Energy savings in transportation: Setting up an innovative SHM method

Fedele, Rosario; Praticò, Filippo Giammaria; Carotenuto, Riccardo; Corte, Francesco G. Della

doi:10.18280/mmep.050408

Cited by 15 publications

(8 citation statements)

References 39 publications

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“…Apart from the aforementioned technologies, there is significant research built around the use of in situ monitoring systems for the acquisition of accurate information concerning the conditions of the pavement. Such systems allow documenting the level of service of the road asset via the use of embedded sensors and technologies, which can allow for remote and continuous monitoring over the life cycle of the pavement for fatigue [9][10][11][12][13]. Such systems do not require frequent surveys and the state of the pavements can be monitored without any disruption to the traffic or road network, which is an advantage over the systems mentioned before.…”

Section: The Needs Of Current Pavement Maintenance Systems and Practicesmentioning

confidence: 99%

Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies

et al. 2019

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Road agencies are constantly being placed in difficult situations when making road maintenance and rehabilitation decisions as a result of diminishing road budgets and mounting environmental concerns for any chosen strategies. This has led practitioners to seek out new alternative and innovative ways of monitoring road conditions and planning maintenance routines. This paper considers the use of innovative piezo-floating gate (PFG) sensors and conventional strain gauges to continuously monitor the pavement condition and subsequently trigger maintenance activities. These technologies can help develop optimized maintenance strategies as opposed to traditional ad-hoc approaches, which often lead to poor decisions for road networks. To determine the environmental friendliness of these approaches, a case study was developed wherein a life cycle assessment (LCA) exercise was carried out. Observations from accelerated pavement testing over a period of three months were used to develop optimized maintenance plans. A base case is used as a guide for comparison to the optimized systems to establish the environmental impacts of changing the maintenance workflows with these approaches. On the basis of the results, the proposed methods have shown that they can, in fact, produce environmental benefits when integrated within the pavement management maintenance system.

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Section: The Needs Of Current Pavement Maintenance Systems and Practicesmentioning

confidence: 99%

Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies

et al. 2019

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“…It is important to underline that the above platform is able to apply an innovative SHM method [18,26,37]. This method aims at identifying and monitoring the structural conditions (e.g., occurrence and propagation of structural damage) of an asset through specific analyses of the vibro-acoustic signature of the monitored asset.…”

Section: Discussionmentioning

confidence: 99%

A Real-Time Decision Platform for the Management of Structures and Infrastructures

et al. 2019

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Natural disasters and the poor management of civil engineering structures and infrastructures require timely action and new tools such as specially designed structural health monitoring platforms. This paper proposes an innovative platform based on a network of wirelessly connected, low-power, and renewable-energy-fed sensor units. The platform is a multipurpose tool for diagnostics, maintenance, and supervision, capable of simultaneously carrying out damage detection, localization, identification, and “multiclass” and “multi-material” level quantification of different types of failures. In addition, it works as a decision support tool for emergency management and post-disaster assessment, here tailored for an Italian theme park. The platform uses innovative algorithms based on the concept of the vibro-acoustic signature of the asset monitored. The vibro-acoustic signatures of the monitored assets are gathered by the microphones and accelerometers of the platform’s sensor units. Then, almost simultaneously, they are analyzed using specifically designed wavelet-based and convolutional-neural-network-based algorithms, which are able to extract crucial information about the structural and environmental conditions of both the asset and the areas of the thematic park. In addition, the platform shows escape routes during an emergency, indicating meeting points and helping people to proceed safely along a recognizable escape route to a safe place, as demonstrated by the simulations.

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“…The method considers the road pavement as a filter of the acoustic and seismic waves (see Figure 1) [39][40][41]. These latter are generated by a mechanical source (i.e., the vehicular traffic), travel into the medium (i.e., the road pavement), and finally are detected by a receiver (i.e., a microphone isolated from the airborne noise, attached on the road surface in a nondestructive way, connected to an external soundcard, in turn connected to a laptop running the Matlab code used to record sounds).…”

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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Energy savings in transportation: Setting up an innovative SHM method

Cited by 15 publications

References 39 publications

Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies

Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies

A Real-Time Decision Platform for the Management of Structures and Infrastructures

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

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