Vehicle dynamic response due to pavement roughness

Barbosa, Roberto Spínola

doi:10.1590/s1678-58782011000300005

Cited by 36 publications

(19 citation statements)

References 5 publications

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“…Several approaches were used to process the data that come from the above mentioned solutions. Noteworthy examples applied for the structural health monitoring (SHM) of road pavements refer to (i) pothole detection using a fuzzy c-mean method based on morphological 2D image reconstruction [19], (ii) pavement roughness estimation based on analyses in the frequency domain [20], or the principal component analysis [21], or wavelet transform [22], (iii) automatic surface crack detection using wavelet-based analysis [23,24], (iv) crack detection using visual features extracted using Gabor filter [25], and particle filter [26], (v) surface crack detection using Otsu's based method [27], (vi) surface crack classification by mean of support vector machine [28], (vii) use the fuzzy c-mean method to find the relationship between traffic emission (e.g., NOx) and the related built environment factors (e.g., short road in city center, bus stations density, ramps, and residential-commercial land proportion), and the relationship between built environment and the 24-hour congestion pattern [29,30], and (viii) ML-based approaches.…”

Section: State Of the Art About Technological Solutions Used To Detecmentioning

confidence: 99%

Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach

et al. 2020

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The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (using acoustic-sensors, located at the roadside) and classifying the pavement’s SHS through ML models. Different ML classifiers (i.e., multilayer perceptron, MLP, convolutional neural network, CNN, random forest classifier, RFC, and support vector classifier, SVC) were used and compared. Results show the possibility of associating with great accuracy (i.e., MLP = 91.8%, CNN = 95.6%, RFC = 91.0%, and SVC = 99.1%) a specific vibro-acoustic signature to a differently cracked road pavement. These results are encouraging and represent the bases for the application of the proposed method in real contexts, such as monitoring roads and bridges using wireless sensor networks, which is the target of future studies.

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Section: State Of the Art About Technological Solutions Used To Detecmentioning

confidence: 99%

Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach

et al. 2020

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“…The time delay between the front and rear axle is taken into account with the following equation [23]:…”

Section: Model Of the Double Cabin Fire Truck Suspensionmentioning

confidence: 99%

Development of a simulation model for analyzing vibrations of a double cabin fire truck and their effects on firefighters

Hajdú¹,

Kuti²

2019

International Advanced Research Journal in Science, Engineering

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Various vibrations occur in vehicles, which are generated by the vehicle's equipment or by the road itself. Several studies have been carried out on the investigation of vibrations in passenger cars or different purpose trucks, but there is little literature available on the analysis of special superstructure vehicles such as fire trucks. During our research therefore a simulation model for analyzing the vibrations of a double-cabin special fire truck was created. The main purpose of our research is investigating their effects on people traveling in fire-fighting vehicles. In this paper the developed model and the computer simulation using it is presented. With our results we want to draw the attention to the importance of the topic and to help the safe operation of fire-fighting vehicles. V. CONCLUSIONSDuring our research a simulation model that is suitable for the examination of vibrations of persons traveling in double cabin vehicles with special superstructure was developed. The model was tested with sinusoidal road profiles of a motorway and an urban road. Based on computer simulation it can be concluded that the resulting vibrations are more harmful in case of urban roads. It was also found out that in case of normal condition roads the resulting vibrations in the examined time interval are not harmful, but are uncomfortable to people traveling in them. Next task in our research is to examine the effects of road failures on firefighters and using more precise road profiles. We have come to the conclusion that proper simulation models are essential for results that can be used in practice. We consider it important to develop appropriate models for accurate research on firefighting vehicles as they are essential for exploring the theoretical and practical phenomena and helping to identify further research directions.

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“…Forcing signal is the irregularity of the road in the form of a shifting threshold [17,19] -semicircular model, where other types of thresholds are also not excluded [11,20,21]. The equation that describes the obstacle being tested is given in formula 1.…”

Section: Phenomenological Model Of a Research Objectmentioning

confidence: 99%

“…The following is a road profile which has been described by the function (eq. 1, 2, 3) as below [18,19]:…”

Section: Phenomenological Model Of a Research Objectmentioning

confidence: 99%

Preliminary Studies of Vertical Acceleration of a Passenger Car Passing Through the Speed Bump for Various Driving Speeds

Haniszewski

Michta

2019

Transport Problems

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The article proposes a phenomenological model of a passenger car. The model includes the biomechanical model of human bodies in the sitting position of Muksian and Nash acting as passengers, while the driver's weight has been reduced to the concentrated weight of the loading seat. The research carried out in this study consisted of passing the vehicle through a transverse obstacle along the so-called speed bump. Vehicle passes through the threshold at three different speeds: 10km/h, 20km/h, and 30km/h. The purpose of the conducted experiments was to initially estimate the values of accelerations acting especially on the passenger at selected driving speeds. On the basis of conducted tests, it was possible to verify the numerical model of the vehicle overcoming the obstacle on the road. This approach allows the use of the proposed model to verify the forces and accelerations affecting the passenger, specified before any selected specific biomechanical model of the human person, for example, a model of pregnant women in this configuration allows us to estimate the forces and accelerations acting on the embryo while overcoming some obstacles on the road.

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Vehicle dynamic response due to pavement roughness

Cited by 36 publications

References 5 publications

Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach

Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach

Development of a simulation model for analyzing vibrations of a double cabin fire truck and their effects on firefighters

Preliminary Studies of Vertical Acceleration of a Passenger Car Passing Through the Speed Bump for Various Driving Speeds

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