Archeologische opgraving Heusden-Zolder Aanhofstraat

Dit rapport werd ingediend bij het agentschap samen met een aantal afzonderlijke digitale bijlagen. Een aantal van deze bijlagen zijn niet inbegrepen in dit pdf document en zijn niet online beschikbaar. Sommige bijlagen (grondplannen, fotos, spoorbeschrijvingen, enz.) kunnen van belang zijn voor een betere lezing en interpretatie van dit rapport. Indien u deze bijlagen wenst te raadplegen kan u daarvoor contact opnemen met: [email protected]

Archeologische prospectie met ingreep in de bodem Bree - Kuilenstraat

Dit rapport werd ingediend bij het agentschap samen met een aantal afzonderlijke digitale bijlagen. Een aantal van deze bijlagen zijn niet inbegrepen in dit pdf document en zijn niet online beschikbaar. Sommige bijlagen (grondplannen, fotos, spoorbeschrijvingen, enz.) kunnen van belang zijn voor een betere lezing en interpretatie van dit rapport. Indien u deze bijlagen wenst te raadplegen kan u daarvoor contact opnemen met: [email protected]

Automated Classification of Heritage Buildings for As-Built BIM using Machine Learning Techniques

Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects. In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, SupportVector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.status: publishe

Classification of sensor independent point cloud data of building objects using random forests

© 2018 Elsevier Ltd The Architectural, Engineering and Construction (AEC) industry is looking to integrate Building Information Modeling (BIM) for existing buildings. Currently these as-built models are created manually, which is time-consuming. An important step in the automated Scan-to-BIM procedure is the interpretation and classification of point cloud data. This is computationally challenging due to the sheer size of point cloud data for an entire building. Additionally, the variety of objects makes classification problematic. Existing methods integrate prior knowledge from the sensors or environment to improve the results. However, these approaches are therefore often case specific and thus have limited applicability. The goal of this research is to provide a method that is independent of any sensor or scene within a building environment. Furthermore, our method processes the entire building simultaneously, resulting in more distinct local and contextual features. This paper presents a generic approach to automatically identify structural elements for the purposes of Scan-to-BIM. More specifically, a Random Forests classifier is employed for the classification of the floors, ceilings, roofs, walls and beams. As input, our algorithm takes a set of planar primitives that are pre-segmented from the point cloud. This significantly reduces the data while maintaining accuracy. Both contextual and geometric features are used to describe the observed patches. The algorithm is evaluated using realistic data for a wide variety of existing buildings including houses, school facilities, a factory, a castle and a church. The experiments prove that the proposed algorithm is capable of properly labeling 87% of the structural elements with an average precision of 85% in highly cluttered environments without the support of the sensors position. In future work, the classified patches will be processed by class-specific reconstruction algorithms to create BIM geometry.status: publishe

Automated Semantic Labelling of 3D Vector Models for Scan-to-BIM

With the increasing popularity of Building Information modelling (BIM), the demand for accurate as-built models of existing buildings is rising. However, the manual creation of these models is labour intensive and error prone. Therefore, automation of the process is a must. One of the key factors in the automated Scan-to-BIM process is the labelling of the data for further reconstruction. Currently, semantic labelling is still ongoing research. This paper presents a flexible method to automatically label highly cluttered vector models of existing buildings. In our proposed method, a reasoning framework is used that exploits geometric and contextual information. A major advantage to our approach is that our algorithm can label both cluttered environments and large data sets very efficiently. Unlike other solutions, this allows us to label entire buildings at once. In addition, the implementation of our algorithm and the platform we use allows for flexible data processing, visualisation of the results and improvement of the labelling process. Our work covers the entire labelling phase and allows the user to label data sets with a minimal amount of effort.status: publishe

Standalone Terrestrial Laser Scanning for Efficiently Capturing AEC Buildings for As-Built BIM

With the increasing popularity of as-built building models for the architectural, engineering and construction (AEC) industry, the demand for highly accurate and dense point cloud data is rising. The current data acquisition methods are labour intensive and time consuming. In order to compete with indoor mobile mapping systems (IMMS), surveyors are now opting to use terrestrial laser scanning as a standalone solution. However, there is uncertainty about the accuracy of this approach. The emphasis of this paper is to determine the scope for which terrestrial laser scanners can be used without additional control. Multiple real life test cases are evaluated in order to identify the boundaries of this technique. Furthermore, this research presents a mathematical prediction model that provides an indication of the data accuracy given the project dimensions. This will enable surveyors to make informed discussions about the employability of terrestrial laser scanning without additional control in mid to large-scale projects.status: publishe

Segmentation of Large Unstructured Point Clouds Using Octree-Based Region Growing And Conditional Random Fields

© 2017 Authors. Point cloud segmentation is a crucial step in scene understanding and interpretation. The goal is to decompose the initial data into sets of workable clusters with similar properties. Additionally, it is a key aspect in the automated procedure from point cloud data to BIM. Current approaches typically only segment a single type of primitive such as planes or cylinders. Also, current algorithms suffer from oversegmenting the data and are often sensor or scene dependent. In this work, a method is presented to automatically segment large unstructured point clouds of buildings. More specifically, the segmentation is formulated as a graph optimisation problem. First, the data is oversegmented with a greedy octree-based region growing method. The growing is conditioned on the segmentation of planes as well as smooth surfaces. Next, the candidate clusters are represented by a Conditional Random Field after which the most likely configuration of candidate clusters is computed given a set of local and contextual features. The experiments prove that the used method is a fast and reliable framework for unstructured point cloud segmentation. Processing speeds up to 40,000 points per second are recorded for the region growing. Additionally, the recall and precision of the graph clustering is approximately 80%. Overall, nearly 22% of oversegmentation is reduced by clustering the data. These clusters will be classified and used as a basis for the reconstruction of BIM models.status: publishe

IFC Wall reconstruction from unstructured point clouds

The automated reconstruction of Building Information Modelling (BIM) objects from point cloud data is still ongoing research. A key aspect is the accurate creation of wall geometry as it forms the basis for further reconstruction of objects in a BIM. After segmenting and classifying the initial point cloud, the labelled segments are interpreted and the wall topology is reconstructed. However, the process is challenging due to noise, occlusions and the complexity of the input data. In this work, a method is presented to automatically reconstruct consistent wall geometry from point clouds. More specifically, the use of room information is proposed to aid the wall topology creation. First, a set of partial walls is constructed based on classified planar primitives. Next, the rooms are identified using the retrieved wall information along with the floors and ceilings. The wall topology is computed by the intersection of the partial walls conditioned on the room information. The final wall geometry is defined by creating IfcWallStandardCase objects conform the IFC4 standard. The result is a set of walls according to the as-built conditions of a building. The experiments prove that the used method is a reliable framework for wall reconstruction from unstructured point cloud data. Also, the implementation of room information reduces the rate of false positives for the wall topology. Given the walls, ceiling and floor information, 94% of the rooms is correctly identified. A key advantage of the proposed method is that it deals with complex rooms and is not dependent on storey information.status: Published onlin

Evaluation of data acquisition techniques and workflows for Scan to BIM

With the increasing popularity of as-built Building Information Modelling (BIM) for existing buildings, the demand for highly accurate and dense point cloud data is rising. However, the current data acquisition methods are labor intensive and time consuming. Among other factors, the use of total station measurements to establish survey control, is a major cost behind data acquisition workflows. Over the recent years, there have been major innovations in the fields of surveying and robotics, such as the development of Indoor Mobile Mapping systems (IMMS). With these technological advancements, more cost-effective workflows for capturing existing buildings can be realized. In this paper, several state-of-the-art data acquisition techniques and workflows are discussed for Architectural, Engineering and Construction (AEC) industry buildings. Furthermore, a workflow is proposed with a standalone terrestrial laser scanner capturing data in high overlap, using loop-closure and optimization algorithms to guaranty accuracy. Real life test cases of AEC industry buildings are presented, proving that the proposed workflow can provide highly dense point cloud data within project specifications more efficiently.status: publishe

The fate of the free flap pedicle after free tissue transfer to the head and neck area

Little is understood about what happens to the vascular pedicle following free tissue transfer in the head and neck region. The viability of a free flap completely depends on the vascular supply by its vascular pedicle until neovascularization occurs from surrounding tissues. The aim of this study is to find out how long a vascular pedicle lasts following free tissue transfer in the head and neck region.Patients were recruited from the Maxillofacial Unit at the Royal Brisbane & Women's Hospital. A Doppler ultrasound was used to map the vascular pedicle immediately postoperatively, at 2weeks, 6weeks, 3months and 6months.Fifty-seven consecutive free flaps underwent colour Doppler ultrasonography at the timepoints described demonstrating the status of the vascular pedicle. All the patients underwent reconstructive head and neck surgery with a wide variety of soft tissue and composite free flaps.This study is the first to document the fate of the vascular pedicle over a long time period for a wide variety of head and neck free flaps. This information is important when undertaking revision surgery to the free flap, or planning the vascular supply for a second or third free flap to the head and neck region