• Structural Engineering and Mechanics
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• 제84권2호
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• Steel and Composite Structures
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• 제52권2호
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• 대한방사선치료학회지
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• 제26권1호
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• pp.11-19
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• 2014

목 적 : 최적화 알고리즘에 적용되는 최적화 인자들의 영향을 고려하여, 가장 적합한 인자 값을 도출함으로써 이상적인 치료계획을 쉽게 설계할 수 있도록 하고자 한다. 대상 및 방법 : 본 연구의 세기조절방사선치료에서 선량계산 알고리즘은 PBC(Pencil Beam Convolution)이고, 최적화 알고리즘은 DVO(Dose Volume Optimizer 10.0.28)이다. 두경부 환자의 세기조절방사선치료에서 치료계획용적의 처방선량은 동시에 2.2 Gy와 2.0 Gy가 될 수 있도록 하였다. 치료계획은 6 MV, 7개의 조사야로 역선량계산방법으로 수립하였다. 최적화 알고리즘 인자는 용적선량-조건강도(Priority, Constrain), 선량부 드럼강도(Smooth)로 선정하고, 각 인자들의 변화량에 따른 치료계획의 영향을 분석하였다. 용적선량-조건강도는 기준 조건강도를 정하고, 비율은 같지만 절대 값은 다른 최적화 과정을 실시하였다. 또한 조건강도의 절대 값에 변화에 따른 치료용적과 주변 정상장기들을 평가하였다. 선량부드럼강도는 기준 조건의 단순 변화와 용적선량-조건강도와 관련시킨 변화를 치료계획에 반영시켰다. 치료계획은 처방선량지수(Conformal Index, CI), 처방선량포함지수(Paddick's Conformal Index, PCI), 선량균질지수(Homogeneity Index, HI)와 각 장기의 평균선량으로 평가하였다. 결 과 : 용적선량-조건강도의 비율을 동일하게 하고 절대 값을 변화 시켰을 때 CI값은 다르지만, PCI는 $1.299{\pm}0.006$, HI는 $1.095{\pm}0.004$, D5%/D95%는 $1.090{\pm}1.011$으로 처방선량에 대한 영향은 유사하였다. 이하선의 평균선량은 용적선량-조건강도의 절대 값이 40, 60, 70, 90으로 증가될 때, 67.4, 50.3, 51.2, 47.1 Gy로 감소하였다. 각각의 치료계획에서 선량부드럼강도를 증가시켰을 때, PCI는 $1.338{\pm}0.006$로 증가된 값을 보였다. 결 론 : 용적선량-조건강도는 절대적인 값보다 각 조건의 비율에 따라 최적화 알고리즘에 영향을 주었다. 절대 값이 다르더라도 같은 비율을 유지하면 유사한 치료계획이 수립되었다. 성공적인 치료계획을 수립하기 위해 특히 보호해야할 정상장기의 용적선량-조건강도는 치료용적의 용적선량-조건강도의 50%이상 되어야한다. 선량부드럼강도는 용적선량-조건강도에 따라 비례하여 증가하거나 감소하여야 한다. 단순히 절대 값으로 적용하면 용적선량-조건강도는 그 조건을 충분히 만족시키지 못한다.

• 한국지진공학회논문집
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• 제8권3호
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• pp.33-42
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• 2004

비선형시스템을 등가의 선형시스템으로 치환하는 것은 해석이 간단하다는 매우 중요한 장점을 제공하지만 구조물의 실제 비선형거동을 정확하게 모델링하지 못하기 때문에 능력스펙트럼법의 정확도는 정확한 등가주기와 등가감쇠비의 산정과 구해진 등가감쇠비에 따른 탄성응답스펙트럼의 수정방법과 그에 따른 요구곡선의 산정에 영향을 받는다. 본 논문에서는 요구곡선의 산정방법에 따른 능력스펙트럼법의 정확성을 분석하였다. 이를 위해 ATC-40과 Euro Code에서 제안한 감소 계수 등의 유효성을 평가하였다. Newmark와 Hall의 수정계수에 기초로 한 ATC-40에서 주어진 감소 계수에 의해 구해진 가속도 응답에 비해 Euro Code에서 주어진 감소 계수를 이용하여 구한 가속도 응답이 실제 평균 응답에 보다 유사함을 알 수 있었다. 그리고 유사가속도 응답을 이용한 방법과 절대가속도 응답을 이용한 방법을 이용하여 요구곡선을 산정하여 능력스펙트럼법의 정확성을 검증해 보았다. 절대가속도 응답을 이용한 결과가 전반적으로 유사가속도 응답을 이용한 결과에 비해 커짐을 알 수 있었고, 능력스펙트럼법이 전반적으로 응답을 과소평가하는 경향이 있어서 이러한 큰 값을 주는 것이 좀 더 정확한 결과를 줌을 알 수 있었다. 하지만 탄성 최대 강도에 대한 항복 강도의 비가 커질수록 그리고 항복 후 강성비가 커질수록 이러한 결과의 차이는 거의 없어짐을 알 수 있었다.

• 한국건축시공학회지
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• 제13권5호
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• pp.491-497
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• 2013

본 연구의 목적은 구조용 경량골재 콘크리트의 배합설계 절차를 확립하고, 설계강도로부터 콘크리트 목표 기건밀도의 범위를 평가하는 것이다. 본 절차를 확립하기 위해, 기존 347 실험데이터의 비선형 회귀분석 및 두 경계조건 (절대용적 및 콘크리트 기건밀도)에 기반한 수학적 모델을 구성하였다. 배합설계 모델제시 결과, 설계강도에 대한 물-시멘트비와 콘크리트 기건밀도는 굵은골재 체적비의 증가와 함께 감소하는데, 이 경향은 모래 경량보다는 전 경량골재 콘크리트에서 현저하였다. 경량골재 콘크리트의 기건단위는 설계강도에 따라 임의의 범위에서 설정되어야 하는데, 이는 제시된 모델에 의해 평가될 수 있다.

• 한국도로학회논문집
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• 제12권4호
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• pp.111-120
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• 2010

본 연구는 준고온 공법을 이용한 재생 아스팔트 콘크리트의 강도특성을 평가하기 위해 이루어졌다. 굵은 골재 최대치수 13mm의 화강암과 침입도 60-80인 신규 바인더 60-80을 재생 혼합물을 제조하는데 사용하였다. 배합설계는 RAP(굵은 입자 : 잔입자=6 : 4) 첨가비율 20%와 30%를 사용하였고 GPC, 침입도, 절대점도, 동점도를 준고온 첨가제(Evotherm와 Sasobit)의 첨가 함량을 결정하기 위하여 측정하였다. LD(low-density poly ethylene)를 본 연구에서 준고온 재생 아스팔트 혼합물의 개질제로 사용하였다. 본 연구에서는 8개의 준고온 재생 혼합물(2 RAP함량 ${\times}$ 2 준고온 첨가제 ${\times}$ 2 개질제)뿐만 아니라 2개의 일반 재생 혼합물, 1개의 가열혼합 일반혼합물(control)까지 총 11개의 혼합물을 제조하였다. 변형강도 시험, 간접인장강도 시험, 수분민감성 시험, wheel tracking을 통한 소성변형 시험을 준고온 재생혼합물의 기본 특성을 평가하기 위하여 수행하였다.

• Geomechanics and Engineering
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• 제33권1호
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Geomechanics and Engineering
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• 제33권3호
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• Steel and Composite Structures
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• 제45권2호
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• Journal of Welding and Joining
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• 제33권3호
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• pp.12-18
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• 2015

The ultimate goal of developing body is revealed the "lightweight" at latest EuroCarBody conference 2012 and the most core technology is joining process to make lightweight car body design. Accordingly, in this study, the car body assembly line for the assembly process applies to any introduction, particularly in the assembly of aluminum alloy and composite materials applied by the process for the introductory approached. Process were largely classified by welding (laser, arc, resistance, and friction stir welding), bonding (epoxy bonding) and mechanical fastening (FDS, SPR, Bolting and clinching). Applications for each process issues in the case and the applicable award was presented, based on the absolute strength of the test specimens and joining characteristics for comparative analysis were summarized. Finally, through this paper, we would tried to establish the characteristics of the joint for lightweight structure.