IoT for structural health monitoring

Scuro, Carmelo; Sciammarella, Paolo F.; Lamonaca, Francesco; Olivito, Renato Sante; Carnì, Domenico Luca

doi:10.1109/mim.2018.8573586

Cited by 84 publications

(31 citation statements)

References 15 publications

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“…The implementation of this type of tool may be implemented in the near future within the developing BIM concept. In conjunction with the IoT technology [66], it will allow for permanent monitoring of building structures along with the simultaneous assessment of the risk from the impact of the industrial environment, which also includes the impact of mining activities. In turn, automatic data archiving and updating the damage risk model will contribute to a more detailed understanding of this phenomenon, which may bring great socio-economic benefits.…”

Section: Discussionmentioning

confidence: 99%

Selected Artificial Intelligence Methods in the Risk Analysis of Damage to Masonry Buildings Subject to Long-Term Underground Mining Exploitation

2021

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This paper presents an advanced computational approach to assess the risk of damage to masonry buildings subjected to negative kinematic impacts of underground mining exploitation. The research goals were achieved using selected tools from the area of artificial intelligence (AI) methods. Ultimately, two models of damage risk assessment were built using the Naive Bayes classifier (NBC) and Bayesian Networks (BN). The first model was used to compare results obtained using the more computationally advanced Bayesian network methodology. In the case of the Bayesian network, the unknown Directed Acyclic Graph (DAG) structure was extracted using Chow-Liu’s Tree Augmented Naive Bayes (TAN-CL) algorithm. Thus, one of the methods involving Bayesian Network Structure Learning from data (BNSL) was implemented. The application of this approach represents a novel scientific contribution in the interdisciplinary field of mining and civil engineering. The models created were verified with respect to quality of fit to observed data and generalization properties. The connections in the Bayesian network structure obtained were also verified with respect to the observed relations occurring in engineering practice concerning the assessment of the damage intensity to masonry buildings in mining areas. This allowed evaluation of the model and justified the utility of the conducted research in the field of protection of mining areas. The possibility of universal application of the Bayesian network, both in the case of damage prediction and diagnosis of its potential causes, was also pointed out.

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Section: Discussionmentioning

confidence: 99%

Selected Artificial Intelligence Methods in the Risk Analysis of Damage to Masonry Buildings Subject to Long-Term Underground Mining Exploitation

2021

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“…The node synchronization is another challenge for a distributed IoT, as stated in the previous section. Scuro et al [ 152 ] published a paper that was devoted to this problem, and a solution was proposed with each node equipped with a clock; the nodesexchange synchronization messages to evaluate the frequency and the offset of their clock with respect to the one taken as a reference (master) or with respect to its neighbor sensor node. This solution implies an additional overhead, since extra messages and re-synchronization periods are required.…”

Section: Hardware Developments Of Wired and Wireless Sensor Network (Wsns) For Shm And Validation Testsmentioning

confidence: 99%

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Capineri

Bulletti

2021

Sensors

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This review article is focused on the analysis of the state of the art of sensors for guided ultrasonic waves for the detection and localization of impacts for structural health monitoring (SHM). The recent developments in sensor technologies are then reported and discussed through the many references in recent scientific literature. The physical phenomena that are related to impact event and the related main physical quantities are then introduced to discuss their importance in the development of the hardware and software components for SHM systems. An important aspect of the article is the description of the different ultrasonic sensor technologies that are currently present in the literature and what advantages and disadvantages they could bring in relation to the various phenomena investigated. In this context, the analysis of the front-end electronics is deepened, the type of data transmission both in terms of wired and wireless technology and of online and offline signal processing. The integration aspects of sensors for the creation of networks with autonomous nodes with the possibility of powering through energy harvesting devices and the embedded processing capacity is also studied. Finally, the emerging sector of processing techniques using deep learning and artificial intelligence concludes the review by indicating the potential for the detection and autonomous characterization of the impacts.

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“…This led to the development of advance techniques and analytical concepts in Structural Health Monitoring for evaluating the damage severity [7]. With the evolution of Nondestructive methods such as Electro chemical half cell potential method, Linear polarization resistance method, Ground penetrating radar test, Infrared thermography, Coverometer, Acoustic emissions, the in situ structural inspection is facilitated without destroying the elements [8]. But detailed investigation is not possible with these quality assurance tests as they suffer with distinct limitations such as lack of reliability, portability, trained technicians, costly equipment, complicated mechanism and electromagnetic interferences.…”

Section: Introductionmentioning

confidence: 99%

Corrosion assessment of structural components using electro mechanical impedance

Pravallika¹,

Reddy²,

Reddy

2021

E3S Web Conf.

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Rapid innovation in interdisciplinary technology emphasized the data acquisition system to evaluate the structural integrity by combining with advance sensing techniques. These smart sensors with impedance methodology has shown an excellent potential in assessing the structural health condition and provided an alternative to many sophisticated Non destructive monitoring systems. The sensitivity of electro mechanical impedance technique in detecting local incipient damages is enhanced with piezoelectric mechanism of lead zirconate titanate materials and with conductance signatures these materials can determine the dynamic variations in structural properties more effectively. In the present work numerical finite element analysis is conducted on simply supported RCC beam in Abaqus software with reinforcement subjected to corrosion in five stages with different reduction rates and coupled with piezoelectric transducers along the length to implement the impedance strategy. These surface bonded piezoelectric patches are electrically excited with an external voltage under specific frequency range to conduct linear perturbation harmonic analysis and the output conductance responses of healthy and corroded beams from different sensor locations are captured and compared. The peak shifting nature of signature pattern will serve as an indicator to diagnose the corrosion severity and propagation in structural components. The simulation results of proposed impedance technique showed the feasibility of employing piezoelectric materials to identify corrosion activity in structural members with electromechanical conductivity signatures.

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IoT for structural health monitoring

Cited by 84 publications

References 15 publications

Selected Artificial Intelligence Methods in the Risk Analysis of Damage to Masonry Buildings Subject to Long-Term Underground Mining Exploitation

Selected Artificial Intelligence Methods in the Risk Analysis of Damage to Masonry Buildings Subject to Long-Term Underground Mining Exploitation

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Corrosion assessment of structural components using electro mechanical impedance

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