scholarly journals CONDITION ASSESSMENT OF RC BRIDGES. INTEGRATING MACHINE LEARNING, PHOTOGRAMMETRY AND BIM

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 201-208 ◽  
Author(s):  
P. Borin ◽  
F. Cavazzini
Keyword(s):  
Neural Network ◽  
Point Cloud ◽  
Concrete Beam ◽  
Three Dimensional ◽  
Concrete Cover ◽  
Color Recognition ◽  
Automated Method ◽  
Beam Bridge

<p><strong>Abstract.</strong> The survey of building pathologies is focused on reading the state of conservation of the building, composed by the survey of constructive and decorative details, the masonry layering, the crack pattern, the degradation and the color recognition. The drawing of these representations is a time-consuming task, accomplished by manual work by skilled operators who often rely on in-situ analysis and on pictures. In this project three-dimensional an automated method for the condition survey of reinforced concrete spalling has been developed. To realize the automated image-based survey it has been exploited the Mask R-CNN neural network. The training phase has been executed over the original model, providing new examples of images with concrete cover detachments. At the same time, a photogrammetry process involved the images, in order to obtain a point cloud which acts as a reference to a Scan to BIM process. The BIM environment serves as a collector of information, as it owns the ontology to recreate entities and relationships. The information as extracted by neural network and photogrammetry serve to create the pictures which depict the concrete spalling in the BIM environment. A process of projecting information from the images to the BIM recreates the shapes of the pathology on the objects of the model, which becomes a decision support system for the built environment. A case study of a concrete beam bridge in northern Italy demonstrates the validity of the process.</p>

Automated Surface Inspection Using 3D Point Cloud Data in Manufacturing: A Case Study

2018 ◽  
Author(s):  
Romina Dastoorian ◽  
Ahmad E. Elhabashy ◽  
Wenmeng Tian ◽  
Lee J. Wells ◽  
Jaime A. Camelio
Keyword(s):  
Point Cloud ◽  
Surface Defect ◽  
Fault Location ◽  
Three Dimensional ◽  
Practical Case ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Defect Inspection ◽  
Cloud Data

With the latest advancements in three-dimensional (3D) measurement technologies, obtaining 3D point cloud data for inspection purposes in manufacturing is becoming more common. While 3D point cloud data allows for better inspection capabilities, their analysis is typically challenging. Especially with unstructured 3D point cloud data, containing coordinates at random locations, the challenges increase with higher levels of noise and larger volumes of data. Hence, the objective of this paper is to extend the previously developed Adaptive Generalized Likelihood Ratio (AGLR) approach to handle unstructured 3D point cloud data used for automated surface defect inspection in manufacturing. More specifically, the AGLR approach was implemented in a practical case study to inspect twenty-seven samples, each with a unique fault. These faults were designed to cover an array of possible faults having three different sizes, three different magnitudes, and located in three different locations. The results show that the AGLR approach can indeed differentiate between non-faulty and a varying range of faulty surfaces while being able to pinpoint the fault location. This work also serves as a validation for the previously developed AGLR approach in a practical scenario.

scholarly journals Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption

2017 ◽  
Vol 17 (2) ◽  
pp. 1187-1205 ◽  
Author(s):  
Guangliang Fu ◽  
Fred Prata ◽  
Hai Xiang Lin ◽  
Arnold Heemink ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Volcanic Ash ◽  
Three Dimensional ◽  
Forecast Accuracy ◽  
Improve Model ◽  
Powerful Approach ◽  
Using Data ◽  
D Model

Abstract. Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations.In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space.Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h.

scholarly journals A virtual diorama. Mapping archives in situ at places of cultural significance

1970 ◽  
pp. 22-36
Author(s):  
Jonathan Westin ◽  
Gunnar Almevik
Keyword(s):  
Spatial Information ◽  
Three Dimensional ◽  
Physical Space ◽  
Cultural Significance ◽  
Dimensional Reconstruction ◽  
Historical Photographs ◽  
New Applications ◽  
Archive Material

Using the wooden church of Södra Råda as a case study, this article concerns new applications of technology to contextualise and activate archive material in situ at places of cultural significance. Using a combination of augmented reality and virtual reality, we describe a process of turning historical photographs and two-dimensional reconstruction drawings into three-dimensional virtual models that can be lined up to a physical space. The leading questions for our investigation concern how archive material can be contextualised, and how the result may be made accessible in situ and contribute to place development. The result of this research suggests possibilities for using historical photographs to faithfully reconstruct lost historical spaces as three-dimensional surfaces that contextualise documentation and offer spatial information.

Ship Classification Method for Point Cloud Images Based on Three-Dimensional Convolutional Neural Network

2020 ◽  
Vol 57 (16) ◽  
pp. 161022
Author(s):  
任永梅 Ren Yongmei ◽  
杨杰 Yang Jie ◽  
郭志强 Guo Zhiqiang ◽  
陈奕蕾 Chen Yilei
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Three Dimensional ◽  
Classification Method ◽  
Ship Classification

scholarly journals Underwater-Sonar-Image-Based 3D Point Cloud Reconstruction for High Data Utilization and Object Classification Using a Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1763
Author(s):  
Minsung Sung ◽  
Jason Kim ◽  
Hyeonwoo Cho ◽  
Meungsuk Lee ◽  
Son-Cheol Yu
Keyword(s):  
Neural Network ◽  
Point Cloud ◽  
Field Experiments ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Object Classification ◽  
High Data ◽  
3D Geometry ◽  
Underwater Object ◽  
3D Point Cloud Reconstruction

This paper proposes a sonar-based underwater object classification method for autonomous underwater vehicles (AUVs) by reconstructing an object’s three-dimensional (3D) geometry. The point cloud of underwater objects can be generated from sonar images captured while the AUV passes over the object. Then, a neural network can predict the class given the generated point cloud. By reconstructing the 3D shape of the object, the proposed method can classify the object accurately through a straightforward training process. We verified the proposed method by performing simulations and field experiments.

Three-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering

2020 ◽  
Vol 160 ◽  
pp. 195-207 ◽  
Author(s):  
Shangpeng Sun ◽  
Changying Li ◽  
Peng W. Chee ◽  
Andrew H. Paterson ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Segmentation ◽  
Cotton Bolls

scholarly journals Improved the in-Situ Remediation Effect of Sediment Microbial Fuel Cells by Optimizing the Anode Structure

2021 ◽  
Author(s):  
Henan Li ◽  
Guohong Liu ◽  
Chao Li ◽  
Yongli Sun ◽  
Yujie Feng
Keyword(s):  
Microbial Fuel Cells ◽  
River Sediment ◽  
Three Dimensional ◽  
Honeycomb Structure ◽  
Pollutant Removal ◽  
In Situ Remediation ◽  
Higher Power ◽  
Sediment Carbon

Abstract Six 60-L benthic microbial electrochemical systems (BMES) were built for the bioremediation of river sediment. Carbon mesh anodes with honeycomb-structure supports were compared with horizontal anodes, and the system was tested using different cover depths and anode densities. The pollutant removal, electricity generation, and electrochemistry of the six BMES with different anodes was examined using the Ashi River (Harbin, China) as a case study. Total organic carbon (TOC) and total nitrogen (TN) removal from sediments in BMES with three-dimensional anodes were 20%~30% and 20%~33% higher for the other reactors. Moreover, the honeycomb-structure of the anode also resulted in higher power density and improved humus removal.

Sign in / Sign up

Export Citation Format

Share Document