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

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 Malaysi

Data quality issues that hinder the implementation of Artificial Neural Network (ANN) for cost estimation of construction projects in Malaysia

The Artificial Neural Network (ANN), which is one of the Artificial Intelligence (AI) tools, has been identified as a great technique to be used for construction cost estimation in the project. With the optimum quality of data input into the ANN model, it could produce an optimum and reliable cost estimation output. Nonetheless, the construction industry lacks the breadth and depth of data required as input into ANN. Though many online databases have been made available for data consumers, data quality problems remain unresolved. Thus, this study aims to identify data quality issues that can hinder the implementation of ANN for cost estimation of a construction project. Literature review and semi- structured interview were employed for the data collection of this research. The content analysis method was used to analyse the information obtained through the literature review. Meanwhile, the data collected from the semi-structured interview with nine (9) respondents was analysed using both content analysis and descriptive statistics analysis methods. The findings revealed six data quality issues that can hinder the ANN implementation for cost estimation of construction projects in Malaysia which are inaccurate data, outdated data, data access barriers, insufficient data, noise in training data, and data input degree of influence. Academically, this study contributes to the body of knowledge about theimplementation of ANN for cost estimation of construction projects in Malaysia