Towards the development of a project decision support framework for adoption of an integrated building information model using a model server

This chapter discusses an action research study towards the development of a decision framework to support a fully integrated multi disciplinary Building Information Model (BIM) using a Model Server. The framework was proposed to facilitate multi disciplinary collaborative BIM adoption through, informed selection of a project specific BIM approach and tools contingent upon project collaborators’ readiness, tool capabilities and workflow dependencies. The aim of the research was to explore the technical concerns in relation to Model Servers to support multi disciplinary model integration and collaboration; however it became clear that there were both technical and non technical issues that needed consideration. The evidence also suggests that there are varying levels of adoption which impacts upon further diffusion of the technologies. Therefore the need for a decision framework was identified based on the findings from an exploratory study conducted to investigate industry expectations. The study revealed that even the market leaders who are early technology adopters in the Australian industry in many cases have varying degrees of practical experiential knowledge of BIM and hence at times low levels of confidence of the future diffusion of BIM technology throughout the industry. The study did not focus on the benefits of BIM implementation as this was not the intention, as the industry partners involved are market leaders and early adopters of the technology and did not need convincing of the benefits. Coupled with this there are various other past studies that have contributed to the ‘benefits’ debate. There were numerous factors affecting BIM adoption which were grouped in to two main areas; technical tool functional requirements and needs, and non technical strategic issues. The need for guidance on where to start, what tools were available and how to work through the legal, procurement and cultural challenges was evidenced in the exploratory study. Therefore a BIM decision framework was initiated, based upon these industry concerns. Eight case studies informed the development of the framework and a summary of the key findings is presented. Primary and secondary case studies from firms that have adopted a structured approach to technology adoption are presented. The Framework consists of four interrelated key elements including a strategic purpose and scoping matrix, work process mapping, technical requirements for BIM tools and Model Servers, and framework implementation guide. The BIM framework was presented in draft format again to key industry stakeholders and considered in comparison with current best practice BIM adoption to further validate the framework. There was no request to change any part of the Framework. However, it is an ongoing process and it will be presented again to industry through the various project partners. The Framework may be refined within the boundaries of the action research process as an ongoing activity as more experiential knowledge can be incorporated.

The language of design resolution in a digital building information model

Information in construction industry is delivered and interpreted in a language specific to the industry in which large complex objects are only partially described and with much information being implicit in the language used. Successful communication therefore relies on participants in the industry leaming how to interpret the language through many years of education, training and experience. With the introduction of computer technology, and in particular the detailed digital building information model (DB 1M), the accepted language currently in use is no longer a valid method of describing the building. At all stages in the paper based design and documentation process it is generally readily apparent which parts of the design require further completion and which are fully resolved. This is able to be achieved through the complex graphical language currently in use. In the DBIM, all information appears at the same level of resolution making difficult the interpretation of implicit information embedded in the model. This compromises the collaborative design environment which is being described as a fundamental characteristic of the future construction industry. This paper focuses on two areas. The first analyses design resolution and the role uncertain information plays in the design process. It then discusses the manner in which designers and the industry in general deal with incomplete or unresolved information. The second describes a theoretical model in which a design resolution (DR) environment incorporates the level of design resolution as an operable element in a collaborative DBIM. The development and implementation of this model will allow designers to better share, understand and interpret design knowledge from the shared information during the various stages of digital design and before full resolution is achieved.

Multi-scale rheological model for discontinuous phenomena in materials under deformation conditions

A study of possibilities given by the developed Cellular Automata–Finite Element (CAFE) multi-scale model for prediction of the initiation and propagation of micro-shear bands and shear bands in metallic materials subjected to plastic deformation is described in the paper. Particular emphasis in defining the criterion for initiation of micro-shear and shear bands, as well as in defining the transition rules for the cellular automata, is put on accounting for the physical aspects of those phenomena occurring in two different scales in the material. The proposed approach led to the creation of the real multi-scale model of strain localization. This model predicts material behavior in various thermo-mechanical processes. Selected examples of applications of the developed model to simulations of metal forming processes, which involve strain localization, are presented in the paper. An approach based on the Smoothed Particle Hydrodynamic, which allows to overcome difficulties with remeshing in the traditional CAFE method, is presented in the paper as well. In this approach remeshing becomes possible and mesh distortion, which limits application of the CAFE method to simple deformation processes, is eliminated.

Development of the multi-scale analysis model to simulate strain localization occurring during material processing

Abstract A detailed description of possibilities given by the developed Cellular Automata—Finite Element (CAFE) multi scale model for prediction of the initiation and propagation of micro shear bands and shear bands in metallic materials subjected to plastic deformation is presented in the work. Particular emphasis in defining the criterion for initiation of micro shear and shear bands, as well as in defining the transition rules for the cellular automata, is put on accounting for the physical aspects of these phenomena occurring in two different scales in the material. The proposed approach led to the creation of the real multi scale model of strain localization phenomena. This model predicts material behavior in various thermo-mechanical processes. Selected examples of applications of the developed model to simulations of metal forming processes, which involve strain localization, are presented in the work. An approach based on the Smoothed Particle Hydrodynamic, which allows to overcome difficulties with remeshing in the traditional CAFE method, is a subject of this work as well. In the developed model remeshing becomes possible and difficulties limiting application of the CAFE method to simple deformation processes are solved. Obtained results of numerical simulaA detailed description of possibilities given by the developed Cellular Automata—Finite Element (CAFE) multi scale model for prediction of the initiation and propagation of micro shear bands and shear bands in metallic materials subjected to plastic deformation is presented in the work. Particular emphasis in defining the criterion for initiation of micro shear and shear bands, as well as in defining the transition rules for the cellular automata, is put on accounting for the physical aspects of these phenomena occurring in two different scales in the material. The proposed approach led to the creation of the real multi scale model of strain localization phenomena. This model predicts material behavior in various thermo-mechanical processes. Selected examples of applications of the developed model to simulations of metal forming processes, which involve strain localization, are presented in the work. An approach based on the Smoothed Particle Hydrodynamic, which allows to overcome difficulties with remeshing in the traditional CAFE method, is a subject of this work as well. In the developed model remeshing becomes possible and difficulties limiting application of the CAFE method to simple deformation processes are solved. Obtained results of numerical simulations are compared with the experimental results of cold rolling process to show good predicative capabilities of the developed model.tions are compared with the experimental results of cold rolling process to show good predicative capabilities of the developed model.

A multi-component assessment model for monitoring training distress among athletes

We report the initial validation of a multi-component assessment model for monitoring training distress among athletes. The model combines measures of mood disturbance with measures of perceived stress and symptom intensity. Altogether, 492 athletes completed the 10-item Perceived Stress Scale (PSS-10; Cohen et al., 1983), the 24-item Brunel Mood Scale (BRUMS; Terry et al., 2003), and a checklist of 19 symptoms associated with acute overtraining (Fry et al., 1994). Six training distress factors were identified by an exploratory factor analysis: ‘‘depressed mood’’, ‘‘perceived vigour’’, ‘‘physical symptoms’’, ‘‘sleep disturbance’’, ‘‘perceived stress’’, and ‘‘general fatigue’’. Comparisons of group means of these factors with a previously validated inventory were consistent with theoretical predictions and provided evidence of construct validity. Internal consistency of the subscales was also confirmed, with Cronbach alphas ranging from 0.72 to 0.86. Together, these findings suggest that this multi-component model provides a sound conceptual basis for the assessment of training distress among athletes.

Multi-agent model for robotic assembly system

In this paper, a multi-agent based model for a robotic assembly system is presented. Firstly, an organization model is used to construct the multi-agent model. Secondly, a dynamic self-organizing method is then put forward for the multi-agent robotic system to bid and contract the operations. Thirdly, a real multi-agent robotic system is built and assembly experiments are carried out. Finally, the experimental results confirm that the present multi-agent robotic system has flexibility, adaptation and stability.

Using information flow model to specify the Kanban-controlled shopfloor control system

The implementation of Kanban-based production control systems may be difficult in make-to-order environments such as job shops. The flexible manufacturing approach constitutes a promising solution to adapt the Kanban method to such environments. This paper presents an information flow modelling approach for specifying the operational planning and control functions of the Kanban-controlled shopfloor control system (KSCS) in a flexible manufacturing environment. By decomposing the KSCS control functionalities, we have created the system information flow model through the data flow diagrams of Structured Systems Analysis Methodology. The data flow diagrams serve effective system specifications for communicating the system operations to participants of different disciplines as well as the system model for the design and development of KSCS.

A maturity model of enterprise business intelligence

The implementation of an enterprise-level business intelligence initiative is a large-scale and complex undertaking, involving significant expenditure and multiple stakeholders over a lengthy period. It is therefore imperative to have systematic guidelines for business intelligence stakeholders in referring business intelligence maturity levels. Draw upon the prudent concepts of the Capability Maturity Model, this research proposes a multi-dimensional maturity model with distinct maturity levels for managing enterprise business intelligence initiatives. The maturity model, named Enterprise Business Intelligence Maturiy (EBIM), consists of five core maturity levels and four key dimensions, namely information quality, master data management, warehousing architecture, and analytics. It can be used to assist enterprises in benchmarking their business intelligence maturity level and identifying the critical areas to attain higher level of maturity.

Collaborating differently on different topics: a multi-relational approach to multi-task learning

Multi-task learning offers a way to benefit from synergy of multiple related prediction tasks via their joint modeling. Current multi-task techniques model related tasks jointly, assuming that the tasks share the same relationship across features uniformly. This assumption is seldom true as tasks may be related across some features but not others. Addressing this problem, we propose a new multi-task learning model that learns separate task relationships along different features. This added flexibility allows our model to have a finer and differential level of control in joint modeling of tasks along different features. We formulate the model as an optimization problem and provide an efficient, iterative solution. We illustrate the behavior of the proposed model using a synthetic dataset where we induce varied feature-dependent task relationships: positive relationship, negative relationship, no relationship. Using four real datasets, we evaluate the effectiveness of the proposed model for many multi-task regression and classification problems, and demonstrate its superiority over other state-of-the-art multi-task learning models