Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. This paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.

This work aims to evaluate the energy savings that can be achieved in domestic hot water (DHW) production using consumption forecasting through statistical modeling. It uses our forecast algorithm and aims at investigating how it can improve energy efficiency depending on the system configuration. Especially, the influence of the DHW production type used is evaluated as well as the water tank insulation. To that end, real consumption measurements are used for model training. Then simulations are run on using TRNSYS software to compute the total energy consumption of DHW production systems over 1 year. Simulations are also based on real consumption measurements for realistic results. To appraise the energy savings, we compared simulations that consider either no forecast (reactive control), perfect forecast (to estimate the control ability to consider forecast), or the forecast provided by our algorithm. The measurements and simulations are run on 26 different but real dwellings to assess results variability. Several system configurations are also compared with varying thermal insulation indices for a complete benchmark of the approach so that an overall performance of the system and the anticipation strategy could be evaluated.

In mass customization, software configurators enable novice end-users to design customized products and services according to their needs and preferences. However, traditional configurators hardly provide an engaging experience while avoiding the burden of choice. We propose a Design Participation Model to facilitate navigating the design space, based on two modules. Modeler enables designers to create customizable designs as parametric models, and Navigator subsequently permits novice end-users to explore these designs. While most parametric designs support direct manipulation of low-level features, we propose interpolation features to give customers more flexibility. In this paper, we focus on the implementation of such interpolation features into Navigator and its user interface. To assess our approach, we designed and performed user experiments to test and compare Modeler and Navigator, thus providing insights for further developments of our approach. Our results suggest that barycentric interpolation between qualitative parameters provides a more easily understandable interface that empowers novice customers to explore the design space expeditiously.

An innovative approach has been developed for discovering better design methodologies that is based on simulating the design process using a multiagent system that mimics the behavior of a design team. The system implements a knowledge-based model of design in which highly specialized knowledge from expert sources is applied to synthesize a design. The agents activate the pieces of design knowledge when they become applicable. The use of knowledge by agents is recorded by tracing the steps that the agents have taken during a design project. Many traces are generated by solving a large number of design projects that differ in their requirements. A set of design methodologies is constructed by using inductive learning techniques to generalize the traces generated. These methodologies then can be used to guide human design teams through future design projects.

In part-selection problems, parts are selected from catalogs and connected to meet the following problem requirements: functionality, specifications, and constraints. This paper formally defines the part-selection problem, enumerates a set of design properties that are useful during a search for a design solution, and provides an algorithm for solving part-selection problems based on a novel set of operators for manipulating portions of the design space.

This paper deals with the system of agents treated as a concurrent modular system, which is able to support the designer in solving complex design tasks. The behavior of design agents is modeled by sets of grammar rules. Each agent manages a graph grammar and a database of facts concerning the subtask for which it is responsible. The course of designing is determined by the interaction between cooperating specialized agents. The design context is expressed by the environment in which agents act and predicates describing design criteria. The organization, design methodology, and a semantic model of a grammar-based multiagent design system are presented. The notions of a valid design solution and a design solution consistent with the design criteria are also introduced. The proposed approach is illustrated by the example of designing a house estate.

A platform for experimenting with population-based design exploration algorithms is presented, called Dexen. The platform has been developed in order to address the needs of two distinct groups of users loosely labeled as researchers and designers. Whereas the researchers group focuses on creating and testing customized toolkits, the designers group focuses on applying these toolkits in the design process. A platform is required that is scalable and extensible: scalable to allow computationally demanding population-based exploration algorithms to be executed on distributed hardware within reasonable time frames, and extensible to allow researchers to easily implement their own customized toolkits consisting of specialized algorithms and user interfaces. In order to address these requirements, a three-tier client–server system architecture has been used that separates data storage, domain logic, and presentation. This separation allows customized toolkits to be created for Dexen without requiring any changes to the data or logic tiers. In the logic tier, Dexen uses a programming model in which tasks only communicate through data objects stored in a key-value database. The paper ends with a case study experiment that uses a multicriteria evolutionary algorithm toolkit to explore alternative configurations for the massing and façade design of a large residential development. The parametric models for developing and evaluating design variants are described in detail. A population of design variants are evolved, a number of which are selected for further analysis. The case study demonstrates how evolutionary exploration methods can be applied to a complex design scenario without requiring any scripting.

Under the concept of a global economy, enterprises are assigning design and production environments around the world in different areas. A serious issue of information exchange emerges as companies use traditional hardware and very distinct software appropriate to their field of expertise. To overcome the problem of low productivity due to the interruption of information, the concept of simultaneous engineering and concurrent design becomes very significant. In this article an agent-based ship design system is developed to support cooperation in distributed ship design environments. Above all, the conflicts that occur while sharing knowledge in the system must be resolved. One approach is to adopt a case-based conflict resolution strategy formulated to resolve current conflict on the basis of similar previously resolved cases in agent-based collaborative design system environments. To do this, conflict cases that occur in the initial ship design stage are extracted. On the basis of the extracted cases, a case base is constructed. In addition, a conflict resolution handler located in the superagent called a facilitator, an agent to control other subagents, is developed to treat conflict problems effectively by case-based reasoning. The case-based conflict resolution strategy is evaluated by applying it to a collaborative design process in the initial ship design stage, especially the machinery outfitting design, the preliminary design, the hullform design, and the structural design. Through the help of the collaboration of the design agents, the facilitator, the conflict resolution handler, and the case-based system, a designer can make decisions based on similar previously resolved cases.

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.

Little research has considered fuzzy scheduling and sequencing problem in operating rooms. Multiple-period fuzzy scheduling and sequencing of patients in operating rooms optimization models are proposed in this research taking into consideration patient‘s preference. The objective of the scheduling optimization model is obtaining minimal undertime and overtime and maximum patients' satisfaction about the assigned date. The objective of sequencing the optimization model is both to minimize overtime and to maximize patients' satisfaction about the assigned time. A real-life case study from a hospital that offers comprehensive surgical procedures for all surgical specialties is considered for illustration. Research results showed that the proposed models efficiently scheduled and sequenced patients while considering their preferences and hospitals operating costs. In conclusion, the proposed optimization models may result in improving patient satisfaction, utilizing hospital's resources efficiently, and providing assistance to decision makers and planners in solving effectively fuzzy scheduling and sequencing problems of operating rooms.

This paper describes how genetic programming has been used as an invention machine to automatically synthesize complete designs for four optical lens systems that duplicated the functionality of previously patented lens systems. The automatic synthesis of the complete design is done ab initio, that is, without starting from a preexisting good design and without prespecifying the number of lenses, the topological arrangement of the lenses, or the numerical or nonnumerical parameters associated with any lens. One of the genetically evolved lens systems infringed a previously issued patent, whereas the others were noninfringing novel designs that duplicated (or improved upon) the performance specifications contained in the patents. One of the patents was issued in the 21st century. The designs were created in a substantially similar and routine way, suggesting that the approach described in the paper can be readily applied to other similar problems in the field of optical design. The genetically evolved designs are instances of human-competitive results produced by genetic programming in the field of optical design.

The efficient use of critical machines or equipment in a manufacturing system requires reliable information about their current operating conditions. This information is often used as a basis for machine condition monitoring and fault diagnosis—which essentially is an endeavor of knowledge extraction. Rough set theory provides a novel way to knowledge acquisition, especially when dealing with vagueness and uncertainty. It focuses on the discovery of patterns in incomplete and/or inconsistent data. However, rough set theory requires the data analyzed to be in discrete manner. This paper proposes a novel approach to the treatment of continuous-valued attributes in multi-concept classification for mechanical diagnosis using rough set theory. Based on the proposed approach, a prototype system called RClass-Plus has been developed. RClass-Plus is validated using a case study on mechanical fault diagnosis. Details of the validation are described.

The configuration task is commonly defined as composing a complex product from a set of predefined component types while taking into account a set of well-defined restrictions on how components belonging to these types can be combined. Configuration, always a successful artificial intelligence (AI) application area ever since the R1/XCON system of the early 1980s, has recently attracted renewed research interest. This is also demonstrated by an annual series of workshops on configuration that have been held at the AAAI, ECAI, and IJCAI conferences since 1999. Important real-world industrial configuration tasks are encountered in marketing, manufacturing, and design. They usually involve physical products, such as telecommunication switches, computers, elevators, large diesel engines, automation systems, or vehicles (some of which appear as application domains in the articles in this issue), but can also pertain to financial or other services or software.

This paper proposes to contribute to our understanding of the fundamental cognitive processes essential to designing by exploring the experiences of people who have different information processing behaviors to those found in most people. In particular, we focus on people with autism spectrum conditions (ASC) because they are known to have information processing behaviors that are both maladaptive and exceptional. Central to our study is the question: what can we learn from people with ASC about cognitive processes essential to designing? The scholarship on cognitive behaviors associated with the autism spectrum and narratives on the experiences with design practice by individuals with ASC are discussed in relation to cognitive processes associated with designing. In turn, the individuals commented upon the analysis of cognitive processes associated with designing in light of their personal experiences with design practice. We conclude that the weak central coherence theory of autism provides a useful prediction of the cognitive processes necessary for expertise in design, and that both the framework for expertise in design and the way it is studied may require updating.

The word “topology” is derived from the Greek word “τοπος,” which means “position” or “location.” A simplified and thus partial definition has often been used (Croom, 1989, page 2): “topology deals with geometric properties which are dependent only upon the relative positions of the components of figures and not upon such concepts as length, size, and magnitude.” Topology deals with those properties of an object that remain invariant under continuous transformations (specifically bending, stretching, and squeezing, but not breaking or tearing). Topological notions and methods have illuminated and clarified basic structural concepts in diverse branches of modern mathematics. However, the influence of topology extends to almost every other discipline that formerly was not considered amenable to mathematical handling. For example, topology supports design and representation of mechanical devices, communication and transportation networks, topographic maps, and planning and controlling of complex activities. In addition, aspects of topology are closely related to symbolic logic, which forms the foundation of artificial intelligence. In the same way that the Euclidean plane satisfies certain axioms or postulates, it can be shown that certain abstract spaces—where the relations of points to sets and continuity of functions are important—have definite properties that can be analyzed without examining these spaces individually. By approaching engineering design from this abstract point of view, it is possible to use topological methods to study collections of geometric objects or collections of entities that are of concern in design analysis or synthesis. These collections of objects and or entities can be treated as spaces, and the elements in them as points.

Research efforts to implement a Bayesian belief-network-based expert system to solve a real-world diagnostic problem – the diagnosis of integrated circuit (IC) testing machines – are described. The development of several models of the IC tester diagnostic problem in belief networks also is described, the implementation of one of these models using symbolic probabilistic inference (SPI) is outlined, and the difficulties and advantages encountered are discussed. It was observed that modeling with interdependencies in belief networks simplifies the knowledge engineering task for the IC tester diagnosis problem, by avoiding procedural knowledge and focusing on the diagnostic component’s interdependencies. Several general model frameworks evolved through knowledge engineering to capture diagnostic expertise that facilitated expanding and modifying the networks. However, model implementation was restricted to a small portion of the modeling, that of contact resistance failures, which were due to time limitations and inefficiencies in the prototype inference software we used. Further research is recommended to refine existing methods, in order to speed evaluation of the models created in this research. With this accomplished, a more complete diagnosis can be achieved.

The potential relationship between service demands and remanufacturing services (RMS) is essential to make the decision of a RMS plan accurately and improve the efficiency and benefit. In the traditional association rule mining methods, a large number of candidate sets affect the mining efficiency, and the results are not easy for customers to understand. Therefore, a mining method based on binary particle swarm optimization ant colony algorithm to discover service demands and remanufacture services association rules is proposed. This method preprocesses the RMS records, converts them into a binary matrix, and uses the improved ant colony algorithm to mine the maximum frequent itemset. Because the particle swarm algorithm determines the initial pheromone concentration of the ant colony, it avoids the blindness of the ant colony, effectively enhances the searchability of the algorithm, and makes association rule mining faster and more accurate. Finally, a set of historical RMS record data of straightening machine is used to test the validity and feasibility of this method by extracting valid association rules to guide the design of RMS scheme for straightening machine parts.

This paper investigates the use of typological knowledge in the visual modality through a computer framework that combines multidisciplinary technologies from computer science, that is, artificial intelligence, software engineering, database system, and programming language, to help provide solutions and services to building designers. The solving of design problems frequently involves visual thinking, which has to do with the intensive use of visual knowledge like pictures, images, and other types of visual displays. The recognized power of typological knowledge in design problem solving is applied to support the exploration of a diversity of possible design solutions represented in a pictorial mode. The innovative use of computer science technologies enables a smooth link of visual typological knowledge with the design goals. Within the framework, a core technology was designed to respond to a designer's specific needs through dynamic user viewpoint generation, so that design solutions are associated with relevant (retrieved) visual typologies from the knowledge base. This has been achieved in a two-way process, in which the designer establishes an interactive dialogue with an experimental computerized framework.

Change mode and effects analysis (CMEA) is a powerful technique for measuring product flexibility toward future changes and diminishing the cost of redesign as well as shortening time to market. As a systematic methodology, it provides an in-depth view for the investigation of potential changes, causes, and effects in designs, products, and processes. Traditional CMEA determines the risk priorities of change modes by using change potential number, which requires the risk factors of design flexibility, occurrence, and readiness to be precisely evaluated. However, this is not always possible in real applications due to the uncertainty and subjectivity involved in the early design stages. It has been criticized much for its deficiencies in criteria weighting of the risk factors, change potential number calculation, and risk priorities determination of the change modes. This paper presents a systematic evaluation approach for determining a more rational rank of change modes by combining with the entropy weight method, rough number, and grey relational analysis. In this study, the entropy weight method is adopted to calculate the relative importance of risk factors. Rough number is presented to aggregate individual weights and preferences, and to manipulate the vagueness in the evaluation process. Then a rough number enhanced grey relational analysis is proposed to evaluate the risk ranking of change modes. Finally, a practical example is put forward to validate the performance of the proposed method. The result shows that the proposed change mode evaluation method can effectively overcome the shortcomings of traditional CMEA and strengthen the objectivity of product flexibility measurement.

In cases where a domain theory can be successfully expressed in a logical formalism and can be used to formulate a task in that domain in mathematical terms, the task of building sound knowledge-based systems is greatly facilitated. However, it is not immediately obvious how the design aspects of such tasks, where these are an important feature of problem solving, can be incorporated in this approach. Design issues differ from search problems in that there may be several choices, each valid in some sense, but not (necessarily) equally good or equally appropriate in the current context. A case study is described in which a methodology is used based on the development of proof plans. The ability to conduct research according to the Popperian framework of hypothesis, validation, testing, and modification in response to empirical evidence – the hypothetico-deductive approach – seems essential to any rigorous scientific endeavor. It is believed that proof planning is a method which readily exploits this inherently incrementalist approach and could prove to be a powerful tool in designing AI systems.