Predicting Performance Measurement of Residential Buildings Using an Artificial Neural Network
| Autor: | Salah J. Mohammed, Hesham A. Abdel-khalek, Sherif M. Hafez |
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| Rok vydání: | 2021 |
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Schedule
Environmental Engineering Artificial neural network Correlation coefficient business.industry Computer science Control (management) Building and Construction Geotechnical Engineering and Engineering Geology Machine learning computer.software_genre Backpropagation Earned value management Software Artificial intelligence business computer Predictive modelling Civil and Structural Engineering |
| Zdroj: | Civil Engineering Journal. 7:461-476 |
| ISSN: | 2476-3055 2676-6957 |
| DOI: | 10.28991/cej-2021-03091666 |
| Popis: | Application Earned Value Management (EVM) as a construction project control technique is not very common in the Republic of Iraq, in spite of the benefit from EVA to the schedule control and cost control of construction projects. One of the goals of the present study is the employment machine intelligence techniques in the estimation of earned value; also this study contributes to extend the cognitive content of study fields associated with the earned value, and the results of this study are considered a robust incentive to try and do complementary studies, or to simulate a similar study in alternative new technologies. This paper is aiming at introducing a novel and alternative method of applying Artificial Intelligence Techniques (AIT) for earned value management of the construction projects through using Artificial Neural Networks (ANN) to build mathematical models to be used to estimate the Schedule Performance Index (SPI), Cost Performance Index (CPI) and to Complete Cost Performance Indicator (TCPI) in Iraqi residential buildings before and at execution stage through using web-based software to perform the calculations in the estimation quickly, accurately and without effort. ANN technique was utilized to produce new prediction models by applying the Backpropagation algorithm through Neuframe software. Finally, the results showed that the ANN technique shows excellent results of estimation when it is compared with MLR techniques. The results were interpreted in terms of Average Accuracy (AA%) equal to 83.09, 90.83, and 82.88%, also, correlation coefficient (R) equal to 90.95, 93.00, and 92.30% for SPI, CPI and TCPI respectively. Doi: 10.28991/cej-2021-03091666 Full Text: PDF |
| Databáze: | OpenAIRE |
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