• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.1-8
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• 2015

Wind power systems have gained much attention due to the relatively high reliability, good infrastructures and cost competitiveness to the fossil fuels. Advances have been made to increase the power efficiency of wind turbines while less attention has been focused on structural integrity assessment of structural sub-systems such as towers and foundations. Among many parameters for integrity assessment, the most perceptive parameter may be the induced horizontal displacement at the hub height although it is very difficult to measure particularly in large-scale and high-rise wind turbine structures. This study proposes an indirect displacement estimation scheme based on the combined use of inclinometers and accelerometers for more convenient and cost-effective measurements. To this end, (1) the formulation for data fusion of inclination and acceleration responses was presented and (2) the proposed method was numerically validated on an NREL 5 MW wind turbine model. The numerical analysis was carried out to investigate the performance of the propose method according to the number of sensors, the resolution and the available sampling rate of the inclinometers to be used.

• Smart Structures and Systems
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• v.20 no.3
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• pp.385-396
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• 2017

The recent advancements in sensing technologies allow us to record measurements from target structures at multiple locations and with relatively high spatial resolution. Such measurements can be used to develop data-driven methodologies for condition assessment, control, and health monitoring of target structures. One of the state-of-the-art technologies, Fiber Optic Strain Sensors (FOSS), is developed at NASA Armstrong Flight Research Center, and is based on Fiber Bragg Grating (FBG) sensors. These strain sensors are accurate, lightweight, and can provide almost continuous strain-field measurements along the length of the fiber. The strain measurements can then be used for real-time shape-sensing and operational load-estimation of complex structural systems. While several works have demonstrated the successful implementation of FOSS on large-scale real-life aerospace structures (i.e., airplane wings), there is paucity of studies in the literature that have investigated the potential of extending the application of FOSS into civil structures (e.g., tall buildings, bridges, etc.). This work assesses the feasibility of using FOSS to predict operational loads (e.g., wind loads) on chain-like structures. A thorough investigation is performed using analytical, computational, and experimental models of a 4-story steel building test specimen, developed at the University of Southern California. This study provides guidelines on the implementation of the FOSS technology on building-like structures, addresses the associated technical challenges, and suggests potential modifications to a load-estimation algorithm, to achieve a robust methodology for predicting operational loads using strain-field measurements.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.4
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• pp.281-287
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• 2016

Fiber Bragg grating (FBG) sensors are applied in structural health monitoring (SHM) in various application fields because of their ease of multiplexing and capability of performing absolute measurements. Moreover, the packaging methods of FBG sensors accelerate their commercialization rapidly. However, long-term SHM exposes the FBG sensors to cyclic thermal loads, and a investigation is required because it finally leads to the signal instability of the FBG sensors. In this study, the effects of sensor packaging methods two methods are generally used for the FBGs: (bonding both sides of the FBG or bonding the FBG directly on signal stability of FBG sensors are investigated. Tests are conducted on specimens in a thermal chamber, over a temperature range from $-20^{\circ}C$ to $60^{\circ}C$ for 300 cycles. Signal characteristics such as Bragg wavelength, light intensity and full width at half maximum are examined and are compared with those of the FBG sensors, obtained in a previous study under direct bonding conditions. From the comparison, it is observed that the FBG sensors with bonding on both sides of the FBG demonstrate higher signal stabilities when exposed to cyclic thermal loads during long-term SHM. Consequently, it guarantees more effectiveness when packaging the FBG sensors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.543-550
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• 2018

The rapid growth of IoT technology induced by the fourth industrial revolution has resulted in research into various types of wireless sensors, and applications based on this technology are prevalent in many areas. However, among the various sites where this technology is used, railway bridges and tunnels with lengths of tens of kilometers have problems with data acquisition, due to the signal noise induced by the long distance measurement and EMI induced by the high voltage power feeding system, when conventional electric sensors are used. To overcome these problems, many studies on fiber optic sensors have been conducted as a substitute for the conventional electric sensors. However, restrictions on the types of fiber optic sensors have limited their application in railways. For this reason, a hybrid measurement system with IoT based wireless data communication, in which both electric and fiber optic sensors can be applied simultaneously, has been developed. In this study, in order to evaluate the applicability of the hybrid measurement system developed in the previous study, a real-time test for 4 types of measurement environments, which reflect possible railway sites, is performed. As a result, it was confirmed that the signals from both the electric and fiber optic sensors, which were acquired at a remote area in real-time, showed good agreement with each other and that this measurement system has the potential to handle sensors with a sampling rate of 2.5 kHz. In the future, it is expected that the IoT-based hybrid measurement system will contribute to the improvement of structural safety by enabling real-time structural health monitoring when applied to various measurement sites.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1509-1521
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• 2013

Social demand for the stability of structures lead to the development of the technology to accomplish it. The non-destructive seismic technique, which is able to assess structural integrity of infrastructures, belongs to this category. Seismic technique is focused on the measurement of seismic velocity propagating through the material, and has to utilize sensors coupled to material surface, which does not allow the testing to be performed on the fly. In this paper, a general vocal microphone, which works as a non-contact sensor, was adopted to facilitate seismic testing with mobility and efficiency improved. The target of using microphones was oriented toward quality assessment of compacted subgrade, stiffness evaluation and health monitoring of concrete structures. Experimental parametric study and field applications were performed to investigate reliability and efficiency of microphones. Finally, the optimal test configuration of microphones was suggested for resonance tests and surface-wave tests.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.7
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• pp.51-59
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• 2004

An experimental procedure to identify failure modes of impact damage using sensor signals and to analyze their general features is examined. A series of low-velocity impact tests from low energy to damage-induced high energy were performed on the instrumented drop weight impact tester to monitor the stress wave signals due to failure modes such as matrix cracking, delamination, and fiber breakage. The wavelet transform(WT) and Short Time Fourier Transform(STFT) are used to decompose the piezoelectric sensor signals in this study. The extent of the damage in each case was examined by means of a conventional ultrasonic C-scan. The PVDF sensor signals are shown to carry important information regarding the nature of the impact process that can be extracted from the careful signal processing and analysis.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.4
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• pp.291-298
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• 2007

This paper presents a signal processing algorithm to control the dynamic bandwidth of a single-degree-of-freedom (SDF) dynamic sensor system. An accelerometer is a representative SDF sensor system. In this paper, a moire-fringe-based fiber optic accelerometer is newly used for the test of the algorithm. The accelerometer is composed of one mass, one damper and one spring as a SDF dynamic system. In order to increase the dynamic bandwidth of the accelerometer, it is needed to increase the spring constant or decrease the mass. However, there are mechanical difficulties of this adjustment. Therefore, the presented signal processing algorithm is very effective to overcome the difficulties because it is just adjustment in the signal processing software. In this paper, the novel fiber optic accelerometer is introduced shortly, and the algorithm is applied to the fiber optic accelerometer to control its natural frequency and damping ratio. Several simulations and experiments are carried out to prove the performance of the algorithm. As a result, it is shown that the presented signal processing algorithm is a good way to broaden the dynamic bandwidth of the fiber optic accelerometer.

• Composites Research
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• v.31 no.6
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• pp.429-434
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• 2018

We investigate strain transmissions of a surface bonded distributed optical fiber sensor considering strain variation according to positions. We first derive a strain transmission ratio depending on a wavelength of a strain distribution of the host structure from an analysis model. The strain transmission ratio is compared with numerical results obtained from the finite element method using ABAQUS. We find that the analytical results agree well with the numerical results. The strain transmission ratio is a function of a wavelength, i.e. the strain transmission ratio decreases (increases) as the wavelength of the host strain decreases (increases). Therefore, if an arbitrary strain distribution containing various wavelengths is given to a host structure, a distorted strain distribution will be observed in the distributed optical fiber sensor compare to that of the host structure, because each wavelength shows different strain transmission ratio. The strain transmission ratio derived in this study will be useful for accurately identifying the host strain distribution based on the signal of a distributed optical fiber sensor.

• Smart Structures and Systems
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• v.31 no.4
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• pp.335-349
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• 2023

Bridges constantly undergo deterioration and damage, the most common ones being concrete damage and exposed rebar. Periodic inspection of bridges to identify damages can aid in their quick remediation. Likewise, identifying components can provide context for damage assessment and help gauge a bridge's state of interaction with its surroundings. Current inspection techniques rely on manual site visits, which can be time-consuming and costly. More recently, robotic inspection assisted by autonomous data analytics based on Computer Vision (CV) and Artificial Intelligence (AI) has been viewed as a suitable alternative to manual inspection because of its efficiency and accuracy. To aid research in this avenue, this study performs a comparative assessment of different architectures, loss functions, and ensembling strategies for the autonomous segmentation of bridge components and damages. The experiments lead to several interesting discoveries. Nested Reg-UNet architecture is found to outperform five other state-of-the-art architectures in both damage and component segmentation tasks. The architecture is built by combining a Nested UNet style dense configuration with a pretrained RegNet encoder. In terms of the mean Intersection over Union (mIoU) metric, the Nested Reg-UNet architecture provides an improvement of 2.86% on the damage segmentation task and 1.66% on the component segmentation task compared to the state-of-the-art UNet architecture. Furthermore, it is demonstrated that incorporating the Lovasz-Softmax loss function to counter class imbalance can boost performance by 3.44% in the component segmentation task over the most employed alternative, weighted Cross Entropy (wCE). Finally, weighted softmax ensembling is found to be quite effective when used synchronously with the Nested Reg-UNet architecture by providing mIoU improvement of 0.74% in the component segmentation task and 1.14% in the damage segmentation task over a single-architecture baseline. Overall, the best mIoU of 92.50% for the component segmentation task and 84.19% for the damage segmentation task validate the feasibility of these techniques for autonomous bridge component and damage segmentation using RGB images.