• Steel and Composite Structures
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• v.42 no.4
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• pp.459-475
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• 2022

In this paper a model for the prediction of the ultimate axial compressive capacity of square and rectangular Concrete Filled Steel Tubes, based on an Artificial Neural Network modeling procedure is presented. The model is trained and tested using an experimental database, compiled for this reason from the literature that amounts to 1193 specimens, including long, thin-walled and high-strength ones. The proposed model was selected as the optimum from a plethora of alternatives, employing different activation functions in the context of Artificial Neural Network technique. The performance of the developed model was compared against existing methodologies from design codes and from proposals in the literature, employing several performance indices. It was found that the proposed model achieves remarkably improved predictions of the ultimate axial load.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.135-136
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• 2022

As a recent technology, it is possible to partially grasp the occurrence of displacement of the entire building through artificial intelligence technology for big data through scanning. However, scanning and data processing take a lot of time, so there is a limit to constant monitoring, so constant monitoring technology of building behavior that combines wireless remote sensors and 3D shape scanning is required. Therefore, in this study, artificial intelligence program coding technology is linked. In addition, a technology capable of real-time wireless remote measurement of structure displacement will be developed through technology development in response to safety management that combines existing building technologies such as sensors. Through this, it is possible to establish an integrated management system for safety inspection and diagnosis.

• Steel and Composite Structures
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• v.46 no.3
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• pp.435-449
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• 2023

Time domain method and frequency domain method are commonly used in the current fatigue life calculation theory. The time domain method has complicated procedures and needs a large amount of calculation, while the frequency domain method has poor applicability to different materials and different spectrum, and improper selection of spectrum model will lead to large errors. Considering that artificial neural network has strong ability of nonlinear mapping and generalization, this paper applied this technique to random fatigue life prediction, and the effect of average stress was taken into account, thereby achieving more accurate prediction result of random fatigue life.

• Earthquakes and Structures
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• v.24 no.2
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• pp.111-126
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• 2023

Highly reliable and versatile methods artificial intelligence (AI) have found multiple application in the different fields of science, engineering and health care system. In the present study, we aim to utilize AI method to investigated vibrations in the human leg bone. In this regard, the bone geometry is simplified as a thick cylindrical shell structure. The deep neural network (DNN) is selected for prediction of natural frequency and critical buckling load of the bone cylindrical model. Training of the network is conducted with results of the numerical solution of the governing equations of the bone structure. A suitable optimization algorithm is selected for minimizing the loss function of the DNN. Generalized differential quadrature method (GDQM), and Hamilton's principle are used for solving and obtaining the governing equations of the system. As well as this, in the results section, with the aid of AI some predictions for improving the behaviors of the various sport systems will be given in detail.

• Structural Engineering and Mechanics
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• v.41 no.4
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• pp.527-538
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• 2012

In seismic analyses of structures, additional eccentricity is introduced to take account for oscillations of random and unknown origins. In many codes of practice, the torsion about the vertical axis is considered through empirical accidental eccentricity formulation. Due to the random nature of structural systems, it is very difficult to evaluate the accidental eccentricity in a deterministic way and to specify its effect on the overall seismic response of structures. The aim of this study is to develop a procedure for the evaluation of the accidental eccentricity induced by uncertainties in stiffness and mass of structural members, using the neural network techniques coupled with Monte Carlo simulations. This method gives very interesting results for single story structures. For real structures, this method can be used as a tool to determine the accidental eccentricity in the seismic vulnerability studies of buildings.

• Structural Engineering and Mechanics
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• v.58 no.3
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• pp.459-473
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• 2016

Various vague and unstructured problems encountered the civil engineering/designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.769-777
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• 2019

Low crested coastal structures such as detached breakwaters and submerged breakwaters (artificial reefs) have been commonly used as coastal protection measures. The armour units of these structures are unstable than those in non-overtopped structure cases. The stability of low crested structures armoured by rock has been suggested in existing studies. In this study, the stability of Tetrapods armour units on theses structures has been investigated using two-dimensional hydraulic model tests. The effect of wave steepness and freeboard on the armour stability on crest, front, and the rear slope has been investigated. Armour units were mostly damaged near the upper part of the seaward slope and the crest of the seaward side. From the experimental data, the new empirical formula for the stability coefficients of the Tetrapods was proposed.

• Journal of radiological science and technology
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• v.15 no.1
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• pp.99-105
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• 1992

Visual evaluation of rib shadow and lung marking during high voltage chest radiography. The Purpose of this study is to improvement of visual discrimination of pulmonary structures on the conventional chest radiogram. The author prepared an artificial lung using an acryl plate, 8 cm in thickness, which is nearly equivalent to human lung, and 0.6 cm thickness of an aluminum plate for an artificial rib, and 0.5 cm of an acryl plate as a pulmonary vessel as well. And they were used as objects for experimental radiograms. This study performed with gradual increasing densities of film bases in the sequences of densities of 0.6, 0.9, 1.1 and 1.3. We made two combinations of images after multiple and regular cuts, with width of 1 cm, of 4 radiograms at the above mentioned densities of film bases. One image consisted of alternative combination of radiograms taken at densities of 0.6 and 1.3, and the other did at 0.9 and 1.1. The latter image provided better visual perception of pulmonary structures than the former. Experimental radiograms were also taken with 60 kV and 120 kV respectively. After careful evaluation and comparison to images taken on varieties of different densities with combinations and kV, the author had a conclusion that it is advisable to use a high kV X-ray which makes rib shadow subtle, for better visual delineation of pulmonary structures behind ribcage, eventhough contrast of pulmonary structures are decreased at high kV radiogram.

• Steel and Composite Structures
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• v.17 no.3
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• pp.215-236
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• 2014

The flexural behaviour of steel beams significantly affects the structural performance of the steel frame structures. In particular, the flexural overstrength (namely the ratio between the maximum bending moment and the plastic bending strength) that steel beams may experience is the key parameter affecting the seismic design of non-dissipative members in moment resisting frames. The aim of this study is to present a new formulation of flexural overstrength factor for steel beams by means of artificial neural network (NN). To achieve this purpose, a total of 141 experimental data samples from available literature have been collected in order to cover different cross-sectional typologies, namely I-H sections, rectangular and square hollow sections (RHS-SHS). Thus, two different data sets for I-H and RHS-SHS steel beams were formed. Nine critical prediction parameters were selected for the former while eight parameters were considered for the latter. These input variables used for the development of the prediction models are representative of the geometric properties of the sections, the mechanical properties of the material and the shear length of the steel beams. The prediction performance of the proposed NN model was also compared with the results obtained using an existing formulation derived from the gene expression modeling. The analysis of the results indicated that the proposed formulation provided a more reliable and accurate prediction capability of beam overstrength.

• Wind and Structures
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• v.32 no.2
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• pp.127-142
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• 2021

Wind load acting on a standalone structure is different from that acting on a similar structure which is surrounded by other structures in close proximity. The presence of other structures in the surrounding can change the wind flow regime around the principal structure and thus causing variation in wind loads compared to a standalone case. This variation on wind loads termed as interference effect depends on several factors like terrain category, geometry of the structure, orientation, wind incident angle, interfering distances etc., In the present study, a three building configuration is considered and the mean pressure coefficients on each face of principle building are determined in presence of two interfering buildings. Generally, wind loads on interfering buildings are determined from wind tunnel experiments. Computational fluid dynamic studies are being increasingly used to determine the wind loads recently. Whereas, wind tunnel tests are very expensive, the CFD simulation requires high computational cost and time. In this scenario, Artificial Neural Network (ANN) technique and Support Vector Regression (SVR) can be explored as alternative tools to study wind loads on structures. The present study uses these data-driven approaches to predict mean pressure coefficients on each face of principle building. Three typical arrangements of three building configuration viz. L shape, V shape and mirror of L shape arrangement are considered with varying interfering distances and wind incidence angles. Mean pressure coefficients (Cp mean) are predicted for 45 degrees wind incidence angle through ANN and SVR. Further, the critical faces of principal building, critical interfering distances and building arrangement which are more prone to wind loads are identified through this study. Among three types of building arrangements considered, a maximum of 3.9 times reduction in Cp mean values are noticed under Case B (V shape) building arrangement with 2.5B interfering distance. Effect of interfering distance and building arrangement on suction pressure on building faces has also been studied. Accordingly, Case C (mirror of L shape) building arrangement at a wind angle of 45º shows less suction pressure. Through this study, it was also observed that the increase of interfering distance may increase the suction pressure for all the cases of building configurations considered.