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A platform for research: civil engineering, architecture and urbanism
Inverse Analysis of Pavement Layer Moduli Based on Data Collected by Buried Accelerometers and Geophones
The aim of this study is to develop a method for identifying asphalt pavement layer properties based on readings from accelerometers and geophones that are embedded near the ride surface. These sensors are relatively small in size and easily embeddable, making them an ideal choice for wide-area applications in the live transportation network. As a first step, a section within the IFSTTAR accelerated pavement testing (APT) facility was instrumented with accelerometers and geophones; also installed was an anchored displacement sensor to serve as a reference/validation device. The APT facility offers the ability to control the loading configuration and intensity, travel speed, and wander (i.e., lateral offset) position relative to the sensor locations. Thus, it becomes possible to isolate the task of property identification through inverse analysis from other real-world complications. The paper commences by describing the experimental setup, and presenting some raw sensor measurements during a single pass of the APT’s wheel carriage. Then, assuming a layered-elastic model, a method is proposed and demonstrated for estimating the pavement moduli. The method is based on best-matching measured velocities and accelerations for the geophones and accelerometers (respectively), with the model predictions- without integrating the signals to convert them into deflections. Very good match is obtained for the sensor readings, and the inferred moduli closely agree with reference values. This outcome means that there is great potential in building a pavement condition monitoring system with near-surface accelerometers and geophones.
Inverse Analysis of Pavement Layer Moduli Based on Data Collected by Buried Accelerometers and Geophones
The aim of this study is to develop a method for identifying asphalt pavement layer properties based on readings from accelerometers and geophones that are embedded near the ride surface. These sensors are relatively small in size and easily embeddable, making them an ideal choice for wide-area applications in the live transportation network. As a first step, a section within the IFSTTAR accelerated pavement testing (APT) facility was instrumented with accelerometers and geophones; also installed was an anchored displacement sensor to serve as a reference/validation device. The APT facility offers the ability to control the loading configuration and intensity, travel speed, and wander (i.e., lateral offset) position relative to the sensor locations. Thus, it becomes possible to isolate the task of property identification through inverse analysis from other real-world complications. The paper commences by describing the experimental setup, and presenting some raw sensor measurements during a single pass of the APT’s wheel carriage. Then, assuming a layered-elastic model, a method is proposed and demonstrated for estimating the pavement moduli. The method is based on best-matching measured velocities and accelerations for the geophones and accelerometers (respectively), with the model predictions- without integrating the signals to convert them into deflections. Very good match is obtained for the sensor readings, and the inferred moduli closely agree with reference values. This outcome means that there is great potential in building a pavement condition monitoring system with near-surface accelerometers and geophones.
Inverse Analysis of Pavement Layer Moduli Based on Data Collected by Buried Accelerometers and Geophones
Lecture Notes in Civil Engineering
Chabot, Armelle (editor) / Hornych, Pierre (editor) / Harvey, John (editor) / Loria-Salazar, Luis Guillermo (editor) / Bahrani, Natasha (author) / Levenberg, Eyal (author) / Blanc, Juliette (author) / Hornych, Pierre (author)
Accelerated Pavement Testing to Transport Infrastructure Innovation ; Chapter: 61 ; 592-601
2020-08-26
10 pages
Article/Chapter (Book)
Electronic Resource
English
| BASE | 2020