Prediction of Concrete Strength with P-, S-, R-Wave Velocities by Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Park, Jong Yil; Yoon, Young-Geun; Oh, Tae Keun

doi:10.3390/app9194053

Cited by 42 publications

(15 citation statements)

References 23 publications

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“…Furthermore, the estimation of Ec or fc from Ed has been little studied due to the difficulties in testing enough specimens. Thus, these studies have focused on obtaining more accurate predictions of concrete strength and conditions using (ML) algorithms such as support vector machine (SVM), ensemble, and artificial neural networks (ANNs) [ 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques

et al. 2020

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The static elastic modulus (Ec) and compressive strength (fc) are critical properties of concrete. When determining Ec and fc, concrete cores are collected and subjected to destructive tests. However, destructive tests require certain test permissions and large sample sizes. Hence, it is preferable to predict Ec using the dynamic elastic modulus (Ed), through nondestructive evaluations. A resonance frequency test performed according to ASTM C215-14 and a pressure wave (P-wave) measurement conducted according to ASTM C597M-16 are typically used to determine Ed. Recently, developments in transducers have enabled the measurement of a shear wave (S-wave) velocities in concrete. Although various equations have been proposed for estimating Ec and fc from Ed, their results deviate from experimental values. Thus, it is necessary to obtain a reliable Ed value for accurately predicting Ec and fc. In this study, Ed values were experimentally obtained from P-wave and S-wave velocities in the longitudinal and transverse modes; Ec and fc values were predicted using these Ed values through four machine learning (ML) methods: support vector machine, artificial neural networks, ensembles, and linear regression. Using ML, the prediction accuracy of Ec and fc was improved by 2.5–5% and 7–9%, respectively, compared with the accuracy obtained using classical or normal-regression equations. By combining ML methods, the accuracy of the predicted Ec and fc was improved by 0.5% and 1.5%, respectively, compared with the optimal single variable results.

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Section: Introductionmentioning

confidence: 99%

Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques

et al. 2020

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“…Support vector machines (SVMs) have been shown to achieve good generalization performance over a wide variety of classification problems, where it is seen that SVM tends to minimize generalization errors, that is, classifier errors over new instances. In geometric terms, SVM can be seen as the attempt to find a surface (σ i ) that separates positive examples from negative ones by the widest possible margin [22][23][24].…”

Section: Support Vector Machinesmentioning

confidence: 99%

Currency Crises Prediction Using Deep Neural Decision Trees

et al. 2019

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Featured Application: The superiority of a novel computational technique (deep neural decision trees) for prediction of currency crises over other methodologies and the construction of new crisis prediction models more precise than existing ones.Abstract: Currency crises are major events in the international monetary system. They affect the monetary policy of countries and are associated with risks of vulnerability for open economies. Much research has been carried out on the behavior of these events, and models have been developed to predict falls in the value of currencies. However, the limitations of existing models mean further research is required in this area, since the models are still of limited accuracy and have only been developed for emerging countries. This article presents an innovative global model for predicting currency crises. The analysis is geographically differentiated for regions, considering both emerging and developed countries and can accurately estimate future scenarios for currency crises at the global level. It uses a sample of 162 countries making it possible to account for the regional heterogeneity of the warning indicators. The method used was deep neural decision trees (DNDTs), a technique based on decision trees implemented by deep learning neural networks, which was compared with other methodologies widely applied in prediction. Our model has significant potential for the adaptation of macroeconomic policy to the risks derived from falls in the value of currencies, providing tools that help ensure financial stability at the global level. Appl. Sci. 2019, 9, 5227 2 of 18 effect of contagion on other countries. However, the event was about more than just speculation on the Thai currency and saw the collapse of Asian stock markets. The financial crisis that began with the devaluation of the Thai baht exchange rate resulted in a sharp increase in interest rates and the collapse of many companies, as well as an increase in the cost of credit and a general fall in GDP in the region [1]. This resulted in foreign and national investors pulling out investment. Not only did this crisis affect Asian countries, but it also had a negative impact on other emerging economies, especially in Latin America, showing that currency crises are not limited to a specific economy. Globalization can increase the economic difficulties of societies and affect the structure of national economies after the real economy has suffered a damaging impact [2]. Figure 1 shows the number of currency crises per year at the international level. Appl. Sci. 2019, 8, x FOR PEER REVIEW 2 of 19of contagion on other countries. However, the event was about more than just speculation on the Thai currency and saw the collapse of Asian stock markets. The financial crisis that began with the devaluation of the Thai baht exchange rate resulted in a sharp increase in interest rates and the collapse of many companies, as well as an increase in the cost of credit and a general fall in GDP in the region [1]. This resulted in foreig...

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“…The prediction of elastic modulus of concrete [ 36 ] and recycled aggregate concrete [ 37 , 38 ] was conducted by elephant herding optimization and ANN, respectively. An estimation of the compressive strength of concrete obtained by mechanical wave velocities was conducted using the ANN method [ 39 , 40 ]. The results of the ASTM C1012-95 testing method on sulfate attack of concretes, which were made with different cement types and pozzolanic additives, were predicted by ANN [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of Shrinkage Reducing Admixture on Drying Shrinkage of Concrete with Different w/c Ratios

et al. 2020

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The reduction of the moisture content of concrete during the drying process reduces the concrete’s volume and causes it to shrink. In general, concrete shrinkage is a phenomenon that causes concrete volume to dwindle and can lead to durability problems. There are different types of this phenomenon, among them chemical shrinkage, autogenous shrinkage, drying shrinkage including free shrinkage and restrained shrinkage, and thermal contraction. Shrinkage-reducing admixtures are commercially available in different forms. The present study investigates the effect of liquid propylene glycol ether on mechanical properties and free shrinkage induced by drying at different water-cement (w/c) ratios. Furthermore, the effect of shrinkage-reducing admixtures on the properties of hardened concrete such as compressive and tensile strength, electrical resistivity, modulus of elasticity, free drying shrinkage, water absorption, and depth of water penetration was investigated. The results indicated that shrinkage reducing agents performed better in a low w/c ratio and resulted in up to 50% shrinkage reduction, which was due to the surface reduction of capillary pores. The prediction of free shrinkage due to drying was also performed using an artificial neural network.

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Prediction of Concrete Strength with P-, S-, R-Wave Velocities by Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Cited by 42 publications

References 23 publications

Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques

Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques

Currency Crises Prediction Using Deep Neural Decision Trees

Effect of Shrinkage Reducing Admixture on Drying Shrinkage of Concrete with Different w/c Ratios

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