الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Productivity of construction engineers’ staff represents a significant
concern to all construction organizations. The optimization of the
production of the total organization can be achieved through the
coordinated improvement of the productivity of the engineers’ staff. As
the most valuable asset of any organization is the productive workforce.
If the engineers’ staff is not satisfied with their jobs and not motivated to
fulfill their tasks and achieve their goals, the organization cannot attain
success. Productivity is directly linked to motivation. Nevertheless, just a
few construction organizations consider the human capital as being their
main asset, capable of leading them to success. So, it is necessary to exert
efforts toward improving productivity by heads of organizations in recent
times.
As Egypt is seeking to promote a leading role in the international
community and to overcome most of the problems affecting its
development, construction productivity is of high concern. Construction
engineers’ staff productivity is affected by many factors. Motivation is
one of these factors. Therefore, the first objective of this study focuses on
identifying the most significant factors affecting engineers’ staff
productivity in Egypt in order to find the root solution to motivate and
improve construction engineers’ staff productivity. The second objective
is the development of an Artificial Neural Network model that assists any
construction organization’s decision maker to achieve a reliable
assessment of the expected construction engineers’ staff productivity
improvement.
This thesis is mainly belonging to the engineers in construction industry
in the Greater Cairo region in Egypt. A review for the previous research
results in line with this thesis was performed. Several factors which affect
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productivity are collected from previous studies and construction experts.
A questionnaire survey among construction experts in the Egyptian
market was carried out. Twelve out of 59 factors were identified as the
most significant factors that affect construction engineers’ staff
productivity. from construction projects in Egypt 123 records for the
identified factors were collected. Projects and HR managers at
construction field were provided feedback for the records. An Artificial
Neural Network model was prepared to predict the percentage of
construction engineers’ staff productivity improvement in Egypt.
Educational version for MATLAB Neural Network (Matlab 2014b)
software using feed forward network, and the back propagation algorithm
was used to develop the model. A total of 123 records were collected and
had been divided into 3 parts which are training, validating, and testing
set as follow; 80 (65%), 22 (18%), and 21 (17%) for training, validating,
and testing of the network respectively. Several trials had been carried out
for determining the best model. Therefore, 60 trials with different
network structures were experimented. The selected model for this
problem is that with the least error for training, validating, and test set
and with the large number of neurons in the hidden layer. This model
consists of an input layer with seventeen (17) input nodes, and one hidden
layer with twenty four (24) neurons, and one output layer with three (3)
output nodes. Sigmoid activation function was used, and learning rate is
(0.1), and no. of iteration is (500). The results of the testing process gave
14% test error. This demonstrates the viability of the model as a powerful
tool. Model can help decision-maker in any construction organization to
arrive at a reliable assessment of the expected construction engineers’
staff productivity improvement in any construction project in Egypt.