Abstract:
We cannot imagine the success of labor intensive construction projects by neglecting
construction labor productivity. Therefore, improving construction labor productivity
(CLP) is a vital task to increase project quality and decrease project cost and time. There
are various modeling techniques adopted for predicting production rates of labor that
incorporate the influence of various factors but neural networks are found to have
strong pattern recognition and higher accuracy to get reliable estimates. Therefore the
overall aim of this study was to develop a model using Artificial Neural Networks (ANN)
for estimating the CLP of concreting activity for building projects in Addis Ababa city. The CLP data was collected through questionnaire and direct observation of concreting
activity. Both primary and secondary data sources were used. From review of past
literatures numerous context specific CLP influencing factors were identified. The most
critical influencing parameters were selected by calculating the relative importance index
(RII) of questionnaire survey data. The productivity data that was used for further
analysis and modeling was collected by direct observation of concreting activity. The
strength of relationship between CLP and influencing parameters was analyzed using
correlation coefficient results generated by Python. CLP model which represents the
output of crew within a certain factors was successfully developed using Python.
Five objective and six subjective critical influencing parameters were selected using RII.
Crew experience, age of workers, and placement technique are the top three influencing
parameters which have a strong relation with CLP with correlation coefficient values of
0.5681, 0.5349, and -0.5227 respectively. The model developed within these influencing
parameters have higher capability to predict the output of a labor with coefficient of
determination (R2
) value of 92% and mean squared error value of 0.316%.
The CLP model was successfully developed within the identified most critical eleven
influencing parameters using ANN’s. Therefore the researcher strongly recommends the
application of this model for accurate estimation of productivity. For any interested users
the final optimal model can be easily accessed in Python, Excel programmed sheet and
mathematical formulas.
