scholarly journals A Digital Information Model Framework for UAS-Enabled Bridge Inspection

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6017
Author(s):  
Kamal Achuthan ◽  
Nick Hay ◽  
Mostafa Aliyari ◽  
Yonas Zewdu Ayele
Keyword(s):  
Structural Model ◽  
Three Dimensional ◽  
Information Model ◽  
Unmanned Aerial Systems ◽  
Digital Information ◽  
Bridge Management ◽  
Model Framework ◽  
Inspection Method ◽  
Cloud Data ◽  
Inspection Data

Unmanned aerial systems (UAS) provide two main functions with regards to bridge inspections: (1) high-quality digital imaging to detect element defects; (2) spatial point cloud data for the reconstruction of 3D asset models. With UAS being a relatively new inspection method, there is little in the way of existing framework for storing, processing and managing the resulting inspection data. This study has proposed a novel methodology for a digital information model covering data acquisition through to a 3D GIS visualisation environment, also capable of integrating within a bridge management system (BMS). Previous efforts focusing on visualisation functionality have focused on BIM and GIS as separate entities, which has a number of problems associated with it. This methodology has a core focus on the integration of BIM and GIS, providing an effective and efficient information model, which provides vital visual context to inspectors and users of the BMS. Three-dimensional GIS visualisation allows the user to navigate through a fully interactive environment, where element level inspection information can be obtained through point-and-click operations on the 3D structural model. Two visualisation environments were created: a web-based GIS application and a desktop solution. Both environments develop a fully interactive, user-friendly model which have fulfilled the aims of coordinating and streamlining the BMS process.

scholarly journals Bridge maintenance management based on routine inspection data: a quantitative approach

2021 ◽  
Author(s):  
Gaowei Xu ◽  
Fae Azhari
Keyword(s):  
Three Dimensional ◽  
Random Variable ◽  
The United States ◽  
Bridge Management ◽  
Condition Rating ◽  
Bridge Maintenance ◽  
Markov Decision ◽  
Working Age ◽  
National Bridge Inventory ◽  
Inspection Data

The United States National Bridge Inventory (NBI) records element-level condition ratings on a scale of 0 to 9, representing failed to excellent conditions. Current bridge management systems apply Markov decision processes to find optimal repair schemes given the condition ratings. The deterioration models used in these approaches fail to consider the effect of structural age. In this study, a condition-based bridge maintenance framework is proposed where the state of a bridge component is defined using a three-dimensional random variable that depicts the working age, condition rating, and initial age. The proportional hazard model with a Weibull baseline hazard function translates the three-dimensional random variable into a single hazard indicator for decision-making. To demonstrate the proposed method, concrete bridge decks were taken as the element of interest. Two optimal hazard criteria help select the repair scheme (essential repair, general repair, or no action) that leads to minimum annual expected life-cycle costs.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

scholarly journals Research on the Status Quo of In-use vehicle Environmental Protection Regular Inspection in Tangshan

2020 ◽  
Vol 165 ◽  
pp. 06059
Author(s):  
Xiaowen Zhang ◽  
Jiguang Wang ◽  
Zhenyu Bai
Keyword(s):  
Environmental Protection ◽  
Weighted Average ◽  
Status Quo ◽  
Project Team ◽  
Inspection Method ◽  
The Status ◽  
Inspection Data ◽  
Qualification Rate

The project team collected more than 1 million in-use vehicles environmental protection regular inspection data for analysis, the data of annual inspection in Tangshan area are sorted out and analyzed, the conclusion is as follows: 1. The weighted average qualification rate of each inspection method for in-use vehicles in Tangshan was 87.9%. 2. The weighted average qualification rate of different emission stages of VMAS is 88.7%, the weighted average qualification rate of different emission stages of TSIC is 93.9%, the weighted average qualification rate of FRAC in different emission stages is 98.3%, and the weighted average qualification rate of LUGDOWN in different emission stages is 77.8%. 3. After the implementation of the new standard, the qualified rate of TSIC and LUGDOWN in Tangshan will be greatly reduced, but it has little influence on the qualified rate of VAMS and FA.

scholarly journals Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 201
Author(s):  
Michael Bekele Maru ◽  
Donghwan Lee ◽  
Kassahun Demissie Tola ◽  
Seunghee Park
Keyword(s):  
Optical Sensors ◽  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Depth Camera ◽  
Terrestrial Laser Scanner ◽  
Cloud Data ◽  
3D Point Clouds ◽  
3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

scholarly journals Na3Co2(As0.52P0.48)O4(As0.95P0.05)2O7

2013 ◽  
Vol 69 (12) ◽  
pp. i85-i86 ◽  
Author(s):  
Youssef Ben Smida ◽  
Abderrahmen Guesmi ◽  
Mohamed Faouzi Zid ◽  
Ahmed Driss
Keyword(s):  
Solid State ◽  
Solid State Reaction ◽  
Title Compound ◽  
General Position ◽  
Structural Model ◽  
Three Dimensional ◽  
The Other ◽  
Coordination Polyhedra ◽  
Bond Valence Sum ◽  
Full Occupancy

The title compound, trisodium dicobalt(II) (arsenate/phosphate) (diarsenate/diphosphate), was prepared by a solid-state reaction. It is isostructural with Na3Co2AsO4As2O7. The framework shows the presence of CoX22O12(X2 is statistically disordered with As0.95P0.05) units formed by sharing corners between Co1O6octahedra andX22O7groups. These units form layers perpendicular to [010]. Co2O6octahedra andX1O4(X1 = As0.54P0.46) tetrahedra form Co2X1O8chains parallel to [001]. Cohesion between layers and chains is ensured by theX22O7groups, giving rise to a three-dimensional framework with broad tunnels, running along thea- andc-axis directions, in which the Na+ions reside. The two Co2+cations, theX1 site and three of the seven O atoms lie on special positions, with site symmetries 2 andmfor the Co,mfor theX1, and 2 andm(× 2) for the O sites. One of two Na atoms is disordered over three special positions [occupancy ratios 0.877 (10):0.110 (13):0.066 (9)] and the other is in a general position with full occupancy. A comparison between structures such as K2CdP2O7, α-NaTiP2O7and K2MoO2P2O7is made. The proposed structural model is supported by charge-distribution (CHARDI) analysis and bond-valence-sum (BVS) calculations. The distortion of the coordination polyhedra is analyzed by means of the effective coordination number.

Research on Generation Rules of Chest Width Point Curve Based on 3D Female Mannequin

2013 ◽  
Vol 796 ◽  
pp. 513-518
Author(s):  
Rong Jin ◽  
Bing Fei Gu ◽  
Guo Lian Liu
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Upper Body ◽  
Secondary Development ◽  
Linear Regression Method ◽  
Point Cloud Data ◽  
Body Type ◽  
Cloud Data ◽  
Engineering Software ◽  
The Individual

In this paper 110 female undergraduates in Soochow University are measured by using 3D non-contact measurement system and manual measurement. 3D point cloud data of human body is taken as research objects by using anti-engineering software, and secondary development of point cloud data is done on the basis of optimizing point cloud data. In accordance with the definition of the human chest width points and other feature points, and in the operability of the three-dimensional point cloud data, the width, thickness, and length dimensions of the curve through the chest width point are measured. Classification of body type is done by choosing the ratio values as classification index which is the ratio between thickness and width of the curve. The generation rules of the chest curve are determined for each type by using linear regression method. Human arm model could be established by the computer automatically. Thereby the individual model of the female upper body mannequin modeling can be improved effectively.

Image-Based Comprehensive Maintenance and Inspection Method for Bridges Using Deep Learning

2018 ◽  
Author(s):  
Xuefeng Zhao ◽  
Shengyuan Li ◽  
Hongguo Su ◽  
Lei Zhou ◽  
Kenneth J. Loh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Visual Inspection ◽  
Health State ◽  
Bridge Management ◽  
Inspection Method ◽  
Maintenance Work ◽  
Maintenance And Inspection ◽  
Fine Turning

Bridge management and maintenance work is an important part for the assessment the health state of bridge. The conventional management and maintenance work mainly relied on experienced engineering staffs by visual inspection and filling in survey forms. However, the human-based visual inspection is a difficult and time-consuming task and its detection results significantly rely on subjective judgement of human inspectors. To address the drawbacks of human-based visual inspection method, this paper proposes an image-based comprehensive maintenance and inspection method for bridges using deep learning. To classify the types of bridges, a convolutional neural network (CNN) classifier established by fine-turning the AlexNet is trained, validated and tested using 3832 images with three types of bridges (arch, suspension and cable-stayed bridge). For the recognition of bridge components (tower and deck of bridges), a Faster Region-based Convolutional Neural Network (Faster R-CNN) based on modified ZF-net is trained, validated and tested by utilizing 600 bridge images. To implement the strategy of a sliding window technique for the crack detection, another CNN from fine-turning the GoogLeNet is trained, validated and tested by employing a databank with cropping 1455 raw concrete images into 60000 intact and cracked images. The performance of the trained CNNs and Faster R-CNN is tested on some new images which are not used for training and validation processes. The test results substantiate the proposed method can indeed recognize the types and components and detect cracks for a bridges.

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