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- 저자 : Elhakeem, Ahmed (검색결과 3 건)
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A custom building deterioration model
Hosny, O.A.,Elhakeem, A.A.,Hegazy, T. Techno-Press 2011 Structural Engineering and Mechanics, An Int'l Jou Vol.37 No.6
Developing accurate prediction models for deterioration behavior represents a challenging but essential task in comprehensive Infrastructure Management Systems. The challenge may be a result of the lack of historical data, impact of unforeseen parameters, and/or the past repair/maintenance practices. These realities contribute heavily to the noticeable variability in deterioration behavior even among similar components. This paper introduces a novel approach to predict the deterioration of any infrastructure component. The approach is general as it fits any component, however the prediction is custom for a specific item to consider the inherent impacts of expected and unexpected parameters that affect its unique deterioration behavior.
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Hosny, Ossama A.,Elbarkouky, Mohamed M.G.,Elhakeem, Ahmed Korea Institute of Construction Engineering and Ma 2015 Journal of construction engineering and project ma Vol.5 No.1
This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.
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A custom building deterioration model
O.A. Hosny,A.A. Elhakeem,T. Hegazy 국제구조공학회 2011 Structural Engineering and Mechanics, An Int'l Jou Vol.37 No.6
Developing accurate prediction models for deterioration behavior represents a challenging but essential task in comprehensive Infrastructure Management Systems. The challenge may be a result of the lack of historical data, impact of unforeseen parameters, and/or the past repair/maintenance practices. These realities contribute heavily to the noticeable variability in deterioration behavior even among similar components. This paper introduces a novel approach to predict the deterioration of any infrastructure component. The approach is general as it fits any component, however the prediction is custom for a specific item to consider the inherent impacts of expected and unexpected parameters that affect its unique deterioration behavior.
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