The as: “a computational mechanism able to acquire,

The term
“regression” was devised by Francis Galton in the nineteenth century to
describe a biological phenomenon. Regression analysis is a statistical method
for exploring the correlations between independent (xi) and dependent (y)
variables. Its purpose is to find the equation that most accurately predicts
the dependent variable as a linear function of two or more independent
variables according to the causal relationships among changes in internal
factors. Regression analysis includes many techniques for modeling and
analyzing several variables, when the focus is on the relationship between a
dependent variable and one or more independent variables (or ‘predictors’)

 

Regression analysis
also allows to assess the effects of variables measured on different scales,
these benefits help researchers / data analysts / data scientists to eliminate
and evaluate the best set of variables to be used for building predictive
models.

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1.1.1       Artificial Neural Network (ANN)

Artificial neural
networks are purely data driven models which through training iteratively
transition from a random state to a final model.  They do not depend on expectations about
functional form, probability distribution or smoothness,

 

ANN is a branch of
artificial intelligence (AI) in which structure is based on the biological
nervous system. It can exhibit a surprising number of the human brain’s
characteristics, e.g. learn from experience and generalize from previous
examples to new problems. ANN can provide expressive answers even when the data
to be processed include errors or are incomplete, and can process information
extremely rapidly when applied to solve real world problems (Lippmann 1988;
Smith 1993). Neural Networks (NN) are used in construction for modeling complex
relationships between inputs and outputs or to find patterns in data.

 

An ANN is typically
defined by three types of parameters (Nitin kumara et al 2013): 

1-The interconnection pattern
between different layers of neurons. 

2-The learning process for updating
the weights of the interconnections. 

3-The activation function that
converts a neuron’s weighted input to its output activation. 

 

Garrett (1994) has
given an interesting engineering definition of the ANN as: “a computational
mechanism able to acquire, represent, and compute mapping from one multivariate
space of information to another, given a set of data representing that
mapping.” Artificial neural networks (ANNs) are a functional abstraction of the
biologic neural structures of the central nervous system

 

Zayed and Halpin (2005)
defined ANN as “the process of developing the ability to generalize, which
correctly classify new patterns or to make forecasts and predictions”.

 

earning and
generalization of the problems in the ANN models could be achieved in spite of
incomplete or erroneous data. The data used to test the prediction capability
of the network is selected from the total data set. After a predetermined
number of iterations, the network training and testing is stopped whereas no
amelioration is done in the output.

 

ANN has some advantages
includes (Nitin kumara et al 2013): 

·        
Adaptive learning: An ability to
learn how to do tasks based on the data given for training or initial
experience. 

·        
Self-Organization:  An ANN can create its own organization or
representation of the information it receives during learning time. 

 

 Also, ANN has some disadvantages include
(Nitin kumara et al 2013): 

·        
ANN can be used only if training
data is available. It is not necessary to have a mathematical model of the
problem of interest, and there is no need to provide any form of prior
knowledge. 

·        
The solution obtained from the
learning process usually cannot be interpreted. 

·        
Most neural network architectures
are black boxes. They cannot be checked whether their solution is probable,
i.e. their final state cannot be interpreted in terms of rules. 

 

Applications of ANNs in
construction management go back to the early 1990s. These applications cover
many critical topics in productivity, quality, time estimation, bidding,
performance evaluation.

 

Among the numerous
artificial neural networks that have been proposed, backpropagation networks
have been extremely popular for their unique learning capability. 80% of
practical ANN applications used the backpropagation neural networks (Haykin
1999).

 

Artificial neural
networks (ANNs) models may be called by different names:

 (1) connectionist models;

(2) parallel
distributed processing models;

(3) neuromorphic
systems; and

 (4) neural computing.