Scour is one of the main causes of bridge failures resulting in significant macro-economic impacts, often beyond the direct costs of infrastructure damage. Given the pressure to increase the resili...

Risk-informed asset management to tackle scouring on bridges across transport networks

Ahstract- The objective of civil infrastructure inspection is to evaluate and periodically perform maintenance. This involves properly collecting data on structures, documenting the data, and analyzing the quantitative inspection data over an extended period of operating time, all in order to maintain the structures properly. Recent research on civil infrastructure inspection is mainly based on manual visual inspections, which are often influenced and limited by a professional inspector's knowledge and experience. Thus, the accuracy and reliability of their results are affected. These inspection processes require significant individual labor, which can become quite costly as the inspection areas often require traffic to be limited or evacuation of certain business parking areas while the inspections are performed. In this paper, we present a new development of the autonomous mobile robotic system for automated data collection and inspection. A 4 degree of freedom (DoF) arm is designed and developed then integrated with the robot for efficient image capture using both visual and thermal cameras. The robotic system performs crack detection on the collected data using a state-of-the-art method involving convolutional neural networks (CNN), which is validated on a variety of test images. As inspection results, the robot can output several condition maps of the inspected infrastructure including crack map, thermal map and deterioration map to provide the overall picture of the structure health condition. Our robotic system reduces the overall inspection time compared to the current robotic inspection methods. We further validate our data collection process through showing the correlation between the data collected by each sensor in the system.

A Mobile Robot for Automated Civil Infrastructure Inspection and Evaluation

Full-depth, precast concrete deck panels have been heavily used in bridge construction since the 1970’s. The use of these panels has traditionally been thought to both accelerate construction and improve the long-term durability performance of the bridge deck. While many engineers and academia are intrinsically aware that confined precast can perform equally or better than nonconfined, cast-in-place concrete, evaluation between the two has never been studied holistically. The main objective of this project was to determine the in-service performance of full-depth, precast concrete (FDPC) deck panels compared to conventional cast-in-place (CIP) decks. The secondary objective is to determine successful and problematic details for these members. A survey was developed and distributed to state DOTs. Survey responses were used to develop the FDPC Deck Panel Database. Results from the DOT survey and analysis of the FDPC Deck Panel Database are discussed in this report.

https://rosap.ntl.bts.gov/view/dot/41831/dot_41831_DS1.pdf

Performance Comparison of In-Service, Full-Depth Precast Concrete Deck Panels to Cast-in-Place Decks

While the benefits of monitoring bridges are fairly understood, securing the resources necessary for their development and installation is often difficult. The premise of the article is that infrastructure owners will take action to provide those resources when they can see value accruing to them; therefore narratives around value generation and business cases are required to motivate and support that action. This paper explores the application of the value of information methodology to the challenge of developing a business case for bridge monitoring using an illustrative case study on a simulacrum of a road bridge in the UK. To this end, this paper presents a systematic approach for setting risk-informed bridge monitoring strategies while considering the reliability and accuracy of data capture offered by different monitoring techniques and the gains to be made downstream if it contributes to setting timely maintenance strategies.

A business case for risk-informed bridge monitoring using the value of information

Performance forecasts of highway bridge networks are informative inputs for stakeholders for cost-effective decision-making of funds allocation and asset management. Developing performance forecast models requires establishing a target performance measure (PM) and developing deterioration models accordingly. In the United States, the Federal Highway Administration (FHWA) and state departments of transportation use FHWA’s national PMs to assess highway bridge performance. Incorporating the national PMs for forecast modeling is the focus of this paper. Existing approaches commonly utilize stochastic processes for deterioration modeling, introducing uncertainty into forecasts and creating challenges for decision makers to capture the complete information. Numerical estimation through Monte Carlo simulations is achievable but inefficient. This paper presents an application of the generalized Poisson–binomial distribution as an efficient statistical tool with closed-form solutions to characterize uncertain network PMs based on stochastic forecasts of individual bridges within a network. The study demonstrated and validated the modeling tool with a case study on highway bridges in the United States using historical data (1993–2019). The results showed that this modeling tool achieved a robust and low prediction error. Further, the study demonstrated the feasibility to infer maintenance, repair, and rehabilitation (MR&R) effects when specific MR&R data were not available. 

Performance Forecasts of US Highway Bridge Networks Using Generalized Poisson–Binomial Distribution

The effective management of bridges requires a good understanding of their life expectancies Improved prediction of bridge service life is required to be developed in order to better understand br

Life Cycle Assessment Framework for the U.S. Bridge Inventory

The Long-Term Bridge Performance (LTBP) Program is a 20-year research effort by the U.S. Federal Highway Administration (FHWA) to collect scientific performance field data from a representative sample of bridges nationwide that will help the bridge community better understands bridge deterioration and performance. The products from this program will be a collection of data-driven tools, including predictive and forecasting models, which will enhance the abilities of bridge owners to optimize their management of bridges. The paper describes a key product of the LTBP program, the Bridge Portal, which contains bridge performancerelated data mined from existing sources (National Bridge Inventory, State Highway Agency bridge element level data, national weather data, traffic data, weigh-in-motion data, bridge maintenance data, and other data sources), but it also serves as a central repository for all field data collected through the LTBP Program. Additionally, the Bridge Portal also functions as a research and decision-making tool by implementing bridge life-cycle and deterioration modeling using both mined data sources as well as LTBP-acquired field data to allow users to investigate bridge performance on many different levels.

/pdf/the-long-term-bridge-performance-ltbp-program-bridge-portal-5fprunevut.pdf

Robert Zobel

Papers

Life Cycle Assessment Framework for the U.S. Bridge Inventory

The Long-Term Bridge Performance (LTBP) Program Bridge Portal

Similarities and Differences in Condition Assessment of Concrete Bridge Decks by Visual Inspection and NDE