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Recent advances in deep learning models for image interpretation finally made it possible to automate construction site monitoring processes that rely on remote sensing. However, the major drawback of these models is their dependency on large datasets of training images labeled at pixel level, which must be produced manually by skilled personnel. To reduce the need for training data, this study evaluates weakly and semi-supervised semantic segmentation models for construction site imagery to efficiently automate monitoring tasks. As a case study, we compare fully, weakly and semi-supervised methods for the detection of rebar covers, which are useful for quality control. In the experiments, recent models, i.e., IRNet, DeepLabv3+ and the cross-consistency training model are compared for their ability to segment rebar covers from construction site imagery with minimal manual input. The results show that weakly and semi-supervised models can indeed rival with the performance of fully supervised models with the majority of the target objects being properly found. This study provides construction site stakeholders with detailed information on how to leverage deep learning for efficient construction site monitoring and weigh preprocessing, training, and testing efforts against each other in order to decide between fully, weakly and semi-supervised training.
Suzanna Cuypers; Maarten Bassier; Maarten Vergauwen. Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation. Sensors 2021, 21, 5428 .
AMA StyleSuzanna Cuypers, Maarten Bassier, Maarten Vergauwen. Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation. Sensors. 2021; 21 (16):5428.
Chicago/Turabian StyleSuzanna Cuypers; Maarten Bassier; Maarten Vergauwen. 2021. "Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation." Sensors 21, no. 16: 5428.
Scan-to-BIM of existing buildings is in high demand by the construction industry. However, these models are costly and time-consuming to create. The automation of this process is still subject of ongoing research. Current obstacles include the interpretation and reconstruction of raw point cloud data, which is complicated by the complexity of built structures, the vast amount of data to be processed and the variety of objects in the built environment. This research aims to overcome the current obstacles and reconstruct the structure of buildings in an unsupervised manner. More specifically, a novel method is presented to automatically reconstruct BIM wall objects and their topology. Key contributions of the method are the ability to reconstruct different wall axis and connection types and the simultaneous processing of entire multi-story structures. The method is validated with the Stanford 2D–3D-Semantics Dataset (2D–3D-S).
Maarten Bassier; Maarten Vergauwen. Unsupervised reconstruction of Building Information Modeling wall objects from point cloud data. Automation in Construction 2020, 120, 103338 .
AMA StyleMaarten Bassier, Maarten Vergauwen. Unsupervised reconstruction of Building Information Modeling wall objects from point cloud data. Automation in Construction. 2020; 120 ():103338.
Chicago/Turabian StyleMaarten Bassier; Maarten Vergauwen. 2020. "Unsupervised reconstruction of Building Information Modeling wall objects from point cloud data." Automation in Construction 120, no. : 103338.
Construction site monitoring is currently performed through visual inspections and costly selective measurements. Due to the small overhead in construction projects, additional resources are scarce to frequently conduct a metric quality assessment of the constructed objects. However, contradictory, construction projects are characterised by high failure costs which are often caused by erroneously constructed structural objects. With the upcoming use of periodic remote sensing during the different phases of the building process, new possibilities arise to advance from a selective quality analysis to an in-depth assessment of the full construction site. In this work, a novel methodology is presented to rapidly evaluate a large number of built objects on a construction site. Given a point cloud and a set of as-design BIM elements, our method evaluates the deviations between both datasets and computes the positioning errors of each object. Unlike the current state of the art, our method computes the error vectors regardless of drift, noise, clutter and (geo)referencing errors, leading to a better detection rate. The main contributions are the efficient matching of both datasets, the drift invariant metric evaluation and the intuitive visualisation of the results. The proposed analysis facilitates the identification of construction errors early on in the process, hence significantly lowering the failure costs. The application is embedded in native BIM software and visualises the objects by a simple color code, providing an intuitive indicator for the positioning accuracy of the built objects.
Maarten Bassier; Stan Vincke; Heinder De Winter; Maarten Vergauwen. Drift Invariant Metric Quality Control of Construction Sites Using BIM and Point Cloud Data. ISPRS International Journal of Geo-Information 2020, 9, 545 .
AMA StyleMaarten Bassier, Stan Vincke, Heinder De Winter, Maarten Vergauwen. Drift Invariant Metric Quality Control of Construction Sites Using BIM and Point Cloud Data. ISPRS International Journal of Geo-Information. 2020; 9 (9):545.
Chicago/Turabian StyleMaarten Bassier; Stan Vincke; Heinder De Winter; Maarten Vergauwen. 2020. "Drift Invariant Metric Quality Control of Construction Sites Using BIM and Point Cloud Data." ISPRS International Journal of Geo-Information 9, no. 9: 545.
Interpreting 3D point cloud data of the interior and exterior of buildings is essential for automated navigation, interaction and 3D reconstruction. However, the direct exploitation of the geometry is challenging due to inherent obstacles such as noise, occlusions, sparsity or variance in the density. Alternatively, 3D mesh geometries derived from point clouds benefit from preprocessing routines that can surmount these obstacles and potentially result in more refined geometry and topology descriptions. In this article, we provide a rigorous comparison of both geometries for scene interpretation. We present an empirical study on the suitability of both geometries for the feature extraction and classification. More specifically, we study the impact for the retrieval of structural building components in a realistic environment which is a major endeavor in Building Information Modeling (BIM) reconstruction. The study runs on segment-based structuration of both geometries and shows that both achieve recognition rates over 75% F1 score when suitable features are used.
Maarten Bassier; Maarten Vergauwen; Florent Poux. Point Cloud vs. Mesh Features for Building Interior Classification. Remote Sensing 2020, 12, 2224 .
AMA StyleMaarten Bassier, Maarten Vergauwen, Florent Poux. Point Cloud vs. Mesh Features for Building Interior Classification. Remote Sensing. 2020; 12 (14):2224.
Chicago/Turabian StyleMaarten Bassier; Maarten Vergauwen; Florent Poux. 2020. "Point Cloud vs. Mesh Features for Building Interior Classification." Remote Sensing 12, no. 14: 2224.
The processing of remote sensing measurements to Building Information Modeling (BIM) is a popular subject in current literature. An important step in the process is the enrichment of the geometry with the topology of the wall observations to create a logical model. However, this remains an unsolved task as methods struggle to deal with the noise, incompleteness and the complexity of point cloud data of building scenes. Current methods impose severe abstractions such as Manhattan-world assumptions and single-story procedures to overcome these obstacles, but as a result, a general data processing approach is still missing. In this paper, we propose a method that solves these shortcomings and creates a logical BIM model in an unsupervised manner. More specifically, we propose a connection evaluation framework that takes as input a set of preprocessed point clouds of a building’s wall observations and compute the best fit topology between them. We transcend the current state of the art by processing point clouds of both straight, curved and polyline-based walls. Also, we consider multiple connection types in a novel reasoning framework that decides which operations are best fit to reconstruct the topology of the walls. The geometry and topology produced by our method is directly usable by BIM processes as it is structured conform the IFC data structure. The experimental results conducted on the Stanford 2D-3D-Semantics dataset (2D-3D-S) show that the proposed method is a promising framework to reconstruct complex multi-story wall elements in an unsupervised manner.
Maarten Bassier; Maarten Vergauwen. Topology Reconstruction of BIM Wall Objects from Point Cloud Data. Remote Sensing 2020, 12, 1800 .
AMA StyleMaarten Bassier, Maarten Vergauwen. Topology Reconstruction of BIM Wall Objects from Point Cloud Data. Remote Sensing. 2020; 12 (11):1800.
Chicago/Turabian StyleMaarten Bassier; Maarten Vergauwen. 2020. "Topology Reconstruction of BIM Wall Objects from Point Cloud Data." Remote Sensing 12, no. 11: 1800.
The Web currently hosts a vast amount of 2D images and 3D building models. Each repository has its own data structure and a limited set of semantics according to their own needs. With the advent of Semantic Web Technologies, the opportunity arises to combine these heterogeneous data sets and publish them as Linked Data. It is within the scope of this research to investigate whether online 2D and 3D content can be enriched, published and reused as RDF. The emphasis of this work is on extracting building component information from online building geometry and publishing it as Linked Data. An interpretation framework is presented that takes as input any building mesh and computes its building components through machine learning techniques. Additionally, a Structure-from-Motion pipeline is proposed that provides similar outputs and links the 2D imagery to the reconstructed 3D building geometry. The experiments show that, even though the building content originates from different sources and was not modeled according to any standards, building geometry in online repositories and photogrammetric reconstructions can be semantically enriched with component information using terminology from Linked Building Data ontologies such as BOT, PRODUCT and OMG/FOG/GOM. This is an important step towards making structureless geometric information retrievable, linkable and thus reusable over the Web.
Maarten Bassier; Mathias Bonduel; Jens Derdaele; Maarten Vergauwen. Processing existing building geometry for reuse as Linked Data. Automation in Construction 2020, 115, 103180 .
AMA StyleMaarten Bassier, Mathias Bonduel, Jens Derdaele, Maarten Vergauwen. Processing existing building geometry for reuse as Linked Data. Automation in Construction. 2020; 115 ():103180.
Chicago/Turabian StyleMaarten Bassier; Mathias Bonduel; Jens Derdaele; Maarten Vergauwen. 2020. "Processing existing building geometry for reuse as Linked Data." Automation in Construction 115, no. : 103180.
As-built Building Information Models (BIMs) are becoming increasingly popular in the Architectural, Engineering, Construction, Owner and Operator (AECOO) industry. These models reflect the state of the building up to as-built conditions. The production of these models for existing buildings with no prior BIM includes the segmentation and classification of point cloud data and the reconstruction of the BIM objects. The automation of this process is a must since the manual Scan-to-BIM procedure is both time-consuming and error prone. However, the automated reconstruction from point cloud data is still ongoing research with both 2D and 3D approaches being proposed. There currently is a gap in the literature concerning the quality assessment of the created entities. In this research, we present the empirical comparison of both strategies with respect to existing specifications. A 3D and a 2D reconstruction method are implemented and tested on a real life test case. The experiments focus on the reconstruction of the wall geometry from unstructured point clouds as it forms the basis of the model. Both presented approaches are unsupervised methods that segment, classify and create generic wall elements. The first method operates on the 3D point cloud itself and consists of a general approach for the segmentation and classification and a class-specific reconstruction algorithm for the wall geometry. The point cloud is first segmented into planar clusters, after which a Random Forests classifier is used with geometric and contextual features for the semantic labelling. The final wall geometry is created based on the 3D point clusters representing the walls. The second method is an efficient Manhattan-world scene reconstruction algorithm that simultaneously segments and classifies the point cloud based on point feature histograms. The wall reconstruction is considered an instance of image segmentation by representing the data as 2D raster images. Both methods have promising results towards the reconstruction of wall geometry of multi-story buildings. The experiments report that over 80% of the walls were correctly segmented by both methods. Furthermore, the reconstructed geometry is conform Level-of-Accuracy 20 for 88% of the data by the first method and for 55% by the second method despite the Manhattan-world scene assumption. The empirical comparison showcases the fundamental differences in both strategies and will support the further development of these methods.
Maarten Bassier; Meisam Yousefzadeh; Maarten Vergauwen. Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM. Journal of Information Technology in Construction 2020, 25, 173 -192.
AMA StyleMaarten Bassier, Meisam Yousefzadeh, Maarten Vergauwen. Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM. Journal of Information Technology in Construction. 2020; 25 (11):173-192.
Chicago/Turabian StyleMaarten Bassier; Meisam Yousefzadeh; Maarten Vergauwen. 2020. "Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM." Journal of Information Technology in Construction 25, no. 11: 173-192.
Progress monitoring of construction sites is becoming increasingly popular in the construction industry. Especially with the integration of 4D BIM, the progression and quality of the construction process can be better quantified. A key aspect is the detection of the changes between consecutive epochs of measurements on the site. However, the development of automated procedures is challenging due to noise, occlusions and the associativity between different objects. Additionally, objects are built in stages and thus varying states have to be detected according to the Percentage of Completion.In this work, a framework is presented to derive work progress of construction sites based on point cloud data. More specifically, a methodology is constituted to compute the Percentage of Completion of in-situ cast concrete walls. In the literature study, existing methods are evaluated for their ability to track progress even in highly cluttered environments. In the practical study, we perform an empirical analysis on a set of periodic point clouds to establish the obstacles and feasibility of the methodology. This work leads to a better understanding of the progress monitoring paradigm which is still subject of ongoing research and will serve as the basis for the further development of a set of automated procedures.
Maarten Bassier; S. Vincke; L. Mattheuwsen; R. De Lima Hernandez; J. Derdaele; M. Vergauwen. PERCENTAGE OF COMPLETION OF IN-SITU CAST CONCRETE WALLS USING POINT CLOUD DATA AND BIM. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-5/W2, 21 -28.
AMA StyleMaarten Bassier, S. Vincke, L. Mattheuwsen, R. De Lima Hernandez, J. Derdaele, M. Vergauwen. PERCENTAGE OF COMPLETION OF IN-SITU CAST CONCRETE WALLS USING POINT CLOUD DATA AND BIM. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-5/W2 ():21-28.
Chicago/Turabian StyleMaarten Bassier; S. Vincke; L. Mattheuwsen; R. De Lima Hernandez; J. Derdaele; M. Vergauwen. 2019. "PERCENTAGE OF COMPLETION OF IN-SITU CAST CONCRETE WALLS USING POINT CLOUD DATA AND BIM." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5/W2, no. : 21-28.
The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still subject of ongoing research. A vital step in the process is identifying the observations for each wall object. Given a set of segmented and classified point clouds, the labeled segments should be clustered according to their respective objects. The current processes to perform this task are sensitive to noise, occlusions, and the associativity between faces of neighboring objects. The proper retrieval of the observed geometry is especially important for wall geometry as it forms the basis for further BIM reconstruction. In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each wall segment in order to determine which wall it belongs to. First, a set of classified planar primitives is obtained through algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph. The method is tested on our own data as well as the 2D-3D-Semantics (2D-3D-S) benchmark data of Stanford. Compared to a conventional region growing method, the proposed method reduces the rate of false positives, resulting in better wall clusters. Overall, the method computes a more balanced clustering of the observations. A key advantage of the proposed method is its capability to deal with wall geometry in complex configurations in multi-storey buildings opposed to the presented methods in current literature.
Maarten Bassier; Maarten Vergauwen. Clustering of Wall Geometry from Unstructured Point Clouds Using Conditional Random Fields. Remote Sensing 2019, 11, 1586 .
AMA StyleMaarten Bassier, Maarten Vergauwen. Clustering of Wall Geometry from Unstructured Point Clouds Using Conditional Random Fields. Remote Sensing. 2019; 11 (13):1586.
Chicago/Turabian StyleMaarten Bassier; Maarten Vergauwen. 2019. "Clustering of Wall Geometry from Unstructured Point Clouds Using Conditional Random Fields." Remote Sensing 11, no. 13: 1586.
Currently, existing online 3D databases each have their own structure according to their own needs. Additionally, the majority of online content only has limited semantics. With the advent of Semantic Web technologies, the opportunity arises to semantically enrich the information in these databases and make it widely accessible and queryable. The goal is to investigate whether online 3D content from different repositories can be processed by a single algorithm to produce the desired semantics. The emphasis of this work is on extracting building components from generic 3D building geometry and publish it as Linked Building Data. An interpretation framework is proposed that takes as input any building mesh and outputs its components. More specifically, we use pretrained Support Vector Machines to classify the separate meshes derived from each 3D model. As a preliminary test case, realistic examples from several repositories are processed. The test results depict that, even though the building content originates from different sources and was not modeled according to any standards, it can be processed by a single machine learning application. As a result, building geometry in online repositories can be semantically enriched with component information according to classes from Linked Data ontologies such as BOT and PRODUCT. This is an important step towards making the implicit content of geometric models queryable and linkable over the Web.
Maarten Bassier; Mathias Bonduel; Jens Derdaele; Maarten Vergauwen. Towards the Semantic Enrichment of Existing Online 3D Building Geometry to Publish Linked Building Data. Communications in Computer and Information Science 2019, 134 -148.
AMA StyleMaarten Bassier, Mathias Bonduel, Jens Derdaele, Maarten Vergauwen. Towards the Semantic Enrichment of Existing Online 3D Building Geometry to Publish Linked Building Data. Communications in Computer and Information Science. 2019; ():134-148.
Chicago/Turabian StyleMaarten Bassier; Mathias Bonduel; Jens Derdaele; Maarten Vergauwen. 2019. "Towards the Semantic Enrichment of Existing Online 3D Building Geometry to Publish Linked Building Data." Communications in Computer and Information Science , no. : 134-148.
Construction site monitoring and progress monitoring is becoming increasingly popular in the architecture, engineering and construction (AEC) industry. To this end remote sensing techniques are used to gather consecutive datasets of the construction site. This work focuses on the recording of imagery for photogrammetric processing and the challenging conditions often encountered on construction sites. The constantly evolving character of a such sites requires datasets to be captured as quickly as possible. Furthermore other recording complexities arise such as the presence of auxiliary equipment and clutter or reflections caused by wet surfaces, hindering quick and complete recordings. Apart from these external factors also construction elements themselves often complicate the capturing workflow.This work enumerates several real-world examples of difficulties construction sites pose for the recording of imagery for photogrammetry purposes. Each section provides an insight in a specific challenge, typical for construction sites, and discusses applicable field-tested solutions including an overview of relevant solutions found in literature.
S. Vincke; Maarten Bassier; M. Vergauwen. IMAGE RECORDING CHALLENGES FOR PHOTOGRAMMETRIC CONSTRUCTION SITE MONITORING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-2/W9, 747 -753.
AMA StyleS. Vincke, Maarten Bassier, M. Vergauwen. IMAGE RECORDING CHALLENGES FOR PHOTOGRAMMETRIC CONSTRUCTION SITE MONITORING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-2/W9 ():747-753.
Chicago/Turabian StyleS. Vincke; Maarten Bassier; M. Vergauwen. 2019. "IMAGE RECORDING CHALLENGES FOR PHOTOGRAMMETRIC CONSTRUCTION SITE MONITORING." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9, no. : 747-753.
The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is retrieving the proper observations for each object. After segmenting and classifying the initial point cloud, the labeled segments should be clustered according to their respective objects. However, this procedure is challenging due to noise, occlusions and the associativity between different objects. This is especially important for wall geometry as it forms the basis for further BIM reconstruction. In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each observation in order to determine which wall it belongs too. The emphasis is on the clustering of highly associative walls as this topic currently is a gap in the body of knowledge. First a set of classified planar primitives is obtained using algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph and thus the most likely configuration of the observations. The experiments prove that the used method is a promising framework for wall clustering from unstructured point cloud data. Compared to a conventional region growing method, the proposed method significantly reduces the rate of false positives, resulting in better wall clusters. A key advantage of the proposed method is its capability of dealing with complex wall geometry in entire buildings opposed to the presented methods in current literature.
Maarten Bassier; M. Vergauwen. CLUSTERING OF WALL GEOMETRY FROM UNSTRUCTURED POINT CLOUDS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-2/W9, 101 -108.
AMA StyleMaarten Bassier, M. Vergauwen. CLUSTERING OF WALL GEOMETRY FROM UNSTRUCTURED POINT CLOUDS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-2/W9 ():101-108.
Chicago/Turabian StyleMaarten Bassier; M. Vergauwen. 2019. "CLUSTERING OF WALL GEOMETRY FROM UNSTRUCTURED POINT CLOUDS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9, no. : 101-108.
The structural analysis of buildings requires accurate spatial models. Additionally, spatial information on pathologies such as settlement-induced damage is paramount in the assessment of heritage assets. This spatial information is used as a basis for Finite Element Methods (FEM) to evaluate the stability of the structure. Traditional data acquisition approaches rely on manual measurements which are labor intensive and error prone. Therefore, major simplifications are made to document structures efficiently. The goal of this research is to provide faster and more accurate procedures to capture the spatial information required by a Finite Element (FE) mesh. This paper presents a semi-automated approach to create accurate models of complex heritage buildings for the purpose of structural analysis. By employing remote sensing techniques such as terrestrial laser scanning and photogrammetry, a complex mesh of the structure is created. Also, a methodology is proposed to capture crack information. A stepwise approach is elaborated to illustrate how the spatial information is adapted towards a FE mesh. The results show a significant difference between the geometry of our model and a traditional wire-frame model. Not only does accurate modelling result in deviating loads, it also affects the behavior of the object. Through the proposed approach, experts can develop highly accurate FE meshes to assess the stability of the structure up to as-built conditions and taking into account existing damage patterns.
Maarten Bassier; Gilles Hardy; Leidy Bejarano-Urrego; Anastasios Drougkas; Els Verstrynge; Koen Van Balen; Maarten Vergauwen. Semi-automated Creation of Accurate FE Meshes of Heritage Masonry Walls from Point Cloud Data. High Performance Fiber Reinforced Cement Composites 6 2019, 305 -314.
AMA StyleMaarten Bassier, Gilles Hardy, Leidy Bejarano-Urrego, Anastasios Drougkas, Els Verstrynge, Koen Van Balen, Maarten Vergauwen. Semi-automated Creation of Accurate FE Meshes of Heritage Masonry Walls from Point Cloud Data. High Performance Fiber Reinforced Cement Composites 6. 2019; ():305-314.
Chicago/Turabian StyleMaarten Bassier; Gilles Hardy; Leidy Bejarano-Urrego; Anastasios Drougkas; Els Verstrynge; Koen Van Balen; Maarten Vergauwen. 2019. "Semi-automated Creation of Accurate FE Meshes of Heritage Masonry Walls from Point Cloud Data." High Performance Fiber Reinforced Cement Composites 6 , no. : 305-314.
Maarten Bassier; Bjorn Van Genechten; Maarten Vergauwen. Classification of sensor independent point cloud data of building objects using random forests. Journal of Building Engineering 2019, 21, 468 -477.
AMA StyleMaarten Bassier, Bjorn Van Genechten, Maarten Vergauwen. Classification of sensor independent point cloud data of building objects using random forests. Journal of Building Engineering. 2019; 21 ():468-477.
Chicago/Turabian StyleMaarten Bassier; Bjorn Van Genechten; Maarten Vergauwen. 2019. "Classification of sensor independent point cloud data of building objects using random forests." Journal of Building Engineering 21, no. : 468-477.
Differential soil settlements can induce structural damage to heritage buildings, causing not only economic but also cultural value losses. In 1963, the Saint Jacob’s church in Leuven was permanently closed to the public because of severe settlement-induced damage caused by insufficient bearing capacity of the foundation. Currently, the church is stabilized using a temporary shoring system. This work aims at implementing a practical modelling approach to predict damage on church nave walls subjected to differential settlements. For that purpose, a finite element model of the Saint Jacob’s church nave was generated and validated through on-site monitoring data including levelling, damage survey and laser scanning. The model takes into account the non-linear behavior of the masonry by means of continuum smeared cracking. The paper introduces two approaches to model the settlement on the structure. One of them consists in the direct application of vertical displacements underneath the structure according to the deformation profile measured on-site. In the second approach, interfaces with different stiffness are placed at the base allowing the structure to deform under its self-weight. In addition, the effect of the settlement profile type in the damage level is analyzed.
Leidy Bejarano-Urrego; Els Verstrynge; Anastasios Drougkas; Giorgia Giardina; Maarten Bassier; Maarten Vergauwen; Koen Van Balen. Numerical Analysis of Settlement-Induced Damage to a Masonry Church Nave Wall. RILEM Bookseries 2019, 853 -861.
AMA StyleLeidy Bejarano-Urrego, Els Verstrynge, Anastasios Drougkas, Giorgia Giardina, Maarten Bassier, Maarten Vergauwen, Koen Van Balen. Numerical Analysis of Settlement-Induced Damage to a Masonry Church Nave Wall. RILEM Bookseries. 2019; ():853-861.
Chicago/Turabian StyleLeidy Bejarano-Urrego; Els Verstrynge; Anastasios Drougkas; Giorgia Giardina; Maarten Bassier; Maarten Vergauwen; Koen Van Balen. 2019. "Numerical Analysis of Settlement-Induced Damage to a Masonry Church Nave Wall." RILEM Bookseries , no. : 853-861.
Advancements in remote sensing and communication technology caused a surge in new methods to capture and share information about tangible heritage. The documentation of these monuments is vital to the conservation process. However, current workflows generate an immense amount of information and often fail to properly relay the context of the scene. Additionally, the distribution of information between different stakeholders is paramount in preventive conservation. The goal of this research is to provide heritage experts with the tools to better capture and communicate information about heritage sites. More specifically, an image recording workflow is presented to rapidly acquire a series of panoramic images of the scene and present them accordingly. An online web application is created based on an existing viewer that allows even unskilled users to access the data and intuitively visit the site. The proposed application can be used to distribute information to stakeholders and supports decision makers to constitute a suitable treatment if necessary. Furthermore, the panoramic viewer and accompanying map can be used as a backbone to link to other data such as 3D models, orthographic images and so on.
Maarten Bassier; Tijs DeLoof; Stan Vincke; Maarten Vergauwen. Panoramic Image Application for Cultural Heritage. Privacy Enhancing Technologies 2018, 386 -395.
AMA StyleMaarten Bassier, Tijs DeLoof, Stan Vincke, Maarten Vergauwen. Panoramic Image Application for Cultural Heritage. Privacy Enhancing Technologies. 2018; ():386-395.
Chicago/Turabian StyleMaarten Bassier; Tijs DeLoof; Stan Vincke; Maarten Vergauwen. 2018. "Panoramic Image Application for Cultural Heritage." Privacy Enhancing Technologies , no. : 386-395.
The documentation and information representation of heritage sites is rapidly evolving. With the advancements in remote sensing technology, increasingly more heritage projects look to integrate innovative sensor data into their workflows. Along with it, more complex analyses have become available which require highly detailed inputs. However, there is a gap in the current body of knowledge of how to transfer the outputs from innovative data acquisition workflows to a set of useful deliverables that can be used for analysis. In addition, current procedures are often restricted by proprietary software or require field specific knowledge. As a result, more data are being generated in heritage projects but the tools to process them are lacking. In this work, we focus on methods that convert the raw information from the data acquisition to a set of realistic data representations of heritage objects. The goal is to present the industry with a series of practical solutions that integrate innovative technologies but still closely relate to the current heritage documentation workflows. An extensive literature study was performed discussing the different methods along with their advantages and opportunities. In the practical study, four deliverables were defined: the use of orthomosaics, web-based viewers, watertight mesh geometry and content for serious games. Each section is provided with a detailed overview of the process and realistic test cases that heritage experts can use as a basis for their own applications. The implementations are applicable to any project and provide the necessary information to update existing documentation workflows. Overall, the ideology is to increase the access to innovative technologies, better communicate the data to the different stakeholders and improve the overall usefulness of the information.
Maarten Bassier; Stan Vincke; Roberto De Lima Hernandez; Maarten Vergauwen. An Overview of Innovative Heritage Deliverables Based on Remote Sensing Techniques. Remote Sensing 2018, 10, 1607 .
AMA StyleMaarten Bassier, Stan Vincke, Roberto De Lima Hernandez, Maarten Vergauwen. An Overview of Innovative Heritage Deliverables Based on Remote Sensing Techniques. Remote Sensing. 2018; 10 (10):1607.
Chicago/Turabian StyleMaarten Bassier; Stan Vincke; Roberto De Lima Hernandez; Maarten Vergauwen. 2018. "An Overview of Innovative Heritage Deliverables Based on Remote Sensing Techniques." Remote Sensing 10, no. 10: 1607.
The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is the creation of accurate 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 processed and the wall topology is reconstructed. However, the preocedure 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, ceilings and floors, 94% of the rooms is correctly identified. A key advantage of the proposed method is that it deals with complex rooms and is not bound to single storeys.
Maarten Bassier; R. Klein; B. Van Genechten; M. Vergauwen. IFCWALL RECONSTRUCTION FROM UNSTRUCTURED POINT CLOUDS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018, IV-2, 33 -39.
AMA StyleMaarten Bassier, R. Klein, B. Van Genechten, M. Vergauwen. IFCWALL RECONSTRUCTION FROM UNSTRUCTURED POINT CLOUDS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018; IV-2 ():33-39.
Chicago/Turabian StyleMaarten Bassier; R. Klein; B. Van Genechten; M. Vergauwen. 2018. "IFCWALL RECONSTRUCTION FROM UNSTRUCTURED POINT CLOUDS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2, no. : 33-39.
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.
Maarten Bassier; M. Bonduel; B. Van Genechten; M. Vergauwen. SEGMENTATION OF LARGE UNSTRUCTURED POINT CLOUDS USING OCTREE-BASED REGION GROWING AND CONDITIONAL RANDOM FIELDS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W8, 25 -30.
AMA StyleMaarten Bassier, M. Bonduel, B. Van Genechten, M. Vergauwen. SEGMENTATION OF LARGE UNSTRUCTURED POINT CLOUDS USING OCTREE-BASED REGION GROWING AND CONDITIONAL RANDOM FIELDS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W8 ():25-30.
Chicago/Turabian StyleMaarten Bassier; M. Bonduel; B. Van Genechten; M. Vergauwen. 2017. "SEGMENTATION OF LARGE UNSTRUCTURED POINT CLOUDS USING OCTREE-BASED REGION GROWING AND CONDITIONAL RANDOM FIELDS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W8, no. : 25-30.
The use of Building Information Modeling (BIM) for existing buildings based on point clouds is increasing. Standardized geometric quality assessment of the BIMs is needed to make them more reliable and thus reusable for future users. First, available literature on the subject is studied. Next, an initial proposal for a standardized geometric quality assessment is presented. Finally, this method is tested and evaluated with a case study. The number of specifications on BIM relating to existing buildings is limited. The Levels of Accuracy (LOA) specification of the USIBD provides definitions and suggestions regarding geometric model accuracy, but lacks a standardized assessment method. A deviation analysis is found to be dependent on (1) the used mathematical model, (2) the density of the point clouds and (3) the order of comparison. Results of the analysis can be graphical and numerical. An analysis on macro (building) and micro (BIM object) scale is necessary. On macro scale, the complete model is compared to the original point cloud and vice versa to get an overview of the general model quality. The graphical results show occluded zones and non-modeled objects respectively. Colored point clouds are derived from this analysis and integrated in the BIM. On micro scale, the relevant surface parts are extracted per BIM object and compared to the complete point cloud. Occluded zones are extracted based on a maximum deviation. What remains is classified according to the LOA specification. The numerical results are integrated in the BIM with the use of object parameters.
M. Bonduel; Maarten Bassier; M. Vergauwen; P. Pauwels; R. Klein. SCAN-TO-BIM OUTPUT VALIDATION: TOWARDS A STANDARDIZED GEOMETRIC QUALITY ASSESSMENT OF BUILDING INFORMATION MODELS BASED ON POINT CLOUDS. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W8, 45 -52.
AMA StyleM. Bonduel, Maarten Bassier, M. Vergauwen, P. Pauwels, R. Klein. SCAN-TO-BIM OUTPUT VALIDATION: TOWARDS A STANDARDIZED GEOMETRIC QUALITY ASSESSMENT OF BUILDING INFORMATION MODELS BASED ON POINT CLOUDS. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W8 ():45-52.
Chicago/Turabian StyleM. Bonduel; Maarten Bassier; M. Vergauwen; P. Pauwels; R. Klein. 2017. "SCAN-TO-BIM OUTPUT VALIDATION: TOWARDS A STANDARDIZED GEOMETRIC QUALITY ASSESSMENT OF BUILDING INFORMATION MODELS BASED ON POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W8, no. : 45-52.