Three-dimensional Point Cloud Model Research Articles

Two-stage terrestrial laser scan planning framework for geometric measurement of civil infrastructures

Terrestrial laser scanning plays a crucial role in measuring geometry shapes of civil infrastructures. However, its practical application often encounters challenges such as insufficient model quality and acquisition efficiency. To address these issues, this study proposes a two-stage planning scheme that optimises both scan positioning and single-station scan parameters. Recognising the need to consider both structures and background objects, UAV scanning is employed to capture a three-dimensional point cloud model of the actual scenario. A visibility analysis method based on spherical projection is proposed to enhance computational efficiency of virtual scan simulation. Additionally, an improved particle swarm algorithm is utilised to optimise the scan positioning. During onsite deployment, the scanner’s pose is measured to align structural components with the scanner’s coordinate system, allowing for automated scanning based on occupied angular ranges. The effectiveness of this method is demonstrated through geometry measurements of a small outdoor structure and a suspension bridge.

Soil Modeling and Prediction Methods in Dredging Construction Areas

In the current implementation process for dredging projects, due to the lack of an accurate understanding of underwater soil characteristics, construction teams often find it difficult to accurately understand the soil condition. Not only does this lead to a reduced dredger operation efficiency, but it may also cause delays to the project’s progress, as well as increasing its cost. Therefore, there is an urgent need to closely integrate soil analysis technology with dredging construction to ensure that projects can be completed efficiently and to a high standard. Therefore, this paper proposes a soil modeling and prediction method based on a three-dimensional point cloud model. The research objective is to propose a new method of soil mass identification with a strong generalization ability and function, which can not only be applied to dredging engineering but can also identify and analyze statistics of land soil and its mass. The accuracy of this method, based on a convergent grid, can reach 95%.

Effect of spatial differentiation of Pinus massoniana canopy branches and leaves on rainfall interception – an example from Changting County

ABSTRACT The spatial differentiation of forest canopy branches and leaves affects rainfall interception. However, the scientific expression of the spatial differentiation of forest canopy branches and leaves has not been clarified, and revealing the effect of spatial differentiation of branches and leaves of the vertical structure of the forest canopy on rainfall interception is difficult. Using the Pinus massoniana planted in Changting County, China, as the research object, this study quantified the spatial differentiation of canopy branches and leaves in a hierarchical manner by combining the unmanned aerial vehicle (UAV) three-dimensional point cloud model with the landscape metrics and further simulated the interception mechanism of Pinus massoniana forest canopy on rainfall of different intensities by constructing the principal component regression model. The results showed that the spatial differentiation of canopy branches and leaves in different strata responds differently to different rainfall intensities.

Application of 3D laser scanning technology for mapping and accuracy assessment of the point cloud model for the Great Achievement Palace heritage building

Heritage buildings represent history and act as vessels of human civilisation. The significance of these architectural phenomena increases as society increases, which renders their preservation and ethical use increasingly crucial. Hence, preserving heritage buildings is necessary for societal benefit. The accuracy of three-dimensional (3D) point cloud models of heritage buildings is important to their digital conservation. Traditional mapping methods typically require more time and human resources. Furthermore, the outcome is subject to measurement omissions, errors, and other issues, which are contrary to the protection of the object measured. The complex elements of ancient Chinese architecture render it challenging for traditional measuring techniques to accurately capture spatial structural information. The 3D laser scanning technology is a novel technology to obtain 3D data rapidly. In this paper, the Great Achievement Palace of the Confucian Temple in Yuci District, Jinzhong City, Shanxi Province, was used as an example to examine the application of 3D laser scanning technology to acquire point cloud models of heritage buildings. Field data collection for architectural heritage using 3D laser scanning technology requires measuring station setting analysis to ensure effect accuracy while considering the elements of target location and quantity. For the 3D point cloud model of the Great Achievement Palace, error analysis and accuracy assessment were conducted on the quality elements of alignment accuracy between scanning stations, point cloud model reliability, point cloud data noise condition, and whether the point cloud data were stratified. The findings demonstrated that the improved method quickly and accurately acquired 3D point cloud model data and obtained realistic spatial data models of the heritage building.

Deposit comminution in a weak variably-cemented breccia rock avalanche

Rock avalanches display a distinct and complicated behavior that is not yet completely understood. Analyzing the deposit characteristics is crucial for studying the comminution mechanism during its emplacement. Combining field investigation, image processing, and numerical modeling, we explore the deposit characteristics and comminution process of the Wangjiapo rock avalanche triggered by the Mw6.2 Ludian earthquake. First, we use UAV photogrammetry to obtain a three-dimensional point cloud model and a digital orthophoto, from which we semi-automatically extract the geometry and position of 80,602 blocks by adopting a new block photo-analytical procedure, in addition to a further 4388 coarse blocks that were mapped from manual procedure. We analyze the longitudinal and transversal variation of the block distribution within the deposit. Large boulder content was more abundant in the middle part, while the fine blocks were distributed more evenly. Blocks were prevalently equant-sphere (ES) shaped with no dependence on volume and location, but the roundness increased significantly with runout. Additionally, we find fractal dimensions ranging from 1.42 to 1.83, lower with respect to other rock avalanches, probably due to the high weakness of materials. Furthermore, we propose two comminution models based on the significant differences observed between the eastern deposit II and western deposit III, which were caused by variably-cemented breccias. Finally, numerical modeling indicates that the large amount of fine material damped the grain-grain velocity during runout, leading to a limited spread of the rock avalanche and so explaining the short displacement of the front observed.

Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation.

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.

Digital documentation of three largest subterranean structures of Hagia Sophia: The passage under the esonarthex, the vaults under the atrium and the hypogeum

The terrestrial scanning project of Hagia Sophia's subterranean structures took place in 2020. This initiative facilitated the creation of a three-dimensional point cloud model capturing the underground tunnels and structures. Subsequently, in 2022, a more detailed documentation of the older hypogeum within Hagia Sophia was conducted. This study particularly focused on the three largest subterranean structures on the site. The first of these is the passage beneath the esonarthex, delineating a structural transition between the Theodosian and Justinian periods, spanning from the fifth to the sixth centuries. Notably, the discovery of a previously unnoticed column base on the floor of this structure prompted an update to the plan of the Theodosian atrium. The second focus lies on the vaults beneath Justinian’s atrium, requiring an architectural visualization update since Schneider's documentation in 1941. These subterranean structures define the boundaries of the no longer physically existing Justinian atrium, and the debris within them awaits excavation to provide further insights. The last structure examined is the hypogeum dating back to the 4th century. This structure underwent a more detailed redocumentation following a partial clean-up and has been digitally visualized.

Drilling Path Planning of Rock-Drilling Jumbo Using a Vehicle-Mounted 3D Scanner

Achieving intelligent rock excavation is an important development direction in underground engineering construction. Currently, some rock-drilling jumbos are able to perform autonomous operations under ideal contour surfaces. However, irregular contour surfaces resulting from factors such as rock characteristics, drilling deviation, and blasting effects present a significant challenge for automated drilling under non-ideal surfaces, which constrains the intelligentization of rock excavation. To address this issue, this paper proposes a method for extracting contour surfaces and planning drilling paths based on a vehicle-mounted 3D scanner. This method effectively extracts contour surfaces and optimizes drilling paths, thereby improving work efficiency and safety. Specifically, the proposed method includes: (i) the real-time scanning of cross-sectional contours using a vehicle-mounted 3D scanner to construct an accurate three-dimensional point-cloud model and obtain contour over-digging information; the acquired data are compared with theoretical drilling maps in the vehicle’s coordinate system to re-plan the blasting-hole point set; (ii) the development of a volume-based dynamic search algorithm based on the irregularities of contour surfaces to detect potential collisions between holes; and (iii) the conversion of the drilling sequence planning based on the new blasting hole point set into a traveling salesman problem (TSP), and optimization using a Hybrid Greedy Genetic Algorithm (HGGA) to achieve path traversal of all drilling positions. The effectiveness of the proposed method was verified using rock excavation in a certain mine as an example. The results show that the overall recognition rate of the contour over-digging reached over 80%, the number of arm collisions was significantly reduced, and the distance traveled by the drilling rig was reduced by 35% using the improved genetic algorithm-based rock-drilling rig path planning.

A Method for Predicting Canopy Light Distribution in Cherry Trees Based on Fused Point Cloud Data

A proper canopy light distribution in fruit trees can improve photosynthetic efficiency, which is important for improving fruit yield and quality. Traditional methods of measuring light intensity in the canopy of fruit trees are time consuming, labor intensive and error prone. Therefore, a method for predicting canopy light distribution in cherry trees was proposed based on a three-dimensional (3D) cherry tree canopy point cloud model fused by multiple sources. First, to quickly and accurately reconstruct the 3D cherry tree point cloud model, we propose a global cherry tree alignment method based on a binocular depth camera vision system. For the point cloud data acquired by the two cameras, a RANSAC-based orb calibration method is used to externally calibrate the cameras, and the point cloud is coarsely aligned using the pose transformation matrix between the cameras. For the point cloud data collected at different stations, a coarse point cloud alignment method based on intrinsic shape signature (ISS) key points is proposed. In addition, an improved iterative closest point (ICP) algorithm based on bidirectional KD-tree is proposed to precisely align the coarse-aligned cherry tree point cloud data to achieve point cloud data fusion and obtain a complete 3D cherry tree point cloud model. Finally, to reveal the pattern between the fruit tree canopy structure and the light distribution, a GBRT-based model for predicting the cherry tree canopy light distribution is proposed based on the established 3D cherry tree point cloud model, which takes the relative projected area features, relative surface area and relative volume characteristics of the minimum bounding box of the point cloud model as inputs and the relative light intensity as output. The experiment results show that the GBRT-based model for predicting the cherry tree canopy illumination distribution has good feasibility. The coefficient of determination between the predicted value and the actual value is 0.932, and the MAPE is 0.116, and the model can provide technical support for scientific and reasonable cherry tree pruning.

Research and Practice on the Mining Vehicle Loading Evaluation by Single-Line LIDAR Top View Scanning

There are many challenges associated with the manual accounting of mining vehicle loading when installation of weighbridge is impeded by environmental constraints. This article proposes the method that employs the single-line LIDAR, which was fixed at a height, to obtain the top point cloud data of the passing mining vehicles by overhead scanning, and to configurate three-dimensional point cloud models of the materials for rapid accounting of loading. The single-line LIDAR meanwhile develops the management system of the cloud data to analyze and manage the related results, aiming to fulfil the various needs of data backup and query. Tests of proposed method were conducted via evaluating the loading of both unloaded and fully loaded mining vehicles, the test results show that the average absolute error of the loading evaluation is 1.71% and the average consistency error is 0.28%, both of which meet the accuracy requirements of production, and therefore verify the feasibility of the proposed method.

Robotic Manipulations of Cylinders and Ellipsoids by Ellipse Detection With Domain Randomization

A lot of objects commonly found in industrial and household environments are represented by cylindrical shapes (flange plates) and ellipsoids (oranges). The visual tops of cylindrical objects and the outlines of ellipsoids are approximately formed by elliptic shape primitives. Thus, a robot can manipulate these objects by ellipse detection. However, it poses a challenge for existing methods to detect ellipses that are largely interfered in cluttered environments. The supervised learning method provides a potential solution for ellipse detection in such environments, but manually labeling the ground truth for training the learning model costs much time, money and workload. In this work, we propose a novel approach to employing three-dimensional point-cloud models of cylindrical objects to construct synthetic images with ellipses based on domain randomization technology for training the supervised learning model. Using synthetic data generated in this manner, we build an end-to-end deep neural network with a detection backbone, rotating filters and the rotated region proposal net to do ellipse detection. To our knowledge, this is the first supervised learning model trained only on synthetic data for ellipse detection, enabling a robot to grasp cylindrical and ellipsoid objects. To demonstrate the capabilities of the proposed detector, the performance of the proposed method is superior to those of two state-of-the-art detectors on synthetic and public datasets. The proposed model for ellipse detection and data generation pipeline based on domain randomization in a simulation are evaluated by a series of robotic manipulations implemented in real application scenarios. The results illustrate a high success rate on real-world grasp attempts despite having only been trained on a synthetic dataset.

Construction of a three-dimensional point cloud model for health assessment of outdoor infrastructure by combining LiDAR point cloud and SfM point cloud

Construction of a three-dimensional point cloud model for health assessment of outdoor infrastructure by combining LiDAR point cloud and SfM point cloud

Structural state estimation of earthquake-damaged building structures by using UAV photogrammetry and point cloud segmentation

Post-earthquake rapid structural assessment has been a widely recognized and valuable inspection need, which can accelerate community recovery and contribute to seismic resilience. Nowadays, three-dimensional point cloud models can be collected quickly using unmanned aerial vehicles (UAVs) for building structures. Regarding post-event rapid inspection, timely processing and informative interpretation of field data sets may still be a significant challenge, while limited attempts have been made to automatically extract geometrical features of point cloud models for estimating full-field deformation states of seismic-damaged structures. This study proposes a point cloud-based structural component segmentation approach to realize structural inclination and residual drift estimation at both system-level and story-level. Two real-world multi-story structures are illustrated to validate the segmentation and estimation performance of the proposed method by employing wall and column inclination and full-field residual drift distribution. The results demonstrate that the developed approach can segment the target components and present high-precision deformation estimates.

Poster 211: Anatomic Factors Associated with Osteochondral Allograft Matching for Trochlear Cartilage Defects: A Computer-Simulated Study

Objectives: Osteochondral allograft transplantation (OCA) for cartilage defects provides excellent long-term clinical outcomes. Articular step-off between the donor and recipient has been shown to significantly alter contact pressures, potentially contributing to worse long-term survival. The trochlea in particular is at risk for graft mismatch due to the complexity and variability of trochlear anatomy. Currently, commercial allograft donor selection is primarily based on simple anatomical parameters such as trochlear length, trochlear width, and tibial width. However, there is limited guidance in literature on factors which best predict an adequate match, as defined by ≤1.0 mm articular step-off. We hypothesize that more complex anatomic parameters which better describe trochlear morphology, including sulcus angle, sagittal angle, and radius of curvature, may help optimize donor selection to reduce articular step off for trochlear osteochondral allograft transplantation. Methods: Ten deidentified, cadaveric trochlear specimens were utilized for this study (JRF Ortho, Denver, CO). Computed tomography (CT) images of the specimens were obtained, and three-dimensional (3D) point cloud models of the trochleae were then created and exported using a medical segmentation software program (Mimics, Materialise Inc., Leuven, Belgium). Circular defect models were created virtually in each point-cloud model of the recipient trochlea at both supero-lateral (18 mm and 22.5 mm) and central (18 mm, 22.5 mm, 30 mm) locations (Figure 1A). Circular graft models were created on all possible locations on the articular cartilage surface models of the donor trochleae. The graft models were virtually placed on the defect models, and a mean value of the minimum distances between the graft and recipient surfaces was calculated for each position of the graft model. This was repeated for all points in the articular surface model of the donor patella and the best possible match was identified. Step-off was calculated as the least mean square difference between the defect and graft along the periphery. Ideal match was defined as ≤0.5 mm articular step-off, and an acceptable match was defined as ≤1.0 mm articular step-off. Demographic and anatomic factors measured include age, sex, laterality, lateral facet width, medial facet width, lateral condyle radius of curvature, trochlear groove radius of curvature, trochlear length, and trochlear width. Sulcus angle and sagittal angle at the center of the defect and grafts for the central trochlear location were calculated by a custom-designed program (Figure 1B). Tibial width for each specimen was provided by the tissue bank. Differences between the donor and recipient trochleae were calculated for each factor and the absolute value of the difference was utilized for analysis. Pearson correlation ( r) or Spearman rank correlation (ρ) were calculated for continuous or categorical variables, respectively. Linear regression multivariate analysis for was performed with significant univariate predictors. Statistical analysis was performed in STATAv16.1 (Statacorp, Houston TX) and RStudio (software version 4.1.0, R Foundation for Statistical Computing, Vienna, Austria). Results: Ideal graft matches were identified in all superolateral trochlear defects, while central defects had a decreasing frequency of ideal matches with increasing defect size (18 mm: 53.4%, 22.5 mm: 35.21%, 30 mm: 14.8%) (Table 1). Univariate predictors of articular step-off are presented in Table 2. On multivariate analysis, sulcus angle difference (β=0.019, p=0.002), sagittal angle difference (β=0.018, p=0.001), and lateral condyle radius of curvature (β=0.015, p=0.003) remained significant predictors of articular step-off for 18 mm central defects (R2=0.45). For 22.5 mm central defects, sulcus angle difference (β=0.029, p<0.001), sagittal angle difference (β=0.025, p<.001), medial facet width (β=0.026, p=0.021), and lateral radius of curvature (β=0.013, p=0.025) were significant. Similarly, for 30 mm central defects, sulcus angle difference (β=0.03, p=0.001), lateral radius of curvature (β=.032, p=0.004), and trochlear length (β=0.041, p<0.001), were significant predictors of articular step-off (Figure 2). For 18 mm superolateral defects, medial facet width was the only factor with univariate significance. However, on multivariate analysis considering all factors, no significant predictors were identified. For 22.5 mm superolateral trochlear defects, medial facet width (β=.006, p<0.001) was a significant multivariate predictor of articular step-off. For 22.5 mm superolateral defects, trochlear length (β=.002, p=0.003) was also a significant multivariate predictor. Conclusions: Central trochlear defects of increasing size become more difficult to find ideal and acceptable graft matches for based on articular step-off. Tibial width did not correlate with articular step-off in the setting of a computer-simulated model of osteochondral allograft for central or superolateral trochlear defects. Minimizing mismatch in anatomic factors such as sulcus angle, sagittal angle, lateral condyle radius of curvature, and medial facet width may contribute to a more optimal graft. Limitations of this computer-simulated study include the inability to account for surgeon technique in ultimate graft matching outcome. [Figure: see text][Table: see text][Table: see text][Figure: see text]

Remote inspection method for water conveyance tunnel based on single-beam scanning sonar

Water conveyance tunnels perform an important role in water-diversion projects. With a water conveyance tunnel transferring water, the conflict between urban crowded ecology and agricultural water can be relieved. Research shows that there is a significant need for a more objective and accurate inspection of water conveyance tunnels during operation. Hence, we investigate the use of a single-beam scanning sonar to detect a water conveyance tunnel and generate a three-dimensional point cloud model, which can be used to make objective predictions for surface defects. First, a cobweb-like grid matrix was proposed to grid discrete points to establish the topological relationship of the point cloud. Second, a denoising method fusing acoustic properties and intensity was used to filter the noise in the acoustic point cloud. Third, an optimal echo extraction technology based on a second-order differential model was proposed to obtain a high-precision point cloud of the tunnel. Experiments show that the proposed denoising method and optimal echo extraction method are superior to traditional algorithms commonly used in ground point clouds, with good results achieved at real test sites.

Point cloud registration method for maize plants based on conical surface fitting\u2014ICP

Reconstructing three-dimensional (3D) point cloud model of maize plants can provide reliable data for its growth observation and agricultural machinery research. The existing data collection systems and registration methods have low collection efficiency and poor registration accuracy. A point cloud registration method for maize plants based on conical surface fitting—iterative closest point (ICP) with automatic point cloud collection platform was proposed in this paper. Firstly, a Kinect V2 was selected to cooperate with an automatic point cloud collection platform to collect multi-angle point clouds. Then, the conical surface fitting algorithm was employed to fit the point clouds of the flowerpot wall to acquire the fitted rotation axis for coarse registration. Finally, the interval ICP registration algorithm was used for precise registration, and the Delaunay triangle meshing algorithm was chosen to triangulate the point clouds of maize plants. The maize plant at the flowering and kernel stage was selected for reconstruction experiments, the results show that: the full-angle registration takes 57.32 s, and the registration mean distance error is 1.98 mm. The measured value’s relative errors between the reconstructed model and the material object of maize plant are controlled within 5%, the reconstructed model can replace maize plants for research.

A Contrastive Learning Method for the Visual Representation of 3D Point Clouds

At present, the unsupervised visual representation learning of the point cloud model is mainly based on generative methods, but the generative methods pay too much attention to the details of each point, thus ignoring the learning of semantic information. Therefore, this paper proposes a discriminative method for the contrastive learning of three-dimensional point cloud visual representations, which can effectively learn the visual representation of point cloud models. The self-attention point cloud capsule network is designed as the backbone network, which can effectively extract the features of point cloud data. By compressing the digital capsule layer, the class dependence of features is eliminated, and the generalization ability of the model and the ability of feature queues to store features are improved. Aiming at the equivariance of the capsule network, the Jaccard loss function is constructed, which is conducive to the network distinguishing the characteristics of positive and negative samples, thereby improving the performance of the contrastive learning. The model is pre-trained on the ShapeNetCore data set, and the pre-trained model is used for classification and segmentation tasks. The classification accuracy on the ModelNet40 data set is 0.1% higher than that of the best unsupervised method, PointCapsNet, and when only 10% of the label data is used, the classification accuracy exceeds 80%. The mIoU of part segmentation on the ShapeNet data set is 1.2% higher than the best comparison method, MulUnsupervised. The experimental results of classification and segmentation show that the proposed method has good performance in accuracy. The alignment and uniformity of features are better than the generative method of PointCapsNet, which proves that this method can learn the visual representation of the three-dimensional point cloud model more effectively.

An improved computation method for asphalt pavement texture depth based on multiocular vision 3D reconstruction technology

The depth of an asphalt pavement structure is an important index used to reflect the anti-skid performance of the pavement, which directly affects the driving safety of vehicles. In the current specifications (e.g., China, European and American standards), the mean texture depth (MTD) of an asphalt pavement is generally measured by the sand-patch method (SPM). However, SPM is easily affected by the operator’s subjective experience and experimental environment, resulting in low accuracy and high scatter in the data. In view of such shortcomings, a fast and accurate method to obtain the texture depth of asphalt pavement was proposed by designing a new test tool and a computer-aided calculation method. The multiocular vision theory was adopted to reconstruct a three-dimensional (3D) point cloud model of pavement texture. To reflect the concavity and convexity of pavement texture and eliminate the influence of pavement slope, the iterative closest point (ICP) algorithm was employed to obtain the datum reference plane (DRP). On this basis, the pentahedral volume calculation method suitable for evaluating the texture depth of an asphalt pavement was proposed. Laboratory experiment results using SPM were inverted by equal volume calculation to obtain the texture reference plane (TRP) and MTD'. Meanwhile, a 3D average maximum elevation (Avg.EL) algorithm was obtained by the evolution of two-dimensional mean profile depth (MPD). Furthermore, three parameters, namely MTD, MTD', and Avg.EL, were used to measure the texture depth of two field pavement sections in service. The experimental results showed that the correlation between MTD' and MTD was better than that between Avg.EL and MTD, and the degree of scatter was smaller. MTD' could be used directly as a reference value for MTD. In the present work, the 3D volume calculation method of asphalt pavement texture depth was proposed, which integrates the technical flow of image acquisition, model reconstruction, and algorithm analysis. The developed method provides a technical reference for the application of computer vision technology in the analysis of pavement texture depth.

IoT-based low-cost 3D mapping using 2D Lidar for different materials

Three-dimensional (3D) mapping is the technology of profiling objects in three dimensions to map them in real life. 3D mapping is an excellent tool for analysis, surveying, and other engineering domains. 3D light detection and ranging (Lidar) technology usually does 3D mapping, but the cost of 3D Lidar is very high, which constrains its applicability in the Industry. The need for a low-cost 3D mapping has increased widely. A cost-effective 3D mapping based on the Internet of Things (IoT) using 2D Lidar is proposed. 2D Lidar is lower in cost than 3D Lidar, so it completely replaces the 3D Lidar for applications that need moderate or low accuracy. This research sought to confirm the applicability of this low-cost IoT-based advanced sensing technology, i.e., Lidar, in the engineering domain. A two-dimensional Lidar mapping system prototype is constructed, and the same is used to generate three-dimensional (3D) point cloud models. Lidar measurements are greatly dependent on the materials. Every material has different absorbance and reflectivity, which alter the Lidar measurements. In this research, we have shown how different material affects Lidar accuracy.

A novel heritage BIM (HBIM) framework development for heritage buildings refurbishment based on an investigative study of microorganisms

PurposeThis study aims to demonstrate the possibility of showing the functionality of complex microbial groups, within ancient structures within a process of refurbishment on a heritage building information modelling (BIM) platform.Design/methodology/approachBoth a qualitative and qualitative research method will be used throughout, as observational and scientific results will be obtained and collated. This path being; phenomena – acquisition tools – storage – analysis tools – literature. Using this methodology, one pilot study within the scope of demolition and refurbishment, using suitable methods of collecting and managing data (structural or otherwise), will be used and generated by various software and applications. The principle methods used for the identification of such micro-organisms will incorporate a polymerase chain reaction method (PCR), to amplify DNA and to identify any or all spores present. The BIM/historical BIM (HBIM) process will be used to create a remotely-based survey to obtain and collate data using a laser scanner to produce a three-dimensional point cloud model to evaluate and deduce the condition, make-up and stature of the monument. A documentation management system will be devised to enable the development of plain language questions and an exchange information requirement, to identify such documentation required to enable safe refurbishment and to give health and safety guidance. Four data sampling extractions will be conducted, two for each site, within the research, for each of the periods being assessed, that being the Norman and Tudor areas of the monument.FindingsFrom laboratory PCR analysis, results show a conclusive presence of micro-organism groups and will be represented within a hierarchical classification, from kingdom to species.Originality/valueThe BIM/HBIM process will highlight results in a graphical form to show data collected, particularly within the PCR application. It will also create standardisation and availability for such data from ancient monuments to make available all data stored, as such analysis becomes substantially important to enable the production of data sets for comparison, from within the framework of this research.