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

• Computers and Concrete
• /
• v.5 no.1
• /
• pp.21-36
• /
• 2008

Steel fiber reinforced concrete is increasingly used day by day in various structural applications. An extensive experimentation was carried out with w/cm ratio ranging from 0.25 to 0.40, and fiber content ranging from zero to1.5 percent by volume with an aspect ratio of 80 and silica fume replacement at 5%, 10% and 15%. The influence of steel fiber content in terms of fiber reinforcing index on the compressive strength of high-performance fiber reinforced concrete (HPFRC) with strength ranging from 45 85 MPa is presented. Based on the test results, equations are proposed using statistical methods to predict 28-day strength of HPFRC effecting the fiber addition in terms of fiber reinforcing index. A strength model proposed by modifying the mix design procedure, can utilize the optimum water content and efficiency factor of pozzolan. To examine the validity of the proposed strength model, the experimental results were compared with the values predicted by the model and the absolute variation obtained was within 5 percent.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1999.04a
• /
• pp.325-330
• /
• 1999

The objectives of this study were to compare the development of compressive strength of epoxy resin mortars used as repairing materials with respect to maturity, and to propose a predictive model for strength development of epoxy resin mortar. A series of tests were carried out for the hardener contents of 30, 40 and 50 percentage of resin and compressive strength were measured at the of 6, 12, 24, 72, 120 and 168 hours respectively under temperature of 0, 10, 20 and 3$0^{\circ}C$. The datum temperature was estimated by measured strength, and the maturity is calculated with the estimated datum temperature. The compressive strength of epoxy resin mortar could be predicted by regression analysis from the maturity-compressive strength relationship.

• Proceedings of the Korean Society For Composite Materials Conference
• /
• 2001.10a
• /
• pp.129-132
• /
• 2001

Fiber waviness is one of manufacturing defects encountered frequently in thick composite structures. It affects significantly on the behavior as well as strength of thick composites. The effects of fiber waviness on tensile/compressive nonlinear elastic behavior and strength of thick composite with fiber waviness are studied theoretically and experimentally. FEA(Finite Element Analysis) models are proposed to predict tensile/compressive nonlinear behavior and strength of thick composites. In the FEA models, both material and geometric nonlinearities were incorporated into the model using energy density, iterative mapping and incremental method. Also Tsai-Wu criteria was adopted to predict the strength of thick composites with fiber waviness. Tensile and compressive tests were conducted on the specimens with uniform fiber waviness. It was observed that the degree of fiber waviness in composites significantly affected the nonlinear behavior and strength of the composites

• Computers and Concrete
• /
• v.20 no.6
• /
• pp.645-654
• /
• 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
• /
• v.9 no.2
• /
• pp.153-169
• /
• 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
• /
• v.32 no.3
• /
• pp.263-286
• /
• 2023

Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.

• Advances in concrete construction
• /
• v.13 no.1
• /
• pp.11-23
• /
• 2022

Conventional concrete needs some improvement in the mechanical properties, which can be obtained by different admixtures. However, making concrete samples costume always time and money. In this paper, different types of hybrid algorithms are applied to develop predictive models for forecasting compressive strength (CS) of concretes containing metakaolin (MK) and fly ash (FA). In this regard, three different algorithms have been used, namely multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVR), to predict CS of concretes by considering most influencers input variables. These algorithms integrated with the grey wolf optimization (GWO) algorithm to increase the model's accuracy in predicting (GWMLP, GWRBF, and GWSVR). The proposed MLP models were implemented and evaluated in three different layers, wherein each layer, GWO, fitted the best neuron number of the hidden layer. Correspondingly, the key parameters of the SVR model are identified using the GWO method. Also, the optimization algorithm determines the hidden neurons' number and the spread value to set the RBF structure. The results show that the developed models all provide accurate predictions of the CS of concrete incorporating MK and FA with R² larger than 0.9972 and 0.9976 in the learning and testing stage, respectively. Regarding GWMLP models, the GWMLP1 model outperforms other GWMLP networks. All in all, GWSVR has the worst performance with the lowest indices, while the highest score belongs to GWRBF.

• Tunnel and Underground Space
• /
• v.31 no.6
• /
• pp.440-459
• /
• 2021

Roadheaders have begun to be adopted in Korean tunneling sites. The performance prediction models proposed by the manufacturer are used by Korean construction companies. The models use UCS (uniaxial compressive strength) value to predict the net cutting rate, but the rock specimens conducted for the uniaxial compression test have 1.0 of the diameter to length ratio. It has been reported that the specimen shape generally influences the rock strength. The previous references studying the shape effect were cited, and the UCS data of Korean rocks are also updated to analyze the shape effect on UCS. The cause of effect was discussed by previous theory. The change amount of UCS values of Korean rocks was estimated by the data, and the modified prediction model for NCR was finally suggested.

• Journal of the Korean Geosynthetics Society
• /
• v.10 no.4
• /
• pp.61-70
• /
• 2011

This paper describes prediction results of penetration rate using case history in order to compare empirical models for penetration rate prediction of TBM. The reasonable empirical model is evaluated by comparison with prediction results and measured result. The penetration rate prediction is applied in separate empirical models considering rock characteristics and mechanical characteristics of TBM. The rock of applied filed had almost gneiss and its unconfined compressive strength was irregular due to the exist of weak zones and joint. In prediction results using unconfined compressive strength, Graham's model (1976) had impractical result when it had lower strength. NTNU model (1998) of the separate empirical models used in average penetration rate had the highest accuracy by comparison with the others, because it is a reasonable model which has rock characteristics and mechanical characteristics of TBM. However, Tarkoy's model (1986) based on unconfined compressive strength correspond with the measured values in field. Therefore, it should be considered a rock type, geological characteristic and mechanical characteristic of TBM at prediction of penetration rate.