Bridge Element Data Collection and Use (2022)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

1   Initial efforts to define and collect bridge element data in the United States started in the late 1990s with the development and implementation of bridge management systems (BMSs). Over the years, the bridge management community provided feedback and made suggestions to improve the bridge element inspection methodology. In 2011, the AASHTO Subcommittee on Bridges and Structures approved a new element-level bridge inspec- tion manual (AASHTO 2011). The new AASHTO Manual for Bridge Element Inspection (MBEI) incorporated the experience of inspecting bridge elements by the state departments of transportation (DOTs) over 20 years and improved the guidelines (AASHTO 2013, 2019). MAP-21 required each state and appropriate federal agency to report bridge element-level data for the bridges on the National Highway System (NHS). As a result, state DOTs revised their element inspection manuals and condition databases to adopt the new elements. As of October 1, 2014, the state DOTs were required to begin collecting element-level data; for the April 1, 2015, National Bridge Inventory (NBI) data submission, agencies submitted element-level data for part of their inventories. Since 2014, the state DOTs have been transitioning to the use of element inspection data for documenting bridge conditions. This condition assessment methodology offers a significant opportunity to improve the timing, cost efficiency, and accuracy of bridge maintenance, rehabilitation, and replacement decisions. However, no standard guidance addresses achieving those benefits. Anecdotal evidence suggests that DOTs that receive the new inspection reports are taking numerous approaches to using the data. Many state DOTs rely on general condition ratings (GCRs) reported to the NBI for bridge maintenance and investment decisions. Still other states have begun to incorporate the element-level data into those decisions. In comparison to GCRs, element-level data are more granular and more quantitative. A major motivation for the AASHTO MBEI was to create elements for wear- ing surfaces (e.g., flexible overlays, epoxy overlays), steel protective coatings (e.g., paint, galvanization, weathering steel patina), and concrete protective coatings (e.g., silane/siloxane water proofers, crack sealers). The addition of these new elements, along with already exist- ing elements such as joints, provides a data framework to account for costs and benefits of preservation, which is not available in GCRs. The objective of this synthesis report is to document current state DOT practices and experiences regarding collecting element-level data and ensuring data accuracy. The syn- thesis also examines how the state DOTs are using the data from the inspection reports. This synthesis primarily focuses on current state DOT policies and practice on (1) bridge element definitions, (2) bridge element data collection, (3) processes to ensure the quality of bridge element inspections, (4) definition and use of performance measures based on bridge element data, (5) business processes that use bridge element data, and (6) internal and external communication based on bridge element data. S U M M A R Y Bridge Element Data Collection and Use

2 Bridge Element Data Collection and Use The information in this synthesis was obtained from three sources. First, a literature review was conducted to acquire background information on the history of bridge element data; under pin a discussion of the status of, and related processes for ensuring, bridge element data quality; introduce the most common performance measures based on bridge element data detailed in the literature and in use by the state DOTs; and assemble background information on the bridge element models that are most relevant to asset decision-making. Second, a survey of state DOTs was performed to document current practice and experi- ence regarding collecting and ensuring the accuracy of element-level data. The synthesis also requested information on how the state DOTs are using bridge element data in asset management decision-making. The surveys were sent to 50 state DOTs and the District of Columbia DOT and produced a 100% response. Third, specific information was gathered from six state DOTs—Florida, Kentucky, Michigan, Minnesota, Rhode Island, and Wisconsin—to expand on one or more of the follow- ing five aspects of their efforts: • Existence of practices for measuring preservation benefits based on element data or models. • Existence and use of performance measures based on element data for decision-making. • In-place procedures for ensuring element data quality. • Experience in using element models or modeling frameworks for bridge- and network- level decision-making. • Success in using and communicating element data analysis in agency reports, internal and external communication, and transportation asset management plans. The information obtained from all three resources served as the foundation for develop- ing the findings presented in this synthesis. Survey responses indicate that all state DOTs are collecting National Bridge Element (NBE) and Bridge Management Element (BME) data aligned with federal guidelines while 67% are also gathering data for Agency-Defined Elements (ADEs). State agencies are also collecting data on element defects, but the col- lection of data on environments is less common (43%). Among nondestructive evaluation (NDE) methods, DOTs often use chain drag for bridge deck inspections and sometimes employ electromagnetic and ultrasonic testing for element inspections. NDE methods (e.g., impact-echo, infrared thermography, ground penetrating radar, dye-penetrant testing, and D-Meters) are also used, but less frequently. A majority of the state DOTs (55%) expressed moderate confidence—and most of the remaining DOTs (39%) had high confidence—in the quality of their bridge element data. The state DOTs also indicated that their existing quality control (QC) and quality assurance (QA) programs improve the quality of their bridge element inspections. Less than half of the state DOTs have established project decision rules, decision trees, or performance measures based on bridge element data. One-fourth of the state DOTs reported confidence in their element cost and deterioration models. Most typically, the state DOTs have developed element cost or deterioration models, but they noted that these models need further improvement. In addition, 26 DOTs do not compare element condition data and NBI GCRs. Meanwhile, the remaining 25 state DOTs have developed a conversion profile that needs further improvement, developed a profile in which they are confident, or use a default conversion profile available in their BMS. The most common uses of element data in asset decision-making are the selection of bridge preservation projects; bridge-level decision-making, such as choice of work type or scoping for individual structures; and selection of bridge rehabilitation or replacement

Summary 3   projects. Other common applications are selection of bridge maintenance projects and network-level decision-making. Aside from four DOTs, all state DOTs have some form of use for bridge element data. Compared to decision-making based on element data or models, reported confidence in decision-making based on component data or models is relatively higher. One-fourth of the state DOTs do not use element data or models in asset management decisions. Among the state DOTs that do utilize element data or models in making decisions, 23 of the 38 state DOTs (60%) reported moderate to high confidence in the decisions based on element data or models. Relatively higher confidence in decisions based on component data and models may stem from the longer history of state DOTs using and developing models for component data. The case examples summarize the practice from six state DOTs that reported success in improving the quality of, and expanding the use of, bridge element data. The selected case examples illustrate QA reviews, QC protocols, and automated data queries that aim to con- tinually improve factors such as bridge element data quality, use of ADEs to trigger repairs, advanced bridge management frameworks that are built on bridge element data, bridge element performance measures, and development and use of element deterioration models to trigger recommended work. This synthesis report identified knowledge gaps that could be addressed by future research opportunities. Research on models to identify element costs, deterioration, performance measures, and treatment efficiency—as well as conversion algorithms to map element con- dition to NBI GCRs—would help state DOTs immensely in their use of bridge element data and models. Guidance for developing and customizing bridge element cost and deteriora- tion models and element-based decision-making processes would support agencies with different levels of maturity regarding these efforts. Peer exchanges could also facilitate inter- agency communication and present agencies with learning opportunities. Research on how agencies communicate BMS results (or on the results of analysis based on bridge element data) could provide agencies with examples. Research is also needed to identify potential improvements in the use of bridge element data in asset management communication.