Abstract
Despite the availability of mobile applications designed for evaluating the superficial conditions of pavement structures based on the International Roughness Index (IRI), their effectiveness for urban pavement is not yet proven. The driving conditions and particular road characteristics influence the effect of the variables on the dynamic response captured by the mobile application to estimate the IRI. Additionally, the variability of the IRI is increased due to the complexity of controlling many variables involved. To estimate the IRI in urban roads using a mobile application (IRIm), these values need to be adjusted using mathematical models. Furthermore, some controlled variables must be fixed at specific values to reduce estimation errors. This study analyzes the influence and interaction of controllable variables on the IRIm to reduce their variability. The first stage of the methodology was exploratory, allowing us to select a subset of independent variables. In the second, an experimental design was applied to analyze the effect of the variables and their interactions on the IRIm. Findings suggest that IRIms are sensitive to specific variables. However, in this study, in a vehicle integrated with a low-performance suspension system, setting the tire pressure to three psi below the recommended manufacturer level and driving the vehicle close to 35 km/h, the IRIms were slightly affected by small changes in these factors. These results show that mobile applications can provide consistent and reliable measurements to estimate the roughness of roads under certain conditions. This investigation is a starting point for future research focused to propose a protocol for estimating the IRI using mobile applications.
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Acknowledgements
The authors of this manuscript acknowledge the Faculty of Engineering at the Universidad de Piura, Peru, under the Ingenium second edition contest for providing the funds to develop this research. Additionally, the authors acknowledge the valuable contributions of Edgar Rodriguez, MSc, during the initial stage of the project.
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Chang, G., Moyano, M.P., Quevedo, V., Sánchez, J., Vegas, S. (2024). Establishment of the Conditions for the Estimation of IRI in Urban Roads Using a Mobile Application. In: Akhnoukh, A., Kaloush, K., Souliman, M.I., Chang, C. (eds) A Pathway to Safe, Smart, and Resilient Road and Mobility Networks. IRF IRF 2022 2022. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-47612-9_1
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