New Formulation of Compressive Strength of Preformed-Foam Cellular Concrete: An Evolutionary Approach

Kiani, Behnam; Gandomi, Amir H.; Sajedi, Siavash; Liang, Robert Y.

doi:10.1061/(asce)mt.1943-5533.0001602

Cited by 53 publications

(29 citation statements)

References 40 publications

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“…Most empirical equations for the prediction of the compressive strength of foamed concrete are commonly based on three fundamental models, namely, Balshin's, Feret's, and Power's models (Neville, ). Balshin's model, which is known as the strength‐porosity model (Kiani, Gandomi, Sajedi, & Liang, ), assumes that the compressive strength of concrete is affected by the volume of air voids in concrete (interlayer pores/spaces, gel pores, capillary pores, and entrapped air voids). Based on the strength‐porosity model, Hoff () first proposed an empirical equation as follows:

σ_{y} = σ_{0} {([], \frac{d_{c} ((), 1 + 0.2 ρ_{c})}{((), 1 + k) ρ_{c} γ_{w}})}^{b}

in which

σ_{y}

is the compressive strength of foamed concrete; σ 0 is the theoretical compressive strength of cement paste at absolute zero porosity;

d_{c}

is the density of foamed concrete; k is the water‐to‐cement ratio (by weight);

ρ_{c}

is the specific gravity of ordinary Portland cement that is 3.15 as given in Hoff (); and

γ_{w}

is the unit weight of water.…”

Section: Empirical Equationsmentioning

confidence: 99%

“…The data set of foamed concrete (Data set 1) consists of 177 testing results for different mixtures (density, water‐to‐cement [w/c] ratio, and sand‐to‐cement [s/c] ratio). The samples of the Data set 1 were consistently made up of ordinary Portland cement, water, sand, and preformed foams, curing time of 28 days (Abd & Abd, ; Asadzadeh & Khoshbayan, ; Jones & McCarthy, ; Kiani et al., ; Pan, Hiromi, & Wee, ). The range of density, w/c ratio, and s/c ratio were [430 − 2, 009] kg/m 3 , [0.26 − 0.83], and [0 − 4.3], respectively.…”

Section: Data Set and Performance Indicatormentioning

confidence: 99%

See 1 more Smart Citation

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Nguyen

Kashani

Ngo

et al. 2018

Computer aided Civil Eng

199

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The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high‐order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross‐entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C‐ANN) and second‐order artificial neural network (SO‐ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high‐performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C‐ANN and SO‐ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water‐to‐cement and sand‐to‐cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.

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σ_{y} = σ_{0} {([], \frac{d_{c} ((), 1 + 0.2 ρ_{c})}{((), 1 + k) ρ_{c} γ_{w}})}^{b}

in which

σ_{y}

is the compressive strength of foamed concrete; σ 0 is the theoretical compressive strength of cement paste at absolute zero porosity;

d_{c}

is the density of foamed concrete; k is the water‐to‐cement ratio (by weight);

ρ_{c}

is the specific gravity of ordinary Portland cement that is 3.15 as given in Hoff (); and

γ_{w}

is the unit weight of water.…”

Section: Empirical Equationsmentioning

confidence: 99%

Section: Data Set and Performance Indicatormentioning

confidence: 99%

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Nguyen

Kashani

Ngo

et al. 2018

Computer aided Civil Eng

199

View full text Add to dashboard Cite

show abstract

“…GP is specialization subset of genetic algorithms (GAs) [13], which are based on the principles of genetics and natural selection. GP and its variants have been successfully used for solving a number of different civil engineering problems (e.g., [14,15]). Multi-gene genetic programming (MGGP) is a robust variant of GP that combines the ability of the standard GP in constructing the model structure with the capability of traditional regression in parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

Genetic programming for experimental big data mining: A case study on concrete creep formulation

Gandomi

Sajedi

Kiani

et al. 2016

Automation in Construction

Self Cite

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This paper proposes a new algorithm called multi-objective genetic programming (MOGP) for complex civil engineering systems. The proposed technique effectively combines the model structure selection ability of a standard genetic programming with the parameter estimation power of classical regression, and it simultaneously optimizes both the complexity and goodness-of-fit in a system through a non-dominated sorting algorithm. The performance of MOGP is illustrated by modeling a complex civil engineering problem: the timedependent total creep of concrete. A Big Data is used for the model development so that the proposed concrete creep model-referred to as a "genetic programming based creep model" or "G-C model" in this study-is valid for both normal and high strength concrete with a wide range of structural properties. The G-C model is then compared with currently accepted creep prediction models. The G-C model obtained by MOGP is simple, straightforward to use, and provides more accurate predictions than other prediction models.

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“…Besides these methods, artificial intelligence and machine learning as a quick and powerful tool [23][24][25] can be used to predict and manage the flood. Bardestani et al used ANFIS which is a combination of Neural Network and Fuzzy Logic in Water Resources [26].…”

Section: Rehabilitation System After Floodmentioning

confidence: 99%

Flood Management, Flood Forecasting and Warning System

Hajibabaei¹,

Ghasemi²

2017

IJSEA

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Flood phenomenon annually causes many human and financial losses so that flood has even had many losses in countries which are properly developed in river bank protection and construction of reservoir dams aiming to control flood. Today, controlling flood through its management is discussed and one of efficient and effective tools in this field is non-structural flood management as the complementary of structural ones that can have a very important role in controlling flood and consequently reducing the losses derived by that. Flood forecasting and warning systems are considered as one of the non-structural management tools which has given high importance within recent decades and providing information about flood and fast and easy reaction after receiving mentioned information to have minimum losses in flood areas as well as providing some information to manage water resources are of the aims of this system.

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New Formulation of Compressive Strength of Preformed-Foam Cellular Concrete: An Evolutionary Approach

Cited by 53 publications

References 40 publications

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Genetic programming for experimental big data mining: A case study on concrete creep formulation

Flood Management, Flood Forecasting and Warning System

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