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Author: Nan Hu Publisher: ISBN: Category : Concrete bridges Languages : en Pages : 131
Book Description
This report summarizes a research project aimed at developing degradation models for bridge decks in the state of Michigan based on durability mechanics. A probabilistic framework to implement local-level mechanistic-based models for predicting the chloride-induced corrosion of the RC deck was developed. The methodology is a two-level strategy: a three-phase corrosion process was modeled at a local (unit cell) level to predict the time of surface cracking while a Monte Carlo simulation (MCS) approach was implemented on a representative number of cells to predict global (bridge deck) level degradation by estimating cumulative damage of a complete deck. The predicted damage severity and extent over the deck domain was mapped to the structural condition rating scale prescribed by the National Bridge Inventory (NBI). The influence of multiple effects was investigated by implementing a carbonation induced corrosion deterministic model. By utilizing realistic and site-specific model inputs, the statistics-based framework is capable of estimating the service states of RC decks for comparison with field data at the project level. Predicted results showed that different surface cracking time can be identified by the local deterministic model due to the variation of material and environmental properties based on probability distributions. Bridges from different regions in Michigan were used to validate the prediction model and the results show a good match between observed and predicted bridge condition ratings. A parametric study was carried out to calibrate the influence of key material properties and environmental parameters on service life prediction and facilitate use of the model. A computer program with a user-friendly interface was developed for degradation modeling due to chloride induced corrosion.
Author: Nan Hu Publisher: ISBN: Category : Concrete bridges Languages : en Pages : 131
Book Description
This report summarizes a research project aimed at developing degradation models for bridge decks in the state of Michigan based on durability mechanics. A probabilistic framework to implement local-level mechanistic-based models for predicting the chloride-induced corrosion of the RC deck was developed. The methodology is a two-level strategy: a three-phase corrosion process was modeled at a local (unit cell) level to predict the time of surface cracking while a Monte Carlo simulation (MCS) approach was implemented on a representative number of cells to predict global (bridge deck) level degradation by estimating cumulative damage of a complete deck. The predicted damage severity and extent over the deck domain was mapped to the structural condition rating scale prescribed by the National Bridge Inventory (NBI). The influence of multiple effects was investigated by implementing a carbonation induced corrosion deterministic model. By utilizing realistic and site-specific model inputs, the statistics-based framework is capable of estimating the service states of RC decks for comparison with field data at the project level. Predicted results showed that different surface cracking time can be identified by the local deterministic model due to the variation of material and environmental properties based on probability distributions. Bridges from different regions in Michigan were used to validate the prediction model and the results show a good match between observed and predicted bridge condition ratings. A parametric study was carried out to calibrate the influence of key material properties and environmental parameters on service life prediction and facilitate use of the model. A computer program with a user-friendly interface was developed for degradation modeling due to chloride induced corrosion.
Author: Emily K. Winn Publisher: ISBN: Category : Concrete bridges Languages : en Pages : 168
Book Description
This research documents the development and evaluation of artificial neural network (ANN) models to predict the condition ratings of concrete highway bridge decks in Michigan. Historical condition assessments chronicled in the national bridge inventory (NBI) database were used to develop the ANN models. Two types of artificial neural networks, multi-layer perceptrons and ensembles of neural networks (ENNs), were developed and their performance was evaluated by comparing them against recorded field inspections and using statistical methods. The MLP and ENN models had an average predictive capability across all ratings of 83% and 85%,respectively, when allowed a variance equal to bridge inspectors. A method to extract the influence of parameters from the ANN models was implemented and the results are consistent with the expectations from engineering judgment. An approach for generalizing the neural networks for a population of bridges was developed and compared with Markov chain methods. Thus, the developed ANN models allow modeling of bridge deck deterioration at the project (i.e., a specific existing or new bridge) and system/network levels. Further, the generalized ANN degradation curves provided a more detailed degradation profile than what can be generated using Markov models. A bridge management system (BMS) that optimizes the allocation of repair and maintenance funds for a network of bridges is proposed. The BMS uses a genetic algorithm and the trained ENN models to predict bridge deck degradation. Employing the proposed BMS leads to the selection of optimal bridge repair strategies to protect valuable infrastructure assets while satisfying budgetary constraints. A program for deck degradation modeling based on trained ENN models was developed as part of this project.
Author: Nour Hider Almarahlleh Publisher: ISBN: Category : Bridge failures Languages : en Pages : 82
Book Description
Bridges play a significant role in the U.S. economy. The number of the bridges in the U.S. exceeds six hundred thousand. Almost one third of them are considered structurally deficient and will require more than $164 billion to repair or replace. Identifying the factors that affect the performance of concrete bridge decks during its service life is critical to the development of an accurate condition assessment and deterioration prediction model. Accurate bridge deck deterioration models can provide vital information for predicting short- and long-term behavior of concrete bridge decks and minimizing costly routine inspection and maintenance activities. Therefore, the main goal of this dissertation is to develop a deterioration prediction model for concrete bridge decks that is based on the National Bridge Inventory (NBI) database. To achieve the goal, five deterioration prediction models for concrete bridge decks were developed using Multinomial Logistic Regression, Decision Tree, Artificial Neural Network, k-Nearest Neighbors and Naive Bayesian machine learning techniques. Michigan bridge deck data from NBI between the years 1992 to 2015 were used for training the various prediction models. The results show that the performance of all five developed models were acceptable. However, the artificial neural network achieved the highest accuracy in the validation process. Additionally, bridge decks age, area, average daily traffic, and skew angle are found to be significant factors in the deterioration of concrete bridge decks. Furthermore, it was observed that bridge decks could stay in their condition rating more than the typical 2-year inspection interval, suggesting that inspection schedules could be extended for certain bridges that had slower deterioration rates. The contributions of this work include 1) the development of an optimized deterioration prediction model that can be used in the condition assessment process for concrete bridge decks, 2)the identification of the factors that have the most impact on concrete bridge deck deterioration,and 3) demonstrating that the inspection schedule can be longer than 2 years for bridges that do not deteriorate fast which can lead to cost and time savings. Future work can include the following: (1)developing deterioration prediction models for concrete bridge decks using deep learning techniques; (2) developing deterioration prediction models for other bridge specific elements (i.e., superstructure and substructure) using multivariant analysis; (3) developing deterioration prediction models for other (or all) U.S. states using the framework developed in this research; and (4) investigating the prospect of revising the mandated inspection interval beyond the 2-year period.
Author: Publisher: Transportation Research Board ISBN: 0309129338 Category : Technology & Engineering Languages : en Pages : 96
Book Description
" TRB's second Strategic Highway Research Program (SHRP 2) Report S2-R06A-RR-1: Nondestructive Testing to Identify Concrete Bridge Deck Deterioration identifies nondestructive testing technologies for detecting and characterizing common forms of deterioration in concrete bridge decks.The report also documents the validation of promising technologies, and grades and ranks the technologies based on results of the validations.The main product of this project will be an electronic repository for practitioners, known as the NDToolbox, which will provide information regarding recommended technologies for the detection of a particular deterioration. " -- publisher's description.
Author: Aqeed Mohsin Chyad Publisher: ISBN: Category : Concrete bridges Languages : en Pages : 138
Book Description
Bridges are key elements in the US transportation system. There are more than six hundred thousand bridges on the highway system in the United States. Approximately one third of these bridges are in need of maintenance and will cost more than $120 billion to rehabilitate or repair. Several factors affect the performance of bridges over their life spans. Identifying these factors and accurately assessing the condition of bridges are critical in the development of an effective maintenance program. While there are several methods available for condition assessment, selecting the best technique remains a challenge. Therefore, developing an accurate and reliable model for concrete bridge deck deterioration is a key step towards improving the overall bridge condition assessment process. Consequently, the main goal of this dissertation is to develop an improved bridge deck deterioration prediction model that is based on the National Bridge Inventory (NBI) database. To achieve the goal, deterministic and stochastic approaches have been investigated to model the condition of bridge decks. While the literatures have typically proposed the Markov chain method as the best technique for the condition assessment of bridges, this dissertation reveals that some probability distribution functions, such as Lognormal and Weibull, could be better prediction models for concrete bridge decks under certain condition ratings. A new universal framework for optimizing the performance of prediction of concrete bridge deck condition was developed for this study. The framework is based on a nonlinear regression model that combines the Markov chain method with a state-specific probability distribution function. In this dissertation, it was observed that on average, bridge decks could stay much longer in their condition ratings than the typical 2-year inspection interval, suggesting that inspection schedules might be extended beyond 2 years for bridges in certain condition rating ranges. The results also showed that the best statistical model varied from one state to another and there was no universal statistical prediction model that can be developed for all states. The new framework was implemented on Michigan data and demonstrated that the prediction error in the combined model was less than each of the two models (i.e. Markov and Lognormal). The results also showed that average daily traffic, age, deck area, structure type, skew angle, and environmental factors have significant impact on the deterioration of concrete bridge decks. The contributions of the work presented in this dissertation include: 1) the identification of the significant factors that impact concrete bridge deck deterioration; 2) the development of a universal deterioration prediction framework that can be uniquely tailored for each state’s data; and 3) supporting the possibility of extending inspection schedules beyond the typical 2-year cycles. Future work may involve: 1) evaluating each of the factors that impact the deterioration rates in more depth by refining the investigation ranges; 2) investigating the possibility of revising the regular bridge deck inspection intervals beyond the 2-year cycles; and 3) developing deterioration prediction models for other bridge elements (i.e. superstructure and substructure) using the framework developed in this dissertation.
Author: Tulio Nogueira Bittencourt Publisher: CRC Press ISBN: 1351801368 Category : Technology & Engineering Languages : en Pages : 946
Book Description
Maintenance, Monitoring, Safety, Risk and Resilience of Bridges and Bridge Networks contains the lectures and papers presented at the Eighth International Conference on Bridge Maintenance, Safety and Management (IABMAS 2016), held in Foz do Iguaçu, Paraná, Brazil, 26-30 June, 2016. This volume consists of a book of extended abstracts and a DVD containing the full papers of 369 contributions presented at IABMAS 2016, including the T.Y. Lin Lecture, eight Keynote Lectures, and 360 technical papers from 38 countries. The contributions deal with the state-of-the-art as well as emerging concepts and innovative applications related to all main aspects of bridge maintenance, safety, management, resilience and sustainability. Major topics covered include: advanced materials, ageing of bridges, assessment and evaluation, bridge codes, bridge diagnostics, bridge management systems, composites, damage identification, design for durability, deterioration modeling, earthquake and accidental loadings, emerging technologies, fatigue, field testing, financial planning, health monitoring, high performance materials, inspection, life-cycle performance and cost, load models, maintenance strategies, non-destructive testing, optimization strategies, prediction of future traffic demands, rehabilitation, reliability and risk management, repair, replacement, residual service life, resilience, robustness, safety and serviceability, service life prediction, strengthening, structural integrity, and sustainability. This volume provides both an up-to-date overview of the field of bridge engineering as well as significant contributions to the process of making more rational decisions concerning bridge maintenance, safety, serviceability, resilience, sustainability, monitoring, risk-based management, and life-cycle performance using traditional and emerging technologies for the purpose of enhancing the welfare of society. It will serve as a valuable reference to all involved with bridge structure and infrastructure systems, including students, researchers and engineers from all areas of bridge engineering.
Author: National Research Council (U.S.). Transportation Research Board Publisher: Transportation Research Board National Research ISBN: Category : Technology & Engineering Languages : en Pages : 80
Book Description
"This synthesis will be of special interest and usefulness to bridge engineers and others seeking information on design, construction, and maintenance of bridge decks. Detailed information is presented on the causes, prevention, evaluation, and rehabilitation of deck deterioration related to corrosion of steel reinforcement."--Avant-propos.
Author: Ahmad Shami Publisher: ISBN: Category : Languages : en Pages : 138
Book Description
The ASCE report card 2013 rated bridges at a grade of C+, implying their condition is moderate and require immediate attention. Moreover, the Federal Highway Administration reported that it is required to invest more than $20.5 billion each year to eliminate the bridge deficient backlog by 2028. In Canada 2012, more than 50% of bridges fall under fair, poor, and very poor categories, where more than $90 billion are required to replace these bridges. Therefore, government agencies should have an accurate way to inspect and assess the corrosiveness of the bridges under their management. Numerical Amplitude method is one of the most common used methods to interpret Ground Penetrating Radar (GPR) outputs, yet it does not have a fixed and informative numerical scale that is capable of accurately interpreting the condition of bridge decks. To overcome such problem, the present research aims at developing a numerical GPR-based scale with three thresholds and build deterioration models to assess the corrosiveness of bridge decks. Data, for more than 60 different bridge decks, were collected from previous research works and from surveys of bridge decks using a ground-coupled antenna with the frequency of 1.5 GHz. The amplitude values of top reinforcing rebars of each bridge deck were classified into four categories using k-means clustering technique. Statistical analysis was performed on the collected data to check the best-fit probability distribution and to choose the most appropriate parameters that affect thresholds of different categories of corrosion and deterioration. Monte-Carlo simulation technique was used to validate the value of these thresholds. Moreover, a sensitivity analysis was performed to realize the effect of changing the thresholds on the areas of corrosion. The final result of this research is a four-category GPR scale with numerical thresholds that can assess the corrosiveness of bridge decks. The developed scale has been validated using a case study on a newly constructed bridge deck and also by comparing maps created using the developed scale and other methods. The comparison shows sound and promising results that advance the state of the art of GPR output interpretation and analysis. In addition, deterioration models and curves have been developed using Weibull Distribution based on GPR outputs and corrosion areas. The developed new GPR scale and deterioration models will help the decision makers to assess accurately and objectively the corrosiveness of bridge decks. Hence, they will be able to take the right intervention decision for managing these decks.
Author: Nan Hu
Author: Nan Hu
Author: Emily K. Winn
Author: Nour Hider Almarahlleh
Author: 
Author: Farzad Ghodoosipoor
Author: Ying-Hua Huang
Author: Aqeed Mohsin Chyad
Author: Tulio Nogueira Bittencourt
Author: National Research Council (U.S.). Transportation Research Board
Author: Ahmad Shami