• Structural Engineering and Mechanics
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• v.82 no.2
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• pp.173-189
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• 2022

This study presents an experimental and numerically study about the effects of fiber reinforcement ratio on the behavior of concrete-filled steel tubes (CFST) under dynamic impact loading. In literature have examined the behavior of GFRP and FRP wrapped strengthened CFST elements impact loads. However, since the direction of potential impact force isn't too exact, there is always the probability of not being matched the impact force of the area where the reinforced. Therefore, instead of the fiber textile wrapping method which strengthens only a particular area of CFST element, we used fiber-added concrete-filled elements which allow strengthening the whole element. Thus, the effect of fiber-addition in concrete on the behavior of CFST elements under impact loads was examined. To do so, six simply supported CFST beams were constructed with none fiber, 2% fiber and 10% fiber reinforcement ratio on the concrete part of the CFST beam. CFST beams were examined under two different impact loads (75 kg and 225 kg). The impactors hit the beam from a 2000 mm free fall during the experimental study. Numerical models of the specimens were created using ABAQUS finite element software and validated with experimental data. The obtained results such as; mid-span displacement, acceleration, failure modes and energies from experimental and numerical studies were compared and discussed. Furthermore, the Von Misses stress distribution of the CFST beams with different ratio of fiber reinforcements were investigated numerically. To sum up, there is an optimum amount limit of the fiber reinforcement on CFST beams. Up to this limit, the fiber reinforcement increases the structural performances of the beam, beyond that limit the fiber reinforcement decreases the performances of the CFST beam under transverse impact loadings.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Journal of Conservation Science
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• v.38 no.4
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• pp.277-288
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• 2022

'Eve 58-1', the subject of this study is a statue made of plaster and its structural stability was evaluated by utilizing the CAE program in order to prevent the risk of damage arising from impact and vibration that are generated during the packaging and transportation process given its material characteristics. CAE is an abbreviation for Computer Applied Engineering for realization by predicting changes at the time of application of virtual physical energy. It is applied by reflecting the physical property conditions and each boundary condition of plaster, and the digital images of the internal and external structure of the work were acquired through 3D scanning and CT analysis for interpretation by executing finite element modeling. When acceleration is applied to the work in the direction of its own weight, the left-right side and the front-rear side, it was possible to confirm a maximum displacement value of 15.24 mm in the head section of the front-rear side direction that has been tilted by approximately 27° from the Y-axis and the largest stress value of 12.46 MPa was at the left ankle section. The corresponding results confirmed that the left ankle section is the most vulnerable area and the section for which precautions need to be exercised and supplemented at the time of transporting the work by means of objective values.

• Earthquakes and Structures
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• v.24 no.1
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• pp.37-51
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• 2023

Türkiye has made significant changes and updates in both seismic risk maps and design codes over time, as have other countries with high seismic risk. In this study, the last two seismic design codes and risk maps were compared for the Aegean Region (Western Türkiye) where the earthquake risk has once again emerged with the 2020 Izmir Earthquake (Mw=6.9). In this study, information about the seismicity of the Aegean Region was given. The seismic parameters for all provinces in the region were compared with the last two earthquake risk maps. The spectral acceleration coefficients of all provinces have increased and differentiated with the current seismic hazard map as a result of the design spectra used on a regional basis have been replaced by the geographical location-specific design spectra. In addition, section damage limits were obtained for all provinces within the scope of the last two seismic design codes. Structural analyses for a sample reinforced-concrete building were made separately for each province using pushover analysis. The deformations in the cross-sections were compared with the limit states corresponding to the damage levels specified in the last two seismic design codes for the region. Target displacement requests for all provinces have decreased with the current code. The differentiation of geographical location-specific design spectra both in the last two seismic design code and between provinces has caused changes in section damages and building performance levels. The main aim of this study is to obtain and compare both seismic and structural analysis results for all provinces in the Aegean Region (Western Türkiye).

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.2
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• pp.75-83
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• 2023

In this paper, we present the analysis results on the vibration serviceability of a pedestrian bridge considering the effect of pedestrian moving mass inertia. Using dynamic finite element analysis, we considered different walking scenarios, including pedestrian density, walking speed, random walking, and synchronized walking, to analyze the acceleration response of a 40m long single-span bridge with a steel composite box cross section. We showed that the equivalent fixed mass analysis method did not significantly differ from the moving mass analysis in the random walk scenario and a wider frequency excitation band may be useful to consider when evaluating vibration serviceability in a random walk scenario.

• Computers and Concrete
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• v.29 no.2
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• pp.93-105
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• 2022

The degrees of freedom (DOFs) of high-rise structures increase rapidly due to the need for refined analysis, which poses a challenge toward a computationally efficient method for numerical analysis of high-rise structures using the finite element method (FEM). This paper presented an efficient iterative method, an algebraic multigrid (AMG) with a Jacobi overrelaxation smoother preconditioned conjugate gradient method (AMG-CG) used for solving large-scale structural system equations running on heterogeneous platforms with parallel accelerator graphics processing units (GPUs) enabled. Furthermore, an AMG-CG FEM application framework was established for the numerical analysis of high-rise structures. In the proposed method, the coarsening method, the optimal relaxation coefficient of the JOR smoother, the smoothing times, and the solution method for the coarsest grid of an AMG preconditioner were investigated via several numerical benchmarks of high-rise structures. The accuracy and the efficiency of the proposed FEM application framework were compared using the mature software Abaqus, and there were speedups of up to 18.4x when using an NVIDIA K40C GPU hosted in a workstation. The results demonstrated that the proposed method could improve the computational efficiency of solving structural system equations, and the AMG-CG FEM application framework was inherently suitable for numerical analysis of high-rise structures.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.18 no.1
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• pp.19-25
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• 2022

Nuclear Power Plants (NPP) should be designed to have sufficient safety margins and to ensure seismic safety against earthquake that may occur during the plant life time. After the 9.12 Gyeongju earthquake accident, the structural integrity of nuclear power plants due to the beyond design basis earthquake is one of key safety issues. Accordingly, it is necessary to conduct structural integrity evaluations for domestic NPPs under beyond design basis earthquake. In this study, the Level 3 LBB (Leak Before Break) evaluation was performed by considering the beyond design basis earthquake for the surge line of a OPR1000 plant of which design basis earthquake was set to be 0.2g. The beyond design basis earthquake corresponding to peak ground acceleration 0.4g at the maximum stress point of the surge line was considered. It was confirmed that the moment behaviors of the hot leg and pressurized surge nozzle were lower than the maximum allowable loading in moment-rotation curve. It was also confirmed that the LBB margin could be secured by comparing the LBB margin through the Level 2 method. It was judged that the margin was secured by reducing the load generated through the compliance of the pipe.

• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.19-26
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• 2024

In recent years, an increasing number of experimental studies have shown that the practical application of mature active control systems requires consideration of robustness criteria in the design process, including the reduction of tracking errors, operational resistance to external disturbances, and measurement noise, as well as robustness and stability. Good uncertainty prediction is thus proposed to solve problems caused by poor parameter selection and to remove the effects of dynamic coupling between degrees of freedom (DOF) in nonlinear systems. To overcome the stability problem, this study develops an advanced adaptive predictive fuzzy controller, which not only solves the programming problem of determining system stability but also uses the law of linear matrix inequality (LMI) to modify the fuzzy problem. The following parameters are used to manipulate the fuzzy controller of the robotic system to improve its control performance. The simulations for system uncertainty in the controller design emphasized the use of acceleration feedback for practical reasons. The simulation results also show that the proposed H∞ controller has excellent performance and reliability, and the effectiveness of the LMI-based method is also recognized. Therefore, this dynamic control method is suitable for seismic protection of civil buildings. The objectives of this document are access to adequate, safe, and affordable housing and basic services, promotion of inclusive and sustainable urbanization, implementation of sustainable disaster-resilient construction, sustainable planning, and sustainable management of human settlements. Simulation results of linear and non-linear structures demonstrate the ability of this method to identify structures and their changes due to damage. Therefore, with the continuous development of artificial intelligence and fuzzy theory, it seems that this goal will be achieved in the near future.

• Smart Structures and Systems
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• v.33 no.2
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• pp.93-103
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• 2024

Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long-span cable-stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage.