• Geomechanics and Engineering
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• 제9권6호
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• pp.793-813
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• 2015

Characterization of discontinuous media is an endeavor that poses great challenge to engineers in practice. Since the inherent defects in cracked domains can substantially influence material resistance and govern its behavior, a lot of work is dedicated to efficiently model such effects. In order to overcome difficulties of material instability problems, one needs to comprehensively represent the geometry of cracks along with their impact on the mechanical properties of the intact material. In the present study, stress-strain results from laboratory experiments on Inada granite was used to derive crack tensor as a tool for the evaluation of fractured domain stability. It was found that the formulations proposed earlier could satisfactorily be employed to attain crack tensor via the invariants of which judgment on cracks population and induced anisotropy is possible. The earlier criteria based on crack tensor analyses were reviewed and compared to the results of the current study. It is concluded that the geometrical parameters calculated using mechanical properties could confidently be used to judge the anisotropy as well as strength of the cracked domain.

• Structural Monitoring and Maintenance
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• 제8권4호
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• 생명과학회지
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• 제32권8호
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• pp.659-665
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• 2022

Chlorella vulgaris는 인간과 동물에서 주요한 식품의 원료로 이용되는 수 조류이다. 최근 클로렐라는 어류 양식, 생물연료 및 영양물질과 치료용 단백질의 생산을 위한 이용 가능성과 관련하여 많은 관심을 받고 있다. 최근 본 연구실에서는 빠른 성장, 배양의 용이성, 암 조건에서의 배양성을 특징으로 하는 새로운 C. vulgaris PKVL7422균주를 분리하였으나, 이 균주의 질소 이용에 관련된 유전자에 대하여는 보고된 바가 없다. 본 연구에서는 cDNA 말단의 신속 증폭과 genome walking을 이용하여 C. vulgaris PKVL7422의 질산환원 효소(nitrate reductase, NR)를 동정하였다. C. vulgaris PKVL7422 NR 유전자는 약 8 kb이며, 18개의 인트론과 877개의 아미노산을 암호화 하는 19개의 엑손으로 되어 있다. 기존의 알려진 NR 단백질과의 비교 분석 결과 C. vulgaris PKVL7422 NR 단백질은 식물의 NR에 존재하는 5개의 도메인과 다수의 비변이성 아미노산 잔기를 가지고 있는 것으로 확인되었으며, 이러한 결과는 조류 NR 유전자의 분자생물학적 구조에 대한 새로운 정보를 제공한다.