• Earthquakes and Structures
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• v.21 no.6
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• pp.641-652
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• 2021

Digging well foundation has been widely used in railway bridges due to its good economy and reliability. In other instances, bridges with digging well foundation still have damage risks during earthquakes. In this study, a new type of digging well foundation with prefabricated roots was proposed to reduce earthquake damage of these bridges. Quasi-static tests were conducted to investigate the failure mechanism of the root digging well foundation, and then to analyze seismic behaviors of the new type well foundation. The testing results indicated that these prefabricated roots could effectively limit the rotation and uplift of the digging well foundation and increase the lateral bearing capacity of the digging well foundation. The elastic critical load and ultimate load can be increased by 69% and 36% if prefabricated roots were added in the digging well foundation. The prefabricated roots drived more soil around the foundation to participate in working, the stiffness of the bridge pier with root digging well foundation was improved. Moreover, the root participation could improve the energy dissipation capacity of soil-foundation-pier interaction system. The conclusions obtained in this paper had important guiding significance for the popularization and application of the digging well foundation with prefabricated roots in earthquake-prone zones.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.1
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• pp.175-182
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• 2008

In performing a risk analysis of structure for earthquake, it is imperative to identify the vulnerability of structures associated with various stages of damage. And the earthquake resisting capability is needed for structures like bridge. So the damage analysis of bridges with or without isolator for earthquake effects is necessary. In this paper, the risk analysis of seismic isolated LRB bridges considering earthquake effects such as PGA, PGV, SA, SV, and SI is performed using fragility curves to assure the earthquake resisting capability of the structures. And, the stability of fragility curve is investigated with respect to elastic modulus.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.2 s.54
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• pp.145-152
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• 2009

In performing a risk analysis on structure for earthquake, it is imperative to identify the vulnerability of structures associated with various stages of damage. And the earthquake resisting capability is needed for structures like bridge. So the damage analysis of bridges with or without isolator for earthquake effects is necessary. In this paper, the risk analysis of seismic isolated LRB bridges considering earthquake effects such as PGA, PGV, SA, SV, and SI is performed using fragility curves to assure the earthquake resisting capability of the structures. And, the stability of fragility curve is investigated with respect to input earthquake.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.245-253
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• 2021

RC slab bridges account for the largest portion of deteriorated bridges in Korea. However, most RC slabs are not included in the first and second classes of bridges, which are subject to bridge safety management and maintenance. The highest damaged components in highway bridges are the subsidiary facilities including expansion joints and bearings. In particular, leakage through expansion joints causes deterioration and cracks of concrete and exposure of reinforced bars. Therefore, this study analyzed the effect of adhesion damage at expansion joints on the response of the deck in RC slab bridges. When the spacing between the expansion joints at both ends was closely adhered, cracks occurred in the concrete at both ends of the deck due to the resistance rigidity at the expansion joints. Based on the response results, the correlation analysis between displacements in the longitudinal direction of the expansion joint and concrete stress at both ends of the deck for each damage scenario was performed to investigate the effect of the occurrence of damage on the bridge behavior. When expansion joint devices at both sides were damaged, the correlation between displacement and stress showed a low correlation of 0.18 when the vehicles proceeded along all the lanes. Compared with those in the intact state, the deflections of the deck in the damaged case at both sides showed a low correlation of 0.34 to 0.53 while the vehicle passed and 0.17 to 0.43 after the vehicle passed. This means that the occurrence of cracks in the ends of concrete changed the behavior of the deck. Therefore, data-deriven damage detection could be developed to manage the damage to expansion joints that cause damage and deterioration of the deck.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.231-241
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• 2021

Although the deterioration of bridges may occur due to various causes, one of the representative causes is that the chloride used for deicing in the winter penetrates bridge members and results in corrosion. This study aims to quantify the ageing degree resulting from the corrosion of a bridge, apply it to the inelastic dynamic analysis model of the bridge, perform a seismic fragility analysis, and evaluate the relationship between the ageing degree and the seismic fragility curve. It is important to appropriately define the threshold values for each damage state in seismic fragility analyses considering the ageing degree. The damage state was defined using the results of existing experimental studies on the characteristics of the deterioration in the displacement ductility capacity of the pier, according to the ageing degree. Based on the seismic fragility analyses of six types of bridges divided by three types of bearing devices and two pier heights, it was found that the seismic vulnerability tends to increase with the ageing degree. The difference in seismic vulnerability with respect to the ageing degree exhibits a tendency to increase as the damage state progresses from slight to moderate, severe, and collapse.

• Structural Engineering and Mechanics
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• v.60 no.1
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• pp.71-90
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• 2016

In this paper, the normalized variable V=(log N-B)(log ${\Delta}{\sigma}-C$-C), as derived from the probabilistic S-N field of Castillo and Canteli, is taken as a reference for calculation of damage accumulation and probability of failure using the Miner number in scenarios of variable amplitude loading. Alternative damage measures, such as the classical Miner and logarithmic Miner, are also considered for comparison between theoretical lifetime prediction and experimental data. The suitability of this approach is confirmed for it provides safe lifetime prediction when applied to fatigue data obtained for riveted joints made of a puddle iron original from the Fao bridge, as well as for data from experimental programs published elsewhere carried out for different materials (aluminium and concrete specimens) under distinct variable loading histories.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.779-802
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• 2013

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

• Journal of the Korean Geotechnical Society
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• v.37 no.11
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• pp.61-69
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• 2021

In this study, scale down model bridge piers were fabricated and non-destructive experiments conducted with an impact load to determine scours in the ground adjacent to the bridge piers using the natural frequency of the bridge piers. Three scale-model bridge piers with different heights were fabricated, and they penetrated the ground at a depth of 0.35 m. The scours around the bridge piers were simulated as a side scour and foundation scour. The experiments were conducted in 13 steps, in which scouring around the model bridge piers was performed in 0.05 m excavation units. To derive the natural frequency, the impact load was measured with three accelerometers attached to the model bridge piers. The impact load was applied with an impact hammer, and the top of the model bridge pier was struck perpendicularly to the bridge axis. The natural frequency according to the scour progress was calculated with a fast Fourier transform. The results demonstrated that the natural frequency of each bridge pier tended to decrease with scour progress. The natural frequency also decreased with increasing pier height. With scour progress, a side scour occurred at 70% or higher of the initial natural frequency, and a foundation scour occurred at less than 70%.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.1-11
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• 2024

Bridges crack and become damaged due to age and external factors such as earthquakes, lack of maintenance, and weather conditions. With the number of aging bridge on the rise, lack of maintenance can lead to a decrease in safety, resulting in structural defects and collapse. To prevent these problems and reduce maintenance costs, a system that can monitor the condition of bridge and respond quickly is needed. To this end, existing research has proposed artificial intelligence model that use sensor data to identify the location and extent of cracks. However, existing research does not use data from actual bridge to determine the performance of the model, but rather creates the shape of the bridge through simulation to acquire data and use it for training, which does not reflect the actual bridge environment. In this paper, we propose a bridge safety determination edge AI model that detects bridge abnormalities based on artificial intelligence by utilizing acceleration data from bridge occurring in the field. To this end, we newly defined filtering rules for extracting valid data from acceleration data and constructed a model to apply them. We also evaluated the performance of the proposed bridge safety determination edge AI model based on data collected in the field. The results showed that the F1-Score was up to 0.9565, confirming that it is possible to determine safety using data from real bridge, and that rules that generate similar data patterns to real impact data perform better.

• Journal of the Korean Society of Safety
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• v.20 no.2 s.70
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• pp.119-126
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• 2005

Load bearing structural members in a wide variety of applications accumulate damage over their service life. From a standpoint of both safety and performance, it is desirable to monitor the occurrence, location, and extent of such damage. Structures require complicated element models with a number of degrees of freedom in structural analysis. During experiment much effort and cost is needed for measuring structural parameters. The sparseness and errors of measured data have to be considered during the parameter estimation Of Structures. In this paper we introduces damage identification algorithm by a system identification(S.I) using static and dynamic response. To study the behaviour of the estimators in noisy environment Using Monte Carlo simulation and a data measured perturbation scheme is adopted to investigate the influence of measurement errors on identification results. The assessment result by static and dynamic response were compared, and the efficiency and applicabilities of the proposed algorithm are demonstrated through simulated static and dynamic responses of a truss bridge. The assessment results by each method were compared and we could observe that the 5.1 method is superior to the other conventional methods.