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Instrumentation de chaussées : la route intelligente qui s’auto-détecte ?

Abstract : Recently, the roads supporting a great number of heavy vehicles usually have a thick and little deformable structure. To evaluate the pavement performances, deflection measurement devices have been used. However, these measurements are not accurate and the degradation detection is not enough to detect the start of pavement damage.In order to obtain more accurate and continuous mechanical pavement measurements, highway sections were instrumented with specific sensors (temperature probes, strain gages, geophones). However, the measurements analysis under real traffic generates a great number of data and a variability of measurements. Therefore, this problem requires an original signal sorting process. The study of strain measurements allows analyzing real strain variations which take in account the daily and seasonal variations of environment parameters.Modelling calculations with different assumptions were carried out afterward in order to obtain the best prediction for the mechanical pavement behavior. The study of geophone measurements allows measuring pavement deflections which represent pavement bearing capacity. In addition, different geophones were used to characterize heavy vehicle silhouettes, vehicle speeds and their lateral positions. The thesis work meets the expected requirement of construction managers to monitor continuously their infrastructures under real traffic.
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Submitted on : Wednesday, October 28, 2020 - 2:43:10 PM
Last modification on : Tuesday, November 3, 2020 - 3:02:41 AM


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  • HAL Id : tel-02982181, version 1



Ngoc Son Duong. Instrumentation de chaussées : la route intelligente qui s’auto-détecte ?. Génie civil. École centrale de Nantes, 2017. Français. ⟨NNT : 2017ECDN0033⟩. ⟨tel-02982181⟩



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