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Development of Vehicle Intelligent Monitoring System (VIMS)
Abstract
In an urban highway network system such as Tokyo Metropolitan Expressway, to detect conditions of road pavement and expansion joints is a very important issue. Although accurate surface condition can be captured by using a road profiler system, the operating cost is expensive and development of a simpler and more inexpensive system is really needed to reduce monitoring cost. "Vehicle Intelligent Monitoring System (VIMS)" developed for this purpose is described in this paper. An accelerometer and GPS are installed to an ordinary road patrol car. GPS together with a PC computer are used to measure the road surface condition and to identify the location of the vehicle, respectively. Dynamic response of the vehicle is used as a measure of the road pavements surface condition as well as the expansion joints. A prototype of VIMS is installed to a motor car and measurement is made at the actual roads. Accuracy of measuring result and effectiveness of this system are demonstrated; the outline of the system and some of the measurement results are reported herein.
... This variability can further impact the identified modal frequencies. Some authors 28,29 observed that expansion joints induce bounce and pitch motions in vehicles, and their prevalence in the vehicle dynamic response can hide the bridge response. The authors suggested removing the affected part of the collected signal from the analyses, thus further reducing the length of the processed signal. ...
... Given the time-varying characteristics of VBI, the frequencies of both vehicles and bridges exhibit variations over time. Their findings emphasize the necessity to differentiate the extracted drive-by bridge frequency from that obtained through direct measurements, aligning with previous studies, 24,[26][27][28][29] and underscore that VBI effects should not be generally overlooked. ...
... In such cases, the main challenges were related to the short signals collected on board the travelling vehicles, resulting in a frequency spectrum with low resolution. 33,34 Issues such as frequency shifts due to load variability, 24,[26][27][28][29] and the presence of vehicle dynamics in the response, which can mask the dynamic behavior of the bridge 46 were also encountered. Significant research has been devoted to these issues, finding the main solutions in time-frequency representations of the signal and explicit VBI modeling. ...
Climate-related extreme events are becoming increasingly frequent, posing significant threats to bridges, which are critical components of transportation infrastructure. This paper offers an overview of recent advancements in methodologies and technologies for conducting structural health monitoring (SHM) of bridges over large areas, where deploying sensors on every structure may be cost-prohibitive for local administrations. It specifically examines two approaches that have garnered interest in the past decade: indirect SHM, which involves instrumenting vehicles and analyzing their dynamic responses to infer information about bridges, and satellite interferometric radar data, which offer static displacement measurements for large regions and has recently been exploited for civil SHM purposes. Additionally, it reviews the recent developments in population-based SHM, which facilitates knowledge-sharing among structures with similar characteristics within a population. Through an analysis of the advantages and limitations of these three rapidly developing research areas, the paper outlines future opportunities and lays the roadmap for a comprehensive “regional-scale SHM” approach based on remote and/or crowdsourced data, supported by population-level analyses. Specific topics addressed include strategies for similarity assessment among monitored structures, available data sources, and feature extraction/integration approaches for different scenarios.
... Siringoringo and Fujino [27] 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 [28]. In theoretical simulations and a parametric study, it is shown that bridge frequency can be extracted from the vehicle response. ...
... Other indirect approaches have been developed which aim to measure road surface profile from the acceleration response of a moving vehicle [28,50] . The vehicle intelligent monitoring system (VIMS) presented by Fujino et al. [28] targets highway pavements and bridge expansion joints and also utilises a GPS sensor mounted in the vehicle to identify the position where the acceleration response is recorded. ...
... Other indirect approaches have been developed which aim to measure road surface profile from the acceleration response of a moving vehicle [28,50] . The vehicle intelligent monitoring system (VIMS) presented by Fujino et al. [28] targets highway pavements and bridge expansion joints and also utilises 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. ...
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.
... 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. ...
... 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. ...
... 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. ...
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.
... They concluded that low vehicle speed is beneficial for reliable extraction of bridge frequencies and there should be sufficiently high dynamic excitation on the bridge when the vehicle travels over it [9,12]. Lin and Yang [19], and Fujino et al. [34] in their investigation concluded that as the vehicle runs for a longer duration on the target bridge under low speed, the vehicle response will be less influenced by the bridge surface response and the bridge-related information will be enhanced with high resolution. In contrast to low vehicle speed choice, Yang et al. [12] in their study reported that the vehicle spectrum shows the first few bridge frequencies more visible when the test vehicle runs at high speed over the target bridge with smooth surface roughness. ...
... The research on the influence of the combined effect of the above critical factors is very beneficial for the assessment of the transmission performance of the drive-by inspection vehicle with different dynamic characteristics. Very little attention has been made so far towards the optimum choice of dynamic properties of the test vehicle (vehicle stiffness, mass, and damping) to achieve higher transmissibility and to optimize the effectiveness of drive-by-monitoring of bridges [34]. This paper presents a more detailed and comprehensive numerical investigation study on both the individual and combined influence of several critical factors such as vehicle properties (mass, stiffness, and damping), vehicle speed, bridge surface roughness, and ongoing traffic in extracting multiple bridge frequencies from the vehicle response (either body or axle response, derived contact wheel/contact point response). ...
The drive-by monitoring technique for bridge dynamic properties extraction from the vibration response of the instrumented test vehicle presents several practical benefits. In this paper, a detailed numerical parametric study is conducted to investigate the individual and combined effect of critical factors such as vehicle frequency (dependent on vehicle mass and vehicle stiffness), vehicle damping, vehicle speed, bridge surface roughness, and ongoing traffic on extracting multiple bridge frequencies from the moving vehicle responses. This study helps in providing guidelines for designing a robust field drive-by test vehicle applicable to the majority of bridges and to develop a reliable vehicle scanning method for bridge damage diagnosis. From the numerical investigations, the results show that the identification of multiple bridge frequencies from the response of the test vehicle is largely possible when the vehicle is designed with a frequency greater than the interested higher bridge frequency. Low vehicle speed is recommended to reduce the camel hump phenomenon observed at higher bridge frequencies. Due to the absence of the overshadowing effect of vehicle frequency, high vehicle damping and contact point response serve as a better feature to capture significant dynamics of the bridge. The ongoing traffic suppresses the high-frequency components generated by bridge surface roughness and proves beneficial for the extraction of multiple bridge frequencies.
... 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. ...
... 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. ...
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 low-cost 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.
... The problem is worsened by the drastic increase in traffic. Daily traffic volume in major highways has increased to as large as 15,000 vehicles per lane, in which the ratio of heavy trucks is also high -in some case it is as high as 30% (Fujino 2005). The high intensity and high frequency loading generated by this high traffic volume creates many problems in bridges. ...
... Monitoring System (VIMS) (Fujino 2005) utilizes dynamic response of an instrumented car to capture the condition of road pavements surface as well as the condition expansion joints. The VIMS system consists of: 1) a car with known dynamics properties, 2) an accelerometer to measure the dynamic response of the car, 3) a GPS sensor to identify the position where the dynamic response is recorded, and 4) a portable computer to store the measurement data (Fig. 12). ...
Civil infrastructures are always subjected to various types of hazard and deterioration. These conditions require systematic efforts to assess the exposure and vulnerability of infrastructure, as well as producing strategic countermeasures to reduce the risks. This paper describes the needs for and concept of advanced sensor technologies for risk assessment of civil infrastructure in Japan. Backgrounds of the infrastructure problems such as natural disasters, difficult environment, limited resource for maintenance, and increasing requirement for safety are discussed. The paper presents a concept of risk assessment, which is defined as a combination of hazard and structural vulnerability assessment. An overview of current practices and research activities toward implementing the concept is presented. This includes implementation of structural health monitoring (SHM) systems for environment and natural disaster prevention, improvement of stock management, and prevention of structural failure.
... To better express and quantify the roughness of the pavement surface, researchers have introduced a one-dimensional pavement longitudinal profile index called the international roughness index (IRI), which is widely used in the research on road and traffic engineering [5][6][7][8][9]. In the Mechanistic-Empirical Pavement Design Guide (MEPDG), IRI is also used as a robust design criterion that is critical to measuring the performance of AC pavement. ...
The international roughness index (IRI) for roads is a crucial pavement design criterion in the Mechanistic-Empirical Pavement Design Guide (MEPDG). However, studies have shown that the IRI transfer function in the MEPDG is simply a linear combination of road parameters, so it cannot provide accurate predictions. To solve this issue, this research developed an AdaBoost regression (ABR) model to improve the prediction ability of IRI and compared it with the linear regression (LR) in MEPDG. The development of the ABR model is based on the Python programming language, using the 4265 records from the Long-Term Pavement Performance (LTPP) that include the pavement thickness, service age, average annual daily truck traffic (AADTT), gator cracks, etc., which are reliable data that are preserved after years of monitoring. The results reveal that the ABR model is significantly better than the LR because the correlation coefficient (R²) between the measured and predicted values in the testing set increased from 0.5118 to 0.9751, and the mean square error (MSE) decreased from 0.0245 to 0.0088. By analyzing the importance of variables, there are many additional crucial factors, such as raveling and bleeding, that affect IRI, which leads to the weak performance of the LR model.
... The indirect methods of modal identification and damage detection of bridges from the vehicle response still have some limitations. Most studies use dynamic responses of vehicles with low speed, as pointed out in Lin and Yang [15] and Fujino et al. [34] that lower vehicle speed provides the best accuracy for estimating the bridge frequency. The main reason is that the travel time of the test vehicle moving over the bridge at a high speed may be too short for the bridge to go through a full cycle of vibration, which is detrimental to the spectral analysis [35], especially for the high-speed railway bridges with trains travelling at a typically high speed. ...
The identification of bridge frequencies using dynamic responses of moving vehicles has been studied by many researchers, however, most of them assume that the vehicle moves at a relatively low speed. In the application of high-speed railway bridges, the bridge span is typically short and the train always moves at a very high speed, which implies that the duration of the vehicle traveling on the bridge is very short and the signal recorded from the vehicle is not long enough for signal processing to extract the bridge frequency. The present study proposes a new method to identify the bridge frequency from vehicles moving at a high speed by combining the responses of multiple vehicles. The theoretical analysis is conducted using the simple vehicle-bridge interaction (VBI) model. By subtracting the acceleration responses of adjacent vehicles at the same position on the bridge, the residual vehicle responses were obtained and combined, which eliminated the additional driving frequencies and avoided its interference on the bridge frequency identification. Numerical simulations are then conducted to verify the proposed method, which shows that by combining the acceleration of several vehicles to extend the overall duration of the signal, the frequency resolution can be improved and the bridge frequency can be successfully identified using vehicles travelling at a high speed. The influence of track irregularity, vehicle speed, bridge stiffness, and bridge damping on the identification results has also been studied. In addition, a multi-degree-of-freedom vehicle model is used to further verify the proposed method.
... Traditional techniques in developed countries for pavement condition monitoring by using specially built trucks or wagons with laser scanners, bump wagons or even manually operated rolling straight edges are not very cost effective and those require a lot of expertise in the related field (Sayers & Karamihas, 1998). Vibration based structural health monitoring is promising to determine the pavement condition (Furukawa, 2005, Gonzalez et. al., 2008, Douangphachnh, and Oneyama, 2013. ...
Monitoring and maintenance of road infrastructure is a very important task in all over the world. Surface roughness of the roads affects vehicle maintenance costs, fuel consumption, ride comfort and road safety. Vibration based structural health monitoring can be applied in case of pavement condition monitoring by relating roughness of pavement with vibration generated from it. However, traditional techniques in developed countries for pavement condition monitoring are not very cost effective and those require a lot of expertise in the related field. Nowadays, modern smartphones have different types of sensors like-accelerometer, GPS, sound etc. This paper presents a method to evaluate the pavement condition by utilizing a simple vehicle which is run over the pavement. The vibration generated due to roughness is captured by the smartphone sensor and GPS system. Then MATLAB and ProVAL software are used to analyze the data and to develop a roughness index. The method is applied in different roads of Dhaka city with various roughness condition to show the effectiveness of the method.
... Traditional techniques in developed countries for pavement condition monitoring by using specially built trucks or wagons with laser scanners, bump wagons or even manually operated rolling straight edges are not very cost effective and those require a lot of expertise in the related field (Sayers & Karamihas, 1998). Vibration based structural health monitoring is promising to determine the pavement condition (Furukawa, 2005, Gonzalez et. al., 2008, Douangphachnh, and Oneyama, 2013. ...
... Traditional techniques in developed countries for pavement condition monitoring by using specially built trucks or wagons with laser scanners, bump wagons or even manually operated rolling straight edges are not very cost effective and those require a lot of expertise in the related field (Sayers & Karamihas, 1998). Vibration based structural health monitoring is promising to determine the pavement condition (Furukawa, 2005, Gonzalez et. al., 2008, Douangphachnh, and Oneyama, 2013. ...
Monitoring and maintenance of road infrastructure is a very important task in all over the world. Surface roughness of the roads affects vehicle maintenance costs, fuel consumption, ride comfort and road safety. Vibration based structural health monitoring can be applied in case of pavement condition monitoring by relating roughness of pavement with vibration generated from it. However, traditional techniques in developed countries for pavement condition monitoring are not very cost effective and those require a lot of expertise in the related field. Nowadays, modern smartphones have different types of sensors like-accelerometer, GPS, sound etc. This paper presents a method to evaluate the pavement condition by utilizing a simple vehicle which is run over the pavement. The vibration generated due to roughness is captured by the smartphone sensor and GPS system. Then MATLAB and ProVAL software are used to analyze the data and to develop a roughness index. The method is applied in different roads of Dhaka city with various roughness condition to show the effectiveness of the method.
... Dans la partie qui suivra, nous effectuerons une étude rapide des systèmes conçus pour différentes applications dans différents domaines. [96], [97], dans les systèmes GPS [98], [99], les têtes d'imprimantes [100], [101], les micro-miroirs des projecteurs vidéo [102], dans les téléphones portables et bien d'autres. ...
L'électroporation est un procédé physique qui consiste à appliquer des impulsions de champ électrique pour perméabiliser de manière transitoire ou permanente la membrane plasmique. Ce phénomène est d'un grand intérêt dans le domaine clinique ainsi que dans l'industrie en raison de ses diverses applications, notamment l'électrochimiothérapie qui combine les impulsions électriques à l'administration d'une molécule cytotoxique, dans le cadre du traitement des tumeurs. L'analyse de ce phénomène est traditionnellement réalisée à l'aide des méthodes optique et biochimique (microscopie, cytométrie en flux, test biochimique). Elles sont très efficaces mais nécessitent l'utilisation d'une large gamme de fluorochromes et de marqueurs dont la mise en œuvre peut être laborieuse et coûteuse tout en ayant un caractère invasif aux cellules. Durant ces dernières années, le développement de nouveaux outils biophysiques pour l'étude de l'électroporation a pris place, tels que la diélectrophorèse et la spectroscopie d'impédance (basse fréquence). Outre une facilité de mise en œuvre, ces méthodes représentent un intérêt dans l'étude des modifications membranaires de la cellule. De là vient l'intérêt d'opérer au-delà du GHz, dans la gamme des micro-ondes, pour laquelle la membrane cytoplasmique devient transparente et le contenu intracellulaire est exposé. L'extraction de la permittivité relative suite à l'interaction champ électromagnétique/cellules biologiques reflète alors l'état cellulaire. Cette technique, la spectroscopie diélectrique hyperfréquence, se présente comme une méthode pertinente pour analyser les effets de l'électroporation sur la viabilité cellulaire. De plus, elle ne nécessite aucune utilisation des molécules exogènes (non-invasivité) et les mesures sont directement réalisées dans le milieu de culture des cellules. Deux objectifs ont été définis lors de cette thèse dont les travaux se situent à l'interface entre trois domaines scientifiques : la biologie cellulaire, l'électronique hyperfréquence et les micro-technologies. Le premier objectif concerne la transposition de l'électroporation conventionnelle à l'échelle micrométrique, qui a montré une efficacité aussi performante que la première. La deuxième partie du travail concerne l'étude par spectroscopie diélectrique HyperFréquence de cellules soumises à différents traitements électriques (combinés ou non à une molécule cytotoxique). Ces travaux présentent une puissance statistique et montrent une très bonne corrélation (R2 >0 .94) avec des techniques standards utilisées en biologie, ce qui valide 'biologiquement' la méthode d'analyse HF dans le contexte d'électroporation. Ces travaux montrent en outre que la spectroscopie diélectrique hyperfréquence s'avère être une technique puissante, capable de révéler la viabilité cellulaire suite à un traitement chimique et/ou électrique. Ils ouvrent la voie à l'analyse 'non-invasive' par spectroscopie diélectrique HyperFréquence de cellules électroporées in-situ.
... The Dynamic Response Intelligent Monitoring System (iDRIMS), which consists of vehicle modeling and International Roughness Index (IRI) estimation, was developed to evaluate road conditions using ordinary vehicles. [8][9][10]. However, these methods have limitations. ...
A smartphone-based Dynamic Response Intelligent Monitoring System (iDRIMS) was developed to conduct road evaluations with high efficiency and reasonable accuracy [1]. iDRIMS estimates the International Roughness Index (IRI) based on vehicle responses measured with an iOS application, which obtains three-axis acceleration, angular velocity, and GPS with accurate sampling timing. However, the robustness and accuracy was limited. In this paper, the iDRIMS was improved mainly by employing frequency domain analysis. The algorithm consists of two steps. First, a half car (HC) model was selected as the vehicle model, and vehicle parameters were identified through driving tests over a portable hump of known size. In contrast to the previous approach of parameter identification in the time domain using Unscented Kalman Filter, the parameters were optimized to minimize the difference between the simulation and measured hump responses in the frequency domain, using a genetic algorithm. Then, IRI was estimated by measuring the vertical acceleration responses of ordinary vehicles. The measured acceleration was converted into the acceleration root mean square (RMS) of the sprung mass of a standard quarter car (QC) by multiplying a transfer function. The transfer function, estimated through the simulation of the identified HC model, as opposed to QC model in previous approaches, reflected the vehicle pitching motions and sensor installation location. The RMS was further converted to IRI based on the correlation between these values. Numerical simulation was conducted to investigate the performance in terms of various driving speeds and sensor locations. The experiment was conducted at a 13 km road by comparing three types of vehicles and a profiler. Inaccurate IRI estimation at the speed change section was experimentally investigated and compensated. Furthermore, the improved method was applied to 72 vehicles that were driven more than 180,000 km per year. A data collection and analysis platform was built, which successfully collected and analyzed large-scale data with high efficiency. The results from both numerical simulation and real case application show that the improved method accurately estimates IRI with high robustness and efficiency.
... iDRIMS has been developed to evaluate the road condition easily and objectively based on the dynamic response of a driving vehicle. However, the previous mechanism of iDRIMS [2][3][4] have limitations. First, vehicles are modeled as a quarter car (QC), which cannot reproduce pitching or rolling motion. ...
Smartphone based Dynamic Response Intelligent Monitoring System (iDRIMS) was developed to evaluate International Roughness Index (IRI) based on dynamic responses of ordinary vehicles [1]. However, the robustness and accuracy were limited. In this paper, iDRIMS is improved mainly by employing frequency domain analysis. The algorithm consists of two steps. The first step is to identify the vehicle model and the second step is to estimate the IRI by utilizing the identified vehicle model. In the first step, a half car (HC) model is selected as the vehicle model and its parameters are identified. The vehicle parameters are identified through a drive tests over a portable hump with a known size. As opposed to previous approach in the time domain using Unscented Kalman filter, the parameters are optimized to minimize the difference between simulation and measured hump responses in the frequency domain using genetic algorithm (GA). IRI is then estimated by measuring acceleration responses of ordinary vehicles. Measured acceleration is converted to the acceleration RMS of the sprung mass of standard quarter car by multiplying a transfer function. The transfer function, estimated through the simulation of the identified HC model as opposed to a QC model in previous approaches, reflects the vehicle pitching motions and sensor installation location. The RMS is further converted to IRI. Numerical simulation is conducted to investigate the IRI estimation performance in terms of various drive speeds and sensor locations. Experiment is carried out at a 13km road. Inacurate IRI estimation at speed change section is invesigated and compensated. Results from both simulation and experiment indicate that the proposed method accurately estimate IRI with high robustness and efficiency.
... 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% ...
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 km/h. 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 laserbased inertial profiler validates this relationship. A linear combination of the RIF from different speed bands produces a time-wavelengthintensity-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.
... We emphasize on the use of typical light commercial vehicle as the inspection vehicle in contrast to other study (Lin and Yang 2005) that utilized special vehicle such as truck or trailer. Such inspection vehicle, named Vehicle Intelligent Monitoring System (VIMS), has been developed recently in the authors' research group (Fujino 2005; Asakawa et al. 2008). The current system is limited only to the assessment of road pavement and expansion joint, and is expected to include assessment of bridge condition via frequency estimation. ...
Driving velocity, natural frequency of vehicle and natural frequency of bridge are the main contributing factors to vibration of a vehicle when passing a bridge. By separating contributions of the first two factors, one can estimate the natural frequency of a bridge indirectly from vehicle acceleration response when it crosses a bridge. In this paper, we apply this concept to estimate the bridge fundamental frequency indirectly using the response of a passing instrumented vehicle. The paper first describes analytical formulation and finite element simulation to demonstrate the feasibility of the method. Afterwards, it describes an experimental verification as a proof-of-concept of the method on a full-scale simply-supported short span bridge by using a light commercial vehicle instrumented with accelerometer. Dynamic responses of the vehicle while passing the bridge are recorded and analyzed. Spectra analysis of the vehicle responses reveal that the first natural frequency of the bridge can be estimated with reasonable accuracy when the vehicle moves with constant velocity. The concept of indirect frequency estimation is useful for assessment of short and medium span bridges where permanent instrumentation and routine visual inspection can be too costly. In this method, one can use inspection vehicle instrumented with vibration sensor to conduct periodic measurements by passing the vehicle over several monitored bridges and estimate their fundamental frequencies. When significant change in frequency is detected, detail inspections can be further conducted to investigate the possible damage on the bridge.
... Several examples of microsystems are now used in daily life: accelerometers that are present in modern cars (airbag trigger system) [19,20], and also in Nike shoes (integrated pedometer), Sony Playstation games consoles (measurement of the inclination of the joystick) and GPS systems [21,22], inkjet print heads [23][24][25][26], arrays of micromirrors in video projectors [27,28], etc. Many sectors are now concerned such as optical and radio frequency telecommunications (switches, variable capacitances and inductances) [29][30][31]. ...
Microfluidics is an emerging field that has given rise to a large number of scientific and technological developments over the last few years. This review reports on the use of various materials, such as silicon, glass and polymers, and their related technologies for the manufacturing of simple microchannels and complex systems. It also presents the main application fields concerned with the different technologies and the most significant results reported by academic and industrial teams. Finally, it demonstrates the advantage of developing approaches for associating polymer technologies for manufacturing of fluidic elements with integration of active or sensitive elements, particularly silicon devices.
The International Roughness Index (IRI) has become the reference scale for assessing pavement roughness in many highway agencies worldwide. This research aims to develop two Artificial Neural Network (ANN) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Laos Pavement Management System (PMS) database for National Road Network (NRN). The final database consisted of 269 and 122 observations covering 1850 km of DBST NRN and 718 km of AC NRN, respectively. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The obtained data were randomly divided into training (70%), validation (15%), and testing (15%) datasets. The statistical evaluation results of the training dataset reveal that both ANN models (DBST and AC) have good prediction ability with high values of coefficient of determination (R2 = 0.96 and 0.94) and low values of Mean Absolute Error (MAE = 0.23 and 0.19) and Mean Squared Percentage Error (RMSPE = 7.03 and 9.98). Eventually, the goodness of fit of the proposed ANN models was compared with the Multiple Linear Regression (MLR) models previously developed under the same conditions. The results show that ANN models yielded higher prediction accuracy than MLR models.
Road roughness is a measure of how uncomfortable a ride is, and provides an important indicator for the needs of roadway maintenance or repavement, which is closely tied to the state and federal budget prioritization. As such, accurate and timely monitoring of deteriorating road conditions and following maintenance are essential to improve the overall ride quality on the road. Various technologies, including vehicle‐mounted laser profiling systems, have been developed and adopted for road roughness (e.g., IRI—International Roughness Index) measurement; however, their high cost limits their use. While recent advances in smartphone technologies allow us to use their embedded accelerometers for road roughness monitoring, the complicated process of necessary vehicle calibration hinders the widespread use of the technology in the actual practices. In this work, a deep learning IRI estimation method is proposed with the goal of using anonymous (i.e., calibration‐free) vehicles and their responses measured by smartphones as road roughness sensors. A state‐of‐the‐art deep learning algorithm (i.e., CNN—convolutional neural network) and multimetric vehicle dynamics data (i.e., accelerometer, gyroscope), possibly measured by drivers’ smartphones, are employed for the purpose. Optimized CNN architecture and data configuration have been investigated to achieve the best performance. The efficacy of the proposed method has been numerically validated using real road IRI information (i.e., Speedway, Tucson, AZ), real driving speed profiles, and four different types of vehicle data with associated uncertainties.
This book represents the author’s views and impressions of structural sensing, health
monitoring, and prognosis. These were derived from the vast body of work developed
over the past couple of decades by highly creative and productive researchers. Although
some of the citation lists are lengthy, no attempt has been made to be comprehensive in the
literature reviews. The author welcomes suggestions for improvements and corrections to
the book.
In recent years, the concept of bridge monitoring using indirect measurements from a passing vehicle has been rapidly developed. This concept is known as “drive-by bridge inspection”. Most of the methods proposed under this approach utilize the dynamic characteristics of the bridge as an indicator of damage, such as the natural frequency of the bridge. The natural frequency is often estimated using fast Fourier transform (FFT). However, FFT has a low frequency resolution at the condition of higher velocity of a passing vehicle; therefore, it is not appropriate to be used to monitor the frequency change caused by the degradation of the bridge structural integrity. This paper introduces a new frequency identification technique based on wavelet analysis. Wavelet transform is characterized by its high-frequency resolution and can, therefore, be used to visualize the bridge damage represented as changing the fundamental frequency of the bridge. The paper will implement this approach using an implicit Vehicle-Bridge Interaction (VBI) algorithm to simulate the passage of the inspection vehicle over the bridge. The acceleration signals are then processed using wavelet analysis to extract the bridge frequency. In addition, the study will investigate the use of a subtracted signal from two consecutive axles. The latter point has the advantage of substantially removing the effect of the road roughness from the recorded acceleration history.
Road surface conditions have a significant impact on transport safety and driving comfort, particularly in snowy areas. This paper proposes a new method for estimating road surface conditions by using a motion sensor embedded in a smartphone. The method is based on a mobile sensing framework that can collect sensor data using crowdsourcing. In this study, we have defined new road surface conditions as the estimation target, which takes into account both the substance that covers the road surface, and the shape of the road surface itself. The paper also describes a method of feature selection, comprising two steps: First, an initial feature set is directly calculated using various features published in previous studies, using raw sensor data. Second, three feature selection algorithms are compared: Principal Component Analysis (PCA), Relief-F, and Sequential Forward Floating Search (SFFS), and the most effective of the three chosen. In this study, the SFFS algorithm showed higher accuracy than the others. The road surface condition classification was performed across different speed ranges using the Random Forest Classifier, and results show that the best accuracy, of about 91%, was obtained in the 50 km/h–80 km/h range.
This paper introduces a smart city application to share road conditions. The application is based on a mobile sensing framework to collect sensor data reflecting personal-scale, or microscopic, roadside phenomena using crowdsourcing. To collect data, a driving recorder smartphone application that records not only sensor data but also videos from the driver’s view is used. To extract specific roadside phenomena, collected data are integrated and analyzed at the service platform. One example is estimating road surface conditions. The paper shows our method to estimate road surface type (RST) and road surface shape (RSS). Features are defined in Sequential Forward Floating Search (SFFS) algorithm from collected data. By using random forest as classifier, average recall was about 91% in the – range. The result may support to build a service that provides detected road conditions from up-to-date crowdsourced mobile sensing application.
In order to achieve large scale road evaluation with high efficiency and accuracy, smartphone based Dynamic Response Intelligent Monitoring System (iDRIMS) was developed [4]. iDRIMS evaluates road condition in terms of International Roughness Index (IRI) based on dynamic responses of ordinary vehicles measured with an iOS application (iDRIMS measurement), which obtains three axis acceleration, angular velocity and GPS with accurate sampling timings. However, the robustness and accuracy was limited. In this paper, iDRIMS is improved mainly by employing the frequency domain analysis. The improved algorithm for IRI estimation consists of two steps. At first, a Half-Car (HC) model, which can reproduce both vehicle bouncing and pitching motions and represent sensor installation location in the longitudinal direction, is selected as the vehicle numerical model and identified. The vehicle parameters are identified through a drive tests over a portable hump with a known size. As opposed to previous approach of parameter identification in the time domain using Kalman filter, the parameters are optimized to minimize the difference between simulation and measured hump responses in the frequency domain using Genetic algorithm (GA). Then, IRI is estimated by measuring vertical acceleration responses of ordinary vehicles. Measured acceleration is converted to the acceleration RMS of the sprung mass of standard quarter car by multiplying a transfer function. The transfer function, estimated through the simulation of the identified HC model as opposed to QC model in previous approaches, reflects the vehicle pitching motions and sensor installation location. The RMS is further converted to IRI based on correlation between these values. Numerical simulation is conducted to investigate the performance in terms of various drive speeds and sensor locations. Experiment is carried out at a 13km road by comparing three types of vehicles and profiler. Furthermore, the improved method is applied to about 70 commercial vehicles, which drive over more than 180,000 km per year. Data collection and analysis platform is built, which successfully collected and analyzed large-scale data with high efficiency. Results from both numerical simulation and real case application indicate that the improved method can accurately estimate IRI with high robustness and efficiency.
Road roughness condition information is very important for the maintenance planning and management of road infrastructure. Collecting such information is time and resource consuming because it usually requires either, if not all, skillful manpower, substantial budget, and sophisticated equipment. Therefore, collecting and updating road roughness condition is viewed as a big challenge for many road authorities. The final goal of our research is to explore for a low cost and easy to use approach for the aforementioned purpose by using Android smartphones to estimate road roughness and traffic conditions. In this study we develop a model based on a relationship function of smartphone sensor data and road roughness condition. The relationship function is drawn from our experiment in Vientiane, Laos, in which we have found that the acceleration data from smartphones has a linear relationship with road roughness condition and the significant of the relationship also partly depends on speed. The most important and unique feature of the model is the ability to estimate road roughness condition using data obtained from anonymous drivers. Preliminary numerical examples and simulations using real sensor data have been carried out whereas the findings show that performance of the model and results are very promising.
Vehicle Intelligent Monitoring System (VIMS), which enables
frequent quantitative assessment of road pavement condition, has been
developed. The system, which utilizes an ordinary vehicle equipped with
an accelerometer, GPS, and portable PC, is simple and inexpensive. VIMS
evaluates road roughness by estimating the International Roughness Index
(IRI) from the response of the vehicle traveling at a constant speed. At
first, the repeatability of the dynamic response measurement is
confirmed. Then, an IRI estimation method utilizing vertical responses
of the vehicle has been proposed and its accuracy studied. Furthermore,
IRI estimation calibration to account for differences in measurement
vehicles and driving speeds has been discussed. Finally, VIMS is applied
to expressways in Japan and national roads in the Philippines, which
confirms the diagnostic capability of the system.
This paper discusses the development of bridge monitoring in Japan. Firstly, the background of the development of bridge monitoring is described. The need for monitoring was originally influenced by geographical conditions. Due to the fact that Japan is prone to natural disasters and has a severe environment for deterioration; monitoring of the environment and loading conditions with respect to natural hazards has been conducted for several decades. In the last decade, bridge monitoring has extended its function as an instrument for an efficient stock management. Based on the accumulation of bridge stock and concentrated construction in former years, many bridges in Japan are expected to have serious deterioration problems within the next decade. The second part of the paper describes the concept of bridge monitoring as an essential part of risk reduction. To improve bridge safety, monitoring technologies for risk and vulnerability are implemented. In this concept, structural health monitoring serves as a tool for vulnerability monitoring. The third part of the paper outlines strategies implemented for bridge monitoring in Japan. They are categorised into three main groups according to the purpose of monitoring: natural hazard and environment condition, effective stock management, and failure prevention. Examples of bridge monitoring systems that implement these strategies and the lessons learned from monitoring experiences are also presented in this paper.
The development of health monitoring of bridges in Japan is reviewed from geographical and socioeconomical background to specific applications. The backgrounds are categorized as (i) environment and natural disasters, (ii) stock management, and (iii) risk and safety. In other words, severe environment, limited resource for maintenance, and increased requirement for safety are the key factors for development of structural health monitoring.
To understand the environment and natural disasters, environment and loading have been monitored at various bridges for decades. Although these measurements were intended to measure loading and not necessarily to measure structural condition, analyses indicate usefulness of these measurements for evaluation of structural integrity.
For improvement of stock management, monitoring technologies for common workhorse bridges, preventive maintenance, and improvement in routine inspection are being developed and applied.
For improved safety, several monitoring technologies for risk and vulnerability have been developed and implemented, especially for railway bridges. When a threatening failure mode can be identified, a specific monitoring device is selected for risk management. Also, vulnerability monitoring to evaluate integrity of structures is introduced. This technique can be used to compare relative safety, and prioritize the action for improvement. Structural health monitoring technologies in this domain can be categorized into (i) microscopic monitoring, where damage detection and localization are the main interests; and (ii) macroscopic monitoring, where holistic structural integrity and its comparison are the main focus. The former is the conventional mainstream of structural health monitoring and is advancing steadily, whereas the latter is recently attracting interest, especially from the practical point of view to connect health monitoring and existing inspection methodology.
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