Evaluation of accelerometers to determine pavement deflections under traffic loads

Arraigada, Martín; Partl, Manfred N.; Angelone, Silvia; Martínez, Francisco

doi:10.1617/s11527-008-9423-5

Cited by 32 publications

(19 citation statements)

References 10 publications

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“…Simple double integration amplifies the low frequency noise leading to a large unpredictable drift [6]. Popular techniques for drift correction include fitting some polynomial during the silent periods to estimate drift, and subtracting it from the calculated displacement to correct it [12,1]. However, corrected signals are highly sensitive to the choice of the drift polynomial, and these techniques do not perform as well for low SNR measurements.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 9: Example of pavement deflection due to two-axle truck moving at 50 km/h. Image taken from [1].…”

Section: Pavement-vehicle Interaction Modelmentioning

confidence: 99%

“…The last step is obtained by combining Equations (2) and (1). The unknowns α and σ can be estimated from the measured acceleration (Section 5.2), but β depends on axle type (single or tandem) and pavement properties, and needs to be calibrated using trucks of known weights (Section 5.3).…”

Section: Pavement-vehicle Interaction Modelmentioning

confidence: 99%

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In-pavement wireless weigh-in-motion

Bajwa

Rajagopal

Coleri

et al. 2013

Proceedings of the 12th International Conference on Information Processing in Sensor Networks

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Truck weight data is used in many areas of transportation such as weight enforcement and pavement condition assessment. This paper describes a wireless sensor network (WSN) that estimates the weight of moving vehicles from pavement vibrations caused by vehicular motion. The WSN consists of: acceleration sensors that report pavement vibration; vehicle detection sensors that report a vehicle's arrival and departure times; and an access point (AP) that synchronizes all the sensors and records the sensor data. The paper also describes a novel algorithm that estimates a vehicle's weight from pavement vibration and vehicle detection data, and calculates pavement deflection in the process. A prototype of the system has been deployed near a conventional Weigh-In-Motion (WIM) system on I-80 W in Pinole, CA. Weights of 52 trucks at different speeds and loads were estimated by the system under different pavement temperatures and varying environmental conditions, adding to the challenges the system must overcome. The error in load estimates was less than 10% for gross weight and 15% for individual axle weights. Different states have different requirements for WIM but the system described here outperformed the nearby conventional WIM, and meets commonly used standards in United States. The system also opens up exciting new opportunities for WSNs in pavement engineering and intelligent transportation.

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Section: Related Workmentioning

confidence: 99%

“…Figure 9: Example of pavement deflection due to two-axle truck moving at 50 km/h. Image taken from [1].…”

Section: Pavement-vehicle Interaction Modelmentioning

confidence: 99%

See 1 more Smart Citation

In-pavement wireless weigh-in-motion

Bajwa

Rajagopal

Coleri

et al. 2013

Proceedings of the 12th International Conference on Information Processing in Sensor Networks

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show abstract

“…The work done by Arraigada et al [8] consisted in measuring the pavement deflections using accelerometers and deflectometers. They also used a visco-elastic pavement model to fit the measurements obtained with the deflectograph.…”

Section: Introductionmentioning

confidence: 99%

“…(1) The direct integration process tends to attenuate the low-frequency components of the signal [8].…”

mentioning

confidence: 99%

Alternate Method of Pavement Assessment Using Geophones and Accelerometers for Measuring the Pavement Response

Bahrani¹,

Blanc²,

Huard³

et al. 2020

Infrastructures

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Pavement instrumentation with embeddable in-situ sensors has been a feasible approach to determine pavement deteriorations. Determining pavement deflections during the passage of the load is a promising strategy to determine the overall performance of the pavement. There are different devices that apply loads to the pavements and measure the deflection basin, these include static, vibratory, or impulse loadings. Most commonly used are the static loading like Benkelman beam and impulse loading like the Falling Weight Deflectometer (FWD). However, these techniques are costly and the measurements are recorded infrequently, i.e., once per year or two years. This study focuses on the use of geophones and accelerometers to measure the surface deflections under traffic loading. To develop a method to measure pavement deflections, the sensors were submitted first to laboratory tests, and then tested in situ, in a full scale accelerated pavement test. In the laboratory, the sensors were submitted to different types of loading using a vibrating table. These tests were used to determine the noise and sensitivity of the sensors, and then to evaluate their response to signals simulating pavement deflections under heavy vehicles. The sensor response was compared with measurements of a reference displacement sensor. Different processing techniques were proposed to correct the measurements from geophones and accelerometers, in order to obtain reliable deflection values. Then, the sensors were evaluated in a full scale accelerated test, under real heavy axle loads. Tests were performed at different loads and speeds, and the deflection measurements were compared with a reference anchored deflection sensor. The main advantage of using accelerometers or geophones embedded in the pavement is to enable continuous pavement monitoring, under real traffic. The sensor measurements could also be used to determine the type of vehicles and their corresponding speeds. The study describes in detail the signal analysis needed to measure the pavement deflections accurately. The measurements of pavement deflection can be then used to analyze the pavement behavior in the field, and its evolution with time, and to back-calculate pavement layer properties.

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Inference of Pavement Properties with Roadside Accelerometers

Nielsen

Levenberg

Skar

2020

Lecture Notes in Civil Engineering

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An array of four synchronized single-axis accelerometers was fixed to the surface of an asphalt pavement. Vertical acceleration traces resulting from several nearby passes of a truck with known characteristics were recorded. The work focused on presenting and demonstrating an interpretation method for inferring the mechanical properties of the pavement system based on the recorded accelerations. In general terms, the method was based on carefully low-pass filtering the field-measured acceleration traces, and then best-matching them with a corresponding set of calculated acceleration traces. For this purpose, the pavement system was modeled as a two-layered linear elastic half-space, and a model-guided signal filtering approach was devised to ensure that irrelevant signal content is removed prior to the matching. Based on the analysis of six separate truck passes it was noticed that the inferred upper layer modulus exhibited medium variability while the lower (subgrade) modulus showed little variability. The moduli values displayed fair agreement with those independently estimated from non-destructive and semi-destructive tests. By analyzing many more passes inferred moduli are expected to become more representative. Overall, the method seems workable and scalable, with capacity to handle any number of acceleration sensors as well as other sensor types.

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Evaluation of accelerometers to determine pavement deflections under traffic loads

Cited by 32 publications

References 10 publications

In-pavement wireless weigh-in-motion

In-pavement wireless weigh-in-motion

Alternate Method of Pavement Assessment Using Geophones and Accelerometers for Measuring the Pavement Response

Inference of Pavement Properties with Roadside Accelerometers

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