Building information modelling – A novel parametric modeling approach based on 3D surveys of historic architecture

Building Information Modelling (BIM) appears to be the best answer to simplify the traditional process of design, construction, management and maintenance. On the other hand, the intricate reality of the built heritage and the growing need to represent the actual geometry using 3D models collide with the new paradigms of complexity and accuracy, opening a novel operative perspective for restoration and conservation. The management of complexity through BIM requires a new management approach focused on the development of improve the environmental impact cost, reduction and increase in productivity and efficiency the Architecture, Engineering and Construction (AEC) Industry. This structure is quantifiable in morphological and typical terms by establishing levels of development and detail (LoDs) and changes of direction (ReversLoDs) to support the different stages of life cycle (LCM). Starting from different experiences in the field of HBIM, this research work proposes a dynamic parametric modeling approach that involves the use of laser scanning, photogrammetric data and advanced modelling for HBIM

Blending using ODE swept surfaces with shape control and C1 continuity

Surface blending with tangential continuity is most widely applied in computer aided design, manufacturing systems, and geometric modeling. In this paper, we propose a new blending method to effectively control the shape of blending surfaces, which can also satisfy the blending constraints of tangent continuity exactly. This new blending method is based on the concept of swept surfaces controlled by a vector-valued fourth order ordinary differential equation (ODE). It creates blending surfaces by sweeping a generator along two trimlines and making the generator exactly satisfy the tangential constraints at the trimlines. The shape of blending surfaces is controlled by manipulating the generator with the solution to a vector-valued fourth order ODE. This new blending methods have the following advantages: 1). exact satisfaction of 1C continuous blending boundary constraints, 2). effective shape control of blending surfaces, 3). high computing efficiency due to explicit mathematical representation of blending surfaces, and 4). ability to blend multiple (more than two) primary surfaces

Preserving Design Intent in Feature-Based Parametric CAD Data Exchange

Part 8: Knowledge Management and Information SharingInternational audienceThe proliferation of 3D technology on many different platforms has made CAD data exchange vital to industrial engineering innovation. The need to share and integrate CAD data, information, and knowledge amongst systems involved in product development has become an important requirement. This requirement is largely determined by a proper communication of design intent, which is usually expressed implicitly within CAD models or tacitly as experiential knowledge from domain experts. However, solutions for CAD data exchange are mostly restricted to the exchange of pure static shape information, restricting their applicability in many downstream processes. This paper suggests methods and strategies for preserving design intent in CAD data exchange. A test case is undertaken to demonstrate the idea and as evidenced in the results, the method proposed helps in capturing and preserving design intent

The binomial-neighbour instance-based learner on a multiclass performance measure scheme

This paper presents a novel instance-based learning methodology the Binomial-Neighbour (B-N) algorithm. Unlike to other k-Nearest Neighbour algorithms, B-N employs binomial search through vectors of statistical features and distance primitives. The binomial combinations derived from the search with best classification accuracy are distinct primitives which characterise a pattern. The statistical features employ a twofold role; initially to model the data set in a dimensionality reduction preprocessing, and finally to exploit these attributes to recognise patterns. The paper introduces as well a performance measure scheme for multiclass problems using type error statistics. We harness this scheme to evaluate the B-N model on a benchmark human action dataset of normal and aggressive activities. Classification results are being compared with the standard IBk and IB1 models achieving significantly exceptional recognition performance