Predicting construction labour productivity using optimal artificial neural network, multiple linear regression models : comparative study
Other Title(s)
التنبؤ بإنتاجية العمالة الإنشائية باستخدام النماذج المثلى للشبكات العصبية الاصطناعية : الانحدار الخطي المتعدد : دراسة مقارنة
Dissertant
Thesis advisor
al-Zawini, Faiq Muhammad Sarhan
University
Isra University
Faculty
Faculty of Engineering
Department
Engineering Project Management
University Country
Jordan
Degree
Master
Degree Date
2018
English Abstract
Construction productivity can be considered as an element in project management; therefore, predicting the rate of construction productivity for labor was an important task.
However, the development of the technology tools will enable the planner to best understand the process of estimation and predicting in different stages of construction projects.
The main aim of this research is to develop a novel mathematical model using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) to predict the construction productivity rates because mathematical models and mathematical equations used for finishing stone activity are characterized by uncertainty and lack validity and verification, and traditional methods fail to calculate the construction productivity due to their slowness and lack of accuracy.
Data was collected from three residential building projects in the Hashemite Kingdom of Jordan in the capital city of Amman from July 2017 to December 2017.
The first project was Tebyeh Residential Building (TRB); the second project was Sinokrot Private Villa (SPV); and the third project was Aldada Private Villa (APV).
The results demonstrated that (MLR) is a more powerful technique than (ANN) for construction productivity of finishing stone activity depending on validity through Mean Absolute Percentage Error (MAPE%) and Average Accuracy (AA%), which were equal to 18.615% and 81.3846% respectively; ANN technique (MAPE%) was equal to 27.06 % and (AA%) was equal to 72.94%.
This result can be expressed when using multiple linear regression techniques instead of artificial neural networks in estimating and predicting construction productivity when the data of the variables are homogeneous; otherwise, use of artificial neural networks technique is preferable.
Main Subjects
Topics
No. of Pages
93
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Construction productivity in project management : concepts, trends, and applications.
Chapter Three : Artificial forecasting modelling.
Chapter Four : Modelling of construction productivity utilizing multiple linear regression technique.
Chapter Five : Modelling of construction productivity utilizing artificial neural network.
Chapter Six : Conclusion and recommendation.
References.
American Psychological Association (APA)
al-Amar, Hamzah Muhammad. (2018). Predicting construction labour productivity using optimal artificial neural network, multiple linear regression models : comparative study. (Master's theses Theses and Dissertations Master). Isra University, Jordan
https://search.emarefa.net/detail/BIM-832685
Modern Language Association (MLA)
al-Amar, Hamzah Muhammad. Predicting construction labour productivity using optimal artificial neural network, multiple linear regression models : comparative study. (Master's theses Theses and Dissertations Master). Isra University. (2018).
https://search.emarefa.net/detail/BIM-832685
American Medical Association (AMA)
al-Amar, Hamzah Muhammad. (2018). Predicting construction labour productivity using optimal artificial neural network, multiple linear regression models : comparative study. (Master's theses Theses and Dissertations Master). Isra University, Jordan
https://search.emarefa.net/detail/BIM-832685
Language
English
Data Type
Arab Theses
Record ID
BIM-832685
