Bridge health index considering deterioration mechanism based on statistical analysis of visual inspection data

2014 ◽  
pp. 1973-1980
Author(s):  
Yu Otake ◽  
Yusuke Honjo
Keyword(s):  
Statistical Analysis ◽  
Visual Inspection ◽  
Health Index ◽  
Deterioration Mechanism ◽  
Inspection Data

Detection of Leukemia Using Image Processing

2018 ◽  
Vol 8 (4) ◽  
pp. 61
Author(s):  
A. Sivasangari ◽  
G. Sasikumar
Keyword(s):  
Image Processing ◽  
Statistical Analysis ◽  
Early Detection ◽  
Visual Inspection ◽  
Causes Of Death ◽  
Cure Rate ◽  
Blood Disorders ◽  
Detection And Diagnosis ◽  
The Moment ◽  
Early Detection And Diagnosis

Leukemia   disease   is one   of    the   leading   causes   of death   among   human. Its  cure  rate and  prognosis   depends   mainly   on  the  early  detection   and  diagnosis  of   the  disease. At  the  moment, identification  of  blood  disorders  is  through   visual  inspection  of  microscopic  images  by  examining  changes  like  texture, geometry, colour  and   statistical  analysis  of  images . This  project  aims  to  preliminary  of  developing  a  detection  of  leukemia  types  using   microscopic  blood  sample using MATLAB. Images  are  used  as  they  are  cheap  and  do  not  expensive  for testing  and  lab  equipment.

Bridge life-cycle prediction through visual inspection data updating

2016 ◽  
pp. 1518-1525
Author(s):  
Mariano Angelo Zanini ◽  
Flora Faleschini ◽  
Carlo Pellegrino
Keyword(s):  
Life Cycle ◽  
Visual Inspection ◽  
Inspection Data ◽  
Data Updating

scholarly journals Utilizing Effective Technology In High-Standard Road Defects Inventory System In Estonia

2021 ◽  
Vol 1202 (1) ◽  
pp. 012009
Author(s):  
Marek Truu ◽  
Romet Raun ◽  
Maret Jentson
Keyword(s):  
Data Collection ◽  
Visual Inspection ◽  
High Standard ◽  
Inventory System ◽  
Inventory Systems ◽  
Full Detail ◽  
Cloud Data ◽  
Road Pavement ◽  
Inspection Systems ◽  
Inspection Data

Abstract Road pavement is expected to withstand enormous traffic loads for long time but sooner or later the deterioration reaches levels when its optimal to apply treatment. While easy to measure roughness or rutting in normal traffic speed, defects are in most countries still collected by means of time-consuming visual inspection in low traffic speeds or expensive and difficult- to-use equipment. Also, most visual inspection systems only operate with aggregated inspection data. That makes data-collection expensive and defects-based decision-making inefficient. In Estonia, defects inventory system utilizes high quality panoramic and orthogonal images to enable data collection in traffic speeds and detailed mapping of pavement defects in 10 classes. Defects mapped in full detail means, that location, shape and size of each defect is known and classified data can be effectively used twice in pavement maintenance planning: for section selection planning in road network level when aggregated and for work method selection in design process when analyzed in detail. Combined with measured lidar-based point-cloud data, detailed 3d-basemap saves both road-owner's and road designer’s valuable time in design phase. In period of 2016-2020, around 35000km of state roads were analyzed with one of the most efficient road defects inventory systems in the world. Also, around 25000 km of municipal and forest roads have been captured with same technology covering several pavement types from bicycle paths to multilane streets and motorways. Current presentation discusses outcomes of Estonian defects inventory study in 2020.

Bridge Health Index: Study of Element Condition States and Importance Weights

2017 ◽  
Vol 2612 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Sylvester Inkoom ◽  
John O. Sobanjo ◽  
Paul D. Thompson ◽  
Richard Kerr ◽  
Richard Twumasi-Boakye
Keyword(s):  
Management System ◽  
Support Network ◽  
Cost Models ◽  
Bridge Management ◽  
Health Index ◽  
Bridge Management System ◽  
Government Accounting ◽  
Index Study ◽  
Inspection Data ◽  
Condition State

The AASHTO Pontis bridge management system has been used to support network-level and project-level decision making on the condition and functional obsolescence of bridges. State departments of transportation often develop bridge inspection data collection methods, deterioration models, cost models, and other preservation analysis capabilities to comply with the requirements of the federal Government Accounting Standards Board. The bridge health index (BHI) in the Pontis bridge management system has been used in the evaluation of the condition of bridges and elements at the project and network levels. This paper investigates three issues in the computation of the BHI: the effects of using linear and nonlinear scales for the condition state weights when computing the element health index (EHI); the application of amplification weights to EHI values to emphasize bridge elements in bad condition; and the development of element weights based on element replacement costs, element long-term costs, element vulnerability to hazard risks, and a combination of these measures. Historical condition data from element-based inspection were used to evaluate these effects at the network level.

Probabilistic modeling of sewer deterioration using inspection data

2008 ◽  
Vol 57 (10) ◽  
pp. 1635-1641 ◽  
Author(s):  
J. Dirksen ◽  
F. H. L. R. Clemens
Keyword(s):  
Visual Inspection ◽  
Surface Damage ◽  
Mechanical Action ◽  
Sewer System ◽  
Sewer Systems ◽  
Historical Databases ◽  
Inspection Data ◽  
Need For Rehabilitation ◽  
Sewer Inspection

Accurate prediction of current and future conditions of sewer systems is crucial to manage the sewer system wisely, cost-effectively and efficiently. The application of historical databases of visual inspection data to sewer deterioration modeling seems common sense. However, in The Netherlands, sewer inspection data is only used to determine the direct need for rehabilitation. This paper outlines the possibilities of using inspection data for deterioration modeling and discusses the problems encountered. A case study was performed on the modeling of the condition aspect ‘surface damage by corrosion or mechanical action’ using a Markov model.

scholarly journals ERROR SOURCES IN PROCCESSING LIDAR BASED BRIDGE INSPECTION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 455-459
Author(s):  
H. Bian ◽  
S. E. Chen ◽  
W. Liu
Keyword(s):  
Quality Management ◽  
Data Processing ◽  
Data Quality ◽  
Visual Inspection ◽  
Inspection System ◽  
Bridge Inspection ◽  
Error Sources ◽  
Scanning Angle ◽  
Management Framework ◽  
Inspection Data

Bridge inspection is a critical task in infrastructure management and is facing unprecedented challenges after a series of bridge failures. The prevailing visual inspection was insufficient in providing reliable and quantitative bridge information although a systematic quality management framework was built to ensure visual bridge inspection data quality to minimize errors during the inspection process. The LiDAR based remote sensing is recommended as an effective tool in overcoming some of the disadvantages of visual inspection. In order to evaluate the potential of applying this technology in bridge inspection, some of the error sources in LiDAR based bridge inspection are analysed. The scanning angle variance in field data collection and the different algorithm design in scanning data processing are the found factors that will introduce errors into inspection results. Besides studying the errors sources, advanced considerations should be placed on improving the inspection data quality, and statistical analysis might be employed to evaluate inspection operation process that contains a series of uncertain factors in the future. Overall, the development of a reliable bridge inspection system requires not only the improvement of data processing algorithms, but also systematic considerations to mitigate possible errors in the entire inspection workflow. If LiDAR or some other technology can be accepted as a supplement for visual inspection, the current quality management framework will be modified or redesigned, and this would be as urgent as the refine of inspection techniques.

Integration of practical supplemental measurements into bridge condition visual inspection grading

2021 ◽  
Vol 16 (4) ◽  
pp. 139-153
Author(s):  
Nefize Shabana ◽  
Ozgur Avsar ◽  
Alp Caner
Keyword(s):  
Visual Inspection ◽  
Highway Bridges ◽  
Limited Information ◽  
End User ◽  
Visual Condition ◽  
Bridge Inspection ◽  
Concrete Degradation ◽  
The Difference ◽  
Inspection Data ◽  
Structural Engineers

The reliability of condition assessment of bridges obtained from analysis of visual inspection data is always a big concern among structural engineers. It has been known that the condition grading of a bridge is very subjective and can convey limited information to the end user. To finalize and verify the reported condition grading, inspectors and bridge owners have mainly been relying on images. It has been known that the image observation may not be sufficient to address all kinds of problems associated with visual condition grading. The integration of practical supplemental measurements into condition grading as proposed in this paper will contribute to minimize the errors in visual inspection. Measurement of vehicle induced vibrations through wireless accelerometers can be used to determine the natural frequencies of the bridge that can be reported at each inspection. The change in frequencies can be an indication of deterioration in stiffness of bridge over the years. Taking concrete samples by chipping at the inspection site and analyzing them under scanning electron microscope (SEM) at the laboratory can be used to identify the current problems with concrete degradation. In this scope, the regular bridge inspection procedure with the proposed enhancements has been performed on field inspection of highway bridges in Turkey to determine the practicality of the quick supplemental measurements and to analyze the difference in grading of the three different inspectors with different level of experiences.

Statistical Analysis of Bridge Management System Inspection Data

2019 ◽  
Author(s):  
Ahmed M. Abdelmaksoud ◽  
Tracy C. Becker ◽  
Georgios P. Balomenos
Keyword(s):  
Statistical Analysis ◽  
Condition Index ◽  
Multivariate Regression Analysis ◽  
Prediction Equation ◽  
Maintenance Scheduling ◽  
Bridge Management ◽  
Bridge Inspection ◽  
Bridge Management System ◽  
Inspection And Maintenance ◽  
Inspection Data

<p>Bridge inspection is essential for sustaining safe and well-performing transportation networks. The Ministry of Transportation of Ontario (MTO) bi-yearly inspects over 2800 bridges in Ontario, Canada. Then assigns each bridge a Bridge Condition Index (BCI) representing its performance level and required rehabilitation<span>. </span>As this is a time and resources consuming practice, this study explores the BCI trends which can allow a better control on inspection and maintenance scheduling. First, statistical analysis is conducted to identify the correlation of the bridge parameters with the BCI. The analysis reveals that the main parameters associated with BCI are bridge age, and time since last major and minor maintenances. Then, multivariate regression analysis is performed to establish a BCI prediction equation function of these parameters. The proposed framework can supplement existing practices for smarter inspection and maintenance scheduling.</p>

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