Field Data Acquisition Technologies for Iowa Transportation Agencies, HR-366 August 1994

This report describes the results of the research project investigating the use of advanced field data acquisition technologies for lowa transponation agencies. The objectives of the research project were to (1) research and evaluate current data acquisition technologies for field data collection, manipulation, and reporting; (2) identify the current field data collection approach and the interest level in applying current technologies within Iowa transportation agencies; and (3) summarize findings, prioritize technology needs, and provide recommendations regarding suitable applications for future development. A steering committee consisting oretate, city, and county transportation officials provided guidance during this project. Technologies considered in this study included (1) data storage (bar coding, radio frequency identification, touch buttons, magnetic stripes, and video logging); (2) data recognition (voice recognition and optical character recognition); (3) field referencing systems (global positioning systems [GPS] and geographic information systems [GIs]); (4) data transmission (radio frequency data communications and electronic data interchange); and (5) portable computers (pen-based computers). The literature review revealed that many of these technologies could have useful applications in the transponation industry. A survey was developed to explain current data collection methods and identify the interest in using advanced field data collection technologies. Surveys were sent out to county and city engineers and state representatives responsible for certain programs (e.g., maintenance management and construction management). Results showed that almost all field data are collected using manual approaches and are hand-carried to the office where they are either entered into a computer or manually stored. A lack of standardization was apparent for the type of software applications used by each agency--even the types of forms used to manually collect data differed by agency. Furthermore, interest in using advanced field data collection technologies depended upon the technology, program (e.g.. pavement or sign management), and agency type (e.g., state, city, or county). The state and larger cities and counties seemed to be interested in using several of the technologies, whereas smaller agencies appeared to have very little interest in using advanced techniques to capture data. A more thorough analysis of the survey results is provided in the report. Recommendations are made to enhance the use of advanced field data acquisition technologies in Iowa transportation agencies: (1) Appoint a statewide task group to coordinate the effort to automate field data collection and reporting within the Iowa transportation agencies. Subgroups representing the cities, counties, and state should be formed with oversight provided by the statewide task group. (2) Educate employees so that they become familiar with the various field data acquisition technologies.

Integration of Bridge Damage Detection Concepts and Components (3 volumes), TR-636, 2013

This work is divided into three volumes: Volume I: Strain-Based Damage Detection; Volume II: Acceleration-Based Damage Detection; Volume III: Wireless Bridge Monitoring Hardware. Volume I: In this work, a previously-developed structural health monitoring (SHM) system was advanced toward a ready-for-implementation system. Improvements were made with respect to automated data reduction/analysis, data acquisition hardware, sensor types, and communication network architecture. The statistical damage-detection tool, control-chart-based damage-detection methodologies, were further investigated and advanced. For the validation of the damage-detection approaches, strain data were obtained from a sacrificial specimen attached to the previously-utilized US 30 Bridge over the South Skunk River (in Ames, Iowa), which had simulated damage,. To provide for an enhanced ability to detect changes in the behavior of the structural system, various control chart rules were evaluated. False indications and true indications were studied to compare the damage detection ability in regard to each methodology and each control chart rule. An autonomous software program called Bridge Engineering Center Assessment Software (BECAS) was developed to control all aspects of the damage detection processes. BECAS requires no user intervention after initial configuration and training. Volume II: In this work, a previously developed structural health monitoring (SHM) system was advanced toward a ready-for-implementation system. Improvements were made with respect to automated data reduction/analysis, data acquisition hardware, sensor types, and communication network architecture. The objective of this part of the project was to validate/integrate a vibration-based damage-detection algorithm with the strain-based methodology formulated by the Iowa State University Bridge Engineering Center. This report volume (Volume II) presents the use of vibration-based damage-detection approaches as local methods to quantify damage at critical areas in structures. Acceleration data were collected and analyzed to evaluate the relationships between sensors and with changes in environmental conditions. A sacrificial specimen was investigated to verify the damage-detection capabilities and this volume presents a transmissibility concept and damage-detection algorithm that show potential to sense local changes in the dynamic stiffness between points across a joint of a real structure. The validation and integration of the vibration-based and strain-based damage-detection methodologies will add significant value to Iowa’s current and future bridge maintenance, planning, and management Volume III: In this work, a previously developed structural health monitoring (SHM) system was advanced toward a ready-for-implementation system. Improvements were made with respect to automated data reduction/analysis, data acquisition hardware, sensor types, and communication network architecture. This report volume (Volume III) summarizes the energy harvesting techniques and prototype development for a bridge monitoring system that uses wireless sensors. The wireless sensor nodes are used to collect strain measurements at critical locations on a bridge. The bridge monitoring hardware system consists of a base station and multiple self-powered wireless sensor nodes. The base station is responsible for the synchronization of data sampling on all nodes and data aggregation. Each wireless sensor node include a sensing element, a processing and wireless communication module, and an energy harvesting module. The hardware prototype for a wireless bridge monitoring system was developed and tested on the US 30 Bridge over the South Skunk River in Ames, Iowa. The functions and performance of the developed system, including strain data, energy harvesting capacity, and wireless transmission quality, were studied and are covered in this volume.

Evaluation of Racial Differences in Seat Belt and Child Restraint Use: A Review of Current Literature, 2004

This report presents the results of a literature review conducted to evaluate differences in seat belt use by race. A literature review was conducted to evaluate overall seat belt use, racial differences in seat belt use, overall child restraint use, racial differences in child restraint use, and information about seat belt and child restraint use specific to Iowa. A number of national studies and regional studies were found and are presented. Mixed results were found as to whether racial differences exist in both seat belt use and child restraint use. However, in the course of the literature review, several items that are of interest to safety in Iowa have emerged, although little data specific to Iowa was encountered. First, national seat belt use appears to be lower among African-Americans than for Caucasians or Hispanics. Second, national crash rates among Hispanics appear to be higher than those for Caucasians, particularly when population and lower vehicle miles traveled (VMT) are considered. One issue that should be considered throughout this literature review is that the Hispanic population may be higher than reported due to large numbers of undocumented persons who do not appear in population estimates, driver’s license, or other databases.

GPS to LRM: Integration of Spatial Point Features with Linear Referencing Methods, 2001

Global positioning systems (GPS) offer a cost-effective and efficient method to input and update transportation data. The spatial location of objects provided by GPS is easily integrated into geographic information systems (GIS). The storage, manipulation, and analysis of spatial data are also relatively simple in a GIS. However, many data storage and reporting methods at transportation agencies rely on linear referencing methods (LRMs); consequently, GPS data must be able to link with linear referencing. Unfortunately, the two systems are fundamentally incompatible in the way data are collected, integrated, and manipulated. In order for the spatial data collected using GPS to be integrated into a linear referencing system or shared among LRMs, a number of issues need to be addressed. This report documents and evaluates several of those issues and offers recommendations. In order to evaluate the issues associated with integrating GPS data with a LRM, a pilot study was created. To perform the pilot study, point features, a linear datum, and a spatial representation of a LRM were created for six test roadway segments that were located within the boundaries of the pilot study conducted by the Iowa Department of Transportation linear referencing system project team. Various issues in integrating point features with a LRM or between LRMs are discussed and recommendations provided. The accuracy of the GPS is discussed, including issues such as point features mapping to the wrong segment. Another topic is the loss of spatial information that occurs when a three-dimensional or two-dimensional spatial point feature is converted to a one-dimensional representation on a LRM. Recommendations such as storing point features as spatial objects if necessary or preserving information such as coordinates and elevation are suggested. The lack of spatial accuracy characteristic of most cartography, on which LRM are often based, is another topic discussed. The associated issues include linear and horizontal offset error. The final topic discussed is some of the issues in transferring point feature data between LRMs.