Computational BIM for Building Envelope Sustainability Optimization

Building envelope plays an important role to protect a building from external climatic factors while providing a comfortable indoor environment. However, the choices of construction materials, opening sizes, and glazing types for optimized sustainability performance require discrete analyses and decision-making processes. Thereby this study explores the use of computational building information modelling (BIM) to automate the process of design decision-making for building envelope sustainability optimization. A BIM tool (Revit), a visual programming tool (Dynamo) and multi objective optimization algorithm were integrated to create a computational BIM-based optimization model for building envelope overall thermal transfer value (OTTV) and construction cost. The proposed model was validated through a test case; the results showed that the optimized design achieved 44.78% reduction in OTTV but 19.64% increment in construction cost compared to the original design. The newly developed computational BIM optimization model can improve the level of automation in design process for sustainability

Incisor malocclusion using cut-out method and convolutional neural network

Malocclusion is a condition of misaligned teeth or irregular occlusion of the upper and lower jaws. This condition leads to poor performance of vital functions such as chewing. A common procedure in orthodontic treatment for malocclusion is a conventional diagnostic procedure where a dental health professional takes dental x-rays to examine the teeth to diagnose malocclusion. However, the manual orthodontic diagnostic procedure by dental experts to identify malocclusion is time-consuming and vulnerable to expert bias that results in delayed treatment completion time. Recently, artificial intelligence technology in image processing has gained attention in orthodontics treatment, accelerating the diagnosis and treatment process. However, several issues concerning the dental images as input of the classification model may affect the accuracy of the classification. In addition, unstructured images with varying sizes and the problem of a machine learning algorithm that does not focus on the region of interest (ROI) for incisor features bring challenges in delivering the treatment. This study has developed a malocclusion classification model using the cut-out method and Convolutional Neural Network (CNN). The cut-out method restructures the input images by standardising the sizes and highlighting the incisor sections of the images which assisted the CNN in accurately classifying the malocclusion. From the results, the implementation of the cut-out method generates higher accuracy across all classes of malocclusion compared to the non-implementation of the cut-out method

Computational BIM for Building Envelope Sustainability Optimization

Building envelope plays an important role to protect a building from external climatic factors while providing a comfortable indoor environment. However, the choices of construction materials, opening sizes, and glazing types for optimized sustainability performance require discrete analyses and decision-making processes. Thereby this study explores the use of computational building information modelling (BIM) to automate the process of design decision-making for building envelope sustainability optimization. A BIM tool (Revit), a visual programming tool (Dynamo) and multi objective optimization algorithm were integrated to create a computational BIM-based optimization model for building envelope overall thermal transfer value (OTTV) and construction cost. The proposed model was validated through a test case; the results showed that the optimized design achieved 44.78% reduction in OTTV but 19.64% increment in construction cost compared to the original design. The newly developed computational BIM optimization model can improve the level of automation in design process for sustainability