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

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.

Binary Logistic Regression Approach for Decision Making in Bridge Management

Bridge management professionals need effective tools to help guide the decision-making process and maintain quality infrastructure in a region. A new binary response is herein defined by categorizing bridges as at-risk and not at-risk, based on the existing overall bridge condition scores. Fitting binary logistic regression model for the response, the probability of a bridge being at-risk is expressed in terms of the primary bridge factors age, load, types of construction material and structural design, and conditions of the deck, superstructure, and substructure. These estimated probabilities multiplied by specified consequence values are used to introduce the risk classes and their ranks. Employing the method for training and validating sets of sizes 13,540 and 3,385 in 2017, and 13,481 and 3,370 in 2018 data in National Bridge Inventory (NBI) Indiana, a statistically significant model is established containing age, load, conditions of both superstructure and substructure. Moreover, at-risk bridges are identified from Indiana NBI data in both years and for a subset from Connecticut in 2017. The novel bridge-ranking tool prioritizes bridges for maintenance purposes such as replacing or repairing and hence efficiently guides the management in the decision-making process for capital expenditures, and perhaps, for predicting the missing overall bridge condition.

Evaluation of Illinois Bridge Deterioration Models

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.

Quantifying Bridge Element Vulnerability over Time

The bridge has been a crucial element of the transportation system of the U.S.A. for many years. The National Bridge Inventory (NBI) reported more than 615,000 national bridges in 2018. Maintaining and fixing bridges is a crucial task for transportation agencies to keep the road network connected. Louisiana, which has 12,899 bridges, was selected as the study site for this study. The American Road and Transportation Builders Association (ARTBA) reported in 2019 that 13% of all Louisiana bridges were classified as structurally deficient. This study applies a data mining algorithm, the empirical Bayes geometric mean (EBGM) method, to identify critical patterns of the bridge inventory condition at element level as a measure of vulnerability, using NBI rating data from 2015 to 2018. It finds that severe condition is highly associated with the following elements, regardless of their structural importance: bridge joints, and “bridge rail timber,”“bearing other,” and “superstructure floor beam reinforced concrete” elements. Poor condition is highly associated with elements like “top flange reinforced concrete,”“bearing movable,” and “superstructure floor beam reinforced concrete.” The quantification scores developed in this study could help transportation agencies and bridge engineers to identify more easily the key element or combination of elements associated with poor or severe condition, so that they can make data-driven decisions in maintaining and repairing the most needed bridge elements.

Efficient Prediction of Bridge Conditions using Modified Convolutional Neural Network

Artificial Intelligence (AI) technology has proved itself as a proficient substitute for classical techniques of modeling. AI is a branch of computer science with the help of which machines and software with intelligence similar to humans can be developed. Many problems related to structural as well as civil engineering are exaggerated with uncertainties that are difficult to be solved using traditional techniques. AI proves advantageous in solving these complex problems. Presently, a comprehensive model based on the Convolutional Neural Network technique of Artificial Intelligence is developed. This model is advantageous in accurately predicting the structure of a bridge without the need for actual testing. The firefly algorithm is used as a technique for accurate feature selection. The database is taken from national bridge inventory (NBI) using internet sources. Different performance measures like accuracy, recall, precision, and F1 score are considered for accurate prediction of the bridge structure and also provide advantages in actual monitoring and controlling of bridges. The proposed CNN model is used to measure these parameters and to provide a comparison with the standard CNN model. The proposed model provides a considerable amount of accuracy (97.49 %) as compared to accuracy value (85 %) using the standard CNN model.

A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos

Bridge inspection is an important step in preserving and rehabilitating transportation infrastructure for extending their service lives. The advancement of mobile robotic technology allows the rapid collection of a large amount of inspection video data. However, the data are mainly the images of complex scenes, wherein a bridge of various structural elements mix with a cluttered background. Assisting bridge inspectors in extracting structural elements of bridges from the big complex video data, and sorting them out by classes, will prepare inspectors for the element-wise inspection to determine the condition of bridges. This article is motivated to develop an assistive intelligence model for segmenting multiclass bridge elements from the inspection videos captured by an aerial inspection platform. With a small initial training dataset labeled by inspectors, a Mask Region-based Convolutional Neural Network pre-trained on a large public dataset was transferred to the new task of multiclass bridge element segmentation. Besides, the temporal coherence analysis attempts to recover false negatives and identify the weakness that the neural network can learn to improve. Furthermore, a semi-supervised self-training method was developed to engage experienced inspectors in refining the network iteratively. Quantitative and qualitative results from evaluating the developed deep neural network demonstrate that the proposed method can utilize a small amount of time and guidance from experienced inspectors (3.58 h for labeling 66 images) to build the network of excellent performance (91.8% precision, 93.6% recall, and 92.7% f1-score). Importantly, the article illustrates an approach to leveraging the domain knowledge and experiences of bridge professionals into computational intelligence models to efficiently adapt the models to varied bridges in the National Bridge Inventory.

Prioritization of Texas prestressed concrete bridges for future truck platoon loading

Autonomous truck platoons shall soon be traveling our highway system with greater frequency. The objective of the presented study is to conduct a high-level evaluation of the Texas concrete bridge inventory when subjected to potential truck platoon loading. The National Bridge Inventory (NBI) database is utilized to the greatest extent possible. In addition, a significant literature review is performed to make assumptions allowing estimated load rating calculations for the prestressed concrete bridges likely to support future platoons (nearly 3,000 bridges). The truck platoon load ratings, combined with the NBI structural evaluation condition ratings, are utilized to prioritize each bridge. As a result, bridges are identified for more detailed evaluation prior to future truck platoon implementation. Data analysis was also performed to further understand the impact of various parameters on the load rating and prioritization results. Conclusions were drawn regarding the sensitivity of the (1) original design methodology, (2) bridge span length, (3) truck type, (4) truck spacing and (5) number of trucks within a platoon. In addition, a secondary benefit of the study is a presented framework for other bridge owners to prioritize their bridges that may be subjected to truck platoon or other heavy vehicle loading.

Scenarios for Life Cycle Studies of Bridge Concrete Structure Maintenance

The sustainability of structures during their construction and service life has become a widespread topic of interest. To ensure the sustainability of bridges, maintenance databases can be analyzed to determine the status changes and required maintenance of existing bridges. The results of this analysis can then be used to predict the environmental impacts and costs incurred during ongoing maintenance of new bridges to prepare accordingly for the future. To prepare for future events, this study utilizes the US National Bridge Inventory to analyze changes in the condition rating of bridge decks and substructures according to their service years, and suggests maintenance scenarios for the service life of bridge deck and substructure concrete by investigating the maintenance activities according to service years. The factors for applying the scenarios in Korea and conceptual equations for life cycle studies which apply the scenarios are discussed for further study in the life cycle assessment field of bridges.