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Artificial Intelligence to Model the Performance of Concrete Mixtures and Elements: A Review

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Abstract

Concrete is the most widely used man-made material in the construction of structures, pavements, bridges, dams, and infrastructures. Depending on the type of components and mixture proportions, different behavior can be expected from different types of concretes, which necessitates the study of concrete behavior in designing procedures. The properties of the concrete mixtures and elements can be estimated through expensive and time-taking laboratory-based experiments. Alternatively, these properties can be estimated through predictive models developed using statistical or artificial intelligence (AI) techniques. AI techniques, because of their capabilities in knowledge processing and pattern recognition, are among the leading methods to find solutions for engineering problems. In this paper, the available studies on the applications of AI techniques to model the behavior of concrete elements and estimate the properties of concrete mixtures are reviewed. In addition, the capabilities of various AI techniques in handling different types of data are discussed. This paper also provides recommendations on the selection of the appropriate input variables in developing the predictive models. It is hoped that this paper will provide the interested practicing engineers with the information needed to fully exploit the resources available on the use of AI techniques in the concrete industry. Moreover, this paper will be helpful to the researchers to explore future avenues of research on the applications of AI techniques in the field of concrete mixtures and elements.

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Abbreviations

ABA:

Adaptive boosting approach

ABC:

Artificial bee colony

ACO:

Ant Colony Optimization

AEA:

Air entraining admixtures

AI:

Artificial intelligence

ANFIS:

Adaptive network-based fuzzy inference system

ANN:

Artificial neural network

BANN:

Bagged artificial neural network

BPNN:

Backpropagation neural networks

BBO:

Biogeography-based optimization

BBP:

Biogeography-based programming

DA:

Dragonfly algorithm

DL:

Deep learning

DT:

Decision tree

ECSO:

Enhanced cat swarm optimization

FA:

Fly ash

FFA:

Firefly algorithm

FFANN:

Feedforward artificial neural network

FIS:

Fuzzy inference system

FL:

Fuzzy logic

FNN:

Feedforward neural network

fmGA:

Fast messy genetic algorithm

FRBFNN:

Fuzzy radial basis function neural network

GBANN:

Gradient boosted artificial neural network

GBRT:

Gradient boosted regression tree

GGBFS:

Ground granulated blast furnace slag

GA:

Genetic algorithm

GEP:

Genetic expression programming

GP:

Genetic programming

GPR:

Gaussian process regression

GWPOT:

Genetic weighted pyramid operation tree

ICA:

Imperialist competitive algorithm

IS:

Insertion sequence

IWO:

Invasive weed optimization

LGP:

Linear genetic programming

LSSVR:

Least squares support vector regression

MARS:

Multivariate adaptive regression splines

MART:

Multiple additive regression tree

ML:

Machine learning

MLRA:

Multi-linear regression analysis

OPC:

Ordinary portland cement

PSO:

Particle swarm optimization

QA:

Quality control

QC:

Quality assurance

RF:

Random forest

RIS:

Root insertion sequence

RSM:

Response surface method

SF:

Silica fume

SLR:

Systematic literature review

TS:

Tail size

WCA:

Water cycle algorithm

WOA:

Whale optimization algorithm

WRA:

Water reducer admixture

WSVM:

Weighted support vector machines

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  1. Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall, West Lafayette, IN, 47907-2051, USA

    Ali Behnood

  2. Department of Civil Engineering and Environmental Engineering, Monash University, Melbourne, Australia

    Emadaldin Mohammadi Golafshani

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  1. Ali Behnood
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Behnood, A., Golafshani, E.M. Artificial Intelligence to Model the Performance of Concrete Mixtures and Elements: A Review. Arch Computat Methods Eng 29, 1941–1964 (2022). https://doi.org/10.1007/s11831-021-09644-0

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