About the Project


Executive Summary

Michigan Tech is developing remotely sensed bridge condition signatures that can be used to enhance the effectiveness of bridge inspection teams and improve asset management programs for transportation agencies. Because no single type of sensor can provide all the information needed to assess the condition of a bridge accurately, our approach integrates in-situ field sensors, “local” remote sensing data (such as infrared thermography), and stand-off remote sensing data (such as satellite imagery) to create a unique bridge signature that provides an overall assessment of bridge structural health. These data will be integrated within a decision support system (DSS) that applies information from multiple sources, including: sensor data; previous inspection results; existing bridge management systems; laboratory-validated models; and meteorological inputs. The "signature" will start with a baseline index that can be used to track changes in bridge condition over time.

The system adds value by providing bridge asset managers with a tool for obtaining an assessment of bridge condition without having to instrument the bridge. And by providing inspection teams with relevant preliminary condition assessment data, bridge inspection teams can focus their efforts on trouble spots identified by the sensors as evaluated through the DSS. These indicators, combined with models and algorithms validated in the laboratory and historical bridge inspection records, will provide the inputs to the DSS. The DSS will provide managers and decision makers to prioritize maintenance and rehabilitation efforts based on objective data with analysis capabilities to help monitor condition, changes in condition, and plan for more cost-effective efforts.


Project Goals

The proposed project has three primary goals:
  1. Establish remotely sensed bridge health indicators.
  2. Develop a baseline bridge performance metric, the bridge condition signature, for benchmarking overall bridge condition.
  3. Provide a system that enhances the ability of state and local bridge engineers to prioritize critical repair and maintenance needs for the nation’s bridges.

Background

The condition of the nation’s infrastructure has gained increased attention in recent years, primarily as a result of catastrophic events such as the I-35W collapse in Minneapolis in 2007. However, deteriorating transportation infrastructure has burdened transportation agencies for many years. Bridges continue to age, and funds for the repair and replacement of this infrastructure are insufficient at current funding levels. The U.S. is home to 600,000 highway bridges. Structural deficiency, which describes the condition of significant load-carrying elements and adequacy of waterway openings, typically relates directly to the age of a bridge (AASHTO 2008). Three percent of bridges between 15 and 19 years old, and 53 percent of those 95 to 100 years old, are structurally deficient (Memmott 2007).

In recent years, structural health monitoring (SHM) for bridges has adopted the “Level IV” approach with a primary focus of accurately monitoring in-situ behavior to assess in-service performance, detect damage, and determine condition of a structure (ISIS 2001). Most research efforts have focused on the subsystems of a structural health monitoring system including: 1) static field testing, 2) dynamic field testing, 3) periodic monitoring, and 4) continuous monitoring, but a complete SHM system also requires routine inspection, data management, data interpretation, and decision support. Recent advances in SHM have included novel sensing technologies and assessment methods such as: wireless sensors, strain sensing films and local damage identification, but a complete solution to the challenges described above has yet to be realized. SHM is further complicated by the wide degree of variability in bridge types, materials, operating environments, and structural configurations.




No single SHM method exists that is capable of completely determining the condition of a bridge. Current assessment methods provide critical information about the condition of a bridge, but the data obtained must be interpreted by a skilled professional and are typically limited to local metrics, such as stress, strain, temperature, deflection, moisture, cracking, and delamination.

Remote sensing technologies offer the ability to combine several methods to obtain a more complete assessment. Currently, these methods exhibit a divide between metrics for structural response at the global level and material distress at the local level. The combination of these metrics will provide a better picture of overall bridge condition.


Technical Approach

The Michigan Tech team is investigating the extent to which remote sensing data, particularly from sources such as aerial and satellite imagery, can be used effectively to monitor components of bridge health, such as the condition of the bridge deck or other structural elements. Stand-off remotely sensed data sources have the potential for effectively monitoring condition and quality of the road and bridge surface, if the imagery data can reliably capture indicators of current and changing condition. Because changes over a two-year period can be minimal, we will use the commercial satellite archive available from vendors to determine potential changes over time, while relating condition signatures in imagery to a sample of current different high, low, and moderate quality conditions on bridges. With these methods, we will be able to report on what the remotely sensed data are capable of telling transportation agency bridge assessment teams in a cost-effective and timely manner. The program will achieve this assessment through a combination of controlled laboratory experiments, field demonstrations and data analysis.


On-Site Sensor Applications

On-site sensors make non-contact measurements in close proximity to bridge structures to nondestructively sense the surface and/or interior conditions. The specific aims of the on-site sensor assessment are:

  1. To determine, through controlled measurements, that on-site sensors can make quantitative measurements of bridge component structural health.
  2. To provide data to develop and demonstrate automated data processing algorithms to facilitate the cost effective use of on-site sensors by bridge inspectors and to provide quantitative structural health data to the DSS.
  3. To provide data to determine if comparable remote sensors can make similar quantitative measurements at longer distances.

Remote Sensing Applications

Satellite and airborne-based remote sensing has shown increasing promise in recent years for monitoring the condition of road surface infrastructure. This project will validate and apply this capability to assess and monitor the condition of the bridge elements that are critical to bridge operation.

Three commercial Synthetic Aperture Radar systems (PALSAR, RADARSAT-II, and ENVISAT) can potentially measure both bridge vibration and deflection. We are determining the sensitivity requirements to make these measurements using stand-off remote sensors, and then evaluating both theoretically and experimentally, whether these existing commercial sensors can provide the required information. These SAR systems provide the required temporal synoptic coverage at affordable cost to perform a wide area bridge assessment.


Sensor Validation and Field Studies

Representative bridge component structures, such as concrete bridge decks and supports, with varying levels of damage and material contamination are used to obtain sensor data. The sensor data will be compared with the sample ground truth data to define the quantitative relationship between sensor data and various levels of bridge component damage or contamination. The data collected also will be used to develop and validate data processing algorithms for the DSS.

Once the relationships between sensor signatures and structural damage have been defined and the automated data processing algorithms have been developed, the combined measurement/data processing system will be tested at field sites, identified in conjunction with MDOT, that are representative of typical operational bridge inspection situations. A series of field tests will be conducted to determine an initial bridge signature based on verified technologies.


Decision Support System

The Decision Support System (DSS) will include the ability to apply algorithms used to extract and combine relevant condition information from sensor data, compare current sensor data to historical data to establish trends, and make recommendations to ensure optimal bridge health using cost-effective maintenance and repair protocols. As part of an integrated bridge assessment, the DSS must be able to take advantage of historical data from existing systems. The DSS will be expandable to accommodate other systems in the future.

Combining data from on-site sensors, in-situ sensors, and stand-off remote sensing using appropriate statistical algorithms, the DSS will integrate data to support and expand current bridge monitoring practices.


Technical and Economic Assessment

A technical assessment and economic evaluation will be conducted as part of the project. The technical assessment will evaluate the DSS tool, its technical components (such as models and algorithms), and the sensor inputs to determine how well they perform in demonstrating integrated bridge assessments that are useful for the transportation agency end-user. This includes assessing the accuracy and reliability of bridge inspection measurements made by tested sensors and comparing these measures to standard measures used by bridge.

The economic evaluation will examine the costs of the developed sensing techniques and evaluate these costs in relationship to the added value that the techniques supply in terms of improved bridge health monitoring. Ultimately, this task answers the question of whether the developed techniques are cost effective or not as demonstrated, with an analysis of likely future costs for full implementation.