详情

Advancing the State of Bridge Weigh-In-Motion for the Connecticut Transportation Network

项目名称：	Advancing the State of Bridge Weigh-In-Motion for the Connecticut Transportation Network
摘要：	A well managed, healthy transportation network is vital to prosperity in the State of Connecticut. Critical to the healthy transportation network is the effective movement of people and goods on Connecticut's highways. To design, maintain and optimize the highway network it is important to have accurate and reliable traffic data, and in particular, actual truck characteristic and weight data. Typically, truck weight data are collected at highway speeds using, "weigh-in-motion (WIM)" technology. Current pavement methods to collect weight (WIM) data have limitations including: the risk of workers in the work zone, dependence on pavement condition and pavement life, the influence of vehicle dynamics, as well as cost and accuracy. The category of WIM systems that uses bridges to collect weight data is designated as bridge weigh-in-motion (BWIM). BWIM offers advantages to address some of the short comings of traditional pavement WIM systems. BWIM uses the dynamic response of a bridge to determine gross vehicle weight, speed, and axle spacing of truck traffic to quantify the loads in a transportation network. The advantage of BWIM is that it does not require installation of sensors in the pavement, nor use any axle locators in the roadway. BWIM systems have been typically conducted under very limited circumstances, bridges of short length, simply supported, little skew, and on roads with low volumes of traffic. Recent advances in sensor technology, data acquisition and computing systems provide excellent opportunities to address challenges that were faced during early studies.A test and system was designed and deployed under Connecticut Research Project No. SPR-2265, "Development and Evaluation of a Dual Purpose Bridge Health Monitoring and weigh-in-motion System for a Steel Girder Bridge," to test the system for both BWIM and bridge health monitoring (BHM). Due to the commonality of the sensors and system, it made logical sense to test both the duality and leverage the mutual benefits. After the system was designed and preliminary tests were conducted, Phase II of the study was initiated under Connecticut Research Project No. SPR-2271, "Development and Evaluation of a Dual-Purpose Bridge Health Monitoring and Weigh-In-Motion System for a Steel Girder Bridge - Phase 2," to test the operations and conduct refinements to the system. Accurate and reliable weigh-in-motion data are needed to characterize trucks and loadings on the State's transportation network to support and improve numerous functions. It is preferable to collect these data both non-intrusively and cost effectively. BWIM provides the potential to meet these needs and to supplement traditional WIM sensors on the transportation network. The BWIM, as identified at a March 10, 2014, Connecticut Department of Transportation (ConnDOT) Bridge Weigh-In-Motion (BWIM) Stakeholders Meeting, must: provide accurate WIM data; be easy, safe and quick to install and maintain; and, be relatively inexpensive. Further, to offer options for deployment on the transportation network, a broad range of bridge types must be evaluated to understand the types of bridges that are well suited for BWIIM. The ConnDOT currently collects WIM data from piezoelectric and quartz piezoelectric sensor WIM systems at a limited number of locations. In-pavement WIM systems present many challenges including cost, installation (costs and safety), calibration, maintenance and accuracy. Many locations are not appropriate for the installation of in-pavement WIM sensors, due to the high volume of traffic (safety of installation) or pavement and site conditions. The level of accuracy and uncertainty in data produced from ceramic and polymer piezoelectric sensors may not be appropriate for many applications. An inexpensive and easily installed BWIM system is desired that can provide accurate network data for periods of (at least) 1-2 years. Connecticut has 4,218 bridges, according to the 2013 National Bridge Inventory. BWIM has been successfully demonstrated on a steel girder bridge in Connecticut. Stringer/multi-beam or girder bridges make up 52% (2,173 of the 4,218 bridges) of the bridge inventory and steel bridges 53% (2,256 of the 4,218 bridges). While this is the most common type of bridge in Connecticut and the most logical to study initially, for BWIM to truly be deployable throughout the State, a broad range of bridge types must be considered. The BWIM methodology developed for the steel girder bridges cannot be directly applied to a concrete bridge due to differences in material characteristics and structural behavior. Research should consider BWIM application to different material types, structure types and geometric properties. These include concrete and potentially wood, masonry and aluminum/iron materials; including slab, box beam or girder, truss, arch, culvert and potentially cable stayed and orthotropic structure types; and skew, slope, curved; long-span and continuous geometric properties. Three objectives are identified in this project to ensure the advancement of BWIM toward deployment the Department: (1) to continue BWIM data collection at the Meriden (I-91) Bridge, making data available to the Department in a queriable format and allowing for improvement and further understanding of BWIM data processing; (2) to develop a reliable, easy to deploy portable monitoring system for BWIM in Connecticut for 1-2 day deployments or 1-2 year deployments; and (3) to deploy a BWIM system on various types of bridges in Connecticut to identify best practices and evaluate performance and potential for application by the Department.
状态：	Active
资金：	$302199.00
资助组织：	Connecticut Department of Transportation
项目负责人：	Kissane, Colleen A
执行机构：	University of Connecticut, Storrs
开始时间：	20140730
预计完成日期：	20191231
主题领域：	Bridges and other structures；Data and Information Technology；Highways；Operations and Traffic Management；Vehicles and Equipment