Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory
We propose, calibrate, and validate a crowdsourced approach for estimating power spectral density (PSD) of road roughness based on an inverse analysis of vertical acceleration measured by a smartphone mounted in an unknown position in a vehicle. Built upon random vibration analysis of a half-car mec...
| Main Authors: | , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press (CUP)
2021
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| Online Access: | https://hdl.handle.net/1721.1/133103 |
| _version_ | 1811079803644674048 |
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| author | Botshekan, Meshkat Roxon, Jacob Wanichkul, Athikom Chirananthavat, Theemathas Chamoun, Joy Ziq, Malik Anini, Bader Daher, Naseem Awad, Abdalkarim Ghanem, Wasel Tootkaboni, Mazdak Louhghalam, Arghavan Ulm, Franz-Josef |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Botshekan, Meshkat Roxon, Jacob Wanichkul, Athikom Chirananthavat, Theemathas Chamoun, Joy Ziq, Malik Anini, Bader Daher, Naseem Awad, Abdalkarim Ghanem, Wasel Tootkaboni, Mazdak Louhghalam, Arghavan Ulm, Franz-Josef |
| author_sort | Botshekan, Meshkat |
| collection | MIT |
| description | We propose, calibrate, and validate a crowdsourced approach for estimating power spectral density (PSD) of road roughness based on an inverse analysis of vertical acceleration measured by a smartphone mounted in an unknown position in a vehicle. Built upon random vibration analysis of a half-car mechanistic model of roughness-induced pavement–vehicle interaction, the inverse analysis employs an L2 norm regularization to estimate ride quality metrics, such as the widely used International Roughness Index, from the acceleration PSD. Evoking the fluctuation–dissipation theorem of statistical physics, the inverse framework estimates the half-car dynamic vehicle properties and related excess fuel consumption. The method is validated against (a) laser-measured road roughness data for both inner city and highway road conditions and (b) road roughness data for the state of California. We also show that the phone position in the vehicle only marginally affects road roughness predictions, an important condition for crowdsourced capabilities of the proposed approach. |
| first_indexed | 2024-09-23T11:20:42Z |
| format | Article |
| id | mit-1721.1/133103 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:20:42Z |
| publishDate | 2021 |
| publisher | Cambridge University Press (CUP) |
| record_format | dspace |
| spelling | mit-1721.1/1331032024-06-06T14:43:24Z Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory Botshekan, Meshkat Roxon, Jacob Wanichkul, Athikom Chirananthavat, Theemathas Chamoun, Joy Ziq, Malik Anini, Bader Daher, Naseem Awad, Abdalkarim Ghanem, Wasel Tootkaboni, Mazdak Louhghalam, Arghavan Ulm, Franz-Josef Massachusetts Institute of Technology. Department of Civil and Environmental Engineering We propose, calibrate, and validate a crowdsourced approach for estimating power spectral density (PSD) of road roughness based on an inverse analysis of vertical acceleration measured by a smartphone mounted in an unknown position in a vehicle. Built upon random vibration analysis of a half-car mechanistic model of roughness-induced pavement–vehicle interaction, the inverse analysis employs an L2 norm regularization to estimate ride quality metrics, such as the widely used International Roughness Index, from the acceleration PSD. Evoking the fluctuation–dissipation theorem of statistical physics, the inverse framework estimates the half-car dynamic vehicle properties and related excess fuel consumption. The method is validated against (a) laser-measured road roughness data for both inner city and highway road conditions and (b) road roughness data for the state of California. We also show that the phone position in the vehicle only marginally affects road roughness predictions, an important condition for crowdsourced capabilities of the proposed approach. 2021-10-25T18:22:36Z 2021-10-25T18:22:36Z 2020-12 2020-10 2021-10-21T18:19:08Z Article http://purl.org/eprint/type/JournalArticle 2632-6736 https://hdl.handle.net/1721.1/133103 Botshekan M, Roxon J, Wanichkul A, Chirananthavat T, Chamoun J, Ziq M, Anini B, Daher N, Awad A, Ghanem W, Tootkaboni M, Louhghalam A and Ulm F.-J (2020). Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory. Data-Centric Engineering, 1: e16 en 10.1017/DCE.2020.17 Data-Centric Engineering Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Cambridge University Press (CUP) Cambridge University Press |
| spellingShingle | Botshekan, Meshkat Roxon, Jacob Wanichkul, Athikom Chirananthavat, Theemathas Chamoun, Joy Ziq, Malik Anini, Bader Daher, Naseem Awad, Abdalkarim Ghanem, Wasel Tootkaboni, Mazdak Louhghalam, Arghavan Ulm, Franz-Josef Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory |
| title | Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory |
| title_full | Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory |
| title_fullStr | Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory |
| title_full_unstemmed | Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory |
| title_short | Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory |
| title_sort | roughness induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory |
| url | https://hdl.handle.net/1721.1/133103 |
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