• Structural Engineering and Mechanics
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• v.80 no.6
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• pp.677-688
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• 2021

The building design codes and standards in many countries usually are either fully or partially adopted from the international codes. However, regional conditions like the quality of construction industry and different statistical parameters of load and resistance have essential roles in the code calibration of building design codes. This paper presents a probabilistic approach to assess the reliability level of adopted national building codes by simulating design situations and considering all load combinations. The impact of the uncertainty of wind and earthquake loads, which are entirely regional condition dependent and have a high degree of uncertainty, are quantified. In this study, the design situation is modeled by generating thousands of numbers for load effect ratios, and the reliability level of steel elements for all load combinations and different load ratios is established and compared to the target reliability. This approach is applied to the Iranian structural steel code as a case study. The results indicate that the Iranian structural steel code lacks safety in some load combinations, such as gravity and earthquake load combinations, and is conservative for other load combinations. The present procedure can be applied to the assessment of the reliability level of other national codes.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1577-1586
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• 2023

Safety qualification of a steam generator is a crucial issue related to faulted condition design loads, including earthquake loads, and it should be ensured that the structural integrity of a steam generator does not exceed its design load. Using data from the Gyeongju and Pohang earthquakes, the two most powerful recorded seismic events in Korea, seismic analyses of a typical steam generator are conducted in this study. The modal characteristics are used to develop an input deck for these analyses. With a time history analysis, the responses of the steam generator in the event of an earthquake are obtained. In particular, the displacement, velocity, and acceleration responses are obtained in the time domain, with these outcomes then used for a detailed structural analysis as part of the ensuing assessment. The response spectra are also generated to determine the response characteristics in the frequency domain, focusing on the response comparisons between the Gyeongju and Pohang earthquakes. Structural integrity can be ensured by performing additional analysis using results obtained from the time history analysis considering the input excitations of various earthquakes considered in the design.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Smart Structures and Systems
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• v.26 no.2
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• pp.227-239
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• 2020

Despite proven effectiveness and accuracy in laboratories, the existing damage assessment based on guided ultrasonic waves (GUWs) or acoustic emission (AE) confronts challenges when extended to real-world structural health monitoring (SHM) for railway tracks. Central to the concerns are the extremely complex signal appearance due to highly dispersive and multimodal wave features, restriction on transducer installations, and severe contaminations of ambient noise. It remains a critical yet unsolved problem along with recent attempts to implement SHM in bourgeoning high-speed railway (HSR). By leveraging authors' continued endeavours, an SHM framework, based on actively generated diffuse ultrasonic waves (DUWs) and a benchmark-free condition contrast algorithm, has been developed and deployed via an all-in-one SHM system. Miniaturized lead zirconate titanate (PZT) wafers are utilized to generate and acquire DUWs in long-range railway tracks. Fatigue cracks in the tracks show unique contact behaviours under different conditions of external loads and further disturb DUW propagation. By contrast DUW propagation traits, fatigue cracks in railway tracks can be characterised quantitatively and the holistic health status of the tracks can be evaluated in a real-time manner. Compared with GUW- or AE-based methods, the DUW-driven inspection philosophy exhibits immunity to ambient noise and measurement uncertainty, less dependence on baseline signals, use of significantly reduced number of transducers, and high robustness in atrocious engineering conditions. Conformance tests are performed on HSR tracks, in which the evolution of fatigue damage is monitored continuously and quantitatively, demonstrating effectiveness, adaptability, reliability and robustness of DUW-driven SHM towards HSR applications.

• Smart Structures and Systems
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• v.17 no.2
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• pp.167-194
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• 2016

This paper investigates the Synergetics based Damage Detection Method (SDDM) for frame structures by using surface-bonded PZT (Lead Zirconate Titanate) patches. After analyzing the mechanism of pattern recognition from Synergetics, the operating framework with cooperation-competition-update process of SDDM was proposed. First, the dynamic identification equation of structural conditions was established and the adjoint vector (AV) set of original vector (OV) set was obtained by Generalized Inverse Matrix (GIM).Then, the order parameter equation and its evolution process were deduced through the strict mathematics ratiocination. Moreover, in order to complete online structural condition update feature, the iterative update algorithm was presented. Subsequently, the pathway in which SDDM was realized through the modified Synergetic Neural Network (SNN) was introduced and its assessment indices were confirmed. Finally, the experimental platform with a two-story frame structure was set up. The performances of the proposed methodology were tested for damage identifications by loosening various screw nuts group scenarios. The experiments were conducted in different damage degrees, the disturbance environment and the noisy environment, respectively. The results show the feasibility of SDDM using piezoceramic sensors and actuators, and demonstrate a strong ability of anti-disturbance and anti-noise in frame structure applications. This proposed approach can be extended to the similar structures for damage identification.

• Smart Structures and Systems
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• v.21 no.3
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• pp.305-320
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• 2018

The second half of the 20th century was marked with a significant raise in amount of railway bridges in Austria made of reinforced concrete. Today, many of these bridges are slowly approaching the end of their envisaged service life. Current methodology of assessment and evaluation of structural condition is based on visual inspections, which, due to its subjectivity, can lead to delayed interventions, irreparable damages and additional costs. Thus, to support engineers in the process of structural evaluation and prediction of the remaining service life, the Austrian Federal Railways (${\ddot{O}}$ BB) commissioned the formation of a concept for an anticipatory life cycle management of engineering structures. The part concerning concrete bridges consisted of forming a bridge management system (BMS) in a form of a web-based analysis tool, known as the LeCIE_tool. Contrary to most BMSs, where prediction of a condition is based on Markovian models, in the LeCIE_tool, the time-dependent deterioration mechanisms of chloride- and carbonation-induced corrosion are used as the most common deterioration processes in transportation infrastructure. Hence, the main aim of this article is to describe the background of the introduced tool, with a discussion on exposure classes and crucial parameters of chloride ingress and carbonation models. Moreover, the article presents a verification of the generated analysis tool through service life prediction on a dozen of bridges of the Austrian railway network, as well as a case study with a more detailed description and implementation of the concept applied.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.4
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• pp.281-286
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• 2012

Single frame structures with notches were fractured by applying drop impact loadings at room temperature and low temperature. Johnson-Cook shear failure model has been employed to simulate the fractured single frame structures. Through several numerical analyses, material constants for Johnson-Cook shear failure model have been found producing the cracks resulted from experiments. Fracture strain-stress triaxiality curves at both room temperature and low temperature are presented based on the extracted material constants. It is expected that the fracture strain-stress triaxiality curves can offer objective fracture criteria for the assessment of structural fractures of polar class vessel structures fabricated from DH36 steels. The fracture experiments of single frame structures revealed that the structure on low temperature condition fractures at much lower strain than that on room temperature condition despite the same stress states at both temperatures. In conclusion, the material properties on low temperature condition are essential to estimate the fracture characteristics of steel structures operated in the Northern Sea Route.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.275-282
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• 2001

The reactor pressure vessel is usually cladded with stainless steel to prevent corrosion and radiation embrittlement. Number of subclad cracks may be found during an in-service-inspection due to the presence of cladding. It is specified, in ASME Sec. XI, that a subclad crack is characterized as a surface crack when the thickness of the clad is less than 40% of the crack depth. This condition is provided to keep the crack integrity evaluation conservative. In order to refine the fracture assessment procedures for such subclad cracks under a pressurized thermal shock condition, three dimensional finite element analyses are applied for various subclad cracks existing under cladding. A total of 36 crack geometries are analyzed, and the results are compared with those for surface cracks. The resulting stress intensity factors for subclad cracks are 6 to 44% less than those for surface cracks. It is proven that the flaw characterization condition as specified in ASME Sec. XI can be overly conservative for some subclad cracks.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.2
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• pp.103-111
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• 2014

In this study, the probabilistic internal pressure fragility analysis was performed by using the non-linear finite element analysis method. The target structure is one of the containment buildings of typical domestic pressurized water reactors(PWRs). The 3-dimensional finite element model of the containment building was developed with considering the large equipment hatches. To consider uncertainties in the material properties and structural capacities, we performed the sensitivity analysis of the ultimate pressure capacity with respect to the variation of four important uncertain parameters. The results of the sensitivity analysis were used to the selection of the probabilistic variables and the determination of their probabilistic parameters. To reflect the present condition of the tendon pre-stressing force, the data of the pre-stressing force acquired from the in-service inspections of tendon forces were used for the determination of the median value. Two failure modes(leak, rupture) were considered and their limit states were defined to assess the internal pressure fragility of target containment building. The internal pressure fragilities for each failure mode were evaluated in terms of median internal pressure capacity, high confidence low probability of failure(HCLPF) capacity, and fragility curves with respect to the confidence levels. The HCLPF capacity was 115.9 psig for leak failure mode, and 125.0 psig for rupture failure mode.

• Structural Monitoring and Maintenance
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• v.2 no.3
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• pp.283-300
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• 2015

Visual condition inspections remain paramount to assessing the current deterioration status of a bridge and assigning remediation or maintenance tasks so as to ensure the ongoing serviceability of the structure. However, in recent years, there has been an increasing backlog of maintenance activities. Existing research reveals that this is attributable to the labour-intensive, subjective and disruptive nature of the current bridge inspection method. Current processes ultimately require lane closures, traffic guidance schemes and inspection equipment. This not only increases the whole-of-life costs of the bridge, but also increases the risk to the travelling public as issues affecting the structural integrity may go unaddressed. As a tool for bridge condition inspections, Unmanned Aerial Vehicles (UAVs) or, drones, offer considerable potential, allowing a bridge to be visually assessed without the need for inspectors to walk across the deck or utilise under-bridge inspection units. With current inspection processes placing additional strain on the existing bridge maintenance resources, the technology has the potential to significantly reduce the overall inspection costs and disruption caused to the travelling public. In addition to this, the use of automated aerial image capture enables engineers to better understand a situation through the 3D spatial context offered by UAV systems. However, the use of UAV for bridge inspection involves a number of critical issues to be resolved, including stability and accuracy of control, and safety to people. SLAM (Simultaneous Localisation and Mapping) is a technique that could be used by a UAV to build a map of the bridge underneath, while simultaneously determining its location on the constructed map. While there are considerable economic and risk-related benefits created through introducing entirely new ways of inspecting bridges and visualising information, there also remain hindrances to the wider deployment of UAVs. This study is to provide a context for use of UAVs for conducting visual bridge inspections, in addition to addressing the obstacles that are required to be overcome in order for the technology to be integrated into current practice.