Classifying and Analyzing Visual 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.

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Development of a Visual Inspection Data Collection Tool for Evaluation of Fielded PV Module Condition

10.2172/1050110 ◽

2012 ◽

Cited By ~ 13

Author(s):

Corrine E. Packard ◽

John H. Wohlgemuth ◽

Sarah R. Kurtz

Keyword(s):

Data Collection ◽

Visual Inspection ◽

Data Collection Tool ◽

Pv Module ◽

Inspection Data

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Probabilistic modeling of sewer deterioration using inspection data

Water Science & Technology ◽

10.2166/wst.2008.308 ◽

2008 ◽

Vol 57 (10) ◽

pp. 1635-1641 ◽

Cited By ~ 19

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.

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ERROR SOURCES IN PROCCESSING LIDAR BASED BRIDGE INSPECTION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-455-2017 ◽

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.

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Integration of practical supplemental measurements into bridge condition visual inspection grading

Bridge Structures ◽

10.3233/brs-200179 ◽

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.

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A Bayesian updating approach for structural damage assessment using visual inspection data

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures ◽

10.1201/b16387-356 ◽

2014 ◽

pp. 2459-2464

Author(s):

M Dirbaz ◽

J Mohammadi ◽

M Modares

Keyword(s):

Damage Assessment ◽

Structural Damage ◽

Visual Inspection ◽

Bayesian Updating ◽

Inspection Data

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Indices for Fast Assessment of Bridge Condition in Taiwan

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.752-753.689 ◽

2015 ◽

Vol 752-753 ◽

pp. 689-697

Author(s):

Nie Jia Yau ◽

Hsien Ke Liao

Keyword(s):

Natural Disasters ◽

Management System ◽

Visual Inspection ◽

Site Investigation ◽

Bridge Management ◽

Bridge Management System ◽

Traffic Loads ◽

Collapse Resistance ◽

Flood Resistance ◽

Inspection Data

Developed in 2000, the Taiwan Bridge Management System (TBMS) is an internet-based, nation-wide bridge management system used by all of the bridge management agencies in Taiwan. Currently, the TBMS has an inventory of more than 24,300 bridges with 14 years of visual inspection data and maintenance records. The inspection data of each bridge are input into the TBMS at least once per two years. In order to have a fast assessment of bridge condition in resisting natural disasters and traffic loads, five indices are established in this research: (1) Degree of flood resistance (DF), (2) Degree of mudflow resistance (DM), (3) Degree of earthquake resistance (DE), (4) Degree of loads resistance (DL), and (5) Degree of collapse resistance (DC). Calculation of these five indices is based on the inventory and visual inspection data of each bridge without further thorough site investigation. These indices are used for screening or sieving bridges out of the TBMS inventory that are potentially vulnerable to natural disasters or traffic loads, thus maintenance efforts can be put on such bridges to improve the efficiency of bridge management.

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