• Computers and Concrete
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• v.1 no.3
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• pp.325-354
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• 2004

Slabs in buildings and bridge decks, which are restrained against lateral displacements at the edges, have ultimate strengths far in excess of those predicted by analytical methods based on yield line theory. The increase in strength has been attributed to membrane action, which is due to the in-plane forces developed at the supports. The benefits of compressive membrane action are usually not taken into account in currently available design methods developed based on plastic flow theories assuming concrete to be a rigid-plastic material. By extending the existing knowledge of compressive membrane action, it is possible to design slabs in building and bridge structures economically with less than normal reinforcement. Recent research on building and bridge structures reflects the importance of membrane action in design. This paper describes the finite element modelling of membrane action in reinforced concrete slabs through optimisation of a simple concrete model. Through a series of parametric studies using the simple concrete model in the finite element simulation of eight fully clamped concrete slabs with significant membrane action, a set of fixed numerical model parameter values is identified and computational conditions established, which would guarantee reliable strength prediction of arbitrary slabs. The reliability of the identified values to simulate membrane action (for prediction purposes) is further verified by the direct simulation of 42 other slabs, which gave an average value of 0.9698 for the ratio of experimental to predicted strengths and a standard deviation of 0.117. A 'deflection factor' is also established for the slabs, relating the predicted peak deflection to experimental values, which, (for the same level of fixity at the supports), can be used for accurate displacement determination. The proposed optimised concrete model and finite element procedure can be used as a tool to simulate membrane action in slabs in building and bridge structures having variable support and loading conditions including fire. Other practical applications of the developed finite element procedure and design process are also discussed.

• Geomechanics and Engineering
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• v.12 no.3
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• pp.441-464
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• 2017

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.

• Computers and Concrete
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• v.27 no.3
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• pp.225-239
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• 2021

Carbon fiber reinforced polymer (CFRP) has extensive use in strengthening reinforced concrete structures due to its high strength and elastic modulus, low weight, fast and easy application, and excellent durability performance. Many studies have been carried out to determine the performance of the CFRP confined concrete cylinder. Although studies about the prediction of confined compressive strength using ANN are in the literature, the insufficiency of the studies to predict the strain of confined concrete cylinder using ANN, which is the most appropriate analysis method for nonlinear and complex problems, draws attention. Therefore, to predict both strengths and also strain values, two different ANNs were created using an extensive experimental database. The strength and strain networks were evaluated with the statistical parameters of correlation coefficients (R2), root mean square error (RMSE), and mean absolute error (MAE). The estimated values were found to be close to the experimental results. Mathematical equations to predict the strength and strain values were derived using networks prepared for convenience in engineering applications. The sensitivity analysis of mathematical models was performed by considering the inputs with the highest importance factors. Considering the limit values obtained from the sensitivity analysis of the parameters, the performances of the proposed models were evaluated by using the test data determined from the experimental database. Model performances were evaluated comparatively with other analytical models most commonly used in the literature, and it was found that the closest results to experimental data were obtained from the proposed strength and strain models.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.2
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• pp.151-156
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• 2017

Shot peening is widely used to improve the fatigue life and strength of various mechanical parts and an accurate method is important for the prediction of the compressive residual stress caused by this process. A finite element (FE) model with an elliptical multi-shot is suggested for random-angled impacts. Solutions for compressive residual stress using this model and a normal random vertical-impact one with a spherical multi-shot are obtained and compared. The elliptical multi-shot experimental solution is closer to an X-ray diffraction (XRD) than the spherical one. The FE model's peening coverage also almost reaches the experimental one. The effectiveness of the model based on an elliptical shot ball is confirmed by these results and it can be used instead of previous FE models to evaluate the compressive residual stress produced on the surface of metal by shot peening in various industries.

• Computers and Concrete
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• v.28 no.5
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• pp.507-519
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• 2021

In this study, a new procedure was proposed in order to predict the crack pattern and failure mode of steel fiber reinforced concrete (SFRC) corbels. Moreover, an experimental study was carried out in order to investigate the effect of several parameters, such as compressive strength, tensile strength, steel fiber ratio, shear span on the mechanical behavior of SFRC corbels in detail. Totally, 24 RC and SFRC corbels were prepared for the experimental study. Experimental results indicate that each investigated parameter has noticeable effect on the load capacity and failure mode of SFRC corbels. Moreover, finite element (FE) model of the tested corbels were prepared and efficiency of FE model was investigated for further studies. Comparison of FE and experimental results show that there is an acceptable fit between them regarding load capacity and crack patterns. Thereafter, parametric study was carried out via FE analyses in order to obtain a methodology for crack pattern and failure mode prediction of SFRC corbels. As a result of parametric studies, a new procedure was proposed as flowcharts in order to predict the failure mode of SFRC corbels for normal and high strength concrete class separately.

• The Journal of Engineering Geology
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• v.15 no.1
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• pp.67-76
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• 2005

This paper presents the application of a neural network for prediction of the unconfined compressive strength from physical properties and schmidt hardness number on rock samples. To investigate the suitability of this approach, the results of analysis using a neural network are compared to predictions obtained by statistical relations. The data sets containing 55 rock sample records which are composed of sandstone and shale were assembled in Daegu area. They were used to learn the neural network model with the back-propagation teaming algorithm. The rock characteristics as the teaming input of the neural network are: schmidt hardness number, specific gravity, absorption, porosity, p-wave velocity and S-wave velocity, while the corresponding unconfined compressive strength value functions as the teaming output of the neural network. A data set containing 45 test results was used to train the networks with the back-propagation teaming algorithm. Another data set of 10 test results was used to validate the generalization and prediction capabilities of the neural network.

• Journal of the Korea Institute of Building Construction
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• v.12 no.2
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• pp.194-202
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• 2012

This paper predicts the cementing efficiency of fly-ash(FA) based on mortar test considering binder-water ratio and FA replacement ratio as experimental variables. The cementing efficiency prediction model proposed by statistical analysis enables us to estimate the value according to the binder-water ratio and FA replacement ratio of matrix. When FA replacement ratio is the same, the lower the binder-water ratio, the higher the estimated cementing efficiency. There are significant differences in the values according to binder-water ratio at FA replacement ratios of 15% or less, but there are almost no differences when FA replacement ratio is more than 15%. As the binder-water ratio increases, the variations in the values according to FA replacement ratio are great at FA replacement ratios of 15% or less. As the FA replacement ratios increase, the values increase for FA replacement ratios of 15% or less, but decrease for more than 15%. The values range from -0.71 to 1.24 at binder-water ratio of 1.67-2.86 and FA replacement ratio of 0-70%. The RMSE of the 28-day compressive strength predicted by modified water-cement ratio is 2.2 MPa. The values can be trusted, as there is good agreement between predicted strength and experimental strength.

• Geomechanics and Engineering
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• v.23 no.4
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• pp.339-349
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• 2020

This study investigates the feasibility of using the results of the UPV (ultrasonic pulse velocity) test to assess the UCS (unconfined compressive strength) of unsaturated soil. A series of laboratory tests was conducted on samples of unsaturated lateritic soils of northern Taiwan. Specifically, the unconfined compressive test was combined with the pressure plate test to obtain the unconfined compressive strength and its matric suction (s) of the samples. Soil samples were first compacted at the designated water content and subsequently subjected to the wetting process for saturation and the following drying process to its target suction using the apparatus developed by the authors. The correlations among the UCS, s and UPV were studied. The test results show that both the UCS and UPV significantly increased with the matric suction regardless of the initial compaction condition, but neither the UCS nor UPV obviously varied when the matric suction was less than the air-entry value. In addition, the UCS approximately linearly increased with increasing UPV. According to the investigation of the test results, simplified methods to estimate the UCS using the UPV or matric suction were established. Furthermore, an empirical formula of the matric suction calculated from the UPV was proposed. From the comparison between the predicted values and the test results, the MAPE values of UCS were 4.52-9.98% and were less than 10%, and the MAPE value of matric suction was 17.3% and in the range of 10-20%. Thus, the established formulas have good forecasting accuracy and may be applied to the stability analysis of the unsaturated soil slope. However, further study is warranted for validation.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.565-566
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• 2009

Supplementary cementing materials (SCM), such as silica fume, slag, and low-calcium fly ash, have been widely used as mineral admixtures in high strength and high performance concrete. Due to the chemical and physical effect of SCM on hydration, compared with Portland cement, hydration process of cement incorporating SCM is much more complex. This paper presents a numerical hydration model which is based on multi-component concept and can simulate hydration of cement incorporating SCM. The proposed model starts with mixture proportion of concrete and considers both chemical and physical effect of SCM on hydration. Using this proposed model, this paper predicts the following properties of hydrating cement-SCM blends as a function of hydration time: reaction ratio of SCM, calcium hydroxide content, heat evolution, porosity, chemically bound water and the development of the compressive strength of concrete. The prediction results agree well with experiment results.

• Advances in Computational Design
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• v.5 no.2
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• pp.195-207
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• 2020

The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.