Abstract
This paper develops an enhanced inverse filtering-based methodology for drive-by frequency identification of bridges using smartphones for real-life applications. As the vibration recorded on a vehicle is dominated by vehicle features including suspension system and speed as well as road roughness, inverse filtering aims at suppressing these effects through filtering out vehicle- and road-related features, thus mitigating a few of the significant challenges for the indirect identification of the bridge frequency. In the context of inverse filtering, a novel approach of constructing a database of vehicle vibrations for different speeds is presented to account for the vehicle speed effect on the performance of the method. In addition, an energy-based surface roughness criterion is proposed to consider surface roughness influence on the identification process. The successful performance of the methodology is investigated for different vehicle speeds and surface roughness levels. While most indirect bridge monitoring studies are investigated in numerical and laboratory conditions, this study proves the capability of the proposed methodology for two bridges in a real-life scale. Promising results collected using only a smartphone as the data acquisition device corroborate the fact that the proposed inverse filtering methodology could be employed in a crowdsourced framework for monitoring bridges at a global level in smart cities through a more cost-effective and efficient process.
Highlights
Rapid urban growth in recent decades has created many challenges in terms of city management including pollution and traffic congestion
This was due to the fact that major vibrational sources including the moving frequency of the vehicle and engine vibrations were all speed-dependent and a slight change in the speed of the vehicle changed the position of the major peaks in the spectrum, which was magnified in the inverse filtering process
This paper proposed an enhanced inverse filtering methodology for the real-life apThis paper proposed enhanced inverse filtering methodology for the through real-life plications of the drive-byan frequency identification of bridges using smartphones applications of the drive-by frequency identification of bridgesproposed using smartphones through a new framework
Summary
Rapid urban growth in recent decades has created many challenges in terms of city management including pollution and traffic congestion. The sustainability of these urban systems and cities is contingent upon the adequate performance of their infrastructure [1,2]. Recent technology developments have provided reliable and efficient means for monitoring city infrastructures through smart sensing, computing and communication technologies, defining the new term of Smart City [3,4,5]. The economic development of a city depends on the proper performance of its transportation system [7]. A recent report [8] concludes that almost
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