• Advances in concrete construction
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• v.11 no.5
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• pp.375-386
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• 2021

In this paper, a study of stability and health of a newly-built railway tunnel is presented. The field test was implemented to monitor the secondary lining due to the significant cracking behaviors influenced the stability and health of the tunnel structure. Surface strain gauges were installed for monitoring the status of crack openings, and the monitoring outputs demonstrated that the cracks were still in the developing stage. Additionally, adjacent tunnel and poor condition of surrounding rock were identified as the causes of the lining cracking by systematically characterizing the crack spatial distribution, tunnel site and surrounding rock conditions. Reconstruction of partial lining and reconstruction of the whole secondary lining were designed as the maintenance projects for different cracking regions based on the construction feasibility. For assessing the health conditions of the reinforced lining, embedded strain gauges were set up to continuously measure the strain and the internal force of the reconstructed structures. For the partially reconstructed lining, the outputs show the maximum tensile elongation is 0.018 mm during 227 days, which means the structure has no obvious deformation after maintenance. The one-year monitoring of full-section was implemented in the other two completely reconstructed cross-sections by embedded strain gauge. The outputs show the reconstructed secondary lining has undertaken the pressure of surrounding rock with the time passing. According to the calculated compressive and tensile safety factors, the completely reconstructed lining has been in reliable and safe condition during the past year after reinforcement. It can conclude that the aforementioned maintenance projects can effectively ensure the stability and health of this tunnel.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.3
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• pp.277-283
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• 2016

A high-precision force-feedback 3-axes accelerometer developed in Korea has been investigated and studied for the verification of feasibility in the computational analysis and health monitoring of civil infrastructures. Through a series of experiment, the nonlinearity, bandwidth, low-frequency signal measurement accuracy and bias characteristics of the accelerometer has been thoroughly compared to those of two accelerometers produced by two market leaders in domestic and global accelerometer market. The experiment results shows that the overall measurement performance of the accelerometer has superiority over the performance of the two accelerometers from global market leader companies. Especially, the accelerometer shows a better low-frequency signal measurement accuracy and constant bias characteristic, which are mostly required in the computational analysis and the long-term health monitoring of large-scale civil infrastructures.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.6
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• pp.661-668
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• 2012

Wireless sensor network is one of prospective methods for railway monitoring due to the long-term operation and low-maintenance performances. How to supply power to the wireless sensor nodes has drawn much attention recently. In railway monitoring, the idea of converting ambient vibration energy from vibration of railway track induced by passing trains to electric energy has made it a potential way for powering the wireless sensor nodes. In this paper, a bimorph cantilever piezoelectric energy harvester was designed based on a single degree-of-freedom model. Experimental test was also performed to validate the design. The first natural frequency of the bimorph piezoelectric energy harvester was decreased from 117.1 Hz to 65.2 Hz by adding 4 gram tip mass to the free end of the 8.6 gram energy harvester. In addition, the power generation of the piezoelectric energy harvester with 4 gram tip mass at resonant frequency was increased from 0.14 mW to 0.74 mW from $2.06m/s^2$ base excitation compared to stand-alone piezoelectric energy harvester without tip mass.

• Smart Structures and Systems
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• v.13 no.6
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• pp.943-957
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• 2014

When subjected to fatigue loading, the main failure mode of partially prestressed concrete (PPC) structure is the fatigue fracture of tensile reinforcement. Therefore, monitoring and evaluation of the steel stresses/strains in the structure are essential issues for structural design and healthy assessment. The current study experimentally investigates the possibility of using fiber Bragg grating (FBG) sensors to measure the steel strains in PPC beams in the process of fatigue loading. Six full-scale post-tensioned PPC beams were exposed to fatigue loading. Within the beams, the FBG and resistance strain gauge (RSG) sensors were independently bonded onto the surface of tensile reinforcements. A good agreement was found between the recorded results from the two different sensors. Moreover, FBG sensors show relatively good resistance to fatigue loading compared with RSG sensors, indicating that FBG sensors possess the capability for long-term health monitoring of the tensile reinforcement in PPC structures. Apart from the above findings, it can also be found that during the fatigue loading, there is stress redistribution between prestressed and non-prestressed reinforcements, and the residual strain emerges in the non-prestressed reinforcement. This phenomenon can bring about an increase of the steel stress in the non-prestressed reinforcement.

• 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.

• International Journal of High-Rise Buildings
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• v.4 no.3
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• pp.191-198
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• 2015

It is important to accurately predict structural responses to external excitations such as typhoons and earthquakes when designing structures for serviceability. One of the key procedures to predict reliable vibration responses is to evaluate accurate structural dynamic properties using finite element (FE) models, which properly represent the realistic behavior of buildings. In the case of historic masonry buildings, structural damage could also be caused by ambient vibrations or impacts. Therefore, the preservation plans of historic buildings for low-level vibrations or impacts should be provided by analyzing structural damages within serviceability levels. For this purpose, it is required to provide FE model construction and response analysis methods verified with field measurement data. In this research, long-term field measurement was performed for a cathedral and its dynamic properties were evaluated using measured data. Then, the model was calibrated based on the measured dynamic properties and an overall construction method for the masonry cathedral was proposed. Using the measured accelerations, the vibrations of the belfry were analyzed using the calibrated FE model and finally, the FE model for the cathedral was verified by comparing the measured accelerations with the modeled results.

• Smart Structures and Systems
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• v.25 no.1
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• pp.23-35
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• 2020

It is important to take into account the thermal behavior in assessing the structural condition of bridges. An effective method of studying the temperature effect of long-span bridges is numerical simulation based on the solar radiation models. This study aims to develop a time-varying solar radiation model which can consider the real-time weather changes, such as a cloud cover. A statistical analysis of the long-term monitoring data is first performed, especially on the temperature data between the south and north anchors of the bridge, to confirm that temperature difference can be used to describe real-time weather changes. Second, a defect in the traditional solar radiation model is detected in the temperature field simulation, whereby the value of the turbidity coefficient t_u is subjective and cannot be used to describe the weather changes in real-time. Therefore, a new solar radiation model with modified turbidity coefficient γ is first established on the temperature difference between the south and north anchors. Third, the temperature data of several days are selected for model validation, with the results showing that the simulated temperature distribution is in good agreement with the measured temperature, while the calculated results by the traditional model had minor errors because the turbidity coefficient t_u is uncertainty. In addition, the vertical and transverse temperature gradient of a typical cross-section and the temperature distribution of the tower are also studied.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.814-822
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• 2022

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Steel and Composite Structures
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• v.29 no.4
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• pp.527-544
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• 2018

The paper presents the study on a change in modal parameters and structural stiffness of cable-stayed Fiberline Bridge made entirely of Glass Fiber Reinforced Polymer (GFRP) composite used for 20 years in the fjord area of Kolding, Denmark. Due to this specific location the bridge structure was subjected to natural aging in harsh environmental conditions. The flexural properties of the pultruded GFRP profiles acquired from the analyzed footbridge in 1997 and 2012 were determined through three-point bending tests. It was found that the Young's modulus increased by approximately 9%. Moreover, the influence of the temperature on the storage and loss modulus of GFRP material acquired from the Fiberline Bridge was studied by the dynamic mechanical analysis. The good thermal stability in potential real temperatures was found. The natural vibration frequencies and mode shapes of the bridge for its original state were evaluated through the application of the Finite Element (FE) method. The initial FE model was created using the real geometrical and material data obtained from both the design data and flexural test results performed in 1997 for the intact composite GFRP material. Full scale experimental investigations of the free-decay response under human jumping for the experimental state were carried out applying accelerometers. Seven natural frequencies, corresponding mode shapes and damping ratios were identified. The numerical and experimental results were compared. Based on the difference in the fundamental natural frequency it was again confirmed that the structural stiffness of the bridge increased by about 9% after 20 years of service life. Data collected from this study were used to validate the assumed FE model. It can be concluded that the updated FE model accurately reproduces the dynamic behavior of the bridge and can be used as a proper baseline model for the long-term monitoring to evaluate the overall structural response under service loads. The obtained results provided a relevant data for the structural health monitoring of all-GFRP bridge.