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The building industry, through its structure and its mandate, faces endemic information problems; expert systems are expected to impact positively. Expert systems are suited to situations of uncertainty; knowledge and reasoning are separated, allowing easier updating. Knowledge acquisition from human experts is difficult and problems of information reliability arise, suggesting the scope for cooperation between knowledge engineers and documentalists familiar with the domain. In building, prevailing conditions seem to indicate the appropriateness of expert systems, particularly during the design phase; however, written documentation and general research results are rarely consulted. This highlights the need for an information ‘refining’ stage between production and use. It is easier to set up expert systems for specialised sub‐domains; however, on‐going research is attempting to develop a comprehensive approach to project‐specific information that would be operational from initial design through to completed construction. Criteria for a comprehensive design information system can be listed.

The purpose of this paper is to present a multi‐expert system that can provide designers with suggestions for improvement. The multi‐expert system can analyse a design and provide designers with ideas for changes to designs at an early stage in order to improve assembly later in the manufacturing process.

The whole system consists of four expert systems: computer‐aided design (CAD) expert, automated assembly expert, manual assembly expert and design analysis expert. The design analysis expert includes a sub‐system to collate the information from the assembly experts and to provide costs and advice.

The paper finds that the approach and the systems can reduce manufacturing costs and lead times.

A knowledge‐based reckoning approach to design‐for‐assembly automation is used. The approach and systems can reduce manufacturing costs and lead times. The system can estimate assembly time and cost for manual or automatic assembly and select suitable assembly techniques.

The system can estimate assembly time and cost for manual or automatic assembly and select a suitable assembly technique.

The new system models assembly, product and process design using a natural approach for capturing intelligence. The new approach categorised automated assembly and manual assembly into separate individual experts. Intelligence and knowledge from each is captured and embedded within the individual expert that represented the process. This approach enabled greater flexibility and made the sub‐systems easier to modify, upgrade, extend and reuse.

The purpose of this paper is to first suggest scanning focal areas in new product development (NPD) by comparing with design thinking and, second, to uncover what people in different occupations expect of NPD based on future scenarios.

Authors place scanning and design thinking into a matrix of product-market strategies. In addition, this study adopts several open-end-type questionnaire surveys of employees at Japanese companies who have taken part in idea generation workshops that take a medium- to long-term perspective.

Authors found that innovations generated through scanning can cover the most difficult and uncertain areas in practice compared with design thinking. This manuscript also reveals occupational categories can be divided into two groups according to different expectations of NPD: the rapid-fire NPD expectation group and late-bloomer NPD expectation group. The former group which consists of marketing and engineering experts tends to expect that NPD is simply a response to existing needs and that profit will be gained expeditiously through NPD, while the latter, which comprising design and research experts, tends to expect that NPD will realize future innovations.

This study shows some common and different points between scanning and design thinking by using a theoretical framework of product-market strategies. Also, this study reveals who will lead innovation based on foresight in business.

Companies needing to establish competitive advantage must ensure their products are brought to market quickly, with the quality features sought by potential customers. Concurrent Engineering techniques can reduce time scales, but quality issues are more difficult, as customers’ needs are not readily available to the project team throughout the design process, so design effort may focus on satisfying a functional specification, imperfectly translated from customer requirements, rather than on satisfying the real customer needs as perceived in the market. The research reported here demonstrates how a Concurrent Engineering environment and Quality Function Deployment techniques can be brought together to provide an extended design team with valuable, shared information throughout the design process.

Expert systems technology as an area of artificial intelligence is coming to the field of structural mechanics. A number of expert systems have been developed or are under development. This paper consists of two parts. A brief discussion of the basics of expert systems and their concepts is given in the first part. The second part reviews the prototype of expert systems developed as an aid for finite element analysis and design optimization. Twelve different expert systems are described. A partial list of books on expert systems in general is given in the Appendix.

Most alcohol education programs are designed by experts, with the target audience largely excluded from this process. Theoretically, application of co-creation which comprises co-design and co-production offers an opportunity to better orient programs to meet audience needs and wants and thereby enhance program outcomes. To date, research has centered on value co-creation, with content co-design receiving limited research attention. This study aims to understand how a target audience would design an intervention and continues by contrasting an audience-designed program with the earlier implemented expert-designed program.

Six co-design sessions were conducted with 58 Year 10 adolescents, aged between 14-16 years old, who had participated in Game On: Know Alcohol, a program developed by experts to address binge drinking. The data were content analyzed.

Analysis revealed that a co-designed program would differ substantially from the expert-driven Game On: Know Alcohol program recently trialed. The results indicate that adolescents prefer interactive activities that engage and challenge. Three alternative program solutions, catering to identified segments in the target audience, are suggested for future implementation and evaluation.

This sample is limited to adolescents from Catholic schools in one state of Australia, and future research is recommended to extend findings beyond this group. This study is limited to establishment of audience (adolescent) preferences, and future experimental field research is needed to develop, implement and evaluate a co-designed program.

This study details a co-design process highlighting differences between expert-designed and audience-designed programs. Future research investigating whether a co-designed program will deliver superior outcomes to an expert-designed program is recommended.

Creativity is an important skill for design teams to reach new and useful solutions. Designers often use one or more of creativity and innovation techniques (CITs) to achieve the desired creative potential during new product development (NPD). The selection of adequate CITs requires considerable expertise, given the multiple application contexts and the extensive number of techniques available. The purpose of this study is to present a creativity support system able to manage this amount of information and provide valuable knowledge to improve NPD.

This study presents a knowledge-based system prototype using artificial intelligence (AI) to support knowledge management on the selection of CITs for design. CITs assertion is modelled through a double inference process using five categories, correlating over 500 different entry scenarios to 24 implemented CITs. The techniques are classified according to: design stage, innovation focus, team relationship, execution method and difficult of use. Prototype outputs explanations on the inference process and chosen techniques information.

To demonstrate the system scope, two opposite design cases are presented. The system was validated by experts in knowledge management and mechanical engineering design. The validation process demonstrates relevance of the approach and improvement directions for future developments.

Though literature contains toolkits and taxonomy for CITs, no work applies AI to identify design scenarios, select best CITs and instruct about their use. Validators reported to know less than half of the available techniques, showing a clear knowledge gap among design experts.

The purpose of this paper is twofold: to analyze the computational complexity of the cogeneration design problem; to present an expert system to solve the proposed problem, comparing such an approach with the traditional searching methods available.

The complexity of the cogeneration problem is analyzed through the transformation of the well-known knapsack problem. Both problems are formulated as decision problems and it is proven that the cogeneration problem is np-complete. Thus, several searching approaches, such as population heuristics and dynamic programming, could be used to solve the problem. Alternatively, a knowledge-based approach is proposed by presenting an expert system and its knowledge representation scheme.

The expert system is executed considering two case-studies. First, a cogeneration plant should meet power, steam, chilled water and hot water demands. The expert system presented two different solutions based on high complexity thermodynamic cycles. In the second case-study the plant should meet just power and steam demands. The system presents three different solutions, and one of them was never considered before by our consultant expert.

The expert system approach is not a “blind” method, i.e. it generates solutions based on actual engineering knowledge instead of the searching strategies from traditional methods. It means that the system is able to explain its choices, making available the design rationale for each solution. This is the main advantage of the expert system approach over the traditional search methods. On the other hand, the expert system quite likely does not provide an actual optimal solution. All it can provide is one or more acceptable solutions.

The most common reaction to an acute physical illness is anxiety, which may be followed by depression. In patients with chronic diseases, the prevalence of anxiety disorders and depression is almost twice as high as in other diseases. This study aims to extract prominent components in the design of treatment spaces on reducing hospitalized patients’ depression from both experts and patients/users’ point of views. A final model is also presented based on the findings.

This research used an exploratory mixed method. The effective components were extracted through the administration of two Likert-scale researcher-made questionnaires in two phases. Q factor analysis was conducted to reach the components. A total of 205 patients were admitted to Namazi Hospital in Shiraz, and 20 architecture and psychology experts participated in the survey. Final modeling of the data was done through path analysis.

Six factors were found to be effective by experts in reducing depression in therapeutic spaces: nature-oriented space, targeted social space, diverse space, visual comfort, logical process and safe space. On the part of patients, seven components were deemed to be effective: visual perception, naturalism, functionalism, physical security, logical process, psychological safety and diversity. Also, four main cycles were extracted from the final model with the direct effect of diversity and the other five cycles were mediated by naturalism.

A total of 15 interviews with architects and psychologists, who were available at the time of the study, were conducted in January 2018. The only general question during interviews was “In your opinion, what factors are effective in reducing the level of depression of patients in the design of treatment spaces?” This may have limited the range of factors that could be surveyed in the study. After collecting the effective factors from the aforementioned expert’s points of view, the questionnaire of experts was designed (Appendix). The expert questionnaires were distributed and edited in two stages in January 2019 among 20 architect experts who were available at the time of the study. The one-year interval between designing and administering the questionnaires occurred because of the limitations posed by the COVID-19 pandemic situation. However, the interval did not pose methodological obstacles for the study.

Evidence-based investigation of the effectiveness of proper design components of therapeutic spaces in reducing the symptoms of patients with chronic secondary depression has received little attention in the literature. Using a “conceptual model,” the present study brought the issue into its focus so as to find effective components in the design of treatment spaces that can alleviate depression symptoms in chronically hospitalized patients.

The purpose of this paper is to describe an innovative information and decision support tool (ToolSHeD™) developed to help construction designers to integrate the management of OHS risk into the design process. The underlying structure of the prototype web‐based system and the process of knowledge acquisition and modelling are described.

The ToolSHeD™ research and development project involved the capture of expert reasoning regarding design impacts upon occupational health and safety (OHS) risk. This knowledge was structured using an innovative method well‐suited to modelling knowledge in the context of uncertainty and discretionary decision‐making. Example “argument trees” are presented, representing the reasoning used by a panel of experts to assess the risk of falling from height during roof maintenance work. The advantage of using this method for modelling OHS knowledge, compared to the use of simplistic rules, is discussed

The ToolSHeD™ prototype development and testing reveals that argument trees can represent design safety risk knowledge effectively.

The translation of argument trees into a web‐based decision support tool is described and the potential impact of this tool in providing construction designers (architects and engineers) with easy and inexpensive access to expert OHS knowledge is discussed.

The paper describes a new computer application, currently undergoing testing in the Australian building and construction industry. Its originality lies in the fact that ToolSHeD™ deploys argument trees to represent expert OHS reasoning, overcoming inherent limitations in rule‐based expert systems.