IMPROVEMENT OF CONCRETE FORMWORKS MANAGEMENT SYSTEMS
MOHAMED MOATAZ IBRAHIM ALLAM;
Abstract
Early understanding of construction cost represents a critical factor of a feasibility study in the early design phase of a project. All parties involved in the construction of a project are in need of reliable information about the cost in the early stages of the project. Therefore, this research work attempts to identify the major factors affecting the concrete formwork costs and to develop a consistent model of forecasting early design construction cost of concrete formwork.
At this time, researchers suffer lack of information in the early phases of project to identify and analyze parameters and their relationship related to concrete formwork costs. Formwork costs depend on two main components; cost of materials and cost of workmanship. The cost of workmanship depends mainly on the production rate of workforce.
As the human-dependent factor, production rate cannot be calculated using mathematical equations. Thus, this research aims to develop concrete formwork production rates prediction models of concrete formwork based on statistical analysis.
It focuses on the factors affecting the formwork production rates, their weighting as well as the validation of the use of Artificial Neural Networks for such a job.
Neural network stability and accuracy characteristics were the focus of the research. The use of descriptive, and where possible quantitative, data collection techniques proved to increase neural networks stability. Furthermore, the development of a dual neural network system using both classification and prediction produced very accurate results. Supervised Kohonen classification networks were used as means of defining an activity to a specific prediction network.
The input used to develop the mathematical were gathered from different sources mainly the tendering department of the Arab Contractors (O.A.O) and the Egyptian-French Joint Venture for Civil Works assigned to design and construct the third line of Greater Cairo Metro Project.
As the result of the study, the main factors affecting the concrete formwork production rates were clearly determined, weightings identified, optimum gang size determination method using virtual techniques applied and formwork production rates using ANN models developed and compared with results obtained from other methods.
Keywords: Construction, Formwork, Concrete, Prediction, Production Rate, Productivity, Artificial Intelligence, Artificial Neural Networks, Simulation.
At this time, researchers suffer lack of information in the early phases of project to identify and analyze parameters and their relationship related to concrete formwork costs. Formwork costs depend on two main components; cost of materials and cost of workmanship. The cost of workmanship depends mainly on the production rate of workforce.
As the human-dependent factor, production rate cannot be calculated using mathematical equations. Thus, this research aims to develop concrete formwork production rates prediction models of concrete formwork based on statistical analysis.
It focuses on the factors affecting the formwork production rates, their weighting as well as the validation of the use of Artificial Neural Networks for such a job.
Neural network stability and accuracy characteristics were the focus of the research. The use of descriptive, and where possible quantitative, data collection techniques proved to increase neural networks stability. Furthermore, the development of a dual neural network system using both classification and prediction produced very accurate results. Supervised Kohonen classification networks were used as means of defining an activity to a specific prediction network.
The input used to develop the mathematical were gathered from different sources mainly the tendering department of the Arab Contractors (O.A.O) and the Egyptian-French Joint Venture for Civil Works assigned to design and construct the third line of Greater Cairo Metro Project.
As the result of the study, the main factors affecting the concrete formwork production rates were clearly determined, weightings identified, optimum gang size determination method using virtual techniques applied and formwork production rates using ANN models developed and compared with results obtained from other methods.
Keywords: Construction, Formwork, Concrete, Prediction, Production Rate, Productivity, Artificial Intelligence, Artificial Neural Networks, Simulation.
Other data
| Title | IMPROVEMENT OF CONCRETE FORMWORKS MANAGEMENT SYSTEMS | Authors | MOHAMED MOATAZ IBRAHIM ALLAM | Issue Date | 2014 |
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