Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

Tesfamariam, Solomon; Najjaran, Homayoun

doi:10.1061/(asce)0899-1561(2007)19:7(550)

Cited by 38 publications

(15 citation statements)

References 38 publications

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“…The neurofuzzy approaches have been commonly applied to civil engineering problems [35,37,40,[45][46][47][48][49][50]. Muzzammil [51] proposed an adaptive neurofuzzy inference system (ANFIS) to predict the maximum possible scour depth for bridge abutments.…”

Section: Neurofuzzy Modelingmentioning

confidence: 99%

Modeling of Magnetorheological Dampers under Various Impact Loads

Arsava

Kim

2015

Shock and Vibration

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Magnetorheological (MR) damper has received great attention from structural control engineering because it provides the best features of both passive and active control systems. However, many studies on the application of MR dampers to large civil structures have tended to center on the modeling of MR dampers under seismic excitations, while, to date, there has been minimal research regarding the MR damper model under impact loads. Hence, this paper investigates nonlinear models of MR dampers under a variety of impact loads and control signals. Two fuzzy models are proposed for modeling the nonlinear impact behavior of MR dampers. They are compared with mechanical models, the Bingham and Bouc-Wen models. Experimental studies are performed to generate sets of input and output data for training, validating, and testing the models: the deflection, acceleration, velocity, and current signals. It is demonstrated that the proposed fuzzy models are effective in predicting the complex nonlinear behavior of the MR damper subjected to a variety of impact loads and control signals. The proposed fuzzy model resulted in an accuracy of 99% to predict the impact forces of the MR damper.

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Section: Neurofuzzy Modelingmentioning

confidence: 99%

Modeling of Magnetorheological Dampers under Various Impact Loads

Arsava

Kim

2015

Shock and Vibration

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“…Researchers believe that planning concrete mix design is an art that depends on the experience of the expert providing the mix design. There are many uncertainties in concrete mix proportioning, including those for compressive strength, water-to-cement ratio, cement content, level of workability, quality of the workshop and durability [7,8]. Suitable methods must handle the linguistic and probabilistic nature of concrete mix proportioning.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy System Methodology for Concrete Mixture Design Considering Maximum Packing Density and Minimum Cement Content

2015

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Concrete mix proportioning could be referred to the process of determining the quantities of concrete ingredients using local materials to achieve specific characteristics of the concrete. Among the most important parameters affecting the performance of concrete are the packing density and the grading curve of the aggregates. Better packing of aggregates improves the strength, durability, elastic modulus and creep of the concrete. Accordingly, by increasing packing density and decreasing cement content, environment pollution will decrease. The present study proposes a fuzzybased technique for mix proportioning of normal concrete that increases packing density and decreases cement content. The proposed system utilizes four sub-fuzzy systems to quantify the target compressive strength, water-to-cement ratio, ideal grading curve and free water of the concrete. The results from the proposed fuzzy systems were compared with those obtained from the concrete mix proportioned by field experts. The comparison showed strong agreement between the results for the fuzzy and expert-proportioned mixtures. The proposed system used less cement and had a higher packing density than was found using other mixture proportioning methods.

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“…Tesfamariam and Najjaran applied the ANFIS model to calculate the strength of concrete [28]. Ozgan et al [29] developed an adaptive fuzzy neural system (ANFIS) Sugeno type for predicting stiffness parameters for asphalt concrete.…”

Section: State-of-the-art Application Of Fuzzy Neural Networkmentioning

confidence: 99%

Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks

et al. 2018

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Artificial neural networks, in interaction with fuzzy logic, genetic algorithms, and fuzzy neural networks, represent an example of a modern interdisciplinary field, especially when it comes to solving certain types of engineering problems that could not be solved using traditional modeling methods and statistical methods. They represent a modern trend in practical developments within the prognostic modeling field and, with acceptable limitations, enjoy a generally recognized perspective for application in construction. Results obtained from numerical analysis, which includes analysis of the behavior of reinforced concrete elements and linear structures exposed to actions of standard fire, were used for the development of a prognostic model with the application of fuzzy neural networks. As fire resistance directly affects the functionality and safety of structures, the significance which new methods and computational tools have on enabling quick, easy, and simple prognosis of the same is quite clear. This paper will consider the application of fuzzy neural networks by creating prognostic models for determining fire resistance of eccentrically loaded reinforced concrete columns.

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Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

Cited by 38 publications

References 38 publications

Modeling of Magnetorheological Dampers under Various Impact Loads

Modeling of Magnetorheological Dampers under Various Impact Loads

A Fuzzy System Methodology for Concrete Mixture Design Considering Maximum Packing Density and Minimum Cement Content

Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks

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