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An increasing number of clients are requesting green building design due to its potential to provide high-sustainability performance, monetary savings and health benefits to occupants. However, the practice of designing green building projects to meet overarching sustainability criteria is complex, with issues of poor information exchange synthesis. The building information modelling (BIM) process was created to ensure that the cumbersome green building data is exchanged accurately and in a coordinated manner. However, the implementation of the BIM process in green building design practices remains underexplored in the literature and industry practice. Therefore, this study aims to identify the best practices in BIM process implementation in the early stages of green building design. A quantitative research method was adopted; a questionnaire was used to survey 180 architects working in various construction firms in Malaysia. The questionnaire data was analysed using factor analysis to narrow down the long list of factors (best practices) into a small number of components. The results highlighted the best approaches in BIM process implementation in green building design practices: (1) Selection of a well-trained and competent design team, (2) Use of software with high interoperability to ensure exchange of accurate information, (3) Development of a standard method for BIM process implementation in green building design and (4) Timely identification of critical decision points. The research outcome will enlighten construction professionals on the best practices in implementing the BIM process in green building design, thereby allowing them to deliver building projects with high-sustainability performance. [ABSTRACT FROM AUTHOR]

Optimal designs of stormwater systems rely very much on the rainfall Intensity-Duration-Frequency (IDF) curves. As climate has shown significant changes in rainfall characteristics in many regions, the adequacy of the existing IDF curves is called for particularly when the rainfall are much more intense. For data sparse sites/regions, developing IDF curves for the future climate is even challenging. The current practice for such regions is, for example, to 'borrow' or 'interpolate' data from regions of climatologically similar characteristics. A novel (3-step) Downscaling-Comparison-Derivation (DCD) approach was presented in the earlier study to derive IDF curves for present climate using the extracted Dynamically Downscaled data an ungauged site, Darmaga Station in Java Island, Indonesia and the approach works extremely well. In this study, a well validated (3-step) DCD approach was applied to develop present-day IDF curves at stations with short or no rainfall record. This paper presents a new approach in which data are extracted from a high spatial resolution Regional Climate Model (RCM; 30 × 30 km over the study domain) driven by Reanalysis data. A site in Java, Indonesia, is selected to demonstrate the application of this approach. Extremes from projected rainfall (6-hourly results; ERA40 Reanalysis) are first used to derive IDF curves for three sites (meteorological stations) where IDF curves exist; biases observed resulting from these sites are captured and serve as very useful information in the derivation of present-day IDF curves for sites with short or no rainfall record. The final product of the present-day climate-derived IDF curves fall within a specific range, +38% to +45%. This range allows designers to decide on a value within the lower and upper bounds, normally subjected to engineering, economic, social and environmental concerns. Deriving future IDF curves for Stations with existing IDF curves and ungauged sites with simulation data from RCM driven by global climate model (GCM ECHAM5) (6-hourly results; A2 emission scenario) have also been presented. The proposed approach can be extended to other emission scenarios so that a bandwidth of uncertainties can be assessed to create appropriate and effective adaptation strategies/measures to address climate change and its impacts. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]