A data analytics case study assessing factors affecting pavement deflection values

Seyfi, Majid; Rawat, Rakesh; Weligamage, Justin; Nayak, Richi

doi:10.1504/ijbidm.2013.059024

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…The three model types were chosen because their structures (i.e., dendritic and neuron based) have been shown to be applicable to large and complex data sets, yet they can be trained quickly using a relatively small sample of test data (without overfitting) to produce accurate predictions (Chandra et al 2012;Melhem and Cheng 2003). Furthermore, the model types have been used successfully in related applications which predict road condition from field measurements (Jang et al 2016;Seyfi et al 2013;Nitsche et al 2012;Soleimani and Sahebi 2012).…”

Section: Development Of Predictive Modelsmentioning

confidence: 99%

Study of the Factors Affecting Road Roughness Measurement Using Smartphones

Wang

Burrow

Ghataora

2020

J. Infrastruct. Syst.

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The measurement of road roughness is important for the management of economic road maintenance. Not only is it an indicator of road condition and ride quality, but it also is used to determine road use costs, including travel time, fuel consumption, and vehicle maintenance. Because of the importance of roughness for road asset management decision-making, road agencies spend considerable resources trying to measure road roughness in a repeatable and reproducible manner. However, many road agencies with large road networks are unable to record the condition of the entire network on a sufficiently frequent basis to determine adequately road condition to make informed preventative maintenance decisions. To address this, research has been carried out to develop low cost smartphone based technologies fitted inside vehicles to measure road condition. The trial of these systems has met with varying degrees of success. This paper presents an in-depth parametric study carried out using state-of-the-art vehicle dynamics software, informed by a review of the literature, to appreciate how and to what degree various influencing variables might affect roughness measurements using a smartphone fitted to a moving vehicle. These variables included the type and position of the smartphone; the type, speed, mass, dynamic response, suspension system, and tire pressure of the vehicle in which the smartphone is fitted; and the longitudinal road profile. The results of the parametric analysis were used to build multivariate linear regression and machine learning algorithms which predict road roughness from a measure of a vehicle's vertical acceleration taking into account the predominant influencing variables. The multivariate linear regression equations can be used to predict road roughness with a similar degree of accuracy that is expected from a visual inspection. On the other hand, the machine learning algorithms, when suitably trained, were able to estimate reliably the road roughness on an integer-based rating scale at a level of detail which is suitable for strategic road asset management, provided that the vehicle type and speed and the type of smartphone are taken into account.

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