• Structural Engineering and Mechanics
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• v.16 no.1
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• pp.83-99
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• 2003

The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).

• Computers and Concrete
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• v.19 no.6
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• pp.651-658
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• 2017

High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.10a
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• pp.647-652
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• 2002

In the previous study, authors presented the I-ProConS (Intelligent PREdiction system of CONcrete Strength) using artificial neural networks (ANN) that provides in-place strength information of the concrete to facilitate concrete form removal and scheduling for construction. The serious problem of the system has occured, which it cannot appropriately predict the concrete strength when the curing temperature of a curing day is changed. This is because it uses the single neural networks, which all nodes are fully connected, and thus it cannot smoothly respond for external impact. However this paper presents that the problem can be solved by multiple neural networks, which is composed of five ANNs.

• Computers and Concrete
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• v.6 no.2
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• pp.155-165
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• 2009

Splitting tensile strength (STS) of high-performance concrete (HPC) is one of the important mechanical properties for structural design. This property is related to compressive strength (CS), water/binder (W/B) ratio and concrete age. This paper presents a clustering-based fuzzy model for the prediction of STS based on the CS and (W/B) at a fixed age (28 days). The data driven fuzzy model consists of three main steps: fuzzy clustering, inference system, and prediction. The system can be analyzed directly by the model from measured data. The performance evaluations showed that the fuzzy model is more accurate than the other prediction models concerned.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.25-26
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• 2020

In this study, by applying the concrete compressive strength estimation system Concrete IoT Management System (hereinafter referred to as CIMS) to the concrete slab concrete in the domestic field, the purpose of this study is to confirm the practical use of CIMS and to verify the accuracy of estimating the initial strength of concrete. As a result, it shows a high correlation when the compressive strength and CIMS estimated strength of the specimen for structural management are converted and compared with the integrated temperature. However, in order to determine a more accurate experimental constant, it is necessary to consider the results up to 28 days.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2008.04a
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• pp.399-404
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• 2008

The objective of this study is to develop the predicting method of concrete strength when remove concrete form-work without making cement test piece at construction site. For this purpose, this study catches the Maturity Method by using RFID, the usability of which is now being emphasized at site, accumulates and record the strength data, which can be gained with the results of existing Maturity Method method that is accompanied with strength estimation study, in database, and finally proposes the system structure which can check the estimated strength by Maturity Method. The merits of this method by using of Maturity Method are as follows; More objective, precise, and rapid decision can be made to the concrete strength and about the maintaining period of concrete form and form support. More efficient control of integrated material management system can be possible. Architectural field example using RFID can be suggested more concretely. RFID applicability can be extended by using DB of material integration management system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.23-24
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• 2020

In this study, the concrete compressive strength estimation system Concrete IoT Management System (hereinafter referred to as CIMS) was developed, and CIMS was applied to domestic field structure slabs and wall concrete to check whether CIMS is practically available and to estimate the accuracy of the initial strength estimation of concrete. As a result, it shows a very high correlation when the compressive strength of the specimen for structural management is compared with the estimated strength of CIMS in terms of integrated temperature, and it is expected to be gradually applied to domestic construction sites in the future.

• Journal of Industrial Technology
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• v.41 no.1
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• pp.1-6
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• 2021

Fly ash and silica fume belong to industry by-products that can be used to produce concrete. This study shows the model of a neural network to evaluate the strength development of blended concrete containing fly ash and silica fume. The neural network model has four input parameters, such as fly ash replacement content, silica fume replacement content, water/binder ratio, and ages. Strength is the output variable of neural network. Based on the backpropagation algorithm, the values of elements in the hidden layer of neural network are determined. The number of neurons in the hidden layer is confirmed based on trial calculations. We find (1) neural network can give a reasonable evaluation of the strength development of composite concrete. Neural network can reflect the improvement of strength due to silica fume additions and can consider the reductions of strength as water/binder increases. (2) When the number of neurons in the hidden layer is five, the prediction results show more accuracy than four neurons in the hidden layer. Moreover, five neurons in the hidden layer can reproduce the strength crossover between fly ash concrete and plain concrete. Summarily, the neural network-based model is valuable for design sustainable composite concrete containing silica fume and fly ash.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.05a
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• pp.532-535
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• 2004

This paper presents a nonlinear finite element analysis procedure for the prediction of shear strength of reinforced concrete deep beams. A computer program, named RCAHESTC(Reinforced Concrete Analysis in Higher Evaluation System Technology), for the analysis of reinforced concrete structures was used. Material nonlinearity is taken into account by comprising tensile. compressive and shear models of cracked concrete and a model of reinforcing steel. The smeared crack approach is incorporated. The proposed numerical method for the prediction of shear strength of reinforced concrete deep beams is verified by comparison with the reliable experimental results.

• Smart Structures and Systems
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• v.14 no.5
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• pp.785-809
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• 2014

In this paper, an Adaptive nerou-based inference system (ANFIS) is being used for the prediction of shear strength of high strength concrete (HSC) beams without stirrups. The input parameters comprise of tensile reinforcement ratio, concrete compressive strength and shear span to depth ratio. Additionally, 122 experimental datasets were extracted from the literature review on the HSC beams with some comparable cross sectional dimensions and loading conditions. A comparative analysis has been carried out on the predicted shear strength of HSC beams without stirrups via the ANFIS method with those from the CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94 codes of design. The shear strength prediction with ANFIS is discovered to be superior to CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94. The predictions obtained from the ANFIS are harmonious with the test results not accounting for the shear span to depth ratio, tensile reinforcement ratio and concrete compressive strength; the data of the average, variance, correlation coefficient and coefficient of variation (CV) of the ratio between the shear strength predicted using the ANFIS method and the real shear strength are 0.995, 0.014, 0.969 and 11.97%, respectively. Taking a look at the CV index, the shear strength prediction shows better in nonlinear iterations such as the ANFIS for shear strength prediction of HSC beams without stirrups.