Improvement of Productivity in Buildings Construction
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Date
2023-12
Journal Title
Journal ISSN
Volume Title
Publisher
SSP - JOURNAL OF CIVIL ENGINEERING
Abstract
Improving productivity in construction projects has long been a major concern, and much research has been carried
out to try to ameliorate construction productivity. To this end, this study aims to improve and increase the
productivity rate of flat slab formwork used in residential construction projects. A survey consisting of 150
questionnaires was undertaken to identify the factors that influence on the productivity. Based on the relative
Importance Index (RII), data on eleven factors deemed to affect productivity were selected. A collection of 100
data points from various sites were utilized to develop two models. Firstly, an Artificial Neural Network (ANN)
model was employed, and secondly, a parametric approach was investigated. The data were divided into two sets,
with 70% of the data used for training and the remaining 30% used for testing. The models' performance was
evaluated using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values. In the test
phase, the artificial neural network model yielded an MSE value of 2.6610e-4
and a MAPE value of 4.9227,
whereas the parametric model produced an MSE of 0.040 and a MAPE of 9.525. It was found that the artificial
neural network model provided reliable prediction accuracy compared to the parametric model. However, the
artificial neural network approach can be selected as a robust model in predicting and controlling the productivity
rate in local construction projects by using the developed model based on the identified factors.