• Geomechanics and Engineering
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• 제20권5호
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• pp.385-397
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• 2020

This paper presents an application of artificial neural networks (ANNs) in settlement prediction of a foundation on sandy soil. In order to train the ANN model, a wide experimental database about settlement of foundations acquired from available literatures was collected. The data used in the ANNs model were arranged using the following five-input parameters that covered both geometrical foundation and sandy soil properties: breadth of foundation B, length to width L/B, embedment ratio D_f/B, foundation net applied pressure q_net, and average SPT blow count N. The backpropagation algorithm was implemented to develop an explicit predicting formulation. The settlement results are compared with the results of previous studies. The accuracy of the proposed formula proves that the ANNs method has a huge potential for predicting the settlement of foundations on sandy soils.

• 설비공학논문집
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• 제13권12호
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• pp.1288-1296
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• 2001

A scheme for on-line fault detection and diagnosis of an air-handling unit is presented. The fault detection scheme uses residuals which are generated by comparing each measurement with analytical redundancies computed from the reference models. In this paper, artificial neural networks (ANNs) are used to estimate analytical redundancy and to classify faults. The Lebenburg-Marquardt algorithm is used to train feed forward ANNs that provide estimates of continuous states and diagnosis results. The simulation result demonstrated that the ANNs can effectively detect and diagnose faults in the highly non-linear and complex HVAC systems.

• Journal of the Korean Physical Society
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• 제73권12호
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• pp.1908-1917
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• 2018

The binding affinity between the T-cell receptors (TCRs) and antigenic peptides mainly determines immunological recognition. It is not a trivial task that T cells identify the digital sequences of peptide amino acids by simply relying on the integrated binding affinity between TCRs and antigenic peptides. To address this problem, we examine whether the affinity-based discrimination of peptide sequences is learnable and generalizable by artificial neural networks (ANNs) that process the digital experimental amino acid sequence information of receptors and peptides. A pair of TCR and peptide sequences correspond to the input for ANNs, while the success or failure of the immunological recognition correspond to the output. The output is obtained by both theoretical model and experimental data. In either case, we confirmed that ANNs could learn the immunological recognition. We also found that a homogenized encoding of amino acid sequence was more effective for the supervised learning task.

• 한국의상디자인학회지
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• 제26권2호
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• pp.33-43
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• 2024

This study comprehensively analyzes previous research comparing the similarities between 3D virtual and actual fittings, and identifies the current usage and limitations of virtual fitting programs. The findings reveal that, in most cases, 3D virtual fittings are not perfect substitutes for actual fittings. To address these limitations, this research focuses on the Hohenstein fitting test and BP-ANNs-based garment fit evaluation method, which incorporate various parameters, such as the correlation between wearers and garments, garment pressure, and ease, thus providing objective data, such as data acquired that can enhance subjective evaluations. By integrating such objective assessments, the study suggests potential improvements in virtual fitting accuracy. This research is expected to provide foundational data necessary for the development of a consumer virtual fitting systems alongside advancements in 3D virtual fitting technology.

• Structural Monitoring and Maintenance
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• 제2권1호
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Journal of Construction Engineering and Project Management
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• 제5권1호
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• 대한기계학회논문집A
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• 제34권7호
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• pp.813-820
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• 2010

포틀랜드 시멘트용 분쇄기는 독일과 일본 등 선진국에서 도입된 고가의 대형 기계이다. 따라서 이에 대한 체계적 정비 및 보수가 원활히 진행되어야 포틀랜드 시멘트의 생산설비에 대한 안정성을 확보할 수 있다. 한편 국내에 도입된 수직형 롤러 분쇄기는 포틀랜드 시멘트의 원료인 석회석의 시간당 생산량이 5.5MN이나 되는 세계 최대 규모의 분쇄기로서 설계 수명이 $4{\times}10^{7%}$사이클 정도이나 대략 $4{\times}10^6\;{\sim}\;8{\times}10^6$ 사이클 정도에서 파괴되고 있어 계획 예방 정비에 대한 어려움이 있으며, 수직형 롤러 분쇄기의 보수비용을 절감하기 위하여 롤러 분쇄기에 대한 효과적인 재설계가 필요한 실정이다. 따라서 본 연구에서는 확률론적인 절차가 내재되어 있어 불확실성을 다룰 수 있고, 대량의 복잡한 비선형적인 관계도 단순화의 과정 없이 연관 관계를 자체 조직화할 수 있는 인간의 뇌와 가장 유사한 병렬연산모델인 신경회로망을 수직형 롤러 분쇄기에 적용하여 최적설계를 수행하였다.

• Smart Structures and Systems
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• 제26권1호
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• pp.35-47
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• 2020

This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.

• Structural Monitoring and Maintenance
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• 제1권3호
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• pp.249-271
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• 2014

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

• 한국통신학회논문지
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• 제21권2호
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• pp.541-551
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• 1996

본 연구에서는 인공 신경 회로망을 이용하여 LCD 변수들의 값을 동적으로 변화시킴으로써 보다 개선된 전압 적정유지율을 얻을 수 있는 실시간 ULTC 제어전략이 개발된다. 제안된 전략에서는 수전전압의 변화에 따른 주변압기 송출전압 변화를 인식하는 ANNs, 그리고 ANNs로부터의 전압레벨과 배전선로들의 시간대별 변화패턴을 인식하여, ULTC의 정정치를 동적으로 결정하는 ANNg를 도입함으로서 보다 개선된 전압보상능력을 얻을 수 있도록 하였다. 개발된 제어전략의 성능을 평가하기 위해서 8개의 피더로 구성되는 시험 배전계통에 대해서 부하가 불규칙적으로 변화하였을때, 그리고 부하가 일정한 시간대별 패턴으로 변화하였을때의 ULTC의 전압 보상 전략이 모의된다. 인공 신경회로망은 Fortran 언어로 구현되며 시험계통에 대한 성능평가에서 유용한 결과를 입증하였다.