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.

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This paper introduces a method to automatically estimate vertical and horizontal clearances of highway viaducts and gantries from Mobile Laser Scanner (MLS) point clouds. It is essential to have accurate data on the vertical and horizontal clearances of overhead infrastructure objects along the highway. Accurate clearance data is used for routing oversized transports, infrastructure reconstruction, maintenance and settling legal claims after incidents. The proposed method takes a point cloud of an infrastructure object as input, and as output provides the user with a concise overview of the horizontal and vertical clearances of the object. A point cloud of a highway overpass or gantry is segmented into the different clusters relevant for determining the clearances. The discrete points in these clusters will then be used to approximate their surfaces with B-splines. Subsequently the minimal clearances can be estimated. These clearances are estimated at certain pre-specified locations according to guidelines from the highway authority. The paper also includes a comparison of the inferred clearances from the point clouds with archived measurements performed by third party contractors. For this case study, a Dutch highway section containing 50 gantries and 20 viaducts is selected. Along this stretch of highway the clearances are estimated. The estimated clearances for each structure are then compared with archived in situ measurements. This will give a quantitative analysis of the quality of the estimated clearances. The estimated vertical clearances have an overestimation of 20-30 mm compared to the validation data. The horizontal clearances show a median underestimation of 20 mm.

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This paper describes an investigation into the use of advanced manufacturing techniques for the creation of a new class of intermeshed steel connections that rely on neither welding nor bolting. The project detailed herein lays the groundwork to transform the steel building construction industry by advancing the underlying science and engineering precepts for intermeshed connections created from precise, volumetric cutting. The proposed system enhances the integration between design, fabrication, and installation. Fully automated, precise, volumetric cutting of open steel sections poses challenges regarding the load-Transfer mechanisms and failure modes for intermeshed connections. Implementation of the intermeshed connection would cause a discontinuity in the beam; therefore, the effects of such a configuration on the behavior of the steel frame are investigated in the current paper. Load resistance and design of these connections are also explored with physical tests and finite element modeling to investigate the mechanics of intermeshed connections, including stress and strain concentrations, fracture and failure modes, and connection geometry optimization.

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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).@en

A three-dimensional (3D) geometric model of a bridge plays an important role in inspection, assessment and management of the bridge. As most bridges were built after the second world war, 3D bridge models are rarely available. A recent development in laser scanning offers a cost-efficient method to capture dense, accurate 3D topographic data of the bridge. However, given the typical complexity of the bridge, a current workflow based commercial software to construct the bridge model still requires intensive labour work. This paper introduces a new approach to extract the point cloud of each surface of structural components of a slab/box beam bridge automatically in a sequential order from a superstructure to a substructure. The proposed method first employs a quadtree to decompose the point cloud of the bridge into two dimensional (2D) cells. Second, a kernel density estimation is used to separate a point cloud describing patches of surfaces within the cells. Subsequently, the cell- and voxel-based region growing are developed to segment patches within the cells/voxels for the superstructure and substructure, respectively. Moreover, knowledge of the bridge’s components (e.g. position, orientation, or shape) is introduced to allow the proposed method to identify criteria for filtering irrelevant objects, and to establish criteria for extracting the components. An experimental test shows the proposed method successfully extracts all surfaces of the bridge components.

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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.

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Churches are part of heritage structures that take an important role in Europe ́s cultural identity. As such, these structures must be protected to prevent catastrophic collapse and any damage must be reported timely to establish planning to maintenance and restoration. This can be achievable when the churches are monitored periodically with regular intervals. However, this monitoring strategy has not been available in most of the Europe’s churches for a number of reasons, complexity of the structures and limited budget are just two of them. Laser scanning has been widely used in capturing rich three-dimensional (3D) topographic data of visible surfaces of a structure with high accuracy. This paper presents a methodology to determine the shape and possible deviation from verticality of the church’s tower for monitoring deformation using a terrestrial laser scanner. The 500-year old wooden tower of St. Bavo Church in Haarlem, Netherlands is selected as a case study. First, point clouds of the tower captured from different views are registered into the same coordinate system. Second, a RANSAC method is employed to extract point clouds of a whole façades of the tower. Next, a point and surface-based method is proposed to compute the deformation of the surface from its data points. The results indicate that there is slightly different deformation between the tower facades in the same story and in neighbour stories. Moreover, the maximum total relative deformation at Story 7 of the tower by 0.63m.@en

Purpose: Terrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation. Design/methodology/approach: In practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface S_ref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank. Findings: The study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation. Research limitations/implications: Due to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy. Practical implications: This study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Social implications: The results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure. Originality/value: Although a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.

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This paper proposes a methodology to automatically extract components of an oil storage tank from terrestrial laser scanning (TLS) point clouds, and subsequently to create a three-dimensional (3D) solid model of the tank for numerical simulation. The proposed method is integrated into a smart analysis layer of a digital twin platform consisting of three main layers: (1) smart analysis, (2) data storage, and (3) visualisation and user interaction. In this proposed method, primary components of the tank were automatically extracted in a consecutive order from a shell wall to roof and floor. Voxel-based RANSAC is employed to extract voxels containing point clouds of the shell wall, while a valley-peak-valley pattern based on kernel density estimation is implemented to remove outlier points within voxels representing to the shell wall and re-extract data points within voxels adjoined to the shell wall. Moreover, octree-based region growing is employed to extract a roof and floor from remaining point clouds. An experimental showed that the proposed framework successfully extracted all primary components of the tank and created a 3D solid model of the tank automatically. Resulting point clouds of the shell wall were directly used for estimating deformation and a 3D solid model was imported into finite element analysis (FEA) software to assess the tank in terms of stress-strain. The demonstration shows that TLS point clouds can play an important role in developing the digital twin of the oil storage tank.

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Development of laser scanning has offered great opportunity to capture three-dimensional (3D) topographic information of objects’ surfaces in a highly accurately and efficiently. Particularly, a terrestrial laser scanner is able to acquire millions of points within a second at millimetre accuracy. This technology has been widely used in many civil engineering applications, including surveying, construction management, and infrastructure inspection. Traditionally, tank inspection was carried out on-site by physical inspectors with suitable measurement equipment (e.g. tapes, staffs and a total station). This approach, albeit the most common one, has many downsides: subjective results, slow and expensive procedure, requirement of experienced and trained inspectors and close service of the tank. Additionally, all results are stored as hard copies, which lead to difficulties in tracking damage development and management. To mitigate these disadvantages, this paper proposes a method for (i) automatically extracting a point cloud of a tank wall from a massive data points, and (ii) evaluating the tank wall through its deformation.

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This paper proposes a framework to automatic extract structural elements of reinforced concrete buildings from laser scanning data, which can be used in dimensional quality control and surface defect identification. The framework deploys both spatial information of a point cloud and contextual knowledge of building structures to extract the structural elements in a sequential order: floors and ceilings, walls, columns and beams. The method starts to extract a subset data containing candidate points of the structural elements and segmentation methods and filtered based contextual knowledge subsequently apply to obtain the final points of the elements. In this framework, a combination between kernel density estimation and a cell-patch-based region growing are to extract the floors, ceilings and walls, while the points of the columns and beams are achieved through a voxel-based region growing. 23.5 million data points of one story of the building is used to test a performance of the proposed framework. Results showed all structural components are successfully extracted. Moreover, completeness, correctness, and quality indicated through point-based performance report larger than 96.0%, 96.9% and 93.0%, respectively while overlap rates of the floors, ceilings and walls are no less than 95.3%. Interestingly, an executing time of the proposed method is about 7.7seconds per a million point.

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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.

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Defects in a construction site is inevitable and these defects must be inspected and reported timely to minimize extract cost due to repair the defects. However, in practice, quality of structural elements is often inspected by a site supervisor with traditional tools (e.g. measuring tapes, total stations or levelling) at only specific locations on the structures. That results the current inspection pipeline is subjective and inefficient. As such, a new approach must develop to support project managers reporting surface defects timely and using a digital tool in project management efficiently. A great achievement of laser scanning platforms allows to acquire three-dimensional (3D) topographic data of structures' surfaces in a construction site quickly and accurately. A current terrestrial laser scanner (TLS) can capture more than a million points per second with a sub-millimetre accuracy. This technology has been gradually implemented in evaluating progress, quality and quantity, and visualisation for construction management. However, a processing point cloud requires intensive labour work because of the complexity of the construction site and massive data points. This paper is to develop the algorithm to automatically access structural components for quality control of the construction project, in which the proposed method is focused to evaluate the flatness of the floors and ceiling. The method starts to decompose the point cloud of the building storey into 2D cells by using a quadtree. Subsequently, a combination of kernel density estimation (KDE) and cell-based segmentation (CbS) to extract the data point affiliated surfaces of the floor and ceiling. Next, edges of the slabs are detected and then are used to generate the reference surface to compute deformations by using both point- and cell-surface methods. The proposed method is tested on 23.9-million-point cloud of a storey of the reinforced concrete building acquired from the TLS. An experimental test shows the proposed method successfully extracts all surfaces of the floors and ceiling and a report also shows the slab deformation varying in a range from -36.04mm to 42.03mm for point-surface method and about 75% of the deformation is within a range of mean  standard deviation.@en

In construction projects, inspection of structural components mostly relies on typical measurements (e.g. measuring tapes, levelling or total stations). Additionally, with those methods, only a few points on the structure can be measured, and resulted inspection may not fully reflect the actual, detailed condition. Laser scanning is emerging a remote sensing technology to capture the structures' surfaces in high details accurately and quickly. However, because of complex, massive data points acquired from the construction project, in practice, data processing is still manual work with support of computer aided program. To improve current workflows, this paper proposes a method automatically extracting structural components of the concrete building and subsequently inspects them in a term of deformation. The proposed method explores both spatial information of a point cloud and contextual knowledge of structures (e.g. orientation or shape). Additionally, based on the fundamental design of the structures, component's boundaries are automatically extracted to establish un-deformed surfaces of the components for deformation measurement.

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Increasing traffic weights and aggressive environmental conditions may result in unexpected deterioration of a bridge’s components. Particularly, most bridges in Europe and US over half life span are affected by such impact. Structural deficiencies may cause partial or full collapse of bridges resulting in problems for human life, economy, society and environment. As such, deformation monitoring of the bridge’s components has high priority in bridge inspection and assessment. Laser scanning has been used to capture the three-dimensional (3D) topographic surface of structures accurately and efficiently, which can be subsequently used to measure change of the structures. This paper introduces three approaches called point-to-surface (P2S), point-to-cell (P2C) and cell-to-cell (C2C) to measure the deformation of a structure using laser scanning data. This study also investigates the impact of the quality of a point cloud and selected surface or cell size to the achieved accuracy of deformation detection, which will be demonstrated through an implementation to measure the bridge’s vertical clearance, which is the maximum vertical drop distance from the bottom of the bridge deck to the ground or water level.@en

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.@en

When aerial laser scanning (ALS) is deployed with targeted flight path planning, urban scenes can be captured in points clouds with both high vertical and horizontal densities to support a new generation of urban analysis and applications. As an example, this paper proposes a hierarchical method to automatically extract data points describing road edges, which are then used for reconstructing road edges and identifying accessible passage areas. The proposed approach is a cell-based method consisting of 3 main steps: (1) filtering rough ground points, (2) extracting cells containing data points of the road curb, and (3) eliminating incorrect road curb segments. The method was tested on a pair of 100 m × 100 m tiles of ALS data of Dublin Ireland's city center with a horizontal point density of about 325 points/m2. Results showed the data points of the road edges to be extracted properly for locations appearing as the road edges with the average distance errors of 0.07 m and the ratio between the extracted road edges and the ground truth by 73.2%.

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