Inicio  /  Infrastructures  /  Vol: 5 Par: 1 (2020)  /  Artículo
ARTÍCULO
TITULO

Data Compression Approach for Long-Term Monitoring of Pavement Structures

Mario Manosalvas-Paredes    
Nizar Lajnef    
Karim Chatti    
Kenji Aono    
Juliette Blanc    
Nick Thom    
Gordon Airey and Davide Lo Presti    

Resumen

Pavement structures are designed to withstand continuous damage during their design life. Damage starts as soon as the pavement is open to traffic and increases with time. If maintenance activities are not considered in the initial design or considered but not applied during the service life, damage will grow to a point where rehabilitation may be the only and most expensive option left. In order to monitor the evolution of damage and its severity in pavement structures, a novel data compression approach based on cumulative measurements from a piezoelectric sensor is presented in this paper. Specifically, the piezoelectric sensor uses a thin film of polyvinylidene fluoride to sense the energy produced by the micro deformation generated due to the application of traffic loads. Epoxy solution has been used to encapsulate the membrane providing hardness and flexibility to withstand the high-loads and the high-temperatures during construction of the asphalt layer. The piezoelectric sensors have been exposed to three months of loading (approximately 1.0 million loads of 65 kN) at the French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR) fatigue carrousel. Notably, the sensors survived the construction and testing. Reference measurements were made with a commercial conventional strain gauge specifically designed for measurements in hot mix asphalt layers. Results from the carrousel successfully demonstrate that the novel approach can be considered as a good indicator of damage progression, thus alleviating the need to measure strains in pavement for the purpose of damage tracking.

 Artículos similares

       
 
Jinjia Zhou and Jian Yang    
Compressive Sensing (CS) has emerged as a transformative technique in image compression, offering innovative solutions to challenges in efficient signal representation and acquisition. This paper provides a comprehensive exploration of the key components... ver más
Revista: Information

 
Aristeidis Karras, Anastasios Giannaros, Christos Karras, Leonidas Theodorakopoulos, Constantinos S. Mammassis, George A. Krimpas and Spyros Sioutas    
In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces ... ver más
Revista: Future Internet

 
Rongliang Cheng, Xiaofeng Han and Zhiqiang Wu    
It is of great significance to identify the spatiotemporal stress distribution characteristics to ensure the safety of a super-high arch dam during the initial operation stage. Taking the 285.5 m-high Xiluodu Dam as an example, the spatiotemporal distrib... ver más
Revista: Water

 
Dennis Papenfuß, Bennet Gerlach, Stefan Fischer and Mohamed Ahmed Hail    
The IoT encompasses objects, sensors, and everyday items not typically considered computers. IoT devices are subject to severe energy, memory, and computation power constraints. Employing NDN for the IoT is a recent approach to accommodate these issues. ... ver más
Revista: Future Internet

 
Varsha S. Lalapura, Veerender Reddy Bhimavarapu, J. Amudha and Hariram Selvamurugan Satheesh    
The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local o... ver más
Revista: Algorithms