• Smart Structures and Systems
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• 제29권1호
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• pp.63-75
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• 2022

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.

• Smart Structures and Systems
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• 제20권3호
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• pp.385-396
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• 2017

The recent advancements in sensing technologies allow us to record measurements from target structures at multiple locations and with relatively high spatial resolution. Such measurements can be used to develop data-driven methodologies for condition assessment, control, and health monitoring of target structures. One of the state-of-the-art technologies, Fiber Optic Strain Sensors (FOSS), is developed at NASA Armstrong Flight Research Center, and is based on Fiber Bragg Grating (FBG) sensors. These strain sensors are accurate, lightweight, and can provide almost continuous strain-field measurements along the length of the fiber. The strain measurements can then be used for real-time shape-sensing and operational load-estimation of complex structural systems. While several works have demonstrated the successful implementation of FOSS on large-scale real-life aerospace structures (i.e., airplane wings), there is paucity of studies in the literature that have investigated the potential of extending the application of FOSS into civil structures (e.g., tall buildings, bridges, etc.). This work assesses the feasibility of using FOSS to predict operational loads (e.g., wind loads) on chain-like structures. A thorough investigation is performed using analytical, computational, and experimental models of a 4-story steel building test specimen, developed at the University of Southern California. This study provides guidelines on the implementation of the FOSS technology on building-like structures, addresses the associated technical challenges, and suggests potential modifications to a load-estimation algorithm, to achieve a robust methodology for predicting operational loads using strain-field measurements.

• 한국압력기기공학회 논문집
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• 제12권1호
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• pp.62-69
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• 2016

Current guidance considers that uniaxially loaded specimen with a deep crack is used for the determination of the ductile-to-brittle transition temperature. However, reactor pressure vessel is under biaxial loading in real and the existence of deep crack is not probable through periodic in-service-inspection. The elastic stress intensity factor and the elastic-plastic J-integral which were used for crack-tip stress field and fracture mechanics assessment parameters. The difference of the loading condition and crack geometry can significantly influence on these parameters. Thus, a constraint effect caused by differences between standard specimens and a real structure can over/underestimate the fracture toughness, and it affects the results of the structural integrity assessment, consequentially. The present paper investigates the constraint effects by evaluating the master curve $T_0$ reference temperature of PCVN (Pre-cracked Charpy V-Notch) and small scale cruciform specimens which was designed to simulate biaxial loading condition with shallow crack through the fracture toughness tests and 3-dimensional elastic-plastic finite element analyses. Based on the finite element analysis results, the fracture toughness values of a small scale cruciform specimen were estimated, and the geometry-dependent factors of the cruciform specimen considered in the present study were determined. Finally, the transferability of the test results of these specimens was discussed.

• 대한기계학회논문집A
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• 제25권7호
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• pp.1139-1146
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• 2001

The reactor pressure vessel(RPV) is usually cladded with stainless steel to prevent corrosion and radiation embrittlement, and a number of subclad cracks have been found during an in-service-inspection. These subclad cracks should be assured for a safe operation under normal conditions and faulted conditions such as pressurized thermal shock(PTS). Currently available integrity assessment procedure for an RPV, ASME Code Sec. XI, are built on the basis of linear fracture mechanics (LEFM). In PTS condition, however, thermal stress and mechanical stress give rise to high tensile stress at the cladding and elastic-plastic behavior is expected in this area. Therfore, ASME Code Sec. XI is overly conservative in assessing the structural integrity under PTS condition. In this paper, the fracture parameter (stress intensity factor, K, and RT(sub)NDT) from elastic analysis using ASME Sec. XI and finite element method were validated against 3-D elastic-plastic finite element analyses. The difference between elastic and elastic-plastic analysis became significant with increasing crack depth. Therfore, it is recommended to perform elastic-plastic analysis for the accurate assessment of subclad cracks under TPS which causes plastic deformation at the cladding.

• Smart Structures and Systems
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• 제7권4호
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• pp.241-262
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• 2011

Cast iron pipe has been used as a water distribution technology in North America since the early nineteenth century. The first cast iron pipes were made of grey cast iron which was succeeded by ductile iron as a pipe material in the 1940s. These different iron alloys have significantly different microstructures which give rise to distinct mechanical properties. Insight into the non-destructive structural condition assessment of aging pipes can be advantageous in developing mitigation strategies for pipe failures. This paper examines the relationship between the small-strain and large-strain properties of exhumed cast iron water pipes. Nondestructive and destructive testing programs were performed on eight pipes varying in age from 40 to 130 years. The experimental program included microstructure evaluation and ultrasonic, tensile, and flexural testing. New applications of frequency domain analysis techniques including Fourier and wavelet transforms of ultrasonic pulse velocity measurements are presented. A low correlation between wave propagation and large-strain measurements was observed. However, the wave velocities were consistently different between ductile and grey cast iron pipes (14% to 18% difference); the ductile iron pipes showed the smaller variation in wave velocities. Thus, the variation of elastic properties for ductile iron was not enough to define a linear correlation because all the measurements were practically concentrated in single cluster of points. The cross-sectional areas of the specimens tested varied as a result of minor manufacturing defects and levels of corrosion. These variations affect the large strain testing results; but, surface defects have limited effect on wave velocities and may also contribute to the low correlations observed. Lamb waves are typically not considered in the evaluation of ultrasonic pulse velocity. However, Lamb waves were found to contribute significantly to the frequency content of the ultrasonic signals possibly resulting in the poor correlations observed. Therefore, correlations between wave velocities and large strain properties obtained using specimens manufactured in the laboratory must be used with caution in the condition assessment of aged water pipes especially for grey cast iron pipes.

• 대한조선학회논문집
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• 제46권6호
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• pp.595-602
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• 2009

Great number of ships has been built by Korean Shipyards since early of 2,000 due to the expanding worldwide trade. Most of shipyards have enlarged the weight of erection block and many blocks have been assembled in block fabrication factories outside the shipyards to reduce the shipbuilding period. Especially, Giga blocks that exceed 2,000 tons are often assembled by the block fabrication factories outside the shipyard. Generally, the blocks are transported to building dock in shipyard by towing barges. Accident can be occurred during the sea transportation and it may bring about not only the delay of delivery but also a disaster on the ocean environments. Transportation condition of GPE (Grand Pre-Erection) block differs from the ocean going conditions of marine vessels. Special consideration should be included before transportation work in order to guarantee the safety of GPE blocks and barge carriers. In this paper, several examples, which have been investigated to set up the safety standard of transportation of the GPE blocks on coastal routes, are introduced. For the barge transportation on coastal sea route, the design criteria are discussed, considering the design wave, the acceleration induced by wave, structural strength, and the fixture condition of blocks.

• Structural Engineering and Mechanics
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• 제74권3호
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• pp.313-323
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• 2020

Non-destructive tests are commonly used in construction industry to access the quality and strength of concrete. However, till date there is no non-destructive testing method that can be adopted to evaluate the bond condition of reinforced concrete beams. In this regard, the presented research work details the use of ultra-sonic pulse velocity test method to evaluate the bond condition of reinforced concrete beam. A detailed experimental research was conducted by testing four identical reinforced concrete beam samples. The samples were loaded in equal increments till failure and ultra-sonic pulse velocity readings were recorded along the length of the beam element. It was observed from experimentation that as the cracks developed in the sample, the ultra-sonic wave velocity reduced for the same path length. This reduction in wave velocity was used to identify the initiation, development and propagation of internal micro-cracks along the length of reinforcement. Using the developed experimental methodology, researchers were able to identify weak spots in bond along the length of the specimen. The proposed method can be adopted by engineers to access the quality of bond for steel reinforcement in beam members. This allows engineers to carryout localized repairs thereby resulting in reduction of time, cost and labor needed for strengthening. Furthermore, the methodology to apply the proposed technique in real-world along with various challenges associated with its application have also been highlighted.

• Smart Structures and Systems
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• 제30권6호
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• pp.571-582
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• 2022

Optimal life-cycle management is a challenging issue for deteriorating regional bridges. Due to the complexity of regional bridge structural conditions and a large number of inspection and maintenance actions, decision-makers generally choose traditional passive management strategies. They are less efficiency and cost-effectiveness. This paper suggests a deep reinforcement learning framework employing double-deep Q-networks (DDQNs) to improve the life-cycle management of deteriorating regional bridges to tackle these problems. It could produce optimal maintenance plans considering restrictions to maximize maintenance cost-effectiveness to the greatest extent possible. DDQNs method could handle the problem of the overestimation of Q-values in the Nature DQNs. This study also identifies regional bridge deterioration characteristics and the consequence of scheduled maintenance from years of inspection data. To validate the proposed method, a case study containing hundreds of bridges is used to develop optimal life-cycle management strategies. The optimization solutions recommend fewer replacement actions and prefer preventative repair actions when bridges are damaged or are expected to be damaged. By employing the optimal life-cycle regional maintenance strategies, the conditions of bridges can be controlled to a good level. Compared to the nature DQNs, DDQNs offer an optimized scheme containing fewer low-condition bridges and a more costeffective life-cycle management plan.

• Smart Structures and Systems
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• 제29권1호
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• Structural Engineering and Mechanics
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• 제75권4호
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• pp.519-527
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• 2020

Dynamic analysis of a vehicle-track coupling system is important to structural design, damage detection and condition assessment of the structural system. Deterministic analysis of the vehicle-track coupling system has been extensively studied in the past, however, the structural parameters of the coupling system have uncertainties in engineering practices. It is essential to treat the parameters of the vehicle-track coupling system with consideration of uncertainties. In this paper, a method for predicting the bounds of the vehicle-track coupling system responses with uncertain parameters is presented. The uncertain system parameters are modeled as fuzzy variables instead of conventional random variables with known probability distributions. Then, the dynamic response functions of the coupling system are transformed into a component function based on the high dimensional representation approximation. The Lagrange interpolation method is used to approximate the component function. Finally, the bounds of the system's dynamic responses can be predicted by using Monte Carlo method for the interpolation polynomials of the Lagrange interpolation function. A numerical example is introduced to illustrate the ability of the proposed method to predict the bounds of the system's dynamic responses, and the results are compared with the direct Monte Carlo method. The results show that the proposed method is effective and efficient to predict the bounds of the system's dynamic responses with fuzzy variables.