Abstract
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
Highlights
The measurement of performance has an essential position in the development management process
The author concluded that the results of the authors' work on the development of a regression model, based on artificial neural networks, that enables prediction of the site overhead cost index, The neural network selected to be the core of developed model allows the prediction of the costs' index and aids in the estimation of the site overhead costs in the early stages of a construction project with satisfactory precision
This research has studied the predicting the performance of residential buildings using the artificial neural network approach
Summary
The measurement of performance has an essential position in the development management process. The authors of this study focused on the development, and evaluate the performance of a model that predicts the earned value via the following steps: (a) Identification of model parameters that have an effect on the earned fee index in residential structures project using multiple linear regression techniques, which were performed using SPSS package, (b) Development and assessment of the performance of the proposed ANNs models to predict the earned cost and schedule indexes, and (c) Check the verification and validation of the mathematical models developed. EVPM creates differences, indices of performance for project costs and schedules; the first indications of expected outcomes of the project performance can be provided by predicting project costs and schedules at completion [11, 12]
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