SVM	support vector machine	SAS	surface area of specimen
MLR	multiple linear regression	w/c	water/cement ratio
ANN	artificial neural network	f ′c	compressive strength
GEP	gene expression programming	Lb	bond length
RBFNN	radial basis function neural network	d	diameter of reinforcement bar
MLP	multilayer perceptron	Tb	type of reinforcement bar
LSSVR	least squares support vector regression	fy	yield strength of reinforcement bar
DFP	differential flower pollination	c	concrete cover
SVR	support vector regression	η	corrosion level
MGGP	multi-gene genetic programming	τu	bond strength
GA	genetic algorithm	PF	pullout force
FL	fuzzy logic	zn	normalized output of variable
ML	machine learning	z	variable of input to be normalized
ReLU	rectified linear unit	zmin	minimum value of input variable z
ANFIS	adaptive neuro-fuzzy inference system	zmax	minimum value of input variable z
GMDH	group method of data handling	r	actual output
MARS	multivariate adaptive regression spline	s	projected output
MNLR	multiple nonlinear regression	N	number of points in data set
KSM	Kriging surrogate model	Ni	input parameter (sum of biases, weights, and normalized inputs)
BPANN	back propagation ANN	Xi	normalized input value
RegTree	regression tree	Wi(H-O)	value of weight from hidden to output layer
PSO	particle swarm optimization	Wi(I-H)	value of weight from input to hidden layer
LMA	Levenberg–Marquardt algorithm	B(H-O)	value of bias from hidden to output layer
RMSE	root mean square error	B(I-H)	value of bias from input to hidden layer
MAE	mean absolute error	f(I-H)	AF that is used from input to hidden layer
MAPE	mean absolute percentage error	f(H-O)	AF that is used from hidden to output layer
R	correlation coefficient	Rr	relative rib area
NS	Nash-Sutcliffe efficiency index	Av/S	amount of transverse steel area to spacing ratio
RC	reinforced concrete	ls	splice length
Std.	standard deviation	ρ	splice bar size
MSE	mean square error	c/d	ratio of concrete cover to reinforcement bar diameter
BS	bond strength	Lb/d	ratio of bond length to reinforcement bar diameter
AF	activation function	Surf	reinforcement bar surface treatment
Ns	number of stirrups	Pos	reinforcement bar position/location
As	area of stirrups	Surf/Tr	ratio of reinforcement bar surface to transverse reinforcement bar
Cm	curing method	√f ′c	square root of concrete compressive strength
UHPC	ultra-high-performance concrete	Ad	anchorage depth
Atr	area of transverse reinforcement bar	Sd	surface dimensions of specimen
Ec	elastic modulus of concrete	Cs	crack severity of concrete
UPV	ultrasonic pulse velocity	Es/EFRP	ratio of elasticity modulus of steel reinforcement bars to that of FRP bars
ft	tensile strength of reinforcement bar	IEPSO	improved eliminate particle swarm optimization
St	surface treatment	IEPANN	improved eliminate particle swarm optimization hybridized ANN
i	interface moisture condition	PANN	particle swarm optimization hybridized ANN
Ct	type of concrete	Atr/Snd	ratio of area of transverse reinforcement bar to product (transverse bar spacing, number of developed bars, and bar diameter)