• Earthquakes and Structures
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• v.18 no.4
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• pp.407-421
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• 2020

In-place analysis for offshore platforms is essentially required to make proper design for new structures and true assessment for existing structures, in addition to the structural integrity of platforms components under the maximum and minimum operating loads when subjected to the environmental conditions. In-place analysis have been executed to check that the structural member with all appurtenance's robustness have the capability to support the applied loads in either storm or operating conditions. A nonlinear finite element analysis is adopted for the platform structure above the seabed and pile-soil interaction to estimate the in-place behavior of a typical fixed offshore platform. The SACS software is utilized to calculate the dynamic characteristics of the platform model and the response of platform joints then the stresses at selected members, as well as their nodal displacements. The directions of environmental loads and water depth variations have significant effects in the results of the in-place analysis behavior. The most of bending moment responses of the piles are in the first fourth of pile penetration depth from pile head level. The axial deformations of piles in all load combinations cases of all piles are inversely proportional with penetration depth. The largest values of axial soil reaction are shown at the pile tips levels (the maximum penetration level). The most of lateral soil reactions resultant are in the first third of pile penetration depth from pile head level and approximately vanished after that penetration. The influence of the soil-structure interaction on the response of the jacket foundation predicts that the flexible foundation model is necessary to estimate the force responses demands of the offshore platform with a piled jacket-support structure well.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.6
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• pp.138-147
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• 2018

With the recent series of damage caused by earthquakes in Korea, such as Gyeongju and Pohang, we know that Korea is no longer a safe zone for earthquakes and that we need to be prepared for them. In addition, bridges built prior to the introduction of seismic design concepts remain without adequate seismic reinforcement measures, and earthquake reinforcement should be performed efficiently considering economic and structural safety. Preliminary assessment of seismic performance of existing bridges is divided into four seismic groups, taking into account seismicity, vulnerability and Impact, considering the magnitude of the existing bridge's seismic, and prioritization for further evaluation of seismic performance. In this study, unlike the existing anti-seismic reinforcement priority method, scores are calculated based on the seismic design criteria applied to bridges, importance coefficient of the bridge including the zone coefficient and the Importance, vulnerability index of the bridge including the soil condition and the elapsed years, detail coefficient of the bridge including the superstructure form, the span length, the width, the height, the design load, and the daily traffic volume. The calculated score items will be weighted and grouped according to the results. Using this, a simpler and more efficient algorithm was proposed to determine the priority of seismic reinforcement on a bridge.

• Smart Structures and Systems
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• v.24 no.2
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.207-227
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• 2022

In general, the interaction conditions between the discrete stones are not taken into account by structural engineers during the modeling and analyzing of historical stone bridges. However, many structural damages in the stone bridges occur due to ignoring the interaction conditions between discrete stones. In this study, it is aimed to examine the seismic behavior of a historical stone bridge by considering the interaction stiffness parameters between stone elements. For this purpose, Tokatli historical stone arch bridge was built in 1179 in Karabük-Turkey, is chosen for three-dimensional (3D) seismic analyses. Firstly, the 3D finite-difference model of the Tokatli stone bridge is created using the FLAC3D software. During the modeling processes, the Burger-Creep material model which was not used to examine the seismic behavior of historical stone bridges in the past is utilized. Furthermore, the free-field and quiet non-reflecting boundary conditions are defined to the lateral and bottom boundaries of the bridge. Thanks to these boundary conditions, earthquake waves do not reflect in the 3D model. After each stone element is modeled separately, stiffness elements are defined between the stone elements. Three situations of the stiffness elements are considered in the seismic analyses; a) for only normal direction b) for only shear direction c) for both normal and shear directions. The earthquake analyses of the bridge are performed for these three different situations of the bridge. The far-fault and near-fault conditions of 1989 Loma Prieta earthquake are taken into account during the earthquake analyses. According to the seismic analysis results, the directions of the stiffness parameters seriously changed the earthquake behavior of the Tokatli bridge. Moreover, the most critical stiffness parameter is determined for seismic analyses of historical stone arch bridges.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.6
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• pp.447-454
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• 2013

A model-based method is proposed to diagnose the gear crack in the gearbox under variable loading condition with the objective to apply it to the wind turbine CMS(Condition Monitoring System). A simple test bed is installed to illustrate the approach, which consists of motors and a pair of spur gears. A crack is imbedded at the tooth root of a gear. Tachometer-based order analysis, being independent on the shaft speed, is employed as a signal processing technique to identify the crack through the impulsive change and the kurtosis. Lumped parameter dynamic model is used to simulate the operation of the test bed. In the model, the parameter related with the crack is inversely estimated by minimizing the difference between the simulated and measured features. In order to illustrate the validation of the method, a simulated signal with a specified parameter is virtually generated from the model, assuming it as the measured signal. Then the parameter is inversely estimated based on the proposed method. The result agrees with the previously specified parameter value, which verifies that the algorithm works successfully. Application to the real crack in the test bed will be addressed in the next study.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.350-357
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• 2017

This study was conducted as a post - study on the development of a centralized controller and a hydraulic lift system including structural analysis and remote control for the development of a vertically elevated car. The safety review was carried out through the structural modification of the elevator lift which was developed during the previous research. 3D modeling was performed with Solidworks, and a model of finite element was created through Hypermesh S / W. In addition, the loading environment of the work vehicle for the evaluation is a condition in which the loading amount is 250 kg per position (total, upper, upper, lower, and lower) on the work table, ), The structural analysis was carried out under the condition that the load was 600 kg, and safety was examined in various aspects. As a result, when the allowable load of 250 kg and the excess load of 600 kg are excluded (except Case-11), the stress level is below the yield strength. In the case of Case-11, there is a region exceeding the yield strength at the center support portion of the safety bar at the upper end even after excluding the component which generates the maximum stress, but it does not affect the safety aspect of the whole structure Respectively. Looking at the deflection results, it can be seen that in all cases the maximum deflection occurs in the same table, and the tendency of sagging in both 250 kg and 600 kg is the same.

• Smart Structures and Systems
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• v.20 no.5
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• pp.549-562
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• 2017

The US railroad network carries 40% of the nation's total freight. Railroad bridges are the most critical part of the network infrastructure and, therefore, must be properly maintained for the operational safety. Railroad managers inspect bridges by measuring displacements under train crossing events to assess their structural condition and prioritize bridge management and safety decisions accordingly. The displacement of a railroad bridge under train crossings is one parameter of interest to railroad bridge owners, as it quantifies a bridge's ability to perform safely and addresses its serviceability. Railroad bridges with poor track conditions will have amplified displacements under heavy loads due to impacts between the wheels and rail joints. Under these circumstances, vehicle-track-bridge interactions could cause excessive bridge displacements, and hence, unsafe train crossings. If displacements during train crossings could be measured objectively, owners could repair or replace less safe bridges first. However, data on bridge displacements is difficult to collect in the field as a fixed point of reference is required for measurement. Accelerations can be used to estimate dynamic displacements, but to date, the pseudo-static displacements cannot be measured using reference-free sensors. This study proposes a method to estimate total transverse displacements of a railroad bridge under live train loads using acceleration and tilt data at the top of the exterior pile bent of a standard timber trestle, where train derailment due to excessive lateral movement is the main concern. Researchers used real bridge transverse displacement data under train traffic from varying bridge serviceability levels. This study explores the design of a new bridge deck-pier experimental model that simulates the vibrations of railroad bridges under traffic using a shake table for the input of train crossing data collected from the field into a laboratory model of a standard timber railroad pile bent. Reference-free sensors measured both the inclination angle and accelerations of the pile cap. Various readings are used to estimate the total displacements of the bridge using data filtering. The estimated displacements are then compared to the true responses of the model measured with displacement sensors. An average peak error of 10% and a root mean square error average of 5% resulted, concluding that this method can cost-effectively measure the total displacement of railroad bridges without a fixed reference.

• Corrosion Science and Technology
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• v.14 no.1
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• pp.1-11
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• 2015

Local wall thinning and integrity degradation caused by several mechanisms, such as flow accelerated corrosion (FAC), cavitation, flashing and/or liquid drop impingements, are a main concern in carbon steel piping systems of nuclear power plant in terms of safety and operability. Thinned pipe management program (TPMP) had been developed and optimized to reduce the possibility of unplanned shutdown and/or power reduction due to pipe failure caused by wall thinning in the secondary side piping system. This program also consists of several technical elements such as prediction of wear rate for each component, prioritization of components for inspection, thickness measurement, calculation of actual wear and wear rate for each component. Decision making is associated with replacement or continuous service for thinned pipe components. Establishment of long-term strategy based on diagnosis of plant condition regarding overall wall thinning is also essential part of the program. Prediction models of wall thinning caused by FAC had been established for 24 operating nuclear plants. Long term strategies to manage the thinned pipe component were prepared and applied to each unit, which was reflecting plant specific design, operation, and inspection history, so that the structural integrity of piping system can be maintained. An alternative integrity assessment criterion and a computer program for thinned piping items were developed for the first time in the world, which was directly applicable to the secondary piping system of nuclear power plant. The thinned pipe management program is applied to all domestic nuclear power plants as a standard procedure form so that it contributes to preventing an accident caused by FAC.

• Structural Engineering and Mechanics
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• v.12 no.2
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• pp.201-214
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• 2001

Four finite element (FE) models are examined to find the one that best estimates moment-rotation characteristics of top- and seat-angle with double web-angle connections. To efficiently simulate the real behavior of connections, finite element analyses are performed with following considerations: 1) all components of connection (beam, column, angles and bolts) are discretized by eight-node solid elements; 2) shapes of bolt shank, head, and nut are precisely taken into account in modeling; and 3) contact surface algorithm is applied as boundary condition. To improve accuracy in predicting moment-rotation behavior of a connection, bolt pretension is introduced before the corresponding connection moment being surcharged. The experimental results are used to investigate the applicability of FE method and to check the performance of three-parameter power model by making comparison among their moment-rotation behaviors and by assessment of deformation and stress distribution patterns at the final stage of loading. This research exposes two important features: (1) the FE method has tremendous potential for connection modeling for both monotonic and cyclic loading; and (2) the power model is able to predict moment-rotation characteristics of semi-rigid connections with acceptable accuracy.