Tf Thomas Krijnen

OpenPSTD:The open source pseudospectral time-domain method for acoustic propagation

Abstract An open source implementation of the Fourier pseudospectral time-domain (PSTD) method for computing the propagation of sound is presented, which is geared towards applications in the built environment. Being a wave-based method, PSTD captures phenomena like diffraction, but maintains efficiency in processing time and memory usage as it allows to spatially sample close to the Nyquist criterion, thus keeping both the required spatial and temporal resolution coarse. In the implementation it has been opted to model the physical geometry as a composition of rectangular two-dimensional subdomains, hence initially restricting the implementation to orthogonal and two-dimensional situations. The strategy of using subdomains divides the problem domain into local subsets, which enables the simulation software to be built according to Object-Oriented Programming best practices and allows room for further computational parallelization. The software is built using the open source components, Blender, Numpy and Python, and has been published under an open source license itself as well. For accelerating the software, an option has been included to accelerate the calculations by a partial implementation of the code on the Graphical Processing Unit (GPU), which increases the throughput by up to fifteen times. The details of the implementation are reported, as well as the accuracy of the code. Program summary Program title: openPSTD v1.1 (v1.0 is the version without the GPU acceleration) Catalogue identifier: AFAA_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AFAA_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 45339 No. of bytes in distributed program, including test data, etc.: 4139815 Distribution format: tar.gz Programming language: Python (as comes with Blender 2.72). Computer: Variable. Operating system: Windows, Linux, Mac OS X. RAM: From 250 MB for a typical geometry up to 1 GB for large geometries (with about 4M grid points) Classification: 4.3, 12. External routines: Blender 2.72, NumPy, SciPy, PyFFT, PyOpenCL, PyCUDA Nature of problem: Sound propagation Solution method: Fourier pseudospectral time-domain method Restrictions: Structured grid, two dimensions, real-valued boundary conditions only Unusual features: Implementation of code using Blender/Python including GPU acceleration; subdomain modelling within Fourier pseudospectral method Running time: Depending on the dimension of the problem, calculation times take minutes up to hours

Assessing implicit knowledge in BIM models with machine learning

The promise, which comes along with Building Information Models, is that they are information rich, machine readable and represent the insights of multiple building disciplines within single or linked models. However, this knowledge has to be stated explicitly in order to be understood. Trained architects and engineers are able to deduce non-explicitly explicitly stated information, which is often the core of the transported architectural information. This paper investigates how machine learning approaches allow a computational system to deduce implicit knowledge from a set of BIM models.

An IFC schema extension and binary serialization format to efficiently integrate point cloud data into building models

Abstract In this paper we suggest an extension to the Industry Foundation Classes (IFC) model to integrate point cloud datasets. The proposal includes a schema extension to the core model allowing the storage of points, either as Cartesian coordinates, points in parametric space of associated building element surfaces or as discrete height fields projected as grids onto building elements. To handle the considerable amounts of data generated in the process of scanning building structures, we present intelligent compression approaches combined with the Hierarchical Data Format (HDF) as an efficient serialization and an alternative to clear text encoded ISO 10303 part 21 files. Based on prototypical implementations we show results of various serialization options and their impacts on storage efficiency. In this proposal the deepened semantic relationships have been favoured over compression ratios. Nevertheless, with various near-lossless layers of compression and binary serialization applied, a compression ratio of up to 67.7% is obtained for a building model with integrated point clouds, compared to the raw source data. The binary serialization is able to handle hundreds of millions of points, out of which specific spatial and semantic subsets can rapidly be extracted due to the containerized hierarchical storage model and association to building elements. The authors advocate the use of binary storage for sizeable point cloud scans, but also show how especially the grid discretization can result into usable points cloud segments embedded into text-based IFC models.

Working with Open BIM Standards to Source Legal Spaces for a 3D Cadastre

Much work has already been done on how a 3D Cadastre should best be developed. An inclusive information model, the Land Administration Model (LADM ISO 19152) has been developed to provide an international framework for how this can best be done. This conceptual model does not prescribe the technical data format. One existing source from which data could be obtained is 3D Building Information Models (BIMs), or, more specifically in this context, BIMs in the form of one of buildingSMART’s open standards: the Industry Foundation Classes (IFC). The research followed a standard BIM methodology of first defining the requirements through the use of the Information Delivery Manual (IDM ISO29481) and then translating the process described in the IDM into technical requirements using a Model View Definition (MVD), a practice to coordinate upfront the multidisciplinary stakeholders of a construction project. The proposed process model illustrated how the time it takes to register 3D spatial units in a Land Registry could substantially be reduced compared to the first 3D registration in the Netherlands. The modelling of an MVD or a subset of the IFC data model helped enable the creation and exchange of boundary representations of topological objects capable of being combined into a 3D legal space overview map.

Enrichment and Preservation of Architectural Knowledge

In the context of the EU FP7 DURAARK project (2013–2016), inter-disciplinary methods, technologies and tools have been researched and developed, that support the Long Term Preservation of semantically enriched digital representations of built structures. The results of the research efforts include approaches of semi-automatically deriving building models from point cloud data sets acquired from laser scans and the integration and overlay of such representations with explicit Building Information Models (BIM). We introduce novel ways for the further semantic enrichment of such hybrid building models with contextual data and vocabularies from external resources using Linked Data (LD) and the recognition relevant features and building components. A special focus of the research reported here lies on strategies and policies for their long term archival, information retrieval based on rich semantic metadata and the use of such archival systems in research and commercial scenarios. We introduce a set of prototypical, open-source tools implementing these features that have been integrated into a modular preservation framework called the “DURAARK Workbench”.

Processing BIM and GIS models in practice: experiences and recommendations from a GeoBIM project in the Netherlands

It is widely acknowledged that the integration of BIM and GIS data is a crucial step forward for future 3D city modelling, but most of the research conducted so far has covered only the high-level and semantic aspects of GIS-BIM integration. This paper presents the results of the GeoBIM project, which tackled three integration problems focussing instead on aspects involving geometry processing: (i) the automated processing of complex architectural IFC models; (ii) the integration of existing GIS subsoil data in BIM; and (iii) the georeferencing of BIM models for their use in GIS software. All the problems have been studied using real world models and existing datasets made and used by practitioners in The Netherlands. For each problem, this paper exposes in detail the issues faced, proposed solutions, and recommendations for a more successful integration.

Employing Outlier and Novelty Detection for Checking the Integrity of BIM to IFC Entity Associations

Although Industry Foundation Classes (IFC) provide standards for exchanging Building Information Modeling (BIM) data, authoring tools still require manual mapping between BIM entities and IFC classes. This leads to errors and omissions, which results in corrupted data exchanges that are unreliable and compromise the interoperability of BIM models. This research explored the use of two machine learning techniques for identifying anomalies, namely outlier and novelty detection to determine the integrity of IFC classes to BIM entity mappings. Both approaches were tested on three BIM models, to test their accuracy in identifying misclassifications. Results showed that outlier detection, which uses Mahalanobis distances, had difficulties when several types of dissimilar elements existed in a single IFC class and conversely was not applicable for IFC classes with insufficient number of elements. Novelty detection, using one-class SVM, was trained a priori on elements with dissimilar geometry. By creating multiple inlier boundaries, novelty detection resolved the limitations encountered in the former approach, and consequently performed better in identifying outliers correctly.