International Journal of Scientific Research and Engineering Development

International Journal of Scientific Research and Engineering Development


( International Peer Reviewed Open Access Journal ) ISSN [ Online ] : 2581 - 7175

IJSRED » Archives


Submit Your Manuscript OnlineIJSRED

Construction Site Monitoring and Predictive Analysis Using Artificial Neural Network



   
International Journal of Scientific Research and Engineering Development (IJSRED)
Published Issue : Volume-3 Issue-4
Year of Publication : 2020
Unique Identification Number : IJSRED-V3I4P16
Authors :Gayatri A. Bahire, Prof. S.M.Dhawade, Prof.S.Sabihuddin


MLA

Gayatri A. Bahire, Prof. S.M.Dhawade, Prof.S.Sabihuddin "Construction Site Monitoring and Predictive Analysis Using Artificial Neural Network" International Journal of Scientific Research and Engineering Development (IJSRED) Vol3-Issue4 | 102-106.



Abstract :

Artificial Neural Network (ANN) is a subdivision of Artificial Intelligence are extensively used to answer a complex civil engineering concern. Construction site monitoring tools are based on expert judgments and parametric tools. The successful performance of construction project cannot be achieved without challenges and obstacles. To meet these challenges and hit these obstacles, an organization must have a clear awareness of its performance. Project manager spend most of his time for developing and updating of reports instead of execution and to take in-time decision to finish the work within prescribed time scale. The development of an artificial neural network tool by MATLAB that will help the project manager in this task. In this study, From the literature study construction site monitoring area has been decided and then 30 parameters are identified and questionnaire was prepared. After analysis resulted in 8 factors Objective of this research is to develop Artificial neural network (ANN) models to predict cost performance, schedule performance, quality performance and satisfaction level. This are determined by ANN through its machine learning which is identified by validation, testing and training results.



Full Text:

[ Download Paper ]