• Computers and Concrete
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• v.21 no.1
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.

• Computers and Concrete
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• v.8 no.5
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• pp.597-616
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• 2011

With respect to rehabilitation, strengthening and retrofitting of existing and deteriorated columns in buildings and bridges, CFRP sheets have been found effective in enhancing the performance of existing RC columns by wrapping and bonding CFRP sheets externally around the concrete. Concrete columns and piers that are confined by both lateral steel reinforcement and CFRP are sometimes referred to as "hybrid" concrete columns. With the availability of experimental data on concrete columns confined by steel reinforcement and/or CFRP, the study presents modeling using artificial neural networks (ANNs) to predict the compressive strength of hybrid circular RC columns. The prediction of the ultimate confined compressive strength of RC columns is very important especially when this value is used in estimating the capacity of structures. The present ANN model used as parameters for the confining materials the lateral steel ratio (${\rho}_s$) and the FRP volumetric ratio (${\rho}_{FRP}$). The model gave good predictions for three types of confined columns: (a) columns confined with steel reinforcement only, (b) CFRP confined columns, and (c) hybrid columns confined by both steel and CFRP. The model may be used for predicting the compressive strength of existing circular RC columns confined with steel only that will be strengthened or retrofitted using CFRP.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.04a
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• pp.129-134
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• 2003

The importance of understanding the response of structural composites to impact and CAI cannot be overstated to develop analytical models for impact damage and CAI strength predictions. This paper presents experimental findings observed from quasi-static lateral load tests, low velocity impact tests, CAI strength and open hole compressive strength tests using 3mm thick composite plates ($[45/-45/0/90]_{3s}$ - IM7/8552). The conclusion is drawn that damage areas for both quasi-static lateral load and impact tests are similar and the curves of several drop weight impacts with varying energy levels (between 5.4 J and 18.7 J) fallow the static curve well. In addition, at a given energy the peak force is in good agreement between the static and impact cases. From the CAI strength and open hole compressive strength tests, it is identified that the failure behaviour of the specimens was very similar to that observed in laminated plates with open holes under compression loading. The residual strengths are in good agreement with the measured open hole compressive strengths, considering the impact damage site as an equivalent hole. The experimental findings suggest that simple analytical models for the prediction of impact damage area and CAI strength can be developed on the basis of the failure mechanism observed from the experimental tests.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.149-150
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• 2023

This study proposes the most suitable strength prediction model equation for UHPC by calculating the apparent activation energy of UHPC according to the curing temperature and deriving the integrated temperature and compressive strength prediction equation. The results are summarized as follows. The apparent activation energy was calculated using the Arrhenius function, which was calculated as 21.09 KJ/mol. A model equation suitable for UHPC was calculated, and when the Flowman model equation was used, it was confirmed that it was suitable for the properties of UHPC using a condensation promoting super plasticizing agent.

• Computers and Concrete
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• v.20 no.6
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• pp.645-654
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• 2017

Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete.

• Computers and Concrete
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• v.4 no.4
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• pp.299-316
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• 2007

The compressive strength of concrete is a commonly used criterion in producing concrete. However, the test on the compressive strength is complicated and time-consuming. More importantly, since the test is usually performed 28 days after the placement of the concrete at the construction site, it is too late to make improvements if unsatisfactory test results are incurred. Therefore, an accurate and practical strength estimation method that can be used before the placement of concrete is highly desirable. In this study, the estimation of the concrete strength is performed using support vector regression (SVR) based on the mix proportion data from two ready-mixed concrete companies. The estimation performance of the SVR is then compared with that of neural network (NN). The SVR method has been found to be very efficient in estimation accuracy as well as computation time, and very practical in terms of training rather than the explicit regression analyses and the NN techniques.

• Computational Structural Engineering
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• v.23 no.3
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• pp.61-68
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• 2010

• Computers and Concrete
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• v.9 no.2
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• pp.153-169
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• 2012

The contribution of steel fibers on the 28-day compressive strength of high-performance steel fiber reinforced concrete was investigated, is presented. An extensive experimentation was carried out over water-cementitious materials (w/cm) ratios ranging from 0.25 to 0.40, with silica fume-cementitious materials ratios from 0.05 to 0.15, and fiber volume fractions ($V_f$= 0.0, 0.5, 1.0 and 1.5%) with the aspect ratios of 80 and 53. Based on the test results of 44 concrete mixes, mathematical model was developed using statistical methods to quantify the effect of fiber content on compressive strength of HPSFRC in terms of fiber reinforcing index. The expression, being developed with strength ratios and not with absolute values of strengths, is independent of specimen parameters and is applicable to wide range of w/cm ratios, and used in the mix design of steel fiber reinforced concrete. The estimated strengths are within ${\pm}3.2%$ of the actual values. The model was tested for the strength results of 14 mixes having fiber aspect ratio of 53. On examining the validity of the proposed model, there exists a good correlation between the predicted values and the experimental values of different researchers. Equation is also proposed for the size effect of the concrete specimens.

• Computers and Concrete
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• v.4 no.6
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• pp.477-497
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• 2007

The ultrasonic pulse velocity method has been widely used to evaluate the quality of concrete and assess the structural integrity of concrete structures. But its use for predicting strength is still limited since there are many variables affecting the relationship between strength and pulse velocity of concrete. This study is focused on establishing a complicated correlation between known input data, such as pulse velocity and mixture proportions of concrete, and a certain output (compressive strength of concrete) using artificial neural networks (ANN). In addition, the results predicted by the developed multilayer perceptrons (MLP) networks are compared with those by conventional regression analysis. The result shows that the correlation between pulse velocity and compressive strength of concrete at various ages can be well established by using ANN and the accuracy of the estimates depends on the quality of the information used to train the network. Moreover, compared with the conventional approach, the proposed method gives a better prediction, both in terms of coefficients of determination and root-mean-square error.

• Journal of the Korea Concrete Institute
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• v.14 no.4
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• pp.457-466
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• 2002

An artificial neural network was applied to predict compressive strength, slump value and mix proportion of a concrete. Standard mixed tables were trained and estimated, and the results were compared with those of the experiments. To consider variabilities of material properties, the standard mixed fables from two companies of Ready Mixed Concrete were used. And they were trained with the neural network. In this paper, standard back propagation network was used. The mix proportion factors such as water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate and air entraining admixture were used. For the arrangement on the approval of prediction of mix proportion factor, the standard compressive strength of $180kgf/cm^2{\sim}300kgf/cm^2$, and target slump value of 8 cm, 15 cm were used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of $210kgf/cm^2{\sim}240kgf/cm^2$, and target slump value of 12 cm and 15 cm wore used because these ranges are most frequently used. In results, in the prediction of mix proportion factor, for all of the water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate, air entraining admixture, the predicted values and the values of standard mixed tables were almost the same within the target error of 0.10 and 0.05, regardless of two companies. And in the prediction of compressive strength and slump value, the predicted values were converged well to the values of standard mixed fables within the target error of 0.10, 0.05, 0.001. Finally artificial neural network is successfully applied to the prediction of concrete mixture and compressive strength.