scholarly journals Condition Rating Prediction Using an Interactive Deterioration Model Development Package

2020 ◽  
Vol 10 (24) ◽  
pp. 8946
Author(s):  
Minwoo Chang ◽  
Marc Maguire
Keyword(s):  
Asset Management ◽  
Model Development ◽  
Data Interpretation ◽  
Bridge Inspection ◽  
Explanatory Variables ◽  
Deterioration Model ◽  
Condition Rating ◽  
Initial Dataset ◽  
Stationary Set ◽  
Inspection Data

This paper presents an advanced method to determine explanatory variables required for developing deterioration models without the interference of human bias. Although a stationary set of explanatory variables is ideal for long-term monitoring and asset management, the penalty regression results vary annually due to the innate bias in the inspection data. In this study, weighting factors were introduced to consider the inspection data collected for several years, and the most stationary set was identified. To manage the substantial amount of inspection data effectively, we proposed a software package referred to as the Deterioration Model Development Package (DMDP). The objective of the DMDP is to provide a convenient platform for users to process and investigate bridge inspection data. Using the standardized data interpretation, the user can update an initial dataset for the deterioration model development when new inspection data are archived. The deterministic method and several stochastic approaches were included for the development of the deterioration models. The performances of the investigated methods were evaluated by estimating the error between the predicted and inspected condition ratings; further, this error was used for estimating the most effective number of explanatory variables for a given number of bridges.

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.

scholarly journals Evaluating the role of deterioration models for condition assessment of sewers

2015 ◽  
Vol 17 (5) ◽  
pp. 789-804 ◽  
Author(s):  
Marius Møller Rokstad ◽  
Rita Maria Ugarelli
Keyword(s):  
Failure Modes ◽  
Structural Condition ◽  
Data Availability ◽  
Limited Data ◽  
Sewer Pipes ◽  
Explanatory Variables ◽  
Deterioration Model ◽  
Sewer Rehabilitation ◽  
Inspection Data

Ensuring reliable structural condition of sewers is an important criterion for sewer rehabilitation decisions. Deterioration models applied to sewer pipes support the rehabilitation planning by means of prioritising pipes according to their current and predicted structural status. There is a benefit in applying such models if sufficient inspection data for calibration, an appropriate deterioration model, and adequate covariates to explain the variability in the conditions are available. In this paper it is discussed up to what level the application of sewer deterioration models can be beneficial under limited data availability. The findings show that the indirect nature of the explanatory covariates which are commonly used in sewer deterioration models makes it difficult to harness any benefit from modelling sewer conditions at a network level, but that the deterioration model application still may be beneficial for prioritising inspection candidates. The prediction power of the current sewer deterioration models is limited by the adequacy of the explanatory variables available, and by the fact that different failure modes are mixed in the aggregated condition class, and not modelled explicitly.

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.

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.

Development and Application of a Vehicle Procurement Model for Rural Fleet Asset Management

2005 ◽  
Vol 1927 (1) ◽  
pp. 123-127 ◽  
Author(s):  
Nathan S. Davenport ◽  
Michael D. Anderson ◽  
Phillip A. Farrington
Keyword(s):  
Asset Management ◽  
Management System ◽  
Public Perception ◽  
Federal Grant ◽  
Condition Rating ◽  
Grant Program ◽  
Consistent Basis ◽  
Federal Grant Program ◽  
Asset Management System ◽  
Socioeconomic Data

Advanced asset management systems have emerged as important tools in the management, maintenance, and procurement of vehicles for transit fleet operators. Effective design and use of an asset management system can increase productivity, enhance public perception, and provide a consistent basis for decision making and planning. This paper documents the design and application of a vehicle procurement model in an asset management system created for the Alabama Department of Transportation to manage vehicles purchased and operated through the Section 5311 federal grant program. The vehicle procurement model predicts future vehicle serviceability, or condition rating, by using a combination of factors, including service area socioeconomic data and vehicle usage data. Application of the system helps transportation professionals estimate the overall fleet quality, identify vehicles that will need to be replaced each year, aid in the management of vehicles, and provide a basis for predicting future funding and budgetary needs.

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.

Trip Generation Modeling Using CHAID, a Criterion-Based Segmentation Modeling Tool

1998 ◽  
Vol 1645 (1) ◽  
pp. 24-31 ◽  
Author(s):  
Orlando Strambi ◽  
Karin-Anne Van De Bilt
Keyword(s):  
Model Development ◽  
Generation Model ◽  
Trip Generation ◽  
Modeling Tool ◽  
Explanatory Variables ◽  
Interaction Detection ◽  
Synthetic Populations ◽  
Exploratory Technique ◽  
Population Forecasting ◽  
Chi Squared

Conventional trip generation models are identified, as are the difficulties of model application typical of segmentation problems: identification and categorization of explanatory variables and of the interactions among them. The use of CHAID (Chi-Squared Automatic Interaction Detection), a criterion-based segmentation modeling tool, is explored to analyze household trip generation rates. CHAID models are presented in the form of a tree, each final node representing a group of homogenous households concerning daily trip making. An application to data from an origin-destination survey for São Paulo produced interesting results, in agreement with theoretical expectations and amenable to interpretation based on the likely activity-travel patterns of each group of households generated by the technique. CHAID can be used as an exploratory technique for aiding model development or as a model by itself. The use of CHAID results as a trip generation model was verified through an evaluation of its predictive capability in a cross comparison of two subsamples and through a comparison of observed versus predicted trips at a zone level; the segmentation of households produced by the technique provided good estimates of trip rates and zone totals. The application of a modeling approach requiring a highly disaggregate projection of the population may become possible considering the advances in methods for the generation of synthetic populations. The use of these methods in conjunction with a segmentation model represents an alternative to conventional trip generation models and an opportunity to introduce new population forecasting techniques into transportation planning practice.

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