The Prediction of Road Cracks through Acoustic Signature: Extended Finite Element Modeling and Experiments

Fedele, Rosario; Praticò, Filippo Giammaria; Pellicano, Gianfranco

doi:10.1520/jte20190209

Cited by 18 publications

(6 citation statements)

References 41 publications

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“…The dimensions of the packaging are mainly determined by both the size of the sensor chip and limitations of packaging processing technology. The packaging, as designed, can be used to protect built-in sensor chips, including MEMS accelerometers, temperature sensors, humidity sensors, vibration sensors, pressure sensors, and displacement sensors [ 15 , 16 ].…”

Section: Three-point Bending Test By the Embedded Sensormentioning

confidence: 99%

Optimization of Embedded Sensor Packaging Used in Rollpave Pavement Based on Test and Simulation

Cai

Liu

et al. 2022

Materials

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Rollpave pavement, as a rollable prefabricated asphalt pavement technology, can effectively reduce the overall road closure time required for pavement construction and maintenance. Sensors can be integrated into Rollpave pavement, thereby avoiding sensor damage that may otherwise result from high temperatures and compactive forces during the rolling process, as well as pavement structural damage resulting from cutting and drilling. However, the embedment of sensors into Rollpave pavement still presents certain challenges, namely poor interfacial synergy between the embedded sensor and the asphalt mixture. To solve this problem, three-point bending tests and dynamic response FEM simulations were used to optimize the embedded sensor’s packaging. The influence of sensor embedment on Rollpave pavement under different working conditions was analyzed. Results of these analyses show that low temperature and the epoxy resin negatively affect the bending performance of specimens, and that packaging with cylindrical shape, flat design, and consisting of a material with modulus similar to that of the asphalt mixture should be preferred. This study is conducive to improve the intellectual level and service life of road infrastructure.

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Section: Three-point Bending Test By the Embedded Sensormentioning

confidence: 99%

Optimization of Embedded Sensor Packaging Used in Rollpave Pavement Based on Test and Simulation

Cai

Liu

et al. 2022

Materials

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“…In particular, different experimental configurations and data analysis approaches were used to analyze the data gathered applying the proposed method. In particular, the analysis in time, frequency, and time-frequency domains, features extraction, hierarchical clustering [44], and Finite Element Models (FEM) [45] were used for the purposes mentioned above, while, in this study, different ML models were used. Based on the potentialities of the ML classifiers that were selected for this study and are presented in Section 3.3, good performance in term of automatic crack detection and monitoring was expected.…”

Section: The Methodsmentioning

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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“…Based on the previous studies [31,36] and on the shape of the vibro-acoustic signatures of the sections under investigation (cf. Fig.…”

Section: Acoustic Signature Datamentioning

confidence: 99%

Sensor-based pavement diagnostic using acoustic signature for moduli estimation

Cafiso

Graziano

Fedele

et al. 2020

Int. J. Pavement Res. Technol.

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The diffusion of smart infrastructures for smart cities provides new opportunities for the improvement of both road infrastructure monitoring and maintenance management.Often pavement management is based on the periodic assessment of the elastic modulus of the bound layers (i.e., asphalt concrete layers) by means of traditional systems, such as Ground Penetrating Radar (GPR) and Falling Weight Deflectometer (FWD). Even if these methods are reliable, well-known, and widespread, they are quite complex, expensive, and are not able to provide updated information about the evolving structural health condition of the road pavement. Hence, more advanced, effective, and economical monitoring systems can be used to solve the problems mentioned above.Consequently, the main objective of the study presented in this paper is to present and apply an innovative solution that can be used to make smarter the road pavement monitoring. In more detail, an innovative Non-Destructive Test (NDT)-based sensing unit was used to gather the vibro-acoustic signatures of road pavements with different deterioration levels (e.g. with and without fatigue cracks) of an urban road. Meaningful features were extracted from the aforementioned acoustic signature and the correlation with the elastic modulus defined using GPR and FWD data was investigated.Results show that some of the features have a good correlation with the elastic moduli of the road section under investigation. Consequently, the innovative solution could be used to evaluate the variability of elastic modulus of the asphalt concrete layers, and to monitor with continuity the deterioration of road pavements under the traffic loads.

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The Prediction of Road Cracks through Acoustic Signature: Extended Finite Element Modeling and Experiments

Cited by 18 publications

References 41 publications

Optimization of Embedded Sensor Packaging Used in Rollpave Pavement Based on Test and Simulation

Optimization of Embedded Sensor Packaging Used in Rollpave Pavement Based on Test and Simulation

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

Sensor-based pavement diagnostic using acoustic signature for moduli estimation

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