Artificial Neural Network (ANN) application for cost estimation of construction projects in Malaysia: a study on quality of data

• Main Authors: Mohd Zammari, Alya Farhani, Ayob, Mohd Fairullazi
• Format: Conference or Workshop Item
• Language: English; English
• Published: 2022
• Subjects: T Technology (General); T173.2 Technological change; T55.4 Industrial engineering.Management engineering.; T58.5 Information technology; T58.6 Management information systems; TA177.4 Engineering economy; TA190 Management of engineering works; TA501 Surveying; TH Building construction
• Online Access: http://irep.iium.edu.my/101186/1/Poster%20QS%20Symposium%20Alya%20and%20fairul%205%20aug%202022.pdf; http://irep.iium.edu.my/101186/2/Announcement%20poster%20QS%20Symposium%202022-Gold%20Award.pdf; http://irep.iium.edu.my/101186/

The Artificial Neural Network (ANN), is one of the Artificial Intelligence (AI) tools. It is a great technique that can be applied in the construction project cost estimation to solve classification, prediction, and regression problems (Juszczyk, 2017). ANN is data-driven and is considered sensitive to input data (Tayefeh Hashemi et al., 2020). The ANN relies on the data input to execute tasks like prediction. Thus, to produce the best and most reliable cost estimation output, the best quality of data are required as input into ANN model. The Malaysian construction industry faces a lack of access, accuracy, breadth, and depth of industry data. Although open-data popularity has increased, the problem with data quality remained unresolved (Nikiforova, 2020). The aim of the study is to investigate the quality of data for the implementation of Artificial Neural Networks (ANN) for cost estimation of construction projects in Malaysia