• 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.

• Structural Engineering and Mechanics
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• v.67 no.2
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• pp.155-163
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• 2018

The Early-age construction loading and changing properties of concrete, especially in the multi-story structures can affect the slab deflection, significantly. Based on previously conducted experiment on eight simply-supported one-way slabs this paper investigates the effect of concrete type, fiber type and content, loading value, cracking moment, ultimate moment and applied moment on the instantaneous deflection of Self-Compacting Concrete (SCC) slabs. Two distinct loading levels equal to 30% and 40% of the ultimate capacity of the slab section were applied on the slabs at the age of 14 days. A wide range of the existing models of the effective moment of inertia which are mainly developed for conventional concrete elements, were investigated. Comparison of the experimental deflection values with predictions of the existing models shows considerable differences between the recorded and estimated instantaneous deflection of SCC slabs. Calculated elastic deflection of slabs at the ages of 14 and 28 days were also compared with the experimental deflection of slabs. Based on sensitivity analysis of the effective parameters, a new model is proposed and verified to predict the effective moment of inertia in SCC slabs with and without fiber reinforcing under two different loading levels at the age of 14 days.

• Journal of Biosystems Engineering
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• v.18 no.3
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• pp.262-269
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• 1993

Stress relaxation behaviors of the cucumber under bending moment were tested with UTM at three levels of loading rate and initial deflection ratio. Sample cucumber was selected from three cultivars of cucumber, Cheongjangmadi, Baekdadagi, and Gyeousalicheongjang, because these cultivars are the most popular grown cultivars in Korea. When the bending moment was applied to the cucumber sample, the effective span between simple supports was held a constant value of 116mm with consideration of the selected sample length. The objectives of this study were to develop the rheological models such as linear and nonlinear models of the stress relaxation for the cucumber samples, and to investigate the effects of loading rate and initial deflection ratio on the stress relaxation behavior of the cucumber. The results of this study may be summarized as follows : 1. Stress relaxation behavior of the cucumber could be well described by the generalized Maxwell model for each level of deflection ratio. But the stress relaxation behavior of the sample was found to be initial deflection ratio and time dependent, and it was represented the nonlinear viscoelastic model as a function of initial deflection ratio and time. 2. Stress relaxation behavior of the cucumber samples was very highly affected by the loading rate and the initial deflection ratio. The more loading rate and initial deflection ratio resulted in the more initial bending stress and after stress relaxation progressed more rapidly. 3. At the same test conditions, it was found that the stress relaxation rate of Cheongjangmadi was faster than that of other cultivars.

• Geomechanics and Engineering
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• v.38 no.4
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• pp.409-420
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• 2024

In this research the hygro-thermo-mechanical loading and micromechanical model effects on bending behavior of functionally graded material plates resting on Winkler and Pasternak elastic foundations, the higher order shear deformation theory is used here. The material properties of the plate: young's modulus, thermal coefficient and moisture expansion coefficient are assumed to be graded in the thickness direction according to various micromechanical models starting with the Voigt's model which is commonly used in most functionally graded plates studies to the Reuss's, LRVE's and Mori-Tanaka's models. The principle of virtual displacement is used to determine the equilibrium equations and the a several numerical results are given to validate the precision of the present method for bending behavior of FGM plates subjected to hygro-thermo-mechanical loading resting on elastic foundations. Afterwards, a parametric study is conducted to determine the effect of different parameters on the deflection of the FGM plates like micromechanical models, type of loading and plate geometry. In the lights of the present research, it can be concluded that the present theory is accurate and simple in predicting the deflection behavior of functionally graded plates under hygro-thermo-mechanical effects and micromechanical models.

• Smart Structures and Systems
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• v.26 no.5
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• pp.591-603
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• 2020

Dynamic deflection monitoring is an essential and critical part of structural health monitoring for high-speed railway bridges. Two critical problems need to be addressed when using inclinometer sensors for such applications. These include constructing a general representation model of inclination-deflection and addressing the ill-posed inverse problem to obtain the accurate dynamic deflection. This paper provides a dynamic deflection monitoring method with the placement of optimal inclinometer sensors for high-speed railway bridges. The deflection shapes are reconstructed using the inclination-deflection transformation model based on the differential relationship between the inclination and displacement mode shape matrix. The proposed optimal sensor configuration can be used to select inclination-deflection transformation models that meet the required accuracy and stability from all possible sensor locations. In this study, the condition number and information entropy are employed to measure the ill-condition of the selected mode shape matrix and evaluate the prediction performance of different sensor configurations. The particle swarm optimization algorithm, genetic algorithm, and artificial fish swarm algorithm are used to optimize the sensor position placement. Numerical simulation and experimental validation results of a 5-span high-speed railway bridge show that the reconstructed deflection shapes agree well with those of the real bridge.

• International Journal of Highway Engineering
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• v.9 no.1 s.31
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• pp.39-46
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• 2007

Different backcalculation results for the same material properties are caused by different structural geometry. In this paper, based on real simulation results for typical pavement systems using 3-dimensional FE models, modified AREA graphs are proposed to graphically backcalculate modulus of elasticity of slab and subgrade based on center deflection and AREA. In modified graph for single infinity slab models, deflection and AREA are increased in deeper depth to bedrock. But, effects of depth to bedrock more than 4.0 meters on backcalculation results are negligible. And, center deflection and AREA generated from multifinite slab models are larger than those of single infinity slab models with same depth to bedrock.

• Smart Structures and Systems
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• v.33 no.5
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• pp.333-347
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• 2024

There has been an increasing interest in the construction of smart buildings that can actively monitor and react to their surroundings. The capacity of these intelligent structures to precisely predict and respond to deflection is a crucial feature that guarantees both their structural soundness and efficiency. Conventional techniques for determining deflection often depend on intricate mathematical models and computational simulations, which may be time- and resource-consuming. Artificial intelligence (AI) algorithms have become a potent tool for anticipating and controlling deflection in intelligent structures in response to these difficulties. The term "deflection-aware smart structures" in this sense refers to constructions that have AI algorithms installed that continually monitor and analyses deflection data in order to proactively detect any problems and take appropriate action. These structures anticipate deflection across a range of operating circumstances and environmental factors by using cutting-edge AI approaches including deep learning, reinforcement learning, and neural networks. AI systems are able to predict real-time deflection with high accuracy by using data from embedded sensors and actuators. This capability enables the systems to identify intricate patterns and linkages. Intelligent buildings have the potential to self-correct in order to reduce deflection and maximize performance. In conclusion, the development of deflection-aware smart structures is a major stride forward for structural engineering and has enormous potential to enhance the performance, safety, and dependability of designed systems in a variety of industries.

• Journal of the Korea Concrete Institute
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• v.23 no.4
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• pp.413-420
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• 2011

The test results of high-strength concrete beam specimens, which have various combinations of different types of flexural reinforcement and short fibers, were compared with the prediction results of codes, guidelines and models proposed by researchers. The theoretical calculation based on the ultimate strength method of the KCI and ACI Code underestimated the ultimate moments of FRP bar-reinforced beams without fibers. The models proposed by ACI 544.4R and Campione predicted the ultimate moment capacities inaccurately for the FRP bar-reinforced beam with steel fibers, because these models do not consider the increased ultimate compressive strain of fiber reinforced concrete. Bischoff's deflection model predicted the service load deflections reasonably well, while the deflection model of ACI Committee 440 underestimated the deflection of FRP bar-reinforced beams. Because the ACI 440 expression, used to predict member deflection, cannot directly apply to the beams reinforced with different types of reinforcing bars, an alternative method to estimate the deflections of beams with different types of reinforcing bars using the ACI 440 expression was proposed. In addition, Bischoff's approach for computing deflection was extended to include deflection after yielding of the steel reinforcement in the beams reinforced with steel and FRP bars simultaneously.

• Structural Engineering and Mechanics
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• v.59 no.4
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• pp.775-793
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• 2016

This paper presents a numerical method for estimating the curvature, deflection and moment capacity of FRP-reinforced concrete encased steel composite beams (FRP-RCS). A sectional analysis is first carried out to predict the moment-curvature relationship from which beam deflection and moment capacity are then calculated. Comparisons between theoretical and experimental results of tests conducted elsewhere show that the proposed numerical technique can accurately predict moment capacity and deflection of FRP-RCS composite beam. The numerical results also indicated that beam ductility and stiffness are improved when encased steel is added to FRP reinforced concrete beams. ACI, ISIS and Bischoff models for deflection prediction compared well at low load, however, significantly underestimated the experimental results for high load levels.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.2
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• pp.47-53
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• 2014

The paper investigates the limit state of deflection for short and medium span steel girder bridges. Deflection depends on stiffness of steel girders and integrity of the reinforced concrete slab (composite action). Load and resistance parameters are treated as random variables. A probabilistic model is developed for prediction of the deflection. The structural performance can be affected by deterioration of components, in particular corrosion of steel girders. In addition, the creep of concrete can greatly influence the deflection of composite structures. Therefore, the statistical models for creep and corrosion of structural steel are incorporated in the model. Structures designed according to the AASHTO LRFD Code are considered. Load and resistance models are developed to account for time-variability of the parameters. Monte Carlo simulations are used to estimate the deflections and probabilities of serviceability failure. Different span lengths and girder spacing are considered for structures designed as moment-controlled and deflection-controlled. A summary of obtained results is presented.