• 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.

• Geomechanics and Engineering
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• 제35권4호
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• pp.425-436
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• 2023

An effective tool for researching actual problems in geotechnical and mining engineering is to conduct physical modeling tests using similar materials. A reliable geometric scaled model test requires selecting similar materials and conducting tests to determine physical properties such as the mixing ratio of the mixed materials. In this paper, a method is proposed to determine similar materials that can reproduce target properties using a polynomial model based on experimental results on modeling materials using a gypsum-sand mixture (GSM) to simulate rocks. To that end, a database is prepared using the unconfined compressive strength, elastic modulus, and density of 459 GSM samples as output parameters and the weight ratio of the mixing materials as input parameters. Further, a model that can predict the physical properties of the GSM using this database and a polynomial approach is proposed. The performance of the developed method is evaluated by comparing the predicted and observed values; the results demonstrate that the proposed polynomial model can predict the physical properties of the GSM with high accuracy. Sensitivity analysis results indicated that the gypsum-water ratio significantly affects the prediction of the physical properties of the GSM. The proposed polynomial model is used as a powerful tool to simplify the process of determining similar materials for rocks and conduct highly reliable experiments in a physical modeling test.

• 터널과지하공간
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• 제28권5호
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• pp.476-492
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• 2018

본 연구에서는 한반도 남부 지역에서 취득 가능한 응회암, 현무암, 섬록암 시험편에 대하여 다양한 실내 시험을 수행하였다. 건조/포화 조건으로 대별하여 실내실험을 수행했으며 이를 바탕으로 포화에 따른 암석 물성변화를 실험적으로 고찰하였다. 실험결과, 비교적 공극률이 작은 시험편을 대상으로 했음에도 불구하고 확연한 강도 저하와 변형 특성 변화가 관찰되었다. 실험결과를 바탕으로 암석의 주요 역학적 물성인 일축압축강도, 탄성계수, 간접인장강도를 예측할 수 있는 회귀모델을 구성하였다. 비파괴 물성인 P파 속도, Shore 경도를 독립변수로 이용하였으며 그 결과 만족할 만한 수준의 물성 예측 모델이 구성되었음을 확인하였다.

• 콘크리트학회논문집
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• 제27권5호
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• pp.559-568
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• 2015

본 연구에서는 감수제의 감수 효율에 따른 다성분계 결합재를 사용한 콘크리트의 물리적 특성에 대한 영향을 평가하기 위하여 고성능 감수제의 종류 3수준(0%, 8% 및 16%) 및 물-결합재비 3수준(40%, 45% 및 50%)에 따른 플라이애시 및 고로슬래그 미분말을 사용한 다성분계 콘크리트 배합을 제조하였다. 또한, 신뢰성 확보를 위하여 콘크리트 배합은 3회 반복실험을 실시하였다. 실험결과, 감수제 종류에 따른 압축강도는 약 20% 이상의 압축강도 차이가 발생하였으며, 감수제의 감수 효율이 콘크리트의 품질에 크게 영향을 미치는 것으로 나타났다. 따라서 감수제의 감수 효율을 반영한 다성분계 콘크리트의 압축강도 예측 모델식을 도출하였으며, 90% 이상의 높은 상관성이 있는 것으로 나타났다.

• 한국구조물진단유지관리공학회 논문집
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• 제8권3호
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• pp.169-176
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• 2004

황산염에 의한 원전 콘크리트 구조물의 침식을 예측하기 위하여 경과시간에 따른 팽창응력, 확산계수 등을 종합적으로 고려할 수 있는 Mechanistic 모델을 적용하였다. 적용배합은 원전 구조물 건설에 사용되었던 설계기준강도 385, 280 및 $210kgf/cm^2$의 3종으로 하였으며, 1종과 5종 포틀랜드시멘트를 시용하였다. 또한 시멘트 종류 및 설계기준강도별 로 1년간 10% 황산나트륨 용액에서 침지실험을 실시하여 각 배합별 확산계수 및 압축강도를 구하였으며, 그 결과를 예측모델식에 사용하여 원전 콘크리트 구조물의 황산염 침식을 예측하였다. 대상 배합의 황산염 확산계수는 $0.5763{\sim}3.9002{\times}10^{-12}m^2/sec.$였으며, 원전 콘크리트 구조물의 황산염 침식속도는 0.1~7.1 mm/year로 예측되었다.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2004년도 추계 학술발표회 제16권2호
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• pp.501-504
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• 2004

This paper proposes degree of hydration based shrinkage prediction model of 40MPa HPC. This model shows degree of hydration which is defined as the ratio between the hydrated cement mass and the initial mass of cement is very closely related to shrinkage deformation. In this study, degree of hydration was determined by CEMHYD-3D program of NIST. Verification of the predicted degree of hydration is performed by comparison between test results of compressive strength and estimated one by CEMHYD-3D. Proposed model is determined by statistical nonlinear analysis using the program Origin of Origin Lab. Co. To get coefficients of the model, drying shrinkage tests of four specimen series were followed with basic material tests. Testes were performed in constant temperature /humidity chamber, with difference moisture curing ages to know initial curing time effect. Verification with another specimen, collected construction field of FCM bridge, was given in the same condition as pre-tested specimens. Finally, all test results were compared to propose degree of hydration based model and other code models; AASHTO, ACI, CEB-FIP, JSCE, etc.

• 터널과지하공간
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• 제34권2호
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• pp.143-153
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• 2024

TBM 공법은 발파 공법에 비해 굴착 중 소음과 진동 수준이 낮고, 안정성이 높은 터널 굴착 공법이며, 전세계적으로 터널 프로젝트에 TBM 공법을 적용하는 사례가 증가하는 추세이다. 디스크 커터는 TBM의 커터헤드에 장착되는 굴착 도구로 지속적으로 막장면 지반과 상호작용하며, 이때 필연적으로 마모가 발생한다. 본 연구에서는 지질 조건과 TBM 운영파라미터, 머신러닝 알고리즘들을 이용하여 디스크 커터 마모를 정량적으로 예측하였다. 디스크커터 마모 예측의 입력변수 중 UCS 데이터의 수가 다른 기계 데이터 및 마모 데이터에 비해 매우 부족하기 때문에, 먼저 TBM 기계 데이터를 이용하여 전체 구간에 대한 UCS 추정을 진행하고, 완성된 전체 데이터로 마모율 계수 예측을 수행하였다. 마모율 계수 예측 모델의 성능을 비교해 본 결과 XGBoost 모델의 성능이 가장 높게 나타났으며, 복잡한 예측 모델의 해석을 위해 SHapley Additive exPlanation (SHAP) 분석을 진행하였다.

• Computers and Concrete
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• 제28권6호
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• pp.533-547
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• 2021

Calculating the shear capacity of slender reinforced concrete beams without shear reinforcement was the subject of numerous studies, where the eternal problem of developing a single relationship that will be able to predict the expected shear capacity is still present. Using experimental results to extrapolate formulae was so far the main approach for solving this problem, whereas in the last two decades different research studies attempted to use artificial intelligence algorithms and available data sets of experimentally tested beams to develop new models that would demonstrate improved prediction capabilities. Given the limited number of available experimental databases, these studies were numerically restrained, unable to holistically address this problem. In this manuscript, a new approach is proposed where a numerically generated database is used to train machine-learning algorithms and develop an improved model for predicting the shear capacity of slender concrete beams reinforced only with longitudinal rebars. Finally, the proposed predictive model was validated through the use of an available ACI database that was developed by using experimental results on physical reinforced concrete beam specimens without shear and compressive reinforcement. For the first time, a numerically generated database was used to train a model for computing the shear capacity of slender concrete beams without stirrups and was found to have improved predictive abilities compared to the corresponding ACI equations. According to the analysis performed in this research work, it is deemed necessary to further enrich the current numerically generated database with additional data to further improve the dataset used for training and extrapolation. Finally, future research work foresees the study of beams with stirrups and deep beams for the development of improved predictive models.

• 한국터널지하공간학회 논문집
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• 제25권3호
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• pp.221-243
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• 2023

국내 도심지 터널 공사에서 발파로 인한 진동 및 소음 방지를 위한 대안으로 로드헤더 공법 적용사례가 늘고 있다. 그러나 국내의 암반 대상 로드헤더 적용사례가 극히 적어 로드헤더 장비선정과 굴착효율 평가에 한계가 있다. 특히 로드헤더 굴착효율 평가를 위해 현재는 해외 현장에서 경험적으로 개발된 모델식을 적용하고 있으나 국내 암종 및 지질조건에 대한 검증이 부족한 실정이다. 본 연구에서는 해외 문헌 연구를 통하여 로드헤더 장비사양 결정방법과 굴착효율 평가 모델을 조사하였다. 이를 바탕으로 국내 현장 대상 장비선정을 위한 사양 검토와 더불어 현장 대상 암석강도와 굴착효율의 상관모델식을 제안하고 설계 굴착효율 예측 모델과 비교하였다. 또한 로드헤더 절삭이론 모델식을 이용한 굴착효율 산정의 간편법을 제안함으로써 굴착효율을 평가하고 기존 경험적 예측 모델과 비교 검증하였다.

• Steel and Composite Structures
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• 제44권3호
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• pp.309-324
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• 2022

The post-fire elastic stiffness and performance of concrete-filled steel tube (CFST) columns containing recycled aggregate concrete (RAC) has rarely been addressed, particularly in terms of material properties. This study was conducted with the aim of assessing the modulus of elasticity of recycled aggregate concrete-filled steel tube (RACFST) stub columns following thermal loading. The test data were employed to model and assess the elastic modulus of circular RACFST stub columns subjected to axial loading after exposure to elevated temperatures. The length/diameter ratio of the specimens was less than three to prevent the sensitivity of overall buckling for the stub columns. The gene expression programming (GEP) method was employed for the model development. The GEP model was derived based on a comprehensive experimental database of heated and non-heated RACFST stub columns that have been properly gathered from the open literature. In this study, by using specifications of 149 specimens, the variables were the steel section ratio, applied temperature, yielding strength of steel, compressive strength of plain concrete, and elastic modulus of steel tube and concrete core (RAC). Moreover, parametric and sensitivity analyses were also performed to determine the contribution of different effective parameters to the post-fire elastic modulus. Additionally, comparisons and verification of the effectiveness of the proposed model were made between the values obtained from the GEP model and the formulas proposed by different researchers. Through the analyses and comparisons of the developed model against formulas available in the literature, the acceptable accuracy of the model for predicting the post-fire modulus of elasticity of circular RACFST stub columns was seen.