• Nuclear Engineering and Technology
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• v.42 no.6
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• pp.704-711
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• 2010

Modal testing requires measuring the vibration of many points, for which an accelerometer, a gab sensor and laser vibrometer are generally used. Conventional modal testing requires mounting of these sensors to all measurement points in order to acquire the signals. However, this can be disadvantageous because it requires considerable measurement time and effort when there are many measurement points. In this paper, we propose a method for modal testing using a camera image. A camera can measure the vibration of many points at the same time. However, this task requires that the measurement points be classified frame by frame. While it is possible to classify the measurement points one by one, this also requires much time. Therefore, we try to classify multiple points using pattern recognition. The feasibility of the proposed method is verified by a beam experiment. The experimental results demonstrate that we can obtain good results.

• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.735-745
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• 2018

The Big Bang-Big Crunch (BB-BC) algorithm is an effective global optimization technique of swarm intelligence with drawbacks of being easily trapped in local optimal results and of converging slowly. To overcome these shortages, an improved BB-BC algorithm (IBB-BC) is proposed in this paper with taking some measures, such as altering the reduced form of exploding radius and generating multiple mass centers. The accuracy and efficiency of IBB-BC is examined by different types of benchmark test functions. The IBB-BC is utilized for damage detection of a simply supported beam and the European Space Agency structure with an objective function established by structural frequency and modal data. Two damage scenarios are considered: damage only existed in stiffness and damage existed in both stiffness and mass. IBB-BC is also validated by an existing experimental study. Results demonstrated that IBB-BC is not trapped into local optimal results and is able to detect structural damages precisely even under measurement noise.

• Earthquakes and Structures
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• v.17 no.1
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• pp.75-89
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• 2019

The paper presents experimental and numerical studies carried out on low-rise RC frames, typically found in developing countries. Shake table tests were conducted on 1:3 reduced scaled two-story RC frames that included a code conforming SMRF model and another non-compliant model. The later was similar to the code conforming model, except, it was prepared in concrete having strength 33% lower than the design specified, which is commonly found in the region. The models were tested on shake table, through multiple excitations, using acceleration time history of 1994 Northridge earthquake, which was linearly scaled for multi-levels excitations in order to study the structures' damage mechanism and measure the structural response. A representative numerical model was prepared in finite element based program SeismoStruct, simulating the observed local damage mechanisms (bar-slip and joint shear hinging), for seismic analysis of RC frames having weaker beam-column joints. A suite of spectrum compatible acceleration records was obtained from PEER for incremental dynamic analysis of considered RC frames. The seismic performance of considered RC frames was quantified in terms of seismic response parameters (seismic response modification, overstrength and displacement amplification factors), for critical comparison.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.2
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• pp.14-21
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• 2019

In recent years, the use of composite structures has become commonplace in various fields such as aerospace, architecture, and civil engineering. In this study, A method is proposed to find optimal position of bolt hole for fastening of composite structure. In the case of composites, stress distribution is very complicated, and design optimization based on this phenomenon increases difficulty. In selecting the optimum position of the bolt hole, the response surface method(rsm), which is a method of optimization, was applied. A response surface was created based on design points by multiple finite element analyzes. The position of the bolt hole that minimizes the stress when bolting on the response surface was found. The distribution of the stress at the position of the optimal hole was much lower than that of the initial design. Based on the results of this study, it is possible to increase the design safety factor of the structure by appropriately selecting the position of the bolt hole according to various load types when designing the structure and civil structure.

• Computers and Concrete
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• v.23 no.1
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• pp.49-60
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• 2019

In recent years, multiple experimental studies have been performed on using fiber reinforced polymer (FRP) bars in reinforced concrete (RC) structural members. FRP bars provide a new type of reinforcement that avoids the corrosion of traditional steel reinforcement. In this study, predicting the shear strength of RC beams with FRP longitudinal bars using artificial neural networks (ANNs) is investigated as a different approach from the current specific codes. An ANN model was developed using the experimental data of 104 FRP-RC specimens from an existing database in the literature. Seven different input parameters affecting the shear strength of FRP bar reinforced RC beams were selected to create the ANN structure. The most convenient ANN algorithm was determined as traingdx. The results from current codes (ACI440.1R-15 and JSCE) and existing literature in predicting the shear strength of FRP-RC beams were investigated using the identical test data. The study shows that the ANN model produces acceptable predictions for the ultimate shear strength of FRP-RC beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model provides more accurate predictions for the shear capacity than the other computed methods in the ACI440.1R-15, JSCE codes and existing literature for considering different performance parameters.

• ETRI Journal
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• v.44 no.3
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• pp.476-490
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• 2022

With the recent advances in technology, automatic speech recognition (ASR) has been widely used in real-world applications. The efficiency of converting large amounts of speech into text accurately with limited resources has become more vital than ever. In this study, we propose a method to rapidly recognize a large speech database via a transformer-based end-to-end model. Transformers have improved the state-of-the-art performance in many fields. However, they are not easy to use for long sequences. In this study, various techniques to accelerate the recognition of real-world speeches are proposed and tested, including decoding via multiple-utterance-batched beam search, detecting end of speech based on a connectionist temporal classification (CTC), restricting the CTC-prefix score, and splitting long speeches into short segments. Experiments are conducted with the Librispeech dataset and the real-world Korean ASR tasks to verify the proposed methods. From the experiments, the proposed system can convert 8 h of speeches spoken at real-world meetings into text in less than 3 min with a 10.73% character error rate, which is 27.1% relatively lower than that of conventional systems.

• Journal of Ocean Engineering and Technology
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• v.36 no.1
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• pp.61-81
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• 2022

The available underwater acoustic spectrum is limited. Therefore, it is imperative to avoid frequency interference from overlapping frequencies of underwater acoustic equipment (UAE) for the co-existence of the UAE. Cognitive technology that senses idle spectrum and actively avoids frequency interference is an efficient method to facilitate the collision-free operation of multiple UAE with overlapping frequencies. Cognitive technology is adopted to identify the frequency usage of UAE to apply cognitive technology. To this end, we investigated two principle underwater acoustic sources: UAE and marine animals. The UAE is classified into five types: underwater acoustic modem, acoustic positioning system, multi-beam echo-sounder, side-scan sonar, and sub-bottom profiler. We analyzed the parameters of the frequency band, directivity, range, and depth, which play a critical role in the design of underwater acoustic cognitive technology. Moreover, the frequency band of several marine species was also examined. The mid-frequency band from 10 - 40 kHz was found to be the busiest. Lastly, this study provides useful insights into the design of underwater acoustic cognitive technologies, where it is essential to avoid interference among the UAE in this mid-frequency band.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.539-550
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• 2022

A detailed experimental program was conducted to investigate the flexural behavior of ultra high performance concrete (UHPC) beams reinforced with high strength steel (HSS) rebars with a specified yield strength of 600 MPa via direct tensile test and monotonic four-point bending test. First, two sets of direct tensile test specimens, with the same reinforcement ratio but different yield strength of reinforcement, were fabricated and tested. Subsequently, six simply supported beams, including two plain UHPC beams and four reinforced UHPC beams, were prepared and tested under four-point bending load. The results showed that the balanced-reinforced UHPC beams reinforced with HSS rebars could improve the ultimate load-bearing capacity, deformation capacity, ductility properties, etc. more effectively owing to interaction between high strength of HSS rebar and strain-hardening characteristic of UHPC. In addition, the UHPC with steel rebars kept strain compatibility prior to the yielding of the steel rebar, further satisfied the plane-section assumption. Most importantly, the crack pattern of the UHPC beam reinforced with HSS rebars was prone to transform from single main crack failure corresponding to the normal-strength steel, to multiple main cracks failure under the condition of balanced-reinforced failure, which validated by the conclusion of direct tensile tests cooperated with acoustic emission (AE) source locating technique as well.

• Steel and Composite Structures
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• v.46 no.5
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• pp.709-718
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• 2023

The absence of an important portion of the web plate in steel beams with multiple circular perforations, cellular beams, causes the web plate to undergo distortions prior to and during lateral torsional buckling (LTB). The conventional LTB equations in the codes and literature underestimate the buckling moments of cellular beams due to web distortions. The present study is an attempt to develop analytical methods for estimating the elastic buckling moments of cellular beams. The proposed methods rely on the reductions in the torsional and warping rigidities of the beams due to web distortions and the reductions in the weak-axis bending and torsional rigidities due to the presence of web openings. To test the accuracy of the analytical estimates from proposed solutions, a total of 114 finite element analyses were conducted for six different standard IPEO sections and varying unbraced lengths within the elastic limits. These analyses clearly indicated that the LTB solutions in the AISC 360-16 and AS4100:2020 codes overestimate the buckling loads of cellular beams within elastic limits, particularly at shorter span lengths. The LDB solutions in the literature and the Eurocode 3 LTB solution, on the other hand, provided conservative buckling moment estimates along the entire range of elastic buckling.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.