Search

In bridge design, economically addressing large, lateral earth pressures on bridge abutments is a major challenge. Traditional approaches employ enlargement of the abutment components to resist these pressures. This approach results in higher construction costs. As an alternative, a formal approach using ground anchors to resist lateral soil pressure on bridge abutments is proposed herein. The ground anchors are designed to minimise lateral forces at the pile cap base. Design examples for high stem abutments (heights 6-8 m) are conducted for a simple 33 m long concrete bridge span, with two to three traffic lanes. The abutments are supported by driven, reinforced concrete piles. As lateral forces at the pile cap are significantly reduced, only one row of piles is needed. When compared with common abutment design, the proposed approach halved the number of piles required and decreased the required abutment volume by 37%. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) geometric bridge models play an important role in bridge inspection, assessment, and management. Laser scanning nowadays offers a cost-efficient method to capture dense, accurate 3D topographic data of surfaces of the bridge. However, given the typical complexity of bridges, current workflows using commercial software to construct a bridge model still require intensive labour work. This paper presents a new approach to automatically extract the point cloud of surfaces of structural components of box and slab-beam bridges. The proposed method consists of 3 Parts: (1) point-to-surface, (2) superstructure and (3) substructure extraction. The method uses both spatial point clouds and contextual knowledge to extract point cloud subsets corresponding to surfaces of individual bridge components in a consecutive order from superstructure to substructure. For each bridge component, two levels of extraction are (1) coarse extraction to separate candidate points of the component from the full data set and (2) fine filtering to obtain final 3D points of individual surfaces using cell- or voxel-based region growing (CRG or VRG), followed by a connected surface component (CSC) method. An experimental test on one box-girder and one slab-beam bridges shows that the proposed method successfully extracts all surfaces of bridge components with the lowest F1-score of 0.93 based on a point-based evaluation. Moreover, a shape similarity evaluation also shows that discrepancies between extracted surfaces and ground truth ones are no larger than 0.82 for the area overlap ratio and 0.59 degrees for the angular deviation. The proposed method contributes to the automatic generation of 3D geometric bridge models and to give point clouds of individual surface for damage identification. • Propose a new approach to extract point clouds of individual surfaces of a bridge • Propose cell-based region growing to extract point clouds of planar surfaces • Develop two levels of surface extraction allowing to handle a massive data set • Present a methodology to estimate input parameters for processing point clouds • Succeed to extract surfaces of bridge components with the lowest F1-score above 0.93 [ABSTRACT FROM AUTHOR]

The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications' quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud–Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud–Edge continuum. It focuses on the application's quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications. [ABSTRACT FROM AUTHOR]

Traditional documentation capabilities of laser scanning technology can be further exploited for urban modeling through the transformation of resulting point clouds into solid models compatible for computational analysis. This article introduces such a technique through the combination of an angle criterion and voxelization. As part of that, a k-nearest neighbor (kNN) searching algorithm is implemented using a predefined number of kNN points combined with a maximum radius of the neighborhood, something not previously implemented. From this sample, points are categorized as boundary or interior points based on an angle criterion. Façade features are determined based on underlying vertical and horizontal grid voxels of the feature boundaries by a grid clustering technique. The complete building model involving all full voxels is generated by employing the Flying Voxel method to relabel voxels that are inside openings or outside the façade as empty voxels. Experimental results on three different buildings, using four distinct sampling densities showed successful detection of all openings, reconstruction of all building façades, and automatic filling of all improper holes. The maximum nodal displacement divergence was 1.6% compared to manually generated meshes from measured drawings. This fully automated approach rivals processing times of other techniques with the distinct advantage of extracting more boundary points, especially in less dense data sets (<175 points/m2), which may enable its more rapid exploitation of aerial laser scanning data and ultimately preclude needing a priori knowledge. [ABSTRACT FROM AUTHOR]

Cloud computing could offer good business models for small computational science and engineering (CSE) research groups because these groups often do not have enough human resources and knowledge to manage the complexity of computational and data infrastructure for their research, while cloud computing aims to eliminate that complexity from the user. In this paper, we have analyzed current status of supporting tools for small CSE groups to utilize cloud computing. Although cloud computing is perceived as an interesting model, we have identified several issues that prevent a wide adoption of cloud computing from small CSE research groups. We also present recommendations for addressing these issues in order to attract small CSE groups to utilize cloud computing. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually. [ABSTRACT FROM AUTHOR]

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method). [ABSTRACT FROM AUTHOR]

Over the last decade, particular interest in using state-of-the-art emerging technologies for inspection, assessment, and management of civil infrastructures has remarkably increased. Advanced technologies, such as laser scanners, have become a suitable alternative for labor intensive, expensive, and unsafe traditional inspection and maintenance methods, which encourage the increasing use of this technology in construction industry, especially in bridges. This paper aims to provide a thorough mixed scientometric and state-of-the-art review on the application of terrestrial laser scanners (TLS) in bridge engineering and explore investigations and recommendations of researchers in this area. Following the review, more than 1500 research publications were collected, investigated and analyzed through a two-fold literature search published within the last decade from 2010 to 2020. Research trends, consisting of dominated sub-fields, co-occurrence of keywords, network of researchers and their institutions, along with the interaction of research networks, were quantitatively analyzed. Moreover, based on the collected papers, application of TLS in bridge engineering and asset management was reviewed according to four categories including (1) generation of 3D model, (2) quality inspection, (3) structural assessment, and (4) bridge information modeling (BrIM). Finally, the paper identifies the current research gaps, future directions obtained from the quantitative analysis, and in-depth discussions of the collected papers in this area. [ABSTRACT FROM AUTHOR]

Current processes that create Building Energy Performance Simulation (BEPS) models are time consuming and costly, primarily due to the extensive manual inputs required for model population. In particular, generation of geometric inputs for existing building models requires significant manual intervention due to the absence, or outdated nature of available data or digital measurements. Additionally, solutions based on Building Information Modelling (BIM) also require high quality and precise geometrically-based models, which are not typically available for existing buildings. As such, this work introduces a semi-automated BEPS input solution for existing building exteriors that can be integrated with other related technologies (such as BIM or CityGML) and deployed across an entire building stock. Within the overarching approach, a novel sub-process automatically transforms a point cloud obtained from a terrestrial laser scanner into a representation of a building's exterior façade geometry as input data for a BEPS engine. Semantic enrichment is performed manually. This novel solution extends two existing approaches: (1) an angle criterion in boundary detection and (2) a voxelisation representation to improve performance. The use of laser scanning data reduces temporal costs and improves input accuracy for BEPS model generation of existing buildings. The approach is tested herein on two example cases. Vertical and horizontal accuracies of 1% and 7% were generated, respectively, when compared against independently produced, measured drawings. The approach showed variation in accuracy of model generation, particularly for upper floors of the test case buildings. However, the energy impacts resulting from these variations represented less than 1% of the energy consumption for both cases. • Introduces a semi-automated BEPS input solution for existing, building exteriors • Processes a point cloud to create a buildingŠs geometry as input data for BEPS • Extends angle criterion in boundary detection and voxelisation representation • Laser scanning reduces time and improves input accuracy for BEPS models. • Validated on two cases: vertical and horizontal accuracy of 1 [ABSTRACT FROM AUTHOR]

Road networks are essential elements of a community's infrastructure and need regular inspection. Present practice requires traffic interruptions and safety risks for inspectors. The road detection system based on vehicle-mounted lasers is also quite mature, offering advantages such as high-precision defect detection, high automation, and fast detection speed. However, it does have drawbacks such as high equipment procurement and maintenance costs, limited flexibility, and insufficient coverage range. Therefore, this paper proposes a low-cost unmanned aerial vehicle (UAV)-based alternative using imagery for automatic road pavement inspection focusing on pothole detection and classification. A slicing-based method, entitled the Pavement Pothole Detection Algorithm, is applied to the imagery after it is converted into a three-dimensional point cloud. When compared with manually extracted results, the proposed UAV-structure-from-motion (SfM) method and the associated algorithm achieved 0.01 m level accuracy for pothole depth detection and maximum errors of 0.0053 m3 in volume evaluation for cases studies of both a road and a bridge deck. [ABSTRACT FROM AUTHOR]

Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big data analysis pipelines for manufacturing process data. Thus, this paper characterizes the requirements for process data analysis pipelines and surveys existing platforms from academic literature. The results demonstrate a stronger focus on the storage and analysis phases of pipelines than on the ingestion, communication, and visualization stages. Results also show a tendency towards custom tools for ingestion and visualization, and relational data tools for storage and analysis. Tools for handling heterogeneous data are generally well-represented throughout the pipeline. Finally, batch processing tools are more widely adopted than real-time stream processing frameworks, and most pipelines opt for a common script-based data processing approach. Based on these results, recommendations are offered for each phase of the pipeline. [ABSTRACT FROM AUTHOR]

Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection. [ABSTRACT FROM AUTHOR]

This paper presents the work carried out on a collaborative tripartite project between the USA, Republic of Ireland and Northern Ireland to create and investigate the design, development and testing of a new class of intermeshed steel connections (ISCs) that do not rely on field welding and minimise bolting, thus targeting the facilitation of fast disassembly of steel structures and material reuse. This research took advantage of fully automated, precise, advanced manufacturing cutting technologies (e.g. laser, waterjet and high-definition plasma cutting) to achieve a connection method in steel that previously was only possible in materials such as timber, with the potential to revolutionise the steel construction industry. The paper outlines the ongoing research work by the collaborative team, focusing on the design, fabrication, finite-element analysis (FEA) and scaled experimental testing of side ISCs for the flanges of open sections, which included the use of state-of-the-art digital image correlation technology for non-contact measurements. A simplified connection design procedure is presented based on yielding of the side plates. This design procedure is refined based on the results of experimental testing and FEA of the local axial behaviour of the flange connection, addressing stress concentrations in the flange, fabrication tolerances and material overstrength. [ABSTRACT FROM AUTHOR]