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>

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.

Parameterized Logistic Models for Bridge Inspection and Maintenance Scheduling

2021 ◽  
Vol 26 (10) ◽  
pp. 04021072
Author(s):  
Ahmed M. Abdelmaksoud ◽  
Georgios P. Balomenos ◽  
Tracy C. Becker
Keyword(s):  
Logistic Models ◽  
Maintenance Scheduling ◽  
Bridge Inspection ◽  
Inspection And Maintenance

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.

Indices for Fast Assessment of Bridge Condition in Taiwan

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.

Bridge Inspection Quality Management

2005 ◽  
Vol 1933 (1) ◽  
pp. 2-8
Author(s):  
Jaakko I. Dietrich ◽  
Mikko A. Inkala ◽  
Vesa J. Männistö
Keyword(s):  
Quality Management ◽  
Quality Management System ◽  
Quality Level ◽  
Bridge Management ◽  
Bridge Inspection ◽  
Bridge Networks ◽  
Bridge Inspections ◽  
Inspection Data ◽  
Quality Measurements

Reliable data on the condition of bridge networks are critical for successful bridge management. However, little attention has been paid to the quality of the data gathered in bridge inspections. This paper reviews the most important areas of bridge inspection that cause variation in bridge condition data and presents possible misjudgments made as a result of poor inspection data quality. The main elements of the inspection quality management system adopted in the Finnish Road Administration are presented, and the development of the quality of inspection data in 2002 and 2003 is briefly summarized. The evidence shows that the quality of inspection data has improved considerably but that the current quality level is not yet sufficient. The quality control system could be improved by increasing inspector interaction during control inspections, using an independent consultant in inspection quality measurements and inspector training, increasing the number of quality measurements, and introducing quality targets.

Reliability Importance as a Measure of Bridge Element Condition Index for Deteriorating Bridges

2019 ◽  
Vol 2673 (12) ◽  
pp. 327-338
Author(s):  
Sylvester Inkoom ◽  
John O. Sobanjo
Keyword(s):  
Asset Management ◽  
Condition Index ◽  
Performance Measure ◽  
Cost Models ◽  
Bridge Management ◽  
Expansion Joints ◽  
State Highway ◽  
Optimal Resource ◽  
Inspection Data ◽  
Bridge System

As a concerted effort to improve the outcomes of bridge asset management, state highway agencies and Departments of Transportation (DOT) often improve their modes of bridge performance assessment using advanced deterioration models, optimized cost models, and other multi-objective preservation analysis in bridge monitoring processes. This paper attempts to utilize reliability importance index of a bridge component as a performance measure of the criticality of each element or subsystem to the entire bridge system. This paper investigates three major concepts regarding the importance of bridge elements: (i) the contribution of individual bridge elements to the overall reliability of the bridge system, (ii) the effect of critical elements on bridge deterioration, and (iii) the rank of the importance of elements and the potential economic consequence of their failure for optimal resource allocation. Computational methodologies are applied to legacy inspection data for bridges monitored over 20 years to evaluate the probability of failure of certain bridge elements. Based on the reliability importance criterion, the superstructure and substructure elements were considered to be critical to the survival of bridges. Bridge management and miscellaneous elements (such as expansion joints, railings, and sign structures), though important, did not generally contribute to the overall deterioration of the bridge.

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