furthermore, all types of analogical purpose showed a similar association with team cohesion, whereas solution generation and function finding had a stronger association with team collaboration. We propose that analogizing and mental simulations are strategies that serve valuable functions in engendering enhanced cohesion and collaboration, which might be expected to lead to more effective design outcomes, although this remains an empirical question in need of further corroboration.

This paper presents research in the development of heuristic evolutionary algorithms (EAs) for generating and exploring differentiated force-based structures. The algorithm is weighted toward design exploration of topological differentiation while including specific structural and material constraints. An embryological EA model is employed to “grow” networks of mass-spring elements achieving desired mesh densities that resolve themselves in tensile force (form-active) equilibrium. The primal quadrilateral quadrisection method serves as the foundation for a range of extensible subdivision methods. Unique to this research, the quad is addressed as a “cell” rather than a topological or geometric construct, allowing for the contents of the cell to vary in number of mass-spring elements and orientation. In this research, this approach has been termed the quadrilateral quadrisection with n variable topological transformation method. This research culminates with the introduction of a method for grafting meshes where emergent features from the evolved meshes can be transposed and replicated in an explicit yet informed manner. The EA and grafting methods function within a Java-based software called springFORM, developed in previous research, which utilizes a mass-spring based library for solving force equilibrium and allows for both active (manual) and algorithmic topology manipulation. In application to a specific complex tensile mesh, the design framework, which combines the generative EA and mesh grafting method, is shown to produce emergent and highly differentiated topological arrangements that negotiate the specific relationships among a desired maximal mesh density, geometric patterning, and equalized force distribution.

The standard penetration test (SPT) is the most common test conducted in the field, and it is used to determine in situ properties of different soils. Although it is a matter of debate, these tests are also used for the determination of the consistency of fine-grained soils, whereby the test results can also be utilized to establish numerous empirical correlations to predict the strength of soils in the field. In this study, unsupervised clustering algorithms were employed to classify the SPT standard penetration resistance value (SPT-N) in the field. In this scope, shear strength and liquidity index parameters were used to classify the SPT-N values by taking the classification system of Terzaghi and Peck (1967) into consideration. The results showed that the input parameters were successful for classifying the SPT-N value to an acceptable degree of strength attribute. Therefore, in cases where the SPT tests are unreliable or could not be performed, laboratory tests on undisturbed specimens can give valuable information regarding the consistency and SPT-N value of the soil specimen under investigation. Data in this study is based on several tests that were conducted in a region; nevertheless, it is advised that the results of this study should be evaluated using global data.

The design community has growing familiarity with the concept of affordances and with the utility of this concept in many areas of design. Less emphasis has been placed on natural processes by which people acquire knowledge about affordances. Consequently, little is known about how design might be optimized to enable users to detect the actions that are available in a given human–machine system. We review scientific research about what people do to obtain information about affordances. We discuss implications of this research for design.