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.

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.

<p><strong>Abstract.</strong> By adopting Building Information Modelling (BIM) software, the architecture, engineering and construction (AEC) industry shifted from a two-dimensional approach to a three-dimensional one in the design phase of a building. However, a similar three-dimensional approach for the visualisation of the current state of the construction works is lacking. Currently, progress reports typically include numerous pictures of the construction site or elements, alongside the appropriate parts of the 3D as-design BIM model. If a proper transition to a <i>3D design versus 3D current state</i> were achieved, the evolved type of reports would become more comprehensible, resulting in more well-informed decision-making. This requires a single, unique software platform that is able to import, process, analyse and visualise both the as-design BIM model as well as the recorded data of the current construction state. At present however, the visualisation and interpretation of the different datasets alone requires already multiple software packages.</p><p>As a partial solution this work presents a platform to easily visualise and interpret various data sources such as point clouds, meshes and BIM models and analysis results. Recent advances of gaming engines focus on and allow for an excellent visualisation of mesh data. Therefore all of the aforementioned data sources are converted into mesh objects upon importing. Moreover, gaming engines provide the necessary tools to traverse the scene intuitively allowing construction site managers and other stakeholders to gain a more complete and better oversight of the construction project. Furthermore, these engines also provide the possibility to take the immersion to the next level: incorporating the 3D entities into a Virtual Reality (VR) environment makes the visualised data and the executed analyses even more comprehensible.</p><p>By means of a case study, the potential of the presented approach is showcased. The real-world construction site recordings, models and analyses are visualised and implemented in VR using the Unity gaming engine.</p>

The monitoring of construction sites and the achieved progress is a vital aspect of construction works in the Architecture, Engineering and Construction (AEC) industry. Only if three-dimensional reconstructions require a limited time, the creation of consecutive datasets, portraying the site in subsequent phases, becomes feasible. Moreover, a shared coordinate system between all datasets is essential for monitoring purposes. In this work, a new photogrammetric framework is presented to shift from the current error-prone and tedious manual geo-referencing process to a semi-automated one. The fundament of the method is an accurately processed reference module that repeatedly serves as the starting point for processing subsequent image datasets. By means of overlap between pictures in both datasets, the coordinate system, incorporated in the reference module, is inherited by the subsequent datasets, hence bypassing the indication process. The proposed procedure is able to outperform current methods, while requiring less time considered over all consecutive datasets. In our experiments, we compared an unaltered part of two subsequent datasets, each of them processed via the traditional and our proposed method. The obtained mean disparity was 9 mm, while for the manual approach it was 16 mm. Especially for comparative analyses, the proposed approach yields excellent results as every dataset is registered exactly the same, whereas results diverge more when following manual methods. In conclusion, our approach is favourable over the current one, especially for a multitude of consecutive site reconstructions, as no ground control points (GCPs) must be indicated in each separate subsequent dataset, while yielding similar to even better results.

<p><strong>Abstract.</strong> 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.</p><p>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.</p>

<p><strong>Abstract.</strong> 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.</p><p>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.</p>

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<p><strong>Abstract.</strong> Remote sensing techniques are invaluable for the documentation and preservation of built heritage. The techniques facilitate fast documentation of highly complex heritage structures with improved accuracies. Furthermore they improve the degree of detail substantially. This is extremely useful for the restoration of collapsed elements or the reassembly of dismantled structures. These entities are often challenging to puzzle back together. Moreover, the differential settlements of the elements over time heavily influence the relative position and orientation of the remaining pieces, further complicating the reconstruction. Digital modelling solutions with a 3D model of the current situation as take-off, are desperately needed by the industry to tackle the present obstacles. In this work, a framework is proposed that facilitates a more accurate reassembly of dismantled heritage elements. It consists of three major phases starting with the accurate recording of the current situation as well as the preserved components. Subsequently, the new design is dititally modelled, reducing the necessary time for the reassembly of the structure, which is the last step in the rebuilding workflow. The presented framework allows for an efficient and comprehensible reconstruction of the structure. A key aspect in the approach is the detection of missing components and the estimation of their dimensions for the production of accurate replicas. The potential is showcased by means of two case studies on the reassembly of flying buttresses and rib vaults of the Saint-James church in Leuven, Belgium, which is currently undergoing major stabilisation works. The presented approach allows heritage experts to gain better oversight over their reassembly project and work more efficiently.</p>