Development of vehicle intelligent monitoring system (VIMS)

Fujino, Yozo; Kitagawa, Keisuke; Furukawa, Takashi; Ishii, Hironori

doi:10.1117/12.601727

Cited by 22 publications

(12 citation statements)

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“…Several efforts examined the possibility of estimating the IRI or the elevation profile from accelerometer data but have not derived a theoretical relationship. A research team from the University of Tokyo found that the root-mean-square (RMS) of the accelerometer signal was correlated to the elevation profile data (Fujino et al 2005). A team from the University of Pretoria (South Africa) found that it is possible to train an artificial neural network to estimate the elevation profile from accelerometer data, within 20% accuracy (Ngwangwa et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Connected Vehicle Approach for Pavement Roughness Evaluation

Bridgelall¹

2014

J. Infrastruct. Syst.

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Connected vehicles present an opportunity to monitor pavement condition continuously by analyzing data from vehicle-integrated position sensors and accelerometers. The current practice of characterizing and reporting ride-quality is to compute the international roughness index (IRI) from elevation profile or bumpiness measurements. However, the IRI is defined only for a reference speed of 80 kilometers per hour. Furthermore, the relatively high cost for calibrated instruments and specialized expertise needed to produce the IRI limit its potential for widespread use in a connected vehicle environment. This research introduces the road impact factor (RIF) which is derived from vehicle integrated accelerometer data. The analysis demonstrates that RIF and IRI are directly proportional. Simultaneous data collection with a laser-based inertial profiler validates this relationship. A linear combination of the RIF from different speed bands produces a time-wavelength-intensity-transform (TWIT) that, unlike the IRI, is wavelength-unbiased. Consequently, the TWIT enables low-cost, network-wide and repeatable performance measures at any speed. It can extend models that currently use IRI data by calibrating them with a constant of proportionality.

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Section: Introductionmentioning

confidence: 99%

Connected Vehicle Approach for Pavement Roughness Evaluation

Bridgelall¹

2014

J. Infrastruct. Syst.

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“…Siringoringo and Fujino [30] study a similar approach for the estimation of the bridge fundamental frequency. Theoretical simulations and a full-scale field experiment are carried out to support their approach, which is aimed at periodic bridge inspections using accelerations of a light commercial vehicle [31]. In theoretical simulations and a parametric study, it is shown that bridge frequency can be extracted from the vehicle response.…”

Section: Indirect Bridge Frequencymentioning

confidence: 99%

“…Other indirect approaches have been developed which aim to measure road surface profile from the acceleration response of a moving vehicle [31,51]. The vehicle intelligent monitoring system (VIMS) presented by Fujino et al [31] targets highway pavements and bridge expansion joints and also utilizes a GPS sensor mounted in the vehicle to identify the position where the acceleration response is recorded. The theory behind such algorithms and techniques incorporates optimisation, transfer and correlation functions which may provide a basis to further reduce the influence that road surface profile has on vehicle acceleration measurements.…”

Section: Effect Of Road Surface Profile On Vehicle-bridge Interactionmentioning

confidence: 99%

A Review of Indirect Bridge Monitoring Using Passing Vehicles

Malekjafarian

McGetrick

O’Brien

2015

Shock and Vibration

173

133

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Indirect bridge monitoring methods, using the responses measured from vehicles passing over bridges, are under development for about a decade. A major advantage of these methods is that they use sensors mounted on the vehicle, no sensors or data acquisition system needs to be installed on the bridge. Most of the proposed methods are based on the identification of dynamic characteristics of the bridge from responses measured on the vehicle, such as natural frequency, mode shapes, and damping. In addition, some of the methods seek to directly detect bridge damage based on the interaction between the vehicle and bridge. This paper presents a critical review of indirect methods for bridge monitoring and provides discussion and recommendations on the challenges to be overcome for successful implementation in practice.

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“…Siringoringo and Fujino [14,15] note that expansion joints excite vehicle bounce and pitch motions and their dominance in the vehicle dynamic response can hide the bridge response. Therefore, it is recommended that this part of the signal should not be considered if bridge dynamic parameters are of interest.…”

Section: Bridge Frequency Identificationmentioning

confidence: 99%

“…However, these types of methods, summarised by Malekjafarian et al [7], lack comprehensive experimental verification, with very few field trials reported in the literature. Those reporting successful results have been primarily limited to bridge frequency identification, such as the experiments by Lin and Yang [6] utilising a trucktrailer configuration, or the light commercial vehicle employed by both Siringoringo and Fujino [14] and Fujino et al [15]. In general, speeds below 40 km/h have been found to provide the most accurate bridge frequency identification results due to improved spectral resolution and the reduced influence of road profile on the vehicle response, while modal analysis of the test vehicle is recommended before field testing commences.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a drive-by monitoring system for transport infrastructure utilising smartphone technology and GNSS

McGetrick

Hester

Taylor

2017

J Civil Struct Health Monit

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Ageing and deterioration of infrastructure are a challenge facing transport authorities. In particular, there is a need for increased bridge monitoring in order to provide adequate maintenance, prioritise allocation of funds and guarantee acceptable levels of transport safety. Existing bridge structural health monitoring (SHM) techniques typically involve direct instrumentation of the bridge with sensors and equipment for the measurement of properties such as frequencies of vibration. These techniques are important as they can indicate the deterioration of the bridge condition. However, they can be labour intensive and expensive due to the requirement for on-site installations. In recent years, alternative low-cost indirect vibration-based SHM approaches have been proposed which utilise the dynamic response of a vehicle to carry out 'drive-by' pavement and/or bridge monitoring. The vehicle is fitted with sensors on its axles thus reducing the need for on-site installations. This paper investigates the use of lowcost sensors incorporating global navigation satellite systems (GNSS) for implementation of the drive-by system in practice, via field trials with an instrumented vehicle. The potential of smartphone technology to be harnessed for drive-by monitoring is established, while smartphone GNSS tracking applications are found to compare favourably in terms of accuracy, cost and ease of use to professional GNSS devices.

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Development of vehicle intelligent monitoring system (VIMS)

Cited by 22 publications

References 0 publications

Connected Vehicle Approach for Pavement Roughness Evaluation

Connected Vehicle Approach for Pavement Roughness Evaluation

A Review of Indirect Bridge Monitoring Using Passing Vehicles

Implementation of a drive-by monitoring system for transport infrastructure utilising smartphone technology and GNSS

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