Feasibility Assessment of a Smartphone-Based Application to Estimate Road Roughness

Zeng, Huanghui; Park, Hyung Jun; Smith, Brian L.; Parkany, Emily

doi:10.1007/s12205-017-1008-9

Cited by 27 publications

(20 citation statements)

References 4 publications

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“…The low resolution of accelerometers in mobile devices presents a challenge for high‐frequency measurement in structures. Recently, Zeng et al assessed the feasibility of smartphone‐based application for cost‐effective roughness monitoring of road surfaces. Two Android‐based smart tablets were used to collect data, and they were firmly kept in the boxes sitting on the floor of a car during the experimental trips.…”

Section: Next‐generation Sensing Methodsmentioning

confidence: 99%

A literature review of next-generation smart sensing technology in structural health monitoring

Sony

Laventure

Sadhu

2019

Struct Control Health Monit

397

183

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Summary Advent of computationally efficient smartphones, inexpensive high‐resolution cameras, drones, and robotic sensors has brought a new era of next‐generation intelligent monitoring systems for civil infrastructure. Vibration‐based condition assessment has garnered as a prominent method of evaluating the health of large‐scale infrastructure. The use of contact‐based sensors for acquiring vibration data becomes uneconomical and tedious due to their instrumentation cost, centralized nature, and densification required to collect sufficient data for system identification of modern complex structures. A need to advance and develop alternative methods for efficient sensing system results in next‐generation measurement technology of structural health monitoring. The abundance of handheld smartphones with easily programmable framework has helped in modifying relevant software to acquire vibration data using embedded sensors in the smartphone. The inexpensive cameras have been used to capture images and videos that are utilized to understand the structural behavior with the aid of advanced signal processing techniques. The inaccessible components of structures require noncontact sensors such as unmanned aerial vehicles (UAVs) or so‐called drones and mobile sensors to acquire structural data. To the authors' knowledge, this paper first time presents a comprehensive review of a suite of next‐generation smart sensing technology that has been developed in recent years within the context of structural health monitoring. The state‐of‐the‐art methods have been presented by conducting a detailed literature review of the recent applications of smartphones, UAVs, cameras, and robotic sensors used in acquiring and analyzing the vibration data for structural condition monitoring and maintenance.

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Section: Next‐generation Sensing Methodsmentioning

confidence: 99%

A literature review of next-generation smart sensing technology in structural health monitoring

Sony

Laventure

Sadhu

2019

Struct Control Health Monit

397

183

View full text Add to dashboard Cite

show abstract

“…To evaluate road roughness IRI, the well-known regression relationships between PSD with IRI was investigated in [82,83], so do the root-mean-squared acceleration (RMS) and IRI in [84,85]. A compact road profiler and ArcGIS to measure and evaluate road roughness condition was introduced in [86].…”

Section: Signal Processingmentioning

confidence: 99%

Response-based methods to measure road surface irregularity: a state-of-the-art review

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

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Purpose: With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the stateof-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research. Methods: Available articles regarding response-based methods to measure road surface condition were collected mainly from "Scopus" database and partially from "Google Scholar". The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation. Results: The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking.

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“…Some smartphone-based road condition-sensing approaches use acceleration signals to locate potholes [12], comparatively identify roughness (by setting thresholds) [13] or obtain pavement profiles (directly through double integration or through Fourier analysis) [14,15]. Other approaches rely on calibrated vehicles and dedicated sensors [16] or establish a correlation between the vibration data and road roughness metric(s) [17][18][19][20][21], which are commonly used to prioritize repair and maintenance of roadways [11], or estimate excess fuel consumption [22][23][24][25]; see also [26] for a survey. All of these approaches, however, attempt to establish empirical relationships between the measures of road surface condition and the vibration signal regardless of vehicle dynamics and the stochastic nature of road surface profiles.…”

Section: Introductionmentioning

confidence: 99%

Smartphone-enabled road condition monitoring: from accelerations to road roughness and excess energy dissipation

et al. 2021

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We develop a framework to address the shortcomings of current smartphone-based approaches for road roughness sensing and monitoring through combining vehicle dynamics, random vibration theory and a two-layer inverse analysis. The proposed approach uses in-cabin recordings of the vehicle’s vertical acceleration measured by a smartphone positioned inside the car for the estimation of road roughness. The mechanistic road roughness–vehicle interaction model at the core of the proposed framework links the frequency spectrum of the vehicle’s vertical acceleration to the road roughness power spectral density and lends itself to the quantitative characterization of roughness-induced energy dissipation. We demonstrate that the measure of roughness provided by the stochastic model of car dynamics interacting with a rough road is fully compatible, in a statistical sense, with the spatial but deterministic definition of road roughness, and validate the identification strategy that originates from it against laser measurements of road roughness. The critical crowdsourcing features of the proposed framework, such as the marginal impact of phone position and transferability, are examined and its utility to meld with big data analytics to identify the class of vehicles travelling on a roadway network is demonstrated.

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Feasibility Assessment of a Smartphone-Based Application to Estimate Road Roughness

Cited by 27 publications

References 4 publications

A literature review of next-generation smart sensing technology in structural health monitoring

A literature review of next-generation smart sensing technology in structural health monitoring

Response-based methods to measure road surface irregularity: a state-of-the-art review

Smartphone-enabled road condition monitoring: from accelerations to road roughness and excess energy dissipation

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