Scalable Structural Modal Identification Using Dynamic Sensor Network Data with STRIDEX

Matarazzo, Thomas J.; Pakzad, Shamim N.

doi:10.1111/mice.12298

Cited by 42 publications

(16 citation statements)

References 79 publications

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“…The accuracy dynamic deflection is on the order of 10 mm (horizontal) and 15 mm (vertical). However, the accuracy for dynamic deflection measurement can be improved by post-processing methods [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Road Infrastructure Monitoring: An Experimental Geomatic Integrated System

Barrile

Fotia

Bilotta

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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Road infrastructures systems are critical in many regions of Italy, counting thousands of bridges and viaducts that were built over several decades. A monitoring system is therefore necessary to monitor the health of these bridges and to indicate whether they need maintenance. Different parameters affect the health of an infrastructure, but it would be very difficult to install a network of sensors of various kinds on each viaduct. For this purpose, we want to finalize the use of geomatics technologies to monitor infrastructures for early warning issues and introducing automations in the data acquisition and processing phases. This study describes an experimental sensor network system, based on long term monitoring in real-time while an adaptive neuro-fuzzy system is used to predict the deformations of GPS-bridge monitoring points. The proposed system integrates different data (used to describe the various behaviour scenarios on the structural model), and then it reworks them through machine learning techniques, in order to train the network so that, once only the monitored parameters (displacements) have been entered as input data, it can return an alert parameter. So, the purpose is to develop a real-time risk predictive system that can replicate various scenarios and capable to alert, in case of imminent hazards. The experimentation conducted in relation to the possibility of transmitting an alert parameter in real time (transmitted through the help of an experimental control unit) obtained by predicting the behavior of the structure using only displacement data during monitoring is particularly interesting.

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Section: Methodsmentioning

confidence: 99%

Road Infrastructure Monitoring: An Experimental Geomatic Integrated System

Barrile

Fotia

Bilotta

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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“…Considering three types of dynamic loads, that is, earthquake, wind, and blast loads, Saleh and Adeli (, ) also demonstrate that LQR control algorithm is effective for vibration control of multistory building structures. Further, on the basis of the LQR control algorithm, Adeli and Saleh () present an innovative integrated structural/control optimization solution for active control of smart structures subjected to different dynamic loadings through adroit integration of four different computing paradigms and technologies: control theory, optimization theory (Wang & Szeto, ; Xu, Spencer, Lu, Chen, & Lu, ), sensor/actuator technology (Fernandez‐Luque, Perez, Zapata, & Ruiz, ; Matarazzo & Pakzad, ; Yin, Yuen, Lam, & Zhu, ), and high‐performance computing (Park, Torbol, & Kim, ). The solution is based on simultaneous minimization of structural weight and the required level of control forces using parallel processing on multiprocessor computers (Adeli & Kamal, ).…”

Section: Linear Control Algorithmsmentioning

confidence: 99%

“…control theory, optimization theory (Wang & Szeto, 2017;Xu, Spencer, Lu, Chen, & Lu, 2017), sensor/actuator technology (Fernandez-Luque, Perez, Zapata, & Ruiz, 2016;Matarazzo & Pakzad, 2018;Yin, Yuen, Lam, & Zhu, 2017), and high-performance computing (Park, Torbol, & Kim, 2018). The solution is based on simultaneous minimization of structural weight and the required level of control forces using parallel processing on multiprocessor computers (Adeli & Kamal, 1993).…”

Section: Linear Quadratic Regulator Controlmentioning

confidence: 99%

Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

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Artificial intelligence and expert system remains a key technology in the 21st century. Using active controllers, a structure can adaptively adjust its behaviour during dynamic loads. Such structures with self‐modifying capabilities are referred to as intelligent or smart structures. Smart structure technology has the potential to be a game changer in the structural engineering field. It promises to have enormous consequences in terms of preventing loss of life and damage to structure and their content especially for large structures with hundreds or thousands of components. A key element in successful implementation of smart active control technology is an effective control algorithm to compute the magnitudes of actual forces to be applied to the structure. In this paper, an overview of main active control methodologies for vibration control of smart civil and mechanical structures subjected to external dynamic loads is presented. The advantages and the disadvantages of different control algorithms are discussed. Finally, new trends in control algorithm research are pointed out including multiparadigm strategies, decentralized control, application of deep neural network machine learning techniques, control design for sustainability, and unification of the two fields of structural health monitoring and vibration control.

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“…The recent intensive development of wireless sensor networks (Amezquita-Sanchez, Valtierra-Rodriguez, & Adeli, 2018;Zhang, Tian, Marindra, Sunny, & Zhao, 2017) results in the emergence of new approaches that involve power-efficiency criteria (Hu, Wang, & Ji, 2012). Further challenges arise, for example, from the concept of through-substrate communication (Salehi, Das, Chakrabartty, Biswas, & Burgueño, 2018) or movable sensor networks (Matarazzo & Pakzad, 2017). In modal parameter identification for large-scale civil structures another challenging problem is the identification of closely spaced modes.…”

Section: Introductionmentioning

confidence: 99%

Convex relaxation for efficient sensor layout optimization in large‐scale structures subjected to moving loads

Błachowski

Świercz

Ostrowski

et al. 2020

Computer aided Civil Eng

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This paper proposes a computationally effective framework for load‐dependent optimal sensor placement in large‐scale civil engineering structures subjected to moving loads. Two common problems are addressed: selection of modes to be monitored and computational effectiveness. Typical sensor placement methods assume that the set of modes to be monitored is known. In practice, determination of such modes of interest is not straightforward. A practical approach is proposed that facilitates the selection of modes in a quasi‐automatic way based on the structural response at the candidate sensor locations to typical operational loads. The criterion used to assess sensor placement is based on Kammer's Effective Independence (EFI). However, in contrast to typical implementations of EFI, which treat the problem as a computationally demanding discrete problem and use greedy optimization, an approach based on convex relaxation is proposed. A notion of sensor density is applied, which converts the original combinatorial problem into a computationally tractable continuous optimization problem. The proposed framework is tested in application to a real tied‐arch railway bridge located in central Poland.

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Scalable Structural Modal Identification Using Dynamic Sensor Network Data with STRIDEX

Abstract: This article uses the formulation of the structural identification using expectation maximization (STRIDE) algorithm for compatibility with the trun-

Cited by 42 publications

References 79 publications

Road Infrastructure Monitoring: An Experimental Geomatic Integrated System

Road Infrastructure Monitoring: An Experimental Geomatic Integrated System

Control methodologies for vibration control of smart civil and mechanical structures

Convex relaxation for efficient sensor layout optimization in large‐scale structures subjected to moving loads

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