Utilizing Kriging Metamodeling to Provide Practical and Effective Bridge Weigh-in-Motion
Publication: Journal of Bridge Engineering
Volume 28, Issue 2
Abstract
Emerging technologies are a strategic ally in the efficient management and preservation of pavements and bridges. Toward this end, different methodologies, such as weigh-in-motion (WIM), have been developed for the detection of overweight vehicles and traffic monitoring. Bridge weigh-in-motion (BWIM) systems can be installed and serviced without interrupting traffic flow. This paper presents a new approach for processing traditional strain response data. This proposal extends the BWIM concept of influence area from response time-histories. The dimensionality of the strain response waves is reduced by the calculation of the centroid and the area under the curve. Subsequently, these two dimensions are used as inputs to an ordinary Kriging (OK) metamodel to predict the gross vehicle weight (GVW) of the passing traffic. The OK methodology allows for the strategic selection of vehicles to train the BWIM metamodel. An example of the application of Kriging metamodeling (KM) to BWIM through the instrumentation of an in-service highway bridge located in Costa Rica is presented. Experimental horizontal strain data along with corresponding weight measurements from a static permanent weigh station were available for 90 trucks to validate the proposed enhanced BWIM methodology.
Practical Applications
This research addresses the current concerns regarding United States infrastructure. The ASCE’s 2021 Report Card for America’s Infrastructure ( ASCE 2021) scores a general C- for all 18 categories reported (aviation, D+; bridges, C; ports, B-; rail, B; roads, D; transit, D-; among others). In the bridge category, the use of “living bridges” is mentioned, referring to sensors installed on the structure, as an innovative solution to provide continuous feedback on structural conditions. The proposed methodology in this paper can provide information on the proper instrumented bridge’s structural condition, and the type of traffic traveling over the route the bridge services. This information can be used to feed the databases that support Transportation Asset Management Plans, which are pointed out in the ASCE’s Report Card as federally required tasks to maintain and preserve properly the transportation infrastructure through sceneries and projections.
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Acknowledgments
The authors express their gratitude to University of Costa Rica, in particular to the University Environment of Advanced Studies (UCREA) program for sponsoring the field trip. The authors acknowledge partial support from the University of Connecticut. The authors appreciate Lanamme UCR’s personnel for the support in the field trip (Rolando Castillo, Esteban Villalobos, Hellen Garita, Mauricio Araya, María José Rodríguez, Alejandro Carvajal, Luis Guillermo Vargas, Sergio Álvarez, Melissa Rojas, Francisco González, Greivin Ceciliano, Catalina Vargas, and Jaime Allen). The first author acknowledges the support provided by the University of Costa Rica and Lanamme UCR for supporting his doctoral studies.
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Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 28 • Issue 2 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 25, 2022
Accepted: Sep 23, 2022
Published online: Nov 16, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 16, 2023
ASCE Technical Topics:
- Architectural engineering
- Bridge engineering
- Bridge management
- Bridges
- Bridges (by type)
- Building management
- Engineering fundamentals
- Highway bridges
- Highway transportation
- Infrastructure
- Kriging
- Maintenance and operation
- Material mechanics
- Materials engineering
- Mathematics
- Parameters (statistics)
- Statistics
- Strain
- Structural engineering
- Traffic engineering
- Traffic flow
- Traffic management
- Transportation engineering
- Vehicles
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Cited by
- Debojyoti Paul, Koushik Roy, Application of bridge weigh-in-motion system in bridge health monitoring: a state-of-the-art review, Structural Health Monitoring, 10.1177/14759217231154431, (147592172311544), (2023).