Applications of Computational Intelligence in Behavior Simulation of Concrete Materials

Gandomi, Amir H.; Alavi, Amir H.

doi:10.1007/978-3-642-20986-4_9

Cited by 23 publications

(11 citation statements)

References 27 publications

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“…Kargah-Ostadi et al 56 used in a network-level pavement management system (PMS) to predict future performance of a pavement section and identify the maintenance and rehabilitation needs. There are also various similar studies in the literature in the way of utilising neural networks and parametric studies in various civil engineering applications [57][58][59] .…”

Section: Published Literature About Artificial Neural Network Applicamentioning

confidence: 99%

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

2012

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The testing procedure in order to determine the precise mechanical testing results in Marshall design is very time consuming. Also, the physical properties of the asphalt samples are obtained by further calculations. Therefore if the researchers can obtain the stability and flow values of a standard mixture with the help of mechanical testing, the rest of the calculations will just be mathematical manipulations. Determination of mechanical testing parameters such as strain accumulation, creep stiffness, stability, flow and Marshall Quotient of dense bituminous mixtures by utilising artificial neural networks is important in the sense that, cumbersome testing procedures can be avoided with the help of the closed form solutions provided in this study. Marshall specimens, prepared by utilising polypropylene fibers, were tested by universal testing machine carrying out static creep tests to investigate the rutting potential of these mixtures. On the very well trained data basis, artificial neural network analyses were carried out to propose five separate models for mechanical testing properties. The explicit formulation of these five main mechanical testing properties by closed form solutions are presented for further use for researches.

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Section: Published Literature About Artificial Neural Network Applicamentioning

confidence: 99%

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

2012

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“…The GP solutions are computer programs represented as tree structures and expressed in a functional programming language. [9].…”

Section: Genetic Programming Methodsmentioning

confidence: 99%

A Comparative Study of Machine Learning Methods for Compressive Strength of Concrete

Silva¹,

Moita²

2019

World Congress on Civil, Structural, and Environmental Engineering

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This paper introduces a comparative study for the compressive strength of concrete by employing machine learning approaches such as Genetic Programming (GP) and Artificial Neural Network (ANN). The simulation of concrete strength is strongly needed to better understand its behaviours under different conditions and loads. Since many studies predict the comprehensive strength of conventional concrete from hardened characteristics, based on the data points gathered from different experimental tests, empirical models have been developed and verified in the past years. Proposed models are more reliable if the numbers of tests increase and their repeatability increase as well. However, these models are designed for a specific range of concrete strengths. On the other hand, numerical models are more reliable since they are devised based on theoretical rules which could consider behaviours of concrete under different loading paths. But, the validation of these models is made by different loading paths with different configurations that result in costly experiments and both models use only principal stresses and strains in their formulation. Employing machine learning approaches instead of traditional models makes it possible to develop a better understanding of the compressive strength of concrete. Hence, the focus of this paper is the application of machine learning process and their suitability to model concrete compressive strength compared with early models obtained from the literature and compared with some conventional approaches.

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“…For a more detailed analysis of the classification accuracy of MGGP, its sensitivity, specificity, positive predictivity, and accuracy are obtained using (4)(5)(6)(7). In general, the classification performance is presented by a confusion matrix as shown in Table 2.…”

Section: Application To Structural Engineering Problemsmentioning

confidence: 99%

“…Artificial neural networks (ANNs) are the most widely used pattern-recognition procedures. ANNs have been used for a wide range of materials and structural engineering problems [3][4][5][6][7]. Despite the acceptable performance of ANNs in most cases, they do not usually give a definite function to calculate the outcome using the input values.…”

Section: Introductionmentioning

confidence: 99%

A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

Gandomi

Alavi

2011

Neural Comput & Applic

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267

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This paper presents a new approach for behavioral modeling of structural engineering systems using a promising variant of genetic programming (GP), namely multi-gene genetic programming (MGGP). MGGP effectively combines the model structure selection ability of the standard GP with the parameter estimation power of classical regression to capture the nonlinear interactions. The capabilities of MGGP are illustrated by applying it to the formulation of various complex structural engineering problems. The problems analyzed herein include estimation of: (1) compressive strength of high-performance concrete (2) ultimate pure bending of steel circular tubes, (3) surface roughness in end-milling, and (4) failure modes of beams subjected to patch loads. The derived straightforward equations are linear combinations of nonlinear transformations of the predictor variables. The validity of MGGP is confirmed by applying the derived models to the parts of the experimental results that are not included in the analyses. The MGGP-based equations can reliably be employed for pre-design purposes. The results of MSGP are found to be more accurate than those of solutions presented in the literature. MGGP does not require simplifying assumptions in developing the models.

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Applications of Computational Intelligence in Behavior Simulation of Concrete Materials

Cited by 23 publications

References 27 publications

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

A Comparative Study of Machine Learning Methods for Compressive Strength of Concrete

A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

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