• Steel and Composite Structures
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• 제49권4호
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• pp.419-430
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• 2023

This paper is aimed at investigating the effect of multiple longitudinal stiffeners on the patch loading resistance of slender steel plate girders. Firstly, a numerical study is conducted through geometrically and materially nonlinear analysis with imperfections included (GMNIA), the model is validated with experimental results taken from the literature. The structural responses of girders with multiple longitudinal stiffeners are compared to the one of girders with a single longitudinal stiffener. Thereafter, a patch loading resistance model is developed through machine learning (ML) using symbolic regression (SR). An extensive numerical dataset covering a wide range of bridge girder geometries is employed to fit the resistance model using SR. Finally, the performance of the SR prediction model is evaluated by comparison of the resistances predicted using available formulae from the literature.

• Steel and Composite Structures
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• 제29권3호
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• pp.309-318
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• 2018

Steel girders are the structural members often used for passing long spans. Mostly being subjected to patch loading, or concentrated loading, steel girders are likely to face sudden deformation or damage e.g., web breathing. Horizontal or vertical stiffeners are employed to overcome this phenomenon. This study aims at assessing the feasibility of a machine learning method, namely the support vector machines (SVM) in predicting the patch loading resistance of longitudinally stiffened webs. A database consisting of 162 test data is utilized to develop SVM models and the model with best performance is selected for further inspection. Existing formulations proposed by other researchers are also investigated for comparison. BS5400 and other existing models (model I, model II and model III) appear to yield underestimated predictions with a large scatter; i.e., mean experimental-to-predicted ratios of 1.517, 1.092, 1.155 and 1.256, respectively; whereas the selected SVM model has high prediction accuracy with significantly less scatter. Robust nature and accurate predictions of SVM confirms its feasibility of potential use in solving complex engineering problems.

• Steel and Composite Structures
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• 제45권2호
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• pp.159-173
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• 2022

This paper proposes a hybrid machine-learning model, which is called DANN-IP, that combines a deep artificial neural network (DANN) and an interior-point (IP) algorithm in order to improve the prediction capacity on the patch loading resistance of steel plate girders. For this purpose, 394 steel plate girders that were subjected to patch loading were tested in order to construct the DANN-IP model. Firstly, several DANN models were developed in order to establish the relationship between the patch loading resistance and the web panel length, the web height, the web thickness, the flange width, the flange thickness, the applied load length, the web yield strength, and the flange yield strength of steel plate girders. Accordingly, the best DANN model was chosen based on three performance indices, which included the R^2, RMSE, and a20-index. The IP algorithm was then adopted to optimize the weights and biases of the DANN model in order to establish the hybrid DANN-IP model. The results obtained from the proposed DANN-IP model were compared with of the results from the DANN model and the existing empirical formulas. The comparison showed that the proposed DANN-IP model achieved the best accuracy with an R^2 of 0.996, an RMSE of 23.260 kN, and an a20-index of 0.891. Finally, a Graphical User Interface (GUI) tool was developed in order to effectively use the proposed DANN-IP model for practical applications.

• Structural Engineering and Mechanics
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• 제71권4호
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• pp.407-416
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• 2019

Novel steel fibre reinforced ultra-lightweight cement composite (ULCC) with compressive strength of 87.3MPa and density of $1649kg/m^3$ was developed for the flat slabs in civil buildings. This paper investigated structural behaviours of ULCC flat slabs according to a 4-specimen test program under concentrated loading and some reported test results. The investigated governing parameters on the structural behaviours of the ULCC slabs include volume fraction of the steel fibre and the patch loading area. The test results revealed that ULCC flat slabs with and without flexure reinforcement failed in different failure mode, and an increase in volume fraction of the steel fibre and loading area led to an increase in flexural resistance for the ULCC slabs without flexural reinforcement. Based on the experiment results, the analytical models were developed and also validated. The validations showed that the analytical models developed in this paper could predict the ultimate strength of the ULCC flat slabs with and without flexure reinforcement reasonably well.

• 해양환경안전학회지
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• 제20권2호
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• pp.218-227
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• 2014

본 연구에서는 실험 모형을 이용한 탄소성 대변형 시리즈 해석을 수행하여 플레이트 거더의 파손모드와 최종하중을 예측하였다. 수치해석 모형의 붕괴모드는 재하 시 플랜지에서 소성 힌지가 형성되었으며 실험모형의 붕괴모드와 일치하였다. 또한, 웹에서 항복선이 형성되어 크리플링 붕괴모드가 발생하는 것을 관찰할 수 있었으며 각각의 실험모형과 수치모형 최종하중의 평균값 1.07, 표준편차 0.04, 변동계수 0.04로 선형성을 유지하였으며 전체 최종하중 결과도 대략 8 % 오차를 나타내었다. 이는 수치모형 결과가 실험 및 적용 기준에 매우 만족하고 양호한 결과를 도출하였다고 생각한다. 따라서 알루미늄합금 플레이트 거더의 최종하중 예측 시 실험 및 적용 기준과 함께 병행하여 적용을 한다면 이에 대한 합리적 안전수준을 유지한다면 더 효율적이고 경제적 알루미늄 합금 플레이트 거더의 파손모드 및 최종하중에 대해 예측할 수 있을 거라고 생각한다.