This study aims to propose a simplified equation for estimating the bond strength of corroded reinforcing bars. To this end, extensive parametric analyses were performed using the detailed analysis method presented in the authors’ previous study, where a wide range of critical variables were considered, such as compressive strength of concrete, net cover thickness, and reinforcing bar diameter. The sensitivity in bond strength of the corroded reinforcing bar according to each variable was evaluated. On this basis, a simplified formula for the bond strength of the corroded reinforcing bar was derived through regression analysis. The proposed equation was rigorously tested and verified using the bond test results of corroded reinforcing bars collected from the literature. The results confirmed that the proposed equation could estimate the bond strengths of specimens with better accuracy than the existing models, providing a reliable tool for engineers and researchers. In addition, the proposed equation was used to analyze the development length required for corroded tensile reinforcement to exert its yield strength, and it showed that the cover thickness of concrete must be at least four times the diameter of the reinforcing bar to achieve the yielding strength of reinforcing bar even at a corrosion degree of more than 5.0%.

PURPOSES : To enhance the accuracy of predicting the compressive strength of practical concrete mixtures, this study aimed to develop a machine learning model by utilizing the most commonly employed curing age, specifically, the 28-day curing period. The training dataset consisted of concrete mixture sample data at this curing age, along with samples subjected to a total load not exceeding 2,350 kg. The objective was to train a machine learning model to create a more practical predictive model suitable for real-world applications. METHODS : Three machine learning models—random forest, gradient boosting, and AdaBoost—were selected. Subsequently, the prepared dataset was used to train the selected models. Model 1 was trained using concrete sample data from the 28th curing day, followed by a comprehensive analysis of the results. For Model 2, training was conducted using data from the 28th day of curing, focusing specifically on instances where the total load was 2,350 kg or less. The results were systematically analyzed to determine the most suitable machine learning model for predicting the compressive strength of concrete. RESULTS : The machine learning model trained on concrete sample data from the 28th day of curing with a total weight of 2,350 kg or less exhibited higher accuracy than the model trained on weight-unrestricted data from the 28th day of curing. The models were evaluated in terms of accuracy, with the gradient boosting, AdaBoost, and random forest models demonstrating high accuracy, in that order. CONCLUSIONS : Machine learning models trained using concrete mix data based on practical and real-world scenarios demonstrated a higher accuracy than models trained on impractical concrete mix data. This case illustrates the significance of not only the quantity but also the quality of the data during the machine learning training process. Excluding outliers from the data appears to result in better accuracy for machine learning models. This underscores the importance of using high-quality and practical mixed concrete data for reliable and accurate model training.

PURPOSES : In this study, an optimal model for compressive strength prediction was derived by learning and directly comparing several machine learning models based on the same data. METHODS : Approximately 478 pieces of concrete compressive strength data were obtained to compare the performance of the machine learning models. In addition, five machine learning models were trained based on the obtained data. The performance of the learned model was compared using three performance indicators. Finally, the performance of the model trained using additional data was reviewed. RESULTS : As a result of comparing the performance of machine learning models, the XGB(eXtra Gradient Boost) model showed the best performance. In addition, as a result of the verification based on additional data, highly reliable results can be obtained if the XGB model is used to predict the compressive strength of concrete. CONCLUSIONS : If a concrete strength prediction model is derived based on a machine learning model, a highly reliable model can be derived.

The demand for high-strength steel is rising due to its economic efficiency. Low-cycle fatigue (LCF) tests have been conducted to investigate the nonlinear behaviors of high-strength steel. Accurate material models must be used to obtain reliable results on seismic performance evaluation using numerical analyses. This study uses the combined hardening model to simulate the LCF behavior of high-strength steel. However, it is challenging and complex to determine material model parameters for specific high-strength steel because a highly nonlinear equation is used in the model, and several parameters need to be resolved. This study used the particle swarm algorithm (PSO) to determine the model parameters based on the LCF test data of HSA 650 steel. It is shown that the model with parameter values selected from the PSO accurately simulates the measured LCF curves.

미세구조 특성의 불확실성은 재료 특성에 많은 영향을 준다. 시멘트 기반 재료의 공극 분포 특성은 재료의 역학적 특성에 큰 영향을 미치며, 재료에 랜덤하게 분포되어 있는 많은 공극은 재료의 물성 예측을 어렵게 한다. 공극의 특성 분석과 재료 응답 간의 상관관계 규명에 대한 기존 연구는 통계적 관계 분석에 국한되어 있으며, 그 상관관계가 아직 명확히 규명되어 있지 않다. 본 연구에서는 합성곱 신경망(CNN, convolutional neural network)을 활용한 이미지 기반 데이터 접근법을 통해 시멘트 기반 재료의 역학적 응답을 예측하 고, 공극분포와 재료 응답의 상관관계를 분석하였다. 머신러닝을 위한 데이터는 고해상도 마이크로-CT 이미지와 시멘트 기반 재료의 물성(인장강도)로 구성하였다. 재료의 메시 구조 특성을 분석하였으며, 재료의 응답은 상장균열모델(phase-field fracture model)에 기 반을 둔 2D 직접 인장(direct tension) 유한요소해석 시뮬레이션을 활용하여 평가하였다. 입력 이미지 영역의 기여도를 분석하여 시편 에서 재료 응답 예측에 가장 큰 영향을 미치는 영역을 CNN을 통하여 식별하였다. CNN 과정 중 활성 영역과 공극분포를 비교 분석하 여 공극분포특성과 재료 응답의 상관관계를 분석하여 제시하였다.

최근 지구 온난화의 영향으로 태풍의 파괴력이 증가함에 따라 부유식 해상풍력발전기의 막대한 유실과 붕괴에 대한 우려가 깊어지고 있다. 부유식 해상풍력발전기의 안전한 운영을 위해 새로운 형태의 탈착형 계류 시스템 개발이 요구되고 있다. 본 연구에서 고 려한 새로운 반잠수식 계류 풀리는 기존의 탈착형 계류 장치에 비해 계류 라인으로 부유식 해상풍력 터빈을 보다 쉽게 탈부착할 수 있도 록 고안되었다. 8MW급 부유식 해상풍력발전기에 적용 가능한 반잠수식 계류 풀리의 초기 설계에 대한 구조적 안전성을 검토하기 위해 3D 프린터를 이용하여 축소구조모형을 제작하고, 이 모형에 대한 구조시험을 수행하였다. 축소 모형의 구조시험을 위해 3D 프린팅에 사 용된 ABS 소재의 인장 시편을 제작하고 인장시험을 수행하여 소재의 물성을 평가하였다. 인장시험에서 얻은 재료 특성과 축소모형 구조 시험과 동일한 하중 및 경계 조건을 적용하여 반잠수식 계류 풀리의 유한요소해석을 수행하였다. 유한요소해석을 통해 반잠수식 계류 풀 리의 구조적 취약 부분을 검토하였다. 반잠수식 계류 풀리의 주요 하중조건을 고려하여 구조모형시험을 수행하였으며, 재료의 최대인장 응력 이상이 발생하는 위치에 대해 유한요소해석과 시험 결과를 비교하였다. 유한요소해석과 모형시험의 결과로부터 작동조건에서는 Body와 Wheel의 연결부 구조가 취약한 것으로 파악되었고, 계류조건에서는 Body와 Chain stopper의 연결부 구조가 취약한 것으로 검토되었 다. 축소모형 구조시험에서 나타난 SMP의 구조 취약부는 구조해석의 결과와 일치하는 것으로 나타났다. 연구 결과를 통해 반잠수식 계류 풀리의 초기 설계에 대한 구조적 안전성을 실험적으로 검증할 수 있었다. 또한, 본 연구 결과는 상세설계 단계에서 반잠수식 계류 풀리의 구조 강도를 향상시키는데 유용하게 활용될 수 있을 것으로 판단된다.

고성능 콘크리트(HPC) 압축강도는 추가적인 시멘트질 재료의 사용으로 인해 예측하기 어렵고, 개선된 예측 모델의 개발이 필수적 이다. 따라서, 본 연구의 목적은 배깅과 스태킹을 결합한 앙상블 기법을 사용하여 HPC 압축강도 예측 모델을 개발하는 것이다. 이 논 문의 핵심적 기여는 기존 앙상블 기법인 배깅과 스태킹을 통합하여 새로운 앙상블 기법을 제시하고, 단일 기계학습 모델의 문제점을 해결하여 모델 예측 성능을 높이고자 한다. 단일 기계학습법으로 비선형 회귀분석, 서포트 벡터 머신, 인공신경망, 가우시안 프로세스 회귀를 사용하고, 앙상블 기법으로 배깅, 스태킹을 이용하였다. 결과적으로 본 연구에서 제안된 모델이 단일 기계학습 모델, 배깅 및 스태킹 모델보다 높은 정확도를 보였다. 이는 대표적인 4가지 성능 지표 비교를 통해 확인하였고, 제안된 방법의 유효성을 검증하였다.

일반적으로 콘크리트는 골재, 모래, 시멘트, 담수, 혼합재 등 다양한 재료로 구성되어있으며 재령에 따라서 강도가 증 가한다. 콘크리트에 필요한 각 재료의 비율은 혼합 설계를 통해 결정되지만, 콘크리트의 강도는 실험적으로 측정되기 전까지는 알 수 없다. 이러한 한계를 극복하기 위해 실험을 통해 얻은 데이터를 이용하여 콘크리트의 압축 강도를 예측하기 위해 통계수 학과 기계학습 알고리즘을 이용한 많은 연구가 시도되었다. 이전의 연구는 콘크리트 압축 강도 예측에 신경망 기법이 가장 적 합하다고 제안하였다. 그러나 신경망 기법은 다른 기계학습과 비교하여 모델 학습에 계산 비용이 많이 들어 실제로 적용하기 어려운 문제점이 있다. 최근 몇 년 동안 다양한 회귀 분석 모델이 개발되었으므로 본 연구에서는 신경망 대신 최신 회귀 분석 모델을 이용하여 콘크리트 강도 예측모델을 제시하였다. 이를 위해 최근 개발된 회귀 분석 모델에 대한 교차검증을 시행하여 최적의 모델을 선정하였다. 그리드 검색을 통하여 선정된 각 모델의 하이퍼 파라미터를 최적화하고, 국내외 데이터를 활용하여 기계학습 모델을 훈련하고 검증하였다. 이들 중 CatBoost, LGBMR, RFR, XGBoost 회귀모델이 높은 성능을 보여주었다. 특히 그 중에서 XGBoost 회귀 분석 모델이 가장 작은 오차와 높은 정확도를 보여주었다. 이들 중 오류가 가장 큰 LGBMR 모델도 이전 연구에서 제안된 신경망 및 앙상블 모델보다 성능이 우수하였다. 현장 레미콘 콘크리트에 대한 압축 강도 예측을 시행하여 학 습된 모델의 현장 적용 가능성을 확인하였다.

The contemporary high-tech structures have become enlarged and their functions more diversified. Steel concrete structure and composite material structures are not exceptions. Therefore, there have been on-going studies on fiber reinforcement materials to improve the characteristics of brittleness, bending and tension stress and others, the short-comings of existing concrete. In this study, the purpose is to develop the estimated model with dynamic characteristics following the steel fiber mixture rate and formation ration by using the nerve network in mixed steel fiber reinforced concrete (SFRC). This study took a look at the tendency of studies by collecting and analyzing the data of the advanced studies on SFRC, and facilitated it on the learning data required in the model development. In addition, by applying the diverse nerve network model and various algorithms to develop the optimal nerve network model appropriate to the dynamic characteristics. The accuracy of the developed nerve network model was compared with the experiment data value of other researchers not utilized as the learning data, the experiment data value undertaken in this study, and comparison made with the formulas proposed by the researchers. And, by analyzing the influence of learning data of nerve network model on the estimation result, the sensitivity of the forecasting system on the learning data of the nerve network is analyzed.

Impact damage tolerance is an important design requirement for composite structures. In this study, the effect of post impact damage and hole size of the composite sandwich skin / sandwich with core specimen on compressive strength of the laminate was analyzed. Three specimen tests were performed in this research. Two tests were carried out on pure bending test specimens subjected to impact damage to the skin and specimen with a hole in one of its skin as a damage. Through this test, we compared the reduction of compressive strength due to the size of skin damage and the size of the hole. Also, core-free specimen with an open hole under uniaxial loading were tested to produce reference data for comparison with the series tested earlier. As results of the tests, the sandwich beams with damage size and open hole are almost identical, and we concluded that the prediction of compressive strength reduction after impact of the sandwich skin structure can be predicted using an analytical model assuming skin open hole as impact inputs.

Rockfish was a commercially important fish specie in marine ranching areas in Korea. To estimate density and biomass of rockfish using acoustic method, target strength (TS) information is required on the species. This study measured TS dependence on tilt angle and size on 14 live rockfish individuals at 38, 70, and, 120 kHz by ex-situ measurement (tethered method) and acoustic scattering model (Krichhoff ray mode, KRM). The swimbladdered angle ranged from 18 to 30˚ (mean ± s.d. = 26 ± 4˚ ). The mean TS for all individuals was highest -35.9 dB of tilt angle -17˚ at 38 kHz, -35.4 dB of tilt angle -25˚ at 70 kHz, and -34.9 dB of tilt angle -22˚ at 120 kHz. The ex-situ TS-total length (TL, cm) relationships were TS38kHz = 20log10(TL) - 67.1, TS70kHz = 20log10(TL) - 68.6, and TS120kHz = 20log10(TL) - 69.9, respectively. The model TS-total length (TL, cm) relationships were TS38kHz = 20log10(TL) - 66.4, TS70kHz = 20log10(TL) - 67.0, TS120kHz = 20log10(TL) - 67.0. The two measurements between the ex-situ TS and KRM model for TS-tilt angle and fish size were found to be significantly correlated.

Measuring the exact cable tension force is important to cable supported bridge under construction and on service. This study was planned to propose EM(electro-magnetic) sensor-based method for measuring the tension force of MS(multi-strand) cable in cable-stayed and extradosed bridge. The tension force in each strand is the same due to MS cable construction using Iso-tensioning system. Therefore, In this prosed method, EM sensor was installed directly at a strand and the measurement model was established for estimating the tension force of strand via EM sensor by experiments. The measurement model was derived from the relation of tension force and magnetic permeability. Also, the magnetic permeability is shown to be different according to the magnetization characteristic of 1860MPa and 2200MPa high-strength strand. The difference is increased as tension force increases. Additional experiment was conducted to verify the measurement model. As a result, the distribution of strand tension calculated upon the EM sensor is similar to those of tension measured by load cell. This proposed approach can be an effective tool for monitoring and measuring the cable force of MS cable.

Acoustic target strength (TS) measurement and theoretical acoustic scattering models were applied to estimate the TS for assessing the fish biomass. TS measurement was made of ex situ sailfin sandfish Arctoscopus japonicus at 70 and 120 kHz, and then compared to backscatter model prediction. The live sandfish was tethered in seawater using monofilament lines. Measurements were made versus incidence angle, −50° to 50° relative to dorsal aspect directions. Distorted wave Born approximation (DWBA) model was used to calculated TS pattern. The TS values of sandfish (body length: 16.2 cm) at 70 and 120 kHz were ‒66.94 dB and ‒64.45 dB, respectively, and were about 20 dB lower than TS of other fishes in Korea waters. These TS levels were distributed within the range of the theoretical TS. Ex situ measurement and theoretical TS may be applied to improve acoustical detection and biomass estimation of the sandfish, and is necessary to measure with various lengths.

Although ex situ target strength (TS) measurements using dual- and split-beam systems have become the primary approach of estimating fish abundance, theoretical model estimation is a powerful tool for verifying the measurements, as well as for providing values when making direct measurements is difficult. TS values for 20 samples of live bambooleaf wrasse (Pseudolabrus japonicus) whose target length (TL) ranged between 13.7 and 21.3 cm were estimated theoretically using the Kirchhoff-ray mode model, and the TS values for 18 live fish samples were additionally measured at ~0° tilt angle to the swimming aspect using a tethered method at a frequency of 120 kHz to verify the theoretical values. The digitizing intervals used to extract the fish body and swim bladder morphology in the X-ray photographs significantly affected the calculated TS patterns, but variations based on the speed of sound and density ratio values for the general range of fish flesh were relatively small (within 1 dB). Close agreement was observed between the measured and theoretical TS values, and the correlation between the average TS and body length of the fish could be calculated accurately as <TS120kHz>= 20logTL (cm) –71.6 using the theoretical method.

강도 한계상태 설계에서는 균열이 일어난 이후 철근콘크리트 부재의 인장영역에서 철근이 모든 인장력을 부담하는 것으로 가정한다. 그러나 균열 사이의 콘크리트가 실제 콘크리트 부재에서는 특히 사용하중 수준에서의 어느 정도의 인장 응력을 견디는데, 일조 하는 것으로 보고 있다. 이러한 효과를 Tension stiffening 효과라 한다. 본 연구에서는 Tension stiffening 모델과 고강도 철근 콘크리트 보의 휨 실험결과의 비교를 통해 해석모델의 유효성을 평가 하고자 한다. 이를 통해 선정 된 6가지의 Tension stiffening 모델과 실험에 의한 모멘트-곡률, 하중-처짐등을 관계를 평가하였다. 실험결과 설계기준에서는 ACI 318이 Tension stiffening 모델에서는 Owen & Damjanic이 실험 값과 가장 적은 오차율을 보이며 높은 신뢰도를 보였다.

Acoustical backscattering characteristics of Japanese anchovy can be estimated by Kirchhoffray mode model (KRM model) due to estimate exact body and swim-bladder shape of the fish, the samples were rapidly frozen by dry-ice and alcohol. X-ray photos for ventral and lateral direction for 6 samples were taken and the 3D coordinates of the body swim-bladder were estimated by digitizing from the photos. The angles between the axis of body and swim-bladder were about 9˚ at 38kHz and 7˚ at 120kHz, 200kHz. General formula of TS and BL estimated were 〈 TS38kHz 〉=20logBL-67.3, 〈 TS120kHz 〉=20logBL-66.6, 〈 TS200kHz 〉=20logBL-67.0. As a result, we confirmed KRM model is very useful to estimate TS (Target Strength) for design of experiment and it also can be applied to estimate the abundance of Japanese anchovy distributed by 2 frequency difference method in the survey area.