A scientometric analysis and visualization of global green building research

The creation of as-built Building Information Models requires the acquisition of the as-is state of existing buildings. Laser scanners are widely used to achieve this goal since they permit to collect information about object geometry in form of point clouds and provide a large amount of accurate data in a very fast way and with a high level of details. Unfortunately, the scan-to-BIM (Building Information Model) process remains currently largely a manual process which is time consuming and error-prone. In this paper, a semi-automatic approach is presented for the 3D reconstruction of indoors of existing buildings from point clouds. Several segmentations are performed so that point clouds corresponding to grounds, ceilings and walls are extracted. Based on these point clouds, walls and slabs of buildings are reconstructed and described in the IFC format in order to be integrated into BIM software. The assessment of the approach is proposed thanks to two datasets. The evaluation items are the degree of automation, the transferability of the approach and the geometric quality of results of the 3D reconstruction. Additionally, quality indexes are introduced to inspect the results in order to be able to detect potential errors of reconstruction.

https://hal.archives-ouvertes.fr/hal-02320638/document

From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings

In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.

Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

Review of built heritage modelling: Integration of HBIM and other information techniques

Digital twinning of existing reinforced concrete bridges from labelled point clusters

Building Information Modelling (BIM) is becoming increasingly important within the UK, not least because of a UK Government directive that mandates Level 2 BIM for companies tendering for Government work, with the aim of reducing the cost of construction of public assets by 20–30 %. While this is aimed at new construction, it can be foreseen that a wider introduction of BIM could also result in savings during large refurbishment projects, which form a significant part of construction work in the UK, as well as during the occupancy phase of the building. However, unlike new projects, where the model for the BIM can be obtained from CAD drawings, deriving a detailed BIM for pre-existing structures requires some form of scan-to-BIM operation using laser scanning. To contribute to sustainability in construction, an underlying driver for BIM, the BIM must also be integrated with other data sources. Therefore, once the scan is complete, the resulting point cloud must be converted into geometry objects and geo-referenced for integration with Geographical data such as air quality or noise information. This paper presents an end-to-end example of this process, focusing in particular on the challenges of integrating BIM and GIS into one framework, and highlighting preliminary steps to be carried out during BIM creation in order to enable this to take place.

Towards Integrating BIM and GIS—An End-to-End Example from Point Cloud to Analysis

Chadwick GreenBIM is an initiative to establish a digital
3D as-built model of UCL’s Chadwick Building including embedded environmental data to gain a greater understanding of its operation with the aim of developing concepts for retrofit and sustainable use. This paper investigates the above by reviewing the state of the art in reality capture to generate a parametric model using laser scanning. It
also considers the challenges of managing information for simulation including complex ‘big data’ types (e.g. point clouds). The research in this paper was carried out through an empirical approach to create a data-rich Building Information Model (BIM) under real world conditions.

Real world measurements of environmental conditions were used to validate the effectiveness of simulations using commercial off-the-shelf (COTS) software packages. In addition, the practicalities of the integration of such data inside the BIM were investigated. 

Data capture using the methods described in this
paper requires extraordinary effort and is expensive
in both time and cost. However, advances in BIM
enabling technologies such as Indoor Mobile Mapping (IMM)
seek improved savings in data collection. 

Technological advances will further improve the integration of data capture and extraction of information used by simulation tools. Once complete it is envisaged that the GreenBIM model would be disseminated among a broad community of researchers to investigate various perspectives
in a collaborative manner.

Chadwick GreenBIM: Advancing Operational Understanding of Historical Buildings with BIM to Support Sustainable Use

Building Information Modelling has, more than any previous initiative, established itself as the process by which operational change can occur, driven by a desire to eradicate the inefficiencies in time and value and requiring a change of approach to the whole lifecycle of construction from design through construction to operation and eventual demolition. BIM should provide a common digital platform which allows different stakeholders to supply and retrieve information thereby reducing waste through enhanced decision making. Through the provision of measurement and representative digital geometry for construction and management purposes, surveying is very much a part of BIM. Given that all professions that are involved with construction have to consider the way in which they handle data to fit with the BIM process, it stands to reason that Geomatic or Land Surveyors play a key part. This is further encouraged by the fact that 3D laser scanning has been adopted as the primary measurement technique for geometry capture for BIM. Also it is supported by a laser scanning work stream from the UK Government backed BIM Task Group. Against this backdrop, the research in this thesis investigates the 3D modelling aspects of BIM, from initial geometry capture in the real world, to the generation and storage of the virtual world model, while keeping the workflow and outputs compatible with the BIM process. The focus will be made on a key part of the workflow for capturing as-built conditions: the geometry creation from point clouds. This area is considered a bottleneck in the BIM process for existing assets not helped by their often poor or non-existent documentation. Automated modelling is seen as desirable commercially with the goal of reducing time, and therefore cost, and making laser scanning a more viable proposition for a range of tasks in the lifecycle.

From Point Cloud to Building Information Model: Capturing and Processing Survey Data Towards Automation for High Quality 3D Models to Aid a BIM Process

. A combination of faster, cheaper and more accurate hardware, more sophisticated software, and greater industry acceptance have all laid the foundations for an increased desire for accurate 3D parametric models of buildings. Pointclouds are the data source of choice currently with static terrestrial laser scanning the predominant tool for large, dense volume measurement. The current importance of pointclouds as the primary source of real world representation is endorsed by CAD software vendor acquisitions of pointcloud engines in 2011. Both the capture and modelling of indoor environments require great effort in time by the operator (and therefore cost). Automation is seen as a way to aid this by reducing the workload of the user and some commercial packages have appeared that provide automation to some degree. In the data capture phase, advances in indoor mobile mapping systems are speeding up the process, albeit currently with a reduction in accuracy. As a result this paper presents freely accessible pointcloud datasets of two typical areas of a building each captured with two different capture methods and each with an accurate wholly manually created model. These datasets are provided as a benchmark for the research community to gauge the performance and improvements of various techniques for indoor geometry extraction. With this in mind, non-proprietary, interoperable formats are provided such as E57 for the scans and IFC for the reference model. The datasets can be found at: http://indoor-bench.github.io/indoor-bench .

/pdf/indoor-modelling-benchmark-for-3d-geometry-extraction-ydpusub98p.pdf

Indoor modelling benchmark for 3d geometry extraction

The need for better 3D documentation of the built environment has come to the fore in recent years, led primarily by city modelling at the large scale and Building Information Modelling (BIM) at the smaller scale. Automation is seen as desirable as it removes the time-consuming and therefore costly amount of human intervention in the process of model generation. BIM is the focus of this paper as not only is there a commercial need, as will be shown by the number of commercial solutions, but also wide research interest due to the aspiration of automated 3D models from both Geomatics and Computer Science communities. The aim is to go beyond the current labour-intensive tracing of the point cloud to an automated process that produces geometry that is both open and more verifiable. This work investigates what can be achieved today with automation through both literature review and by proposing a novel point cloud processing process. We present an automated workflow for the generation of BIM data from 3D point clouds. We also present quality indicators for reconstructed geometry elements and a framework in which to assess the quality of the reconstructed geometry against a reference.

https://www.mdpi.com/2072-4292/7/9/11753/pdf

Automatic Geometry Generation from Point Clouds for BIM

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