• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2001년도 봄 학술발표회 논문집
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• pp.207-212
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• 2001

The aim of this study is to present a practical and simple method for decision of ultimate failure mode of high-strength concrete beam members, based on interaction between shear strength and displacement ductility. Four tests were conducted on full-scale beam specimens having concrete compressive strength of 410kgf/$cm^{2}$. Prediction of failure mode from presented method and comparison with test results are also presented

• Structural Engineering and Mechanics
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• 제38권1호
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• pp.1-25
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• 2011

A new nonlinear model was developed to evaluate the shear resistance of steel fiber-reinforced concrete beams (SFRCB) using linear genetic programming (LGP). The proposed model relates the shear strength to the geometrical and mechanical properties of SFRCB. The best model was selected after developing and controlling several models with different combinations of the influencing parameters. The models were developed using a comprehensive database containing 213 test results of SFRC beams without stirrups obtained through an extensive literature review. The database includes experimental results for normal and high-strength concrete beams. To verify the applicability of the proposed model, it was employed to estimate the shear strength of a part of test results that were not included in the modeling process. The external validation of the model was further verified using several statistical criteria recommended by researchers. The contributions of the parameters affecting the shear strength were evaluated through a sensitivity analysis. The results indicate that the LGP model gives precise estimates of the shear strength of SFRCB. The prediction performance of the model is significantly better than several solutions found in the literature. The LGP-based design equation is remarkably straightforward and useful for pre-design applications.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2009년도 춘계 학술대회 제21권1호
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• pp.61-62
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• 2009

철근콘크리트 부재의 전단거동에 대한 다양한 이론모델들과 제안식이 존재한다. 하지만 전단거동에 대한 메커니즘이 매우 복잡하고 영향을 미치는 요소가 다양하기 때문에, 대다수의 제안식들은 경험식이며 그 예측 정확도도 매우 다르다. 이런 결점의 대안으로 인공신경망이 제안되어 여러 연구자들에 의해 연구되었지만 기존의 연구에서는 인공신경망 분석에 사용된 데이터베이스의 양이 충분하지 못한 문제점이 있었다. 그래서 본 논문에서는 방대한 전단실험 데이터베이스와 인공신경망을 이용하여 전단 보강근이 없는 철근콘크리트 보의 전단강도를 예측하고, 그 결과를 ACI 규준식과 비교 분석하였다. 분석 결과 인공신경망은 전단강도에 대한 주요 인자들의 영향을 적절히 반영하여 매우 우수한 예측 정확도를 보여 주었다.

• Computers and Concrete
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• 제2권1호
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• pp.79-96
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• 2005

The use of high-strength concrete (HSC) has significantly increased over the last decade, especially in offshore structures, long-span bridges, and tall buildings. The behavior of such concrete is noticeably different from that of normal-strength concrete (NSC) due to its different microstructure and mode of failure. In particular, the shear capacity of structural members made of HSC is a concern and must be carefully evaluated. The shear fracture surface in HSC members is usually trans-granular (propagates across coarse aggregates) and is therefore smoother than that in NSC members, which reduces the effect of shear transfer mechanisms through aggregate interlock across cracks, thus reducing the ultimate shear strength. Current code provisions for shear design are mainly based on experimental results obtained on NSC members having compressive strength of up to 50MPa. The validity of such methods to calculate the shear strength of HSC members is still questionable. In this study, a new approach based on artificial neural networks (ANNs) was used to predict the shear capacity of NSC and HSC beams without shear reinforcement. Shear capacities predicted by the ANN model were compared to those of five other methods commonly used in shear investigations: the ACI method, the CSA simplified method, Response 2000, Eurocode-2, and Zsutty's method. A sensitivity analysis was conducted to evaluate the ability of ANNs to capture the effect of main shear design parameters (concrete compressive strength, amount of longitudinal reinforcement, beam size, and shear span to depth ratio) on the shear capacity of reinforced NSC and HSC beams. It was found that the ANN model outperformed all other considered methods, providing more accurate results of shear capacity, and better capturing the effect of basic shear design parameters. Therefore, it offers an efficient alternative to evaluate the shear capacity of NSC and HSC members without stirrups.

• 콘크리트학회논문집
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• 제17권3호
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• pp.343-351
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• 2005

본 논문에서는 FRP 외부 부착공법으로 전단 보강된 철근콘크리트 보의 전단강도 예측 모델을 제안하였다. 제안된 모델은 전단 균열각과 전단 경간비와 같은 주요한 설계인자를 고려할 수 있도록 하였다. 제안된 모델의 주요고려사항은 ]nP로 전단 보강된 보의 일반적 파괴 형태인 부착파괴에 대한 전단력 산정이다. 또한 제안된 모델은 기존의 수정 소성이론에 근거한 crack sliding model을 이용하였으며, 아치작용계수를 도입함으로써, 전단 경간비의 영향을 최소화하였다. 최종적으로 본 전단강도 예측 모델을 적용한 해석결과를 실험결과와 비교$\cdot$검증하였으며, 본 제안된 예측 모델이 실험결과와 상당히 일치하는 것을 확인하였다.

• Steel and Composite Structures
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• 제17권5호
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• pp.623-639
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• 2014

Due to recent advancements in the area of Artificial Intelligence (AI) and computational intelligence, the application of these technologies in the construction industry and structural analysis has been made feasible. With the use of the Adaptive-Network-based Fuzzy Inference System (ANFIS) as a modelling tool, this study aims at predicting the shear strength of channel shear connectors in steel concrete composite beam. A total of 1200 experimental data was collected, with the input data being achieved based on the results of the push-out test and the output data being the corresponding shear strength which were recorded at all loading stages. The results derived from the use of ANFIS and the classical linear regressions (LR) were then compared. The outcome shows that the use of ANFIS produces highly accurate, precise and satisfactory results as opposed to the LR.

• Steel and Composite Structures
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• 제42권3호
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• pp.407-426
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• 2022

Superior to traditional welded studs, high strength friction-grip bolted shear connectors facilitate the assembling and demounting of the composite members, which maximizes the potential for efficiency in the construction and retrofitting of new and old structures respectively. Hence, it is necessary to investigate the structural properties of high strength friction-grip bolts used in steel concrete composite beams. By means of push-out tests, an experimental study was conducted on post-installed high strength friction-grip bolts, considering the effects of different bolt size, concrete strength, bolt tensile strength and bolt pretension. The test results showed that bolt shear fracture was the dominant failure mode of all specimens. Based on the load-slip curves, uplifting curves and bolt tensile force curves between the precast concrete slab and steel beam obtained by push-out tests, the anti-slip performance of steel-concrete interface and shear behavior of bolt shank were studied, including the quantitative analysis of anti-slip load, and anti-slip stiffness, frictional coefficient, shear stiffness of bolt shank and ultimate shear capacity. Meanwhile, the interfacial anti-slip stiffness and shear stiffness of bolt shank were defined reasonably. In addition, a total of 56 push-out finite element models verified by the experimental results were also developed, and used to conduct parametric analyses for investigating the shear behavior of high-strength bolted shear connectors in steel-concrete composite beams. Finally, on ground of the test results and finite element simulation analysis, a new design formula for predicting shear capacity was proposed by nonlinear fitting, considering the bolt diameter, concrete strength and bolt tensile strength. Comparison of the calculated value from proposed formula and test results given in the relevant references indicated that the proposed formulas can give a reasonable prediction.

• Earthquakes and Structures
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• 제22권4호
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• pp.355-372
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• 2022

The study is part of an experimental program on full-scale Un-Reinforced Masonry (URM) wall panels strengthened with Textile reinforced mortars (TRM). Eight brick walls (two with and five without central opening), were tested under the diagonal tension (shear) test method in order to investigate the strengthening system effectiveness on the in-plane behaviour of the walls. All the URM panels consist of the innovative components, named "Orthoblock K300 bricks" with vertical holes and a thin layer mortar. Both of them have great capacity and easy application and can be constructed much more rapidly than the traditional bricks and mortars, increasing productivity, as well as the compressive strength of the masonry walls. Several parameters pertaining to the in-plane shear behaviour of the retrofitted panels were investigated, including shear capacity, failure modes, the number of layers of the external TRM jacket, and the existence of the central opening of the wall. For both the control and retrofitted panels, the experimental shear capacity and failure mode were compared with the predictions of existing prediction models (ACI 2013, TA 2000, Triantafillou 1998, Triantafillou 2016, CNR 2018, CNR 2013, Eurocode 6, Eurocode 8, Thomoglou et al. 2020). The experimental work allowed an evaluation of the shear performance in the case of the bidirectional textile (TRM) system applied on the URM walls. The results have shown that some analytical models present a better accuracy in predicting the shear resistance of all the strengthened masonry walls with TRM systems which can be used in design guidelines for reliable predictions.

• Structural Monitoring and Maintenance
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• 제9권4호
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• pp.337-357
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• 2022

The objective of this study is to present a data-driven machine learning (ML) framework for predicting ultimate shear strength and failure modes of reinforced concrete ledge beams. Experimental tests were collected on these beams with different loading, geometric and material properties. The database was analyzed using different ML algorithms including decision trees, discriminant analysis, support vector machine, logistic regression, nearest neighbors, naïve bayes, ensemble and artificial neural networks to identify the governing and critical parameters of reinforced concrete ledge beams. The results showed that ML framework can effectively identify the failure mode of these beams either web shear failure, flexural failure or ledge failure. ML framework can also derive equations for predicting the ultimate shear strength for each failure mode. A comparison of the ultimate shear strength of ledge failure was conducted between the experimental results and the results from the proposed equations and the design equations used by international codes. These comparisons indicated that the proposed ML equations predict the ultimate shear strength of reinforced concrete ledge beams better than the design equations of AASHTO LRFD-2020 or PCI-2020.

• Steel and Composite Structures
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• 제44권5호
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• pp.691-705
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• 2022

Shear failure in reinforced concrete (RC) structures is very hazardous. This failure is rarely predicted and may occur without any prior signs. Accurate shear strength prediction of the RC members is challenging, and traditional methods have difficulty solving it. This study develops a JAYA-GBRT model based on the JAYA algorithm and the gradient boosting regression tree (GBRT) to predict the shear strength of RC slender beams without stirrups. Firstly, 484 tests are carefully collected and divided into training and test sets. Then, the hyperparameters of the GBRT model are determined using the JAYA algorithm and 10-fold cross-validation. The performance of the JAYA-GBRT model is compared with five well-known empirical models. The comparative results show that the JAYA-GBRT model (R² = 0.982, RMSE = 9.466 kN, MAE = 6.299 kN, µ = 1.018, and Cov = 0.116) outperforms the other models. Moreover, the predictions of the JAYA-GBRT model are globally and locally explained using the Shapley Additive exPlanation (SHAP) method. The effective depth is determined as the most crucial parameter influencing the shear strength through the SHAP method. Finally, a Graphic User Interface (GUI) tool and a web application (WA) are developed to apply the JAYA-GBRT model for rapidly predicting the shear strength of RC slender beams without stirrups.