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Abstract: Using the “tipping point” as a metaphor, the paper argues that the construction industry is ready to reap the benefits of using information technologies. Construction IT as an appropriating discipline is enabling us to have devices to speculate about designs, to have innovative modes of collaboration, communication and practice. “Virtuality” is the password for construction professionals to cross over into the experimental realms of installation and operation. The paper identifies the ingredients needed to ‘tip’ the balance for an accelerated penetration of information technologies into the construction industry. Core technologies are described as the prime mover of radical transformation in the construction industry. [Copyright &y& Elsevier]

Abstract: Ontologies are the foundation on which knowledge-intensive problem solvers depend. Ontologies encapsulate domain concepts, tasks, problem-solving knowledge, and methods. This paper reports on a research project on ontology development for constructability assessment in steel frame structures. It discusses the conceptualisation and formal knowledge representation for the problem domain. The resulting knowledge structures facilitate reasoning in intelligent systems. The paper uses a case study to demonstrate the potential applications of ontologies in developing intelligent rule-based agents for constructability assessment, and reports on the experiments conducted to teach problem-solving agents. The results indicate great potential in developing high utility ontology-driven agents for use in organisational corporate knowledge management. The paper discusses the findings from the research project. The authors propose a constructability ontology for steel structures and an agent framework for constructability design knowledge acquisition. Recommendations are given for further work in developing ontologies for construction applications. [Copyright &y& Elsevier]

Building information modeling is instrumental in documenting design, enhancing customer experience, and improving product functionality in capital projects. However, good building models do not happen by accident, but rather as a result of a managed process that involves several participants from different disciplines and backgrounds. Effective management of this process requires an ability to closely monitor the modeling process and correctly measure modelers' performance. Nevertheless, existing methods of performance monitoring in building design practices lack an objective measurement system to quantify modeling progress. The widespread utilization of Building Information Modeling (BIM) tools presents a unique opportunity to retrieve granular design process data and conduct accurate performance measurements. As a building's 3D model is gradually developed, model generation software packages, such as Autodesk Revit, automatically create log files that record design activities. This paper investigates what information these log files contain and how one can extract and further analyze the information to provide insight into the design modeling process. The specific objectives of this study were to: (1) investigate the presence of implicit patterns in 3-D design log files; and (2) to empirically characterize the performance of modelers based on the time it takes them to execute similar modeling tasks. To fulfill these objectives, design log files provided by an international architecture and design firm were analyzed. Using a tailored text file parser, user-model interaction data including modeler characteristics, command type, and command time were extracted from the journal files. To identify implicit command execution patterns, a sequence mining algorithm based on Generalized Suffix Trees (GST) was implemented. It was shown that there is a statistically significant difference between the average time it takes modelers to execute each command sequence. This study extends the existing knowledge by proposing a novel methodology to extract meaningful patterns from time-stamped unstructured design log data. This research contributes to the state of practice by providing a better understanding of information embedded in design log files. [ABSTRACT FROM AUTHOR]

Abstract: Due to the nature of construction projects, collection of sufficient data is usually challenging for knowledge discovery in construction databases. Previous researchers had explored the capabilities of various artificial intelligence (AI) techniques for the mining of construction databases including symbolic reasoning techniques (e.g., case based reasoning, CBR), and numeric reasoning techniques (e.g., artificial neural networks, ANN, and neuro-fuzzy system, NFS). Both of the above paradigms own their merits and drawbacks on data mining. This paper proposes a hybridization of both symbolic and numeric reasoning techniques, to form a new data mining technique that can achieve a higher mining accuracy and overcome the restrictions of traditional numeric reasoning techniques on data scarcity problems. The testing results show the proposed hybridization can improve relative system accuracy by 44% (ANN) and 68% (NFS) compared with traditional CBR, or by 33.62% (ANN) and 72.88% (NFS) compared with traditional ANN and NFS, respectively. It provides profound potential for improving performance of traditional AI techniques in data mining for scarce construction databases. [Copyright &y& Elsevier]

Abstract: Collaborative design has become a research paradigm in design studies. To make effective collaborative design, an information service mechanism for helping collaborators to access related information of specific design situation is getting important. This paper presents an approach of applying data mining techniques to reveal information patterns for managing collaborative design information. A visual interface of linking design information based on revealed patterns are presented and issues are discussed. [Copyright &y& Elsevier]