scholarly journals AN EVALUATING BRIDGE HEALTH INDEX OF STEEL BRIDGE RC DECK CONSIDERING DETERIORATION MECHANISMS BASED ON STATISTICAL ANALYSIS OF BRIDGE INSPECTION DATA

2012 ◽  
Vol 68 (3) ◽  
pp. 683-695 ◽  
Keyword(s):  
Statistical Analysis ◽  
Steel Bridge ◽  
Health Index ◽  
Bridge Inspection ◽  
Inspection Data

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>

A Climbing Robot for Steel Bridge Inspection

2021 ◽  
Vol 102 (4) ◽  
Author(s):  
Son Thanh Nguyen ◽  
Hung Manh La
Keyword(s):  
Steel Bridge ◽  
Climbing Robot ◽  
Bridge Inspection

Implementation of Virtual Reality in Routine Bridge Inspection

2003 ◽  
Vol 1827 (1) ◽  
pp. 29-35 ◽  
Author(s):  
David V. Jáuregui ◽  
Kenneth R. White
Keyword(s):  
Virtual Reality ◽  
Easy Access ◽  
Bridge Management ◽  
Bridge Inspection ◽  
Image Creation ◽  
Bridge Inspections ◽  
Recorded Data ◽  
Media Types ◽  
Inspection Data ◽  
Selection Of

The innovative use of QuickTime Virtual Reality (QTVR) and panoramic image–creation utilities for recording field observations and measurements during routine bridge inspections is reported. A virtual reality approach provides the ability to document a bridge’s physical condition by using different media types at a significantly higher level of detail than is possible in a written bridge inspection report. Digitally recorded data can be stored on compact disc for easy access before, during, or after an inspection. The development of a QTVR bridge record consists of four major steps: selection of the camera stations, acquisition of the digital images, creation of cylindrical or cubic panoramas, and rendering of the QTVR file. Specific details related to these steps are provided, as applied to various bridge inspection projects. The potential impact of QTVR on bridge management—in which routine inspection data are a factor in making decisions regarding the future maintenance, rehabilitation, or replacement of a bridge—is discussed.

Bridge Inspection and Management System Based on Smart Phone

2016 ◽  
Author(s):  
Yanbing Ding ◽  
Ruicong Han ◽  
Hao Liu ◽  
Shengyuan Li ◽  
Xuefeng Zhao ◽  
...  
Keyword(s):  
Management System ◽  
Smart Phone ◽  
Mobile Network ◽  
Technical Condition ◽  
Bridge Inspection ◽  
Condition Rating ◽  
Inspection Process ◽  
Inspection Work ◽  
Inspection Data ◽  
User Friendly

For the traditional inspection methods, the visual inspection data is firstly recorded on the inspection forms and then input manually into computer, which is inefficient and creates errors frequently. This research aims at establishing a smartphone-based bridge inspection and management system that can avoid such inputting errors and facilitate the bridge inspection process. The system enables the inspector to complete the inspection information collection in a portable smart phone. The site photos that related to the investigated structures can be easily added and edited during the inspection work with the help of the smart phone. After the investigation, the inspection report and the technical condition rating of the inspected bridge can be automatically generated. The collected data and the GPS information can be uploaded to the terminal server directly via the mobile network. The interface of the mobile software is user-friendly and easy operation, which provides an opportunity for the public to take part in the bridge inspection work, especially for the bridges in rural and mountainous areas. Then, this paper puts forward the relevant ideas on public participation in bridges’ emergency assessment and disposal after the disaster, which can provide data support for the decision-making and disaster relief work.

Quality of Element-Level Bridge Inspection Data

2020 ◽  
Vol 2674 (2) ◽  
pp. 252-261
Author(s):  
Glenn A. Washer ◽  
Mohammad M. Hammed ◽  
Paul Jensen ◽  
Robert J. Connor
Keyword(s):  
Load Capacity ◽  
Field Trials ◽  
Bridge Management ◽  
Bridge Inspection ◽  
Element Level ◽  
Level Data ◽  
Truck Load ◽  
Inspection Data ◽  
Highway System

Bridge inspection results provide input for several important functions such as maintenance, repair, and rehabilitation, bridge load capacity ratings, truck load routing/permitting, and future safety/condition predictions. As a result, the quality and reliability of inspection data are important for bridge management and to ensure the safety and serviceability of bridges. Element-level data collection has been required nationwide for bridges on the National Highway System since 2014, and therefore is relatively new to some bridge owners. The objective of the research reported here was to assess the quality of element-level bridge inspection data by comparing bridge inspection results between different bridge inspectors assessing the same bridges. This paper reports results from two research studies completed to collect data on the quality (i.e., variability) of element-level inspection data. Results of field trials indicated that there was significant variability in the data for bridge elements reported in the study. Based on these data, the element-level inspection results were widely dispersed—the smallest coefficient of variation calculated from the current studies was 18%, but typical values were found to be greater than 50% in most cases, and often greater than 100%. These data provide examples from a series of field trials that illustrate the need for improving the quality of element-level inspections to ensure the reliability of the data provided.

The Information System for Bridge Networks Condition Monitoring and Prediction

2012 ◽  
Vol 5 (1) ◽  
pp. 1-18
Author(s):  
Khalid Aboura ◽  
Bijan Samali
Keyword(s):  
Information System ◽  
Condition Monitoring ◽  
New South ◽  
Management Systems ◽  
Maintenance Optimization ◽  
Bridge Management ◽  
Bridge Inspection ◽  
South Wales ◽  
Bridge Networks ◽  
Inspection Data

This paper introduces an information system for estimating lifetime characteristics of elements of bridges and predicting the future conditions of networks of bridges. The Information System for Bridge Networks Condition Monitoring and Prediction was developed for the Roads and Traffic Authority of the state of New South Wales, Australia. The conceptual departure from the standard bridge management systems is the use of a novel stochastic process built out of the gamma process. The statistical model was designed for the estimation of infrastructure lifetime, based on the analysis of more than 15 years of bridge inspection data. The predictive curve provides a coherent mathematical model for conducting target level constrained and funding based maintenance optimization.

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.

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.

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