Developing a vision for an nD modelling tool

The design of a building is a complicated process, having to formulate diverse components through unique tasks involving different personalities and organisations in order to satisfy multi-faceted client requirements. To do this successfully, the project team must encapsulate an integrated design that accommodates various social, economic and legislative factors. Therefore, in this era of increasing global competition integrated design has been increasingly recognised as a solution to deliver value to clients.----- The ‘From 3D to nD modelling’ project at the University of Salford aims to support integrated design; to enable and equip the design and construction industry with a tool that allows users to create, share, contemplate and apply knowledge from multiple perspectives of user requirements (accessibility, maintainability, sustainability, acoustics, crime, energy simulation, scheduling, costing etc.). Thus taking the concept of 3-dimensional computer modelling of the built environment to an almost infinite number of dimensions, to cope with whole-life construction and asset management issues in the design of modern buildings. This paper reports on the development of a vision for how integrated environments that will allow nD-enabled construction and asset management to be undertaken. The project is funded by a four-year platform grant from the Engineering and Physical Sciences Research Council (EPSRC) in the UK; thus awarded to a multi-disciplinary research team, to enable flexibility in the research strategy and to produce leading innovation. This paper reports on the development of a business process and IT vision for how integrated environments will allow nD-enabled construction and asset management to be undertaken. It further develops many of the key issues of a future vision arising from previous CIB W78 conferences.

The STRATIFY tool and clinical judgment were poor predictors of falling in an acute hospital setting

Objective: To compare the effectiveness of the STRATIFY falls tool with nurses’ clinical judgments in predicting patient falls. Study Design and Setting: A prospective cohort study was conducted among the inpatients of an acute tertiary hospital. Participants were patients over 65 years of age admitted to any hospital unit. Sensitivity, specificity, and positive predictive value (PPV) and negative predictive values (NPV) of the instrument and nurses’ clinical judgments in predicting falls were calculated. Results: Seven hundred and eighty-eight patients were screened and followed up during the study period. The fall prevalence was 9.2%. Of the 335 patients classified as being ‘‘at risk’’ for falling using the STRATIFY tool, 59 (17.6%) did sustain a fall (sensitivity50.82, specificity50.61, PPV50.18, NPV50.97). Nurses judged that 501 patients were at risk of falling and, of these, 60 (12.0%) fell (sensitivity50.84, specificity50.38, PPV50.12, NPV50.96). The STRATIFY tool correctly identified significantly more patients as either fallers or nonfallers than the nurses (P50.027). Conclusion: Considering the poor specificity and high rates of false-positive results for both the STRATIFY tool and nurses’ clinical judgments, we conclude that neither of these approaches are useful for screening of falls in acute hospital settings.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.