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This paper aims to contribute towards understanding how safety knowledge can be elicited from railway experts for the purposes of supporting effective decision-making.

A consortium of safety experts from across the British railway industry is formed. Collaborative modelling of the knowledge domain is used as an approach to the elicitation of safety knowledge from experts. From this, a series of knowledge models is derived to inform decision-making. This is achieved by using Bayesian networks as a knowledge modelling scheme, underpinning a Safety Prognosis tool to serve meaningful prognostics information and visualise such information to predict safety violations.

Collaborative modelling of safety-critical knowledge is a valid approach to knowledge elicitation and its sharing across the railway industry. This approach overcomes some of the key limitations of existing approaches to knowledge elicitation. Such models become an effective tool for prediction of safety cases by using railway data. This is demonstrated using passenger–train interaction safety data.

This study contributes to practice in two main directions: by documenting an effective approach to knowledge elicitation and knowledge sharing, while also helping the transport industry to understand safety.

By supporting the railway industry in their efforts to understand safety, this research has the potential to benefit railway passengers, staff and communities in general, which is a priority for the transport sector.

This research applies a knowledge elicitation approach to understanding safety based on collaborative modelling, which is a novel approach in the context of transport.

The aim of the autopoietic model of knowledge is to act as a common foundation for KM to overcome the numerous knowledge management failures highlighted by the literature attributed to inaccurate or constantly debated definitions of knowledge. This paper seeks to evaluate such a model.

Participants for this interpretivist evaluation study were selected by convenience sampling. Experts known to the authors were asked to participate, and 12 took part. Face‐to‐face interviews were conducted and lasted between 45 to 60 minutes. Member checking was used during the interviews. The data was analysed using the recursive abstraction method.

The study highlighted the complexities of conducting an expert evaluation of a model that was deemed both too high level and too low level by the experts. The study highlighted the challenge of evaluating a model that is theoretically correct, but required acceptance in the knowledge management discipline. The study also showed that the application of autopoiesis to knowledge management has potential, but is still in its infancy.

The main limitation of this study came from the initial autopoietic model of knowledge: most experts found it too difficult to engage with in the time available. The knowledge management foundation aspired to by applying autopoiesis to the domain is hard to achieve as little value was placed on models by some experts.

The evaluation of the autopoietic model of knowledge presented in this paper represents the first expert evaluation of an autopoietic epistemology. The study is an incremental step towards providing a sound conceptual foundation for knowledge management.

The objective of this paper is to propose a holistic dynamic model for understanding the behavior of a complex and internet‐based kind of knowledge market by considering both social and economic interactions.

A system dynamics (SD) model is formulated in this study to investigate the dynamic characteristics of complex interactions in a fee‐based online question & answer (Q&A) knowledge market. The proposed model considers the dynamic, non‐linear, asymmetric, and reciprocal relationships between its components, and allows the study of the evolution of the market under assumed conditions.

Some illustrative results show that: this market is very sensitive to the prices that the customers choose; low‐priced questions are as important as high‐priced ones; gradually increasing experts' proportion of a question's price reduces customer satisfaction and experts' reputation; and training programs for experts result in higher customer satisfaction and researchers' reputation. Furthermore, three types of customers are identified and discussed.

This model can be used to change, manage, and control this market and also helps to design new similar markets. In addition, the proposed model helps to observe the behavior of a market under one or more policies before applying to the real world.

Since GA was shut down in 2006, the implications of this research serve as a strategic tool (strategic evaluation software) for understanding and examining the effects of policies for many existing similar Q&A business models. Furthermore, the SD approach can provide new insights into the field of online Q&A knowledge markets and overcome traditional econometric treatment of data for understanding the dynamic behavior of these markets.

Understanding the complex social and economic behavior of Q&A markets is one of the most important concerns for academics and practitioners in the areas of online markets' management. The paper shows how SD can provide attractive insights into the field of online fee‐based knowledge markets based on a qualitative and quantitative modeling. However, the background literature lacks a holistic view of these kinds of markets.

The bid/no-bid decision is critical to the success of construction contractors. The factors affecting the bid/no-bid decision are either qualitative or quantitative. Previous studies on modeling the bidding decision have not extensively focused on distinguishing qualitative and quantitative factors. Thus, the purpose of this paper is to improve the bidding decision in construction projects by developing tools that consider both qualitative and quantitative factors affecting the bidding decision.

This study proposes a mixed qualitative-quantitative approach to deal with both qualitative and quantitative factors. The mixed qualitative-quantitative approach is developed by combining a rule-based expert system and fuzzy-based expert system. The rule-based expert system is used to evaluate the project based on qualitative factors and the fuzzy expert system is used to evaluate the project based on the quantitative factors in order to reach the comprehensive bid/no-bid decision.

Three real bidding projects are used to investigate the applicability and functionality of the proposed mixed approach and are tested with experts of a construction company in Alberta, Canada. The results demonstrate that the mixed approach provides a more reliable, accurate and practical tool that can assist decision-makers involved in the bid/no-bid decision.

This study contributes theoretically to the body of knowledge by (1) proposing a novel approach capable of modeling all types of factors (either qualitative or quantitative) affecting the bidding decision, and (2) providing means to acquire, store and reuse expert knowledge. Practical contribution of this paper is to provide decision-makers with a comprehensive model that mimics the decision-making process and stores experts' knowledge in the form of rules. Therefore, the model reduces the administrative burden on the decision-makers, saves time and effort and reduces bias and human errors during the bidding process.

Today the garment industry in México is vulnerable to complex problems. This type of industry is subject to influences which over time, modify the perceptions of those involved in the design process due to the fact that they face problems that have both objective and subjective characteristics. In this study the authors used interviews, direct observation methodology, and theoretical argumentation to obtain the experts knowledge as they describe the problems that arise in the process of garment design for Mexican markets. The purpose of this paper is to generate a methodology so that the expert in this field will become highly specialized, resulting in heightened abilities and reinforcing them with the methodology of soft systems and the design management model (DMM). The results suggest that they are applicable to any area of design.

The success or failure of expert system (ES) depends directly on the acquisition of knowledge (Méndez-Gurrola, 2007, 2012), to accomplish that, three large groups of techniques are used to extract that knowledge: manual, semiautomatic, and automatic. Within the group of manual techniques, interviews, protocol analysis, questionnaires, direct on site observation, and the extraction of closed curves are emphasized. This technique is chosen for its ability to extract the particular type of knowledge being sought. The knowledge of the dominion expert in the design process applied to the clothing industry in México is based on processes and at the same time is episodic, meaning that part of the procedure is automated and each step of the process triggers the next. This knowledge is also based on experience which is of an autobiographical nature.

First, when one simulates human behavior, the hardest thing is to choose a knowledge representation that conforms as closely as possible to its emulation. According to, choosing a given type of knowledge representation is an art that is discovered little by little. And this is true as one designs and assembles a representation, when one realizes how far the authors are from imitating the design of the human brain processes, and discovers or invents methodologies to achieve it, combining the results of investigations into knowledge representation, cognitive psychology, and cognitive engineering. Second, solving any complex situation in the design process function of the clothing industry is no easy task. It requires plenty of experience in the manufacturing process. One needs the ability to identify the signals emitted by complex situations, and being able to stop them in time before they create irreversible damage. By merging the soft system methodology (SSM) and DMM with the experts’ abilities and knowledge (the result being the EXITUS model (EM)), makes knowledge modeling possible. A problem cannot be solved if it is unknown, if the problem persists and grows it becomes more complex. By describing a problem, based on: its origin, its relationship, and its effects, it also confers the ability to solve it. Thereby, an SBC with the characteristics presented in this paper, not only improves the design process function as a whole, it also contributes to achieving corporate success. Finally, it influences directly on: a quality product; market positioning; and good economic results. First, the SBC-EXITUS was tested and endorsed by expert management designers. When a designer identifies a complex problem using the SBC-EXITUS system, he is capable of verifying its existence with facts and real life situations. This enables quicker decision making decisions, which saves time and money, due to the fact that a non-desirable state of affairs may be contemplated in advance. Fourth, in this project an SBC named SBC-EXITUS has been implemented using the SSM and the DMM, with the purpose of detecting possible problems in the design process of the clothing industry. Its implementation is developed by the use of production rules. Fifth, utilizing the methodology and the production rules like knowledge representation technique, make possible to acquire dominion knowledge in complex problems as in the study case clothing industry in México. This approach is also applicable to other areas of design.

Utilizing the methodology and the production rules like knowledge representation technique, make possible to acquire dominion knowledge in complex problems as in the study case clothing industry in México. This approach is also applicable to the garment industry in the international context, as well as to other areas of design such as architecture, furniture, and others. The EM is a generic methodology. In this research and case, it has been applied in a design process within the garment industry, specifically in a case in Mexico. Yet this does not limit its use in a different context and problem situation. Having in mind the difference between countries in aspects such as sponsoring, technology, worker skills, marketing, etc. the tools for diagnostics in the Appendix 2, may be adapted to each context with enriched questions directed to specific aspects involved. Every new application allows facts and production rules that make the use of ES more efficient.

The ES-EXITUS was tested and endorsed by expert management designers. When a designer identifies a complex problem using the ES-EXITUS, he is capable of verifying its existence with facts and real life situations. This enables quicker decision making decisions, which saves time and money, due to the fact that a non-desirable state of affairs may be contemplated in advance.

Solving any complex situation in the design process function of the clothing industry is no easy task. It requires plenty of experience in the manufacturing process. One needs the ability to identify the signals emitted by complex situations, and being able to stop them in time before they create irreversible damage. By merging the SSM and DMM with the experts’ abilities and knowledge (the result being the EM), makes knowledge modeling possible. A problem cannot be solved if it is unknown, if the problem persists and grows it becomes more complex. By describing a problem, based on: its origin, its relationship and its effects, it also confers the ability to solve it. Thereby, an SBC with the characteristics presented in this paper, not only improves the design process function as a whole, it also contributes to achieving corporate success. Finally, it influences directly on a quality product, market positioning, and good economic results.

A wearable expert system (WES) is an expert system designed and implemented to obtain input from and give outputs to wearable devices. Among its distinguishing features are the direct cooperation between domain experts and users, and the interaction with a knowledge maintenance system devoted to dynamically update the knowledge base taking care of the evolving scenario. The paper aims to discuss these issues.

The WES development method is based on the Knowledge Acquisition Framework based on Knowledge Artifact (KAFKA) framework. KAFKA employs multiple knowledge artifacts, each devoted to the acquisition and management of a specific kind of knowledge. The KAFKA framework is introduced from both the conceptual and computational points of view. An example is given which demonstrates the interaction, within this framework, of taxonomies, Bayesian networks and rule-based systems. An experimental assessment of the framework usability is also given.

The most interesting characteristic of WESs is their capability to evolve over time, due both to the measurement of new values for input variables and to the detection of new input events, that can be used to modify, extend and maintain knowledge bases and to represent domains characterized by variability over time.

WES is a new and challenging concept, dealing with the possibility for a user to develop his/her own decision support systems and update them according to new events when they arise from the environment. The system fully supports domain experts and users with no particular skills in knowledge engineering methodologies, to create, maintain and exploit their expert systems, everywhere and when necessary.

Many instructors have implemented decision-based learning (DBL) into their courses. This chapter is a careful qualitative analysis of the narratives in this book done by the editors. The author found common themes among all the narratives. The first theme was that many instructors discovered that they were missing conditional knowledge in their instruction. Second, the author found common issues around the complexity of designing an expert decision model (EDM). Included in this theme are stories about selecting problems and organizing the EDM, building the EDM around specific course learning outcomes, providing just-enough, just-in-time instruction, and introducing the decision model and software to students. Instructors also discovered that assessing the learning of students needed to go beyond traditional goals and began to include new goals related to conditional knowledge. Finally, the author describes the comments made by both faculty and students about the experience of using DBL. Several authors described the value of using DBL in the process of taking students from novice thinkers to expert thinkers. Many students expressed that they enjoyed the process that DBL presented to them and that they had a new level of confidence to be able to approach problems in the content area. Summaries and quotes from the chapters in this book are referenced by the authors’ names and the content areas they were teaching.

Selection of knowledge management strategies (KMS) is one of the most important and effective factors in acquiring the competitive advantage and elevating the knowledge level of the organizations. Those organizations that have taken steps toward knowledge management necessarily need to pay utmost attention to the matter of KMS before taking any further steps in their activities. One of the effective ways in adopting the proper KMS is evaluating the knowledge management maturity level in the organization. The purpose of this paper is to design an expert fuzzy system to adopt the KMS based on Bloodgood model in accordance with the maturity level of the organization.

In this method, with the help of expert fuzzy system, a model has been designed, by using MATLAB software, to adopt the KMS. The KM maturity level, tacit knowledge and explicit knowledge are chosen as inputs, and each one of Bloodgood’s KMS (production, transfer and protecting the knowledge) are chosen as outputs. To perform the system, the maturity level of knowledge management of an industrial organization that has been evaluated by the standard Asian Productivity Organization questionnaire is used as the input, which has been given to expert fuzzy system. Then, considering the output of the system, KMS for the organization have been recommended.

Knowledge management maturity level of the organization is on Level 4; considering the expert fuzzy system that has been designed, “knowledge production” strategy is recommended for the organization under study.

An expert fuzzy system has been designed regarding the maturity of knowledge management and Bloodgood model that can be used as a guide for organizations and academic people as an appropriate practical model for selecting knowledge management strategies.

Bayesian approaches have been widely applied in construction management (CM) research due to their capacity to deal with uncertain and complicated problems. However, to date, there has been no systematic review of applications of Bayesian approaches in existing CM studies. This paper systematically reviews applications of Bayesian approaches in CM research and provides insights into potential benefits of this technique for driving innovation and productivity in the construction industry.

A total of 148 articles were retrieved for systematic review through two literature selection rounds.

Bayesian approaches have been widely applied to safety management and risk management. The Bayesian network (BN) was the most frequently employed Bayesian method. Elicitation from expert knowledge and case studies were the primary methods for BN development and validation, respectively. Prediction was the most popular type of reasoning with BNs. Research limitations in existing studies mainly related to not fully realizing the potential of Bayesian approaches in CM functional areas, over-reliance on expert knowledge for BN model development and lacking guides on BN model validation, together with pertinent recommendations for future research.

This systematic review contributes to providing a comprehensive understanding of the application of Bayesian approaches in CM research and highlights implications for future research and practice.

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.