Brent Phares

Highway Bridge Inspection: State-of-the-Practice Survey

The congressionally mandated National Bridge Inspection Program requires states to inspect periodically all highway bridges on public roads, among other activities; visual inspection (VI) is the primary tool used to perform these inspections. A survey was conducted to help determine current policies and practices that may affect the accuracy and reliability of VI. The survey had three main objectives: to compile a “state of the practice” for bridge inspection, particularly as it pertains to VI; to gather information about bridge inspection management to study how inspection management may influence the reliability of inspections; and to gather data about the current use of nondestructive evaluation technologies and to identify current and future research needs. State departments of transportation, local departments of transportation, and select bridge inspection contractors participated in the survey. Conclusions drawn from this study indicate that the use of nondestructive evaluation has increased since 1993 and that the use of American Society of Nondestructive Testing Level III–certified personnel is increasing. VI was cited as the most frequently used nondestructive evaluation technique; however, vision testing for inspectors is almost nonexistent. Typically, professional engineers were not on site for inspections. State departments of transportation indicated a large demand for future research into the nondestructive evaluation of prestressed concrete superstructures and concrete decks.

Smart sensing skin for detection and localization of fatigue cracks

Fatigue cracks on steel components may have strong consequences on the structure’s serviceability and strength. Their detection and localization is a difficult task. Existing technologies enabling structural health monitoring have a complex link signal-to-damage or have economic barriers impeding large-scale deployment. A solution is to develop sensing methods that are inexpensive, scalable, with signals that can directly relate to damage. The authors have recently proposed a smart sensing skin for structural health monitoring applications to mesosystems. The sensor is a thin film soft elastomeric capacitor (SEC) that transduces strain into a measurable change in capacitance. Arranged in a network configuration, the SEC would have the capacity to detect and localize damage by detecting local deformation over a global surface, analogous to biological skin. In this paper, the performance of the SEC at detecting and localizing fatigue cracks in steel structures is investigated. Fatigue cracks are induced in steel specimens equipped with SECs, and data measured continuously. Test results show that the fatigue crack can be detected at an early stage. The smallest detectable crack length and width are 27.2 and 0.254 mm, respectively, and the average detectable crack length and width are 29.8 and 0.432 mm, respectively. Results also show that, when used in a network configuration, only the sensor located over the formed fatigue crack detect the damage, thus validating the capacity of the SEC at damage localization.

Time-Domain Model for Predicting Aerodynamic Loads on a Slender Support Structure for Fatigue Design

This paper presents the development of a universal model for predicting cyclic aerodynamic loads originating from buffeting, self-excited, and vortex shedding on a slender support structure in the time domain that can be used to predict its fatigue life. To accomplish this development, long-term monitoring was performed on a high mast light pole (HMLP) and the field data were used to validate the developed mathematical model. Wind-tunnel tests were conducted on the dodecagonal (12-sided) cylindrical cross section of the light pole to obtain the necessary aerodynamic parameters such as static force coefficients, Strouhal number, and indicial functions for buffeting that appear in the postulated model. Furthermore, these aerodynamic parameters were cast into a coupled dynamic model for predicting the response of any HMLP in time domain from vortex shedding and buffeting.

RELIABILITY AND ACCURACY OF ROUTINE INSPECTION OF HIGHWAY BRIDGES

Routine inspection is the most common type of highway bridge inspection completed to satisfy the requirements of the National Bridge Inspection Standards. Routine inspections of highway bridges are typically completed using only visual inspection (VI) and rely heavily on subjective assessments made by bridge inspectors. Given this practice and the fact that VI may have limitations that affect its reliability, the Federal Highway Administration completed a comprehensive study to examine the reliability of VI as it is currently practiced in the United States. The accuracy and reliability of condition ratings generated through routine inspections of six in-service and decommissioned highway bridges were studied. These timed inspections were completed in normal summer weather conditions under direct observation. To ensure that results from this study would be applicable to normal bridge inspections, state department of transportation bridge inspectors made up the study sample, which included 49 inspectors from 25 states, representing a diverse cross section of the bridge inspector population. Results of the study indicate that routine inspections are completed with significant variability. Specifically, 95 percent of primary bridge element condition ratings will vary within two rating points of the average, and only 68 percent of these ratings will vary within one rating point. Additionally, the National Bridge Inspection Standards definitions of condition rating may not be sufficiently refined to allow for reliable routine inspection results.

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.

Lateral Live-Load Distribution Characteristics of Simply Supported Steel Girder Bridges Loaded with Implements of Husbandry

This paper discusses the effect of agricultural live-loads on lateral load distribution characteristics of girder bridges on rural roadways in the United States. In this study, load distribution factors for bridges subjected to agricultural vehicles frequently used on rural roads are calculated based upon codified processes, field test results, and simulations. As part of this work, five simply supported steel girder bridges in Iowa were selected for field tests with four agricultural vehicles and a highway-type truck. Strain sensors were mounted on the bottom flanges of girders at midspan of all five bridges. Strain data resulting from the test vehicles were measured and used to determine girder distribution factors for each bridge. These strain data were also used to calibrate analytical models of the bridges. Over 120 agricultural vehicles were identified and used to analytically load the models. Girder distribution factors were then computed using responses from the vehicle-induced model simulations. Findings revealed that the analytical and field distribution factors were in most cases smaller than code-specified values, as has been observed by others. In some cases, however, these factors exceeded code values. Furthermore, the variability in agricultural vehicles’ characteristics had a significant impact on the live-load distribution factors for each bridge.

Field Validation of a Statistical-Based Bridge Damage-Detection Algorithm

This paper describes a field validation of a second-generation, statistical-based damage-detection algorithm and its ability to detect actual damage in bridges accurately. The algorithm had been theoretically validated previously. For the field tests, in lieu of introducing damage to a public bridge, two sacrificial specimens that simulated damage-sensitive locations of the bridge were mounted on the bridge, and different types and levels of damage in the form of cracks and simulated corrosion were induced in the specimens. Using strain data collected from sensors on the sacrificial specimens and on the bridge, the algorithm correctly identified the damage. Analysis of data from sensors far away from the damaged area revealed a relatively high false-positive rate.

Development of a Procedure for Fatigue Design of Slender Support Structures Subjected to Wind-Induced Vibration

Cantilevered signal, sign, and light support structures are used nationwide on major Interstate highways, national highways, local highways, and at local intersections for traffic control. Recently, a number of failures of these structures have been characterized as wind-induced fatigue failures. It is widely accepted that there is considerable lack of accuracy in the calculation of wind-induced loads on high mast light poles (HMLPs) in both the AASHTO and the Canadian Highway Bridge Design Code provisions. A coupled model for predicting buffeting- and vortex shedding–induced response for slender support structures was developed. To accomplish this, monitoring of long-term response behavior of an HMLP subjected to wind-induced vibration and wind tunnel experiments was used to study global behavior and to extract important parameters. From the long-term field monitoring and wind tunnel experiments, the two critical types of wind vibration (natural wind gusts or buffeting and vortex shedding) were individually identified for in-depth analysis. Finally, a coupled dynamic model in time domain was developed for predicting the wind-excited response and was validated by comparing the simulation results with the field-collected data. The fatigue life of a specific HMLP was also estimated with the stress amplitudes predicted by the time-domain model and was validated with statistical extrapolation of the field data.

Bridge Structural Health–Monitoring System Using Statistical Control Chart Analysis

Technological advancements in sensing, computing, and networking have facilitated the application of long-term structural health-monitoring (SHM) and made it a more widely accepted tool to improve bridge management. A fiber-optic strain-based SHM system developed in cooperation with the Iowa Department of Transportation is described; the system continuously monitors bridge performance under ambient traffic loads to detect damage. Although this SHM system has broad potential application to numerous types of structural deterioration or damage, the system is presented here by using a case study associated with fatigue cracking in the web gap region. The described SHM system autonomously collects, reduces, and analyzes strain data and determines the damage occurrence in a near-real-time fashion. In this system, statistical control chart analysis is applied over the strategically defined damage indicator to determine the damage mathematically. Data preprocessing strategies were also studied to limit the impact of major non-structural factors causing strain variations. The effectiveness of the system was demonstrated with field-collected predamage data and synthetic postdamage data. Experimental research is being conducted to validate the system at the field-testing level.

Integral Bridge Abutment to Approach Slab Connection

This paper presents the findings of a performance investigation of two approach slabs (a cast-in-place slab and a precast panel slab) integrally connected to two parallel bridges. The goal of using the integral connection is to eliminate the bump at the end of the bridge. To measure the performance, a long-term structural monitoring system consisting of various vibrating wire transducers was installed. From the year-long monitoring, the following general conclusions were made: (1) the integral connection functions well, with no observed distress or relative movement between the approach slab and bridge; (2) most of the force at the integral connection is induced by forces at the pavement/approach slab expansion joint; and (3) the observed responses generally followed an annual cycle, with short-term ratcheting patterns also apparent.