scholarly journals Bridge maintenance management based on routine inspection data: a quantitative approach

2021 ◽  
Author(s):  
Gaowei Xu ◽  
Fae Azhari
Keyword(s):  
Three Dimensional ◽  
Random Variable ◽  
The United States ◽  
Bridge Management ◽  
Condition Rating ◽  
Bridge Maintenance ◽  
Markov Decision ◽  
Working Age ◽  
National Bridge Inventory ◽  
Inspection Data

The United States National Bridge Inventory (NBI) records element-level condition ratings on a scale of 0 to 9, representing failed to excellent conditions. Current bridge management systems apply Markov decision processes to find optimal repair schemes given the condition ratings. The deterioration models used in these approaches fail to consider the effect of structural age. In this study, a condition-based bridge maintenance framework is proposed where the state of a bridge component is defined using a three-dimensional random variable that depicts the working age, condition rating, and initial age. The proportional hazard model with a Weibull baseline hazard function translates the three-dimensional random variable into a single hazard indicator for decision-making. To demonstrate the proposed method, concrete bridge decks were taken as the element of interest. Two optimal hazard criteria help select the repair scheme (essential repair, general repair, or no action) that leads to minimum annual expected life-cycle costs.

scholarly journals A Digital Information Model Framework for UAS-Enabled Bridge Inspection

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6017
Author(s):  
Kamal Achuthan ◽  
Nick Hay ◽  
Mostafa Aliyari ◽  
Yonas Zewdu Ayele
Keyword(s):  
Structural Model ◽  
Three Dimensional ◽  
Information Model ◽  
Unmanned Aerial Systems ◽  
Digital Information ◽  
Bridge Management ◽  
Model Framework ◽  
Inspection Method ◽  
Cloud Data ◽  
Inspection Data

Unmanned aerial systems (UAS) provide two main functions with regards to bridge inspections: (1) high-quality digital imaging to detect element defects; (2) spatial point cloud data for the reconstruction of 3D asset models. With UAS being a relatively new inspection method, there is little in the way of existing framework for storing, processing and managing the resulting inspection data. This study has proposed a novel methodology for a digital information model covering data acquisition through to a 3D GIS visualisation environment, also capable of integrating within a bridge management system (BMS). Previous efforts focusing on visualisation functionality have focused on BIM and GIS as separate entities, which has a number of problems associated with it. This methodology has a core focus on the integration of BIM and GIS, providing an effective and efficient information model, which provides vital visual context to inspectors and users of the BMS. Three-dimensional GIS visualisation allows the user to navigate through a fully interactive environment, where element level inspection information can be obtained through point-and-click operations on the 3D structural model. Two visualisation environments were created: a web-based GIS application and a desktop solution. Both environments develop a fully interactive, user-friendly model which have fulfilled the aims of coordinating and streamlining the BMS process.

Bridge Management for the 21st Century

2000 ◽  
Vol 1696 (1) ◽  
pp. 197-203 ◽  
Author(s):  
James E. Roberts ◽  
Richard Shepard
Keyword(s):  
Performance Measure ◽  
Bridge Management ◽  
Health Index ◽  
Element Level ◽  
The Past ◽  
Bridge Maintenance ◽  
Asset Value ◽  
Numerical Rating ◽  
Improved Performance ◽  
Inspection Data

Bridge management has been a subject of intense interest and development for the past 10 years. In support of improved bridge management, FHWA funded the development of the Pontis bridge computer program, which is now in use by approximately 40 of the 50 states. In addition, many new guide specifications have been produced to assist bridge managers in their efforts to better manage the nation’s aging bridge inventory. The AASHTO Subcommittee on Bridges and Structures has taken the lead along with FHWA in implementing the improved bridge management systems. California and a few other states have been critical of the current ranking system for bridge maintenance and have been working to develop an improved performance measure. The bridge health index (HI), an improved and more comprehensive numerical rating system that uses the element inspection data to determine the remaining asset value of a bridge or network of bridges, is discussed. The HI is more consistent with the element-level evaluation data collected and reported in the Pontis program. Examples of the application of the HI are included.

scholarly journals Evaluation of Illinois Bridge Deterioration Models

2021 ◽  
Author(s):  
Gongkang Fu ◽  
Keyword(s):  
Probabilistic Models ◽  
Inspection System ◽  
Department Of Transportation ◽  
Deterministic Models ◽  
Bridge Inspection ◽  
Condition Rating ◽  
Bridge Maintenance ◽  
National Bridge Inventory ◽  
Transition Times

The National Bridge Inventory bridge inspection system ranks the condition of bridge components on a scale of zero to nine. The resulting condition ratings represent an important element considered in deciding measures for bridge maintenance, repair, and rehabilitation. Thus, forecasting future condition ratings well is critical to reliable planning for these activities and estimating the costs. The Illinois Department of Transportation currently has deterministic models for this purpose. This study’s objective is to review the current models using condition rating histories gathered from 1980 to 2020 in Illinois for the following bridge components: deck, superstructure, substructure, culvert, and deck beam. The results show the current Illinois Department of Transportation models are inadequate in forecasting condition ratings, producing overestimates of the transition times between two condition rating levels for these components / systems, except for the deck beam, which is underestimated. It is recommended that the mean transition times found in this study from condition rating histories are used to replace the current models as a short-term solution. Further research is recommended to develop probabilistic models as a long-term solution to address observed significant variation or uncertainty in condition rating and transition times between condition rating levels.

Drivers of Bridge Decommissioning in the United States

2019 ◽  
Vol 2674 (8) ◽  
pp. 591-603
Author(s):  
Basak Aldemir Bektas ◽  
Ahmed J. M. Albughdadi
Keyword(s):  
United States ◽  
Service Life ◽  
Asset Management ◽  
Relevant Information ◽  
The United States ◽  
Functional Improvement ◽  
Common Finding ◽  
Bridge Management ◽  
Data Files ◽  
National Bridge Inventory

Extending the useful life of bridges through better design, construction, and management is a shared effort among the bridge management community. Data in the National Bridge Inventory (NBI) is valuable for understanding the behavior of bridges throughout their service lives. While the primary reason for bridge replacement, intuitively, would be condition, research has indicated that bridge replacement may not necessarily be driven by the condition of the bridge. The estimated median service life for bridges, 53 years, is much lower than the desired service life of 75 years. This paper summarizes the results of an NCHRP Project which identified the main drivers for bridge decommissioning in the United States, based on findings from three complementary analyses using historic NBI data files, select agency records, and data from old and new structure pairs. A common finding in previous studies was that a significant portion (15–30%) of decommissioning could not be associated with any particular reason. Although poor condition is a significant factor, the major driver of bridge decommissioning is functional improvement, and this explains the majority of the unexplained cases. Structures replaced due to functional reasons tend to be replaced at a younger age, leading to a decrease in the overall decommissioning age. While decisions on functional improvement projects are not led by bridge offices, bridge networks are substantially affected by these decisions. Coordinating functional improvement decisions at the agency level and integrating relevant information with decision support tools can improve financial planning and asset management processes.

scholarly journals Prediction of Bridge Component Ratings Using Ordinal Logistic Regression Model

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Pan Lu ◽  
Hao Wang ◽  
Denver Tolliver
Keyword(s):  
Regression Model ◽  
External Validation ◽  
Highway Bridges ◽  
The United States ◽  
Information Criteria ◽  
Condition Rating ◽  
Validation Procedure ◽  
National Bridge Inventory ◽  
Error Bias ◽  
Akaike’S Information Criteria

Prediction of bridge component condition is fundamental for well-informed decisions regarding the maintenance, repair, and rehabilitation (MRR) of highway bridges. The National Bridge Inventory (NBI) condition rating is a major source of bridge condition data in the United States. In this study, a type of generalized linear model (GLM), the ordinal logistic statistical model, is presented and compared with the traditional regression model. The proposed model is evaluated in terms of reliability (the ability of a model to accurately predict bridge component ratings or the agreement between predictions and actual observations) and model fitness. Five criteria were used for evaluation and comparison: prediction error, bias, accuracy, out-of-range forecasts, Akaike’s Information Criteria (AIC), and log likelihood (LL). In this study, an external validation procedure was developed to quantitatively compare the forecasting power of the models for highway bridge component deterioration. The GLM method described in this study allows modeling ordinal and categorical dependent variable and shows slightly but significantly better model fitness and prediction performance than traditional regression model.

Use of Recursive Partitioning to Predict National Bridge Inventory Condition Ratings from National Bridge Elements Condition Data

2017 ◽  
Vol 2612 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Başak Aldemir Bektaş
Keyword(s):  
Predictive Accuracy ◽  
Recursive Partitioning ◽  
Classification Trees ◽  
The United States ◽  
Alternative Methods ◽  
Bridge Management ◽  
Core Element ◽  
National Bridge Inventory ◽  
Management Functions ◽  
Transportation Agencies

In the United States, National Bridge Inventory (NBI) condition ratings, since the 1970s, and AASHTO’s commonly recognized (CoRe) element condition data, since the 1990s, have provided two major sources of bridge condition data. Although these separate systems of condition assessment had their individual uses, comparing the two, and mapping one from the other had uses for both state and federal agencies and the bridge management community. Alternative methods for this mapping have been proposed in the literature with varying predictive accuracy. With the publication of the new AASHTO Manual for Bridge Element Inspection in 2013, national bridge elements (NBEs) replace the CoRe element condition data as the comparable condition data for the NBI condition ratings. This paper investigates the use of the recursive partitioning method to develop classification trees that predict NBI condition ratings from NBE condition data. On the basis of data from a 2016 submission and 12 transportation agencies, classification trees were developed that presented the most likely NBI condition ratings for a set of logical conditions based on the relative element quantities and the percentage of element quantities in the condition states. The predictive accuracies for the trees are sufficient, and the percentages of exact matches and matches within one error term are better than other studies in the literature. Although the trees can be improved in the future with the availability of more NBE data submissions, the study presented preliminary decision trees with sufficient predictive accuracy that could be adopted by transportation agencies for a variety of bridge management functions.

scholarly journals UAV Formation Shape Control via Decentralized Markov Decision Processes

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 91
Author(s):  
Md Ali Azam ◽  
Hans D. Mittelmann ◽  
Shankarachary Ragi
Keyword(s):  
Control Problem ◽  
Shape Control ◽  
Formation Control ◽  
Three Dimensional ◽  
Geographical Region ◽  
Dynamic Programming Method ◽  
Theoretic Approach ◽  
Control Approach ◽  
Uav Swarm ◽  
Markov Decision

In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive the UAV swarm from an initial geographical region to another geographical region where the swarm must form a three-dimensional shape (e.g., surface of a sphere). As most decision-theoretic formulations suffer from the curse of dimensionality, we adapt an existing fast approximate dynamic programming method called nominal belief-state optimization (NBO) to approximately solve the formation control problem. We perform numerical studies in MATLAB to validate the performance of the above control algorithms.

scholarly journals Long-Term Employment Outcomes among Female Cancer Survivors

2020 ◽  
Vol 17 (8) ◽  
pp. 2751 ◽  
Author(s):  
Christine C. Ekenga ◽  
Eunsun Kwon ◽  
BoRin Kim ◽  
Sojung Park
Keyword(s):  
Cancer Survivors ◽  
Employment Status ◽  
The United States ◽  
Employment Outcomes ◽  
Hours Worked ◽  
Professional Workers ◽  
Working Age ◽  
The Impact ◽  
Female Cancer Survivors

Advances in early detection and treatment have led to a growing population of female cancer survivors, many of whom are of working age. We examined the relationship between cancer and long-term (>5 years) employment outcomes in a nationally representative sample of working-age women in the United States. Data from nine waves of the Health and Retirement Study were used to examine employment status and weekly hours worked among cancer survivors (n = 483) and women without cancer (n = 6605). We used random slope regression models to estimate the impact of cancer and occupation type on employment outcomes. There was no difference in employment status between cancer survivors and women without cancer at baseline; however, during follow-up, cancer survivors were more likely to be employed than women without cancer (odds ratio (OR) = 1.33, 95% confidence interval (CI): 1.11–1.58). Among 6–10-year survivors, professional workers were less likely (OR = 0.40, 95% CI: 0.21–0.74) to be employed than manual workers. Among >10-year survivors, professional workers averaged fewer weekly hours worked (−2.4 h, 95% CI: −4.4–−0.47) than manual workers. The impact of cancer on long-term employment outcomes may differ by occupation type. Identifying the occupation-specific mechanisms associated with the return to work will be critical to developing targeted strategies to promote employment in the growing female cancer survivor population.

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