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.

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.

In recent years, there has been a significant increase in inspecting and evaluating transport infrastructure. Traditionally, these structural data were collected manually by measuring and redrawing the construction against design documents. In recent decades, laser scanning technology can help collect 3D data rapidly and accurately. The 3D point clouds can provide detailed texture and shape information of complex construction such as bridges. This study aims to develop a 3D mesh model for a finite element simulation from a 3D point cloud of a bridge's Pier collected by Terrestrial Laser Scanning (TLS). The point cloud is structured, and the object boundary points are generated using the marching cube algorithm. The boundary and inside points, which imply the vertex of the solid element in the 3D mesh model, are grouped as a new point cloud. The generated point cloud is input into 3D CAD, and the 3D solid model is manually created. As a result, the 3D mesh model is developed and successfully imported to ANSYS software for the structural behavior simulation. The accuracy of generated mesh model is good, with the relative error of geometric parameters being less than 4%. The distance from the point cloud to the mesh model is approximately 5 mm.

Laser scanning (LS) is an effective technology for accurately capturing point clouds of visible surfaces of objects in 3D scenes. The point clouds were subsequently used for various applications, for example, generating 2D drawings of the floor or building information models (BIM) and structural inspection. However, in practice, the products from point cloud are created mainly by using commercial software, in which the quality primarily depends on users’ experiences and may contain the error caused by technician carelessness. This paper proposed a new method to automatically extract the point clouds of the floor and create a 2D drawing of floor slabs. This method analyses features of the points within cells of a 2D cell grid in the xy plane to extract candidate points of the building and each floor, while the cell- and point-based region growing segmentations were employed to extract the final points of the floor and each edge of the floor, respectively. The proposed method was successfully tested on 7.5 million points of a concrete, two-story building with 17 m long x 7m width x 7m height.

Unmanned aerial vehicles (UAVs) are commonly utilized as cost-effective devices for data collection by capturing photos of target objects. UAV images have been used for many applications, such as civil engineering, transportation, architecture, surveying, and mapping. Although commercial UAV image data processing software is suitable for generating orthoimages and dense point clouds of surfaces, it still requires extensive labor to prepare the appropriate point cloud to create a digital elevation model (DEM). This study proposes a method to automatically create DEM from a point cloud generated from UAV images. The proposed method composes of three main steps: (1) Candidate ground points, (2) Ground points extraction, and (3) Creation of a DEM model. The proposed method was tested on three datasets, covering a total area of approximately 45 hectares from 200 images captured by DJI Phantom 4 drone. As a result, the DEMs are successfully created with a spatial resolution of 1.0 m.

Church towers are key cultural heritage. In theory, towers are vertical, while facade elements are symmetrically positioned around the tower axis. However, during service of a structure, building and lifetime conditions cause deviations, with associated risks. Laser scanning point clouds can be used to assess the structural state but a universal approach was missing. The proposed algorithm first estimates the tower inclination, and tests which multi-axis representation best represents the course of the tower. Next, point cloud spatial expansion recovers relative distances and deviations of facade elements. The resulting procedure was applied to assess two Dutch medieval towers including the Old Church in Delft and the St. Bavo Church in Haarlem, respectively. As results of analysis, significant asymmetry was found with a 1.4° deviation of the multi-modal axis of the St. Bavo Church tower together with variations of 0.1%–1.5% for facade slopes, while 0.1°–3.1° radial deviations were found in the position of the turrets of the Old Church tower.

Current mobile systems are capable of efficiently acquiring dense urban point clouds. Still, operational use of such data is hampered by the lack of efficient object extraction methodology. Notably methodology is lacking for automatically extracting objects that do not belong to the road furniture like street signs and light poles but do belong to the street furniture. As an example, object we consider public garbage bins, that are installed and should be maintained in public areas in every city. However, information about types, locations, and condition of these public garbage bins are rarely updated and only obtained through manual measurements. Therefore, an efficient way of collecting information on such public objects is of interest not only for urban management but also when developing digital twins of a city. This study proposes a new method to automatically extract public garbage bins from large urban mobile laser scanning (MLS) point clouds. The proposed method consists of three main steps: (1) cell-, (2) sub-cell-, and (3) surface-based filtering, in which both spatial information of the point clouds and contextual knowledge of the public garbage bins are incorporated to efficiently remove irrelevant 3D points at an early phase and identify and classify different types of public garbage bins. Contextual knowledge includes shape and dimensions, and the relationship between the public garbage bins and the ground surface. A MLS dataset of the city centre of Rotterdam, the Netherlands, consisting of 2.84 billion points organised in 166 tiles of 50 × 75m, and covering an area of about 750 × 750m was used to test the proposed method. Results show that the method can automatically extract ∼90 public garbage bins with an overall detection rate of 89.1%. Moreover, the executing time for the entire dataset was only about 163.6 minutes, which is equivalent to 3.46 seconds per one million points. Although the method was tested here one public garbage bins, it can be easily tuned for the detection of other street furniture objects, like benches, post boxes or bollards.

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.

In construction projects, inspection of structural components mostly relies on classical measurements obtained by measuring tapes, levelling, or total stations. With those methods, only a few points on the structure can be measured, and the resulting inspection may not fully reflect the actual, detailed condition of the complete object. Laser scanning is an emerging remote sensing technology to accurately and quickly capture surfaces of structures in high details. However, because of the complex, massive point cloud data acquired at a construction project, in practice, data processing is still manual work with computer aided programs. To improve upon current workflows, this paper proposes a method to automatically extract point clouds of individual surfaces of structural components of a concrete building, which subsequently can be used to inspect construction quality based on geometric information of the surfaces. The proposed method explores both spatial point cloud information and contextual knowledge of structures (e.g., orientation or shape) derived from building design specifications and practice. For extracting point clouds of surfaces of each structural component, the proposed method consists of 4 consecutive steps for extracting: (1) floors, ceiling slabs, and walls, (2) columns, and (3) primary and (4) secondary beams. Each step consists of two ingredients: (i) rough extracting the candidate points of the component and (ii) fine filtering of the surface points of the components via cell-based and voxel-based region growing segmentation (CRG and VRG) incorporating contextual knowledge of the structural members. Experimental tests on two different types of concrete buildings showed that the proposed method successfully extracts the structural elements, in which the completeness, correctness, and quality from the point-based evaluation are larger than 96.0%, 96.9%, and 92.0%, respectively. Moreover, the evaluation based on a shape similarity showed that the extracted floor, ceiling slab and wall overlap to the ground truth more than 92.5%.

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.

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.

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.

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.

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.

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.

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.

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.