• Computers and Concrete
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• v.30 no.6
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• pp.445-453
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• 2022

A pre-cambered deep deck-plate system has been developed that can realize a long span by offsetting the deflection caused by a construction load. In this study, finite element (FE) analysis is performed to examine the preload-camber relationship introduced into a deck and calculate the deflection reflecting the ponding effect that arises during concrete pouring. The FE analysis results showed that the stress of the bottom plate was half of the yield stress when the pre-camber of approximately 30 mm was introduced. Based on the FE results, a full-scale deep deck-plate is fabricated, a pre-camber is introduced, and concrete is poured to measure deflection. A deflection calculation formula that reflects the ponding effect is proposed, and the deflections yielded by the proposed model, experimental results, and FE results are compared. Results show that the proposed model can accurately estimate the deflection of non-supported deep deck-plate systems after concrete is poured.

• Steel and Composite Structures
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• v.42 no.6
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• pp.733-745
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• 2022

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

• Computational Structural Engineering
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• v.5 no.1
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• pp.83-89
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• 1992

A practical method of estimating inelastic deflection of reinforced concrete slab under service load is presented. Based on the elastic results of linear finite element analysis and area of reinforcement, inelastic deflection multiplier(.betha.) is evaluated and desired deflection as a measure of serviceability of the designed slab is obtained. Example for the corner supported slab shows that the results from the proposed method agree well with those from the experiment/and nonlinear finite element analysis. Application of the method to the design of irregular slab is also considered.

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.71-72
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• 2010

In case of calculation of an immediate deflection according to EC2, a curvature and average curvature are calculated by reflecting tension stiffening effect. In this study, tension stiffening effects according to MC90 and EC2 were considered, and an immediate deflection was calculated. And also, it was compared to results in KCI provision and experimental data. In results, it has difference around 8~15% with respect to tension stiffening effect, but all of them predict well for the load-deflection behavior after yielding state.

• Structural Engineering and Mechanics
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• v.44 no.3
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• pp.405-416
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• 2012

In this study the investigation of large deflections subject in compliant mechanisms is presented using homotopy perturbation method (HPM). The main purpose is to propose a convenient method of solution for the large deflection problem in compliant mechanisms in order to overcome the difficulty and complexity of conventional methods, as well as for the purpose of mathematical modeling and optimization. For simplicity, a cantilever beam of linear elastic material under horizontal, vertical and bending moment end point load is considered. The results show that the applied method is very accurate and capable for cantilever beams and can be used for a large category of practical problems for the aim of optimization. Also the consequence of effective parameters on the large deflection is analyzed and presented.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.795-798
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• 2010

본 논문에서는 상사진동에서의 응답을 통해 구성된 모드유연도에 의해 추정되는 손상유발 변위를 이용하여 전당빌딩의 손상을 탐지하는 진동기반의 손상탐지 방법을 제시하였다. 이 방법은 전단빌딩의 층간변위를 활용하여 오직 손상이 존재할 때에만 발생하는 Damage-induced inter-story deflection (DI-ID)을 통해 손상탐지를 수행하는 방법이다. 구조물의 전체 자유도에 양의 전단력을 발생시킴으로써 층간변위를 분명히 파악할 수 있도록 하는 양전단력 탐색하중(Positive Shear Inspection Load)을 통해 DI-ID를 산정한다. 제안된 방법의 검증을 위해 5층의 전단빌딩 축소모형을 대상구조물로 선정하여 수치모의실험을 수행했다. 단일손상과 다중손상의 모사를 위해 1층과 3층의 휨강성을 각각 10% 씩 저감시켰고, 수치모의실험 결과, 단일손상과 다중손상 모두 정확히 손상발생 구역을 확인했다.

• Structural Engineering and Mechanics
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• v.3 no.2
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• pp.163-172
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• 1995

A simple numerical method is applied to calculate the large deflection of a cantilever beam under an elastic-plastic deformation by dividing the deformed axis into a number of small segments. Assuming that each segment can be approximated as a circular arc, the method allows large deflections and plastic deformation to be analyzed. The main interests are the load-deflection relationship, curvature distribution along the beam and the length of the plastic region. The method is proved to be easy and particularly versatile. Comparisons with other studies are given.

• Computers and Concrete
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• v.11 no.3
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• pp.237-252
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• 2013

This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN's MSE values are 40 times smaller than the LR's. The test data R value from ANN is 0.9931; thus indicating a high confidence level.

• Smart Structures and Systems
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• v.18 no.3
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• pp.389-403
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• 2016

Structural deflection can be identified from measured strains from long gague sensors, but the sensor layout scheme greatly influences on the accuracy of identified resutls. To determine the optimal sensor layout scheme for accurate deflection identification of the tied arch bridge, the method of optimal layout of long-gauge fiber optic sensors is studied, in which the characteristic curve is first developed by using the bending macro-strain curve under multiple target load conditions, then optimal sensor layout scheme with different number of sensors are determined. A tied arch bridge is studied as an example to verify the effectiveness and robustness of the proposed method for static and dynamic deflection identification.

• Structural Engineering and Mechanics
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• v.59 no.5
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• pp.921-932
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• 2016

This paper presents a theoretical analysis for determining the transverse deflection of simply supported battened beams subjected to a uniformly distributed transverse quasi-static load. The analysis considers not only the shear effect but also the discrete effect of battens on the transverse deflection of the battened beam. The analytical solution is obtained using the principle of minimum potential energy. Numerical validation of the present analytical solution is accomplished using finite element methods. The present analytical solution shows that the shear effect on the transverse deflection of battened beams increases with the cross-section area of the main member but decreases with the cross-section area of the batten. The longer the battened beam is, or the larger the moment of inertia of the main member is, the smaller the shear effect will be.