Developing a preliminary cost estimation model for tall buildings based on machine learning

ABSTRACT

The last half-century has witnessed an astronomical rise in the number of tall building projects in urban centers globally. These projects however frequently experience delays and total abandonment due to economic reasons. This study presents the application of Machine Learning techniques in the systematic development of a model to estimate the preliminary cost of tall building projects. The techniques considered include Multi-Linear Regression Analysis (MLRA), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Multi Classifier Systems. Twelve models were developed and compared using standard performance metrics. The results revealed that the best performing model was based on a Multi Classifier System using KNN as the combining classifier, with a Correlation Coefficient (R²) of 0.81, Root Mean Squared Error (RMSE) of 6.09, and Mean Absolute Percentage Error (MAPE) of 80.95%. This research showed the potential of modern digital technologies such as machine learning to solve problems of the construction industry. The procedure described in this study is of significant value to research and practice in the development of preliminary cost estimation models. The developed model can function as a decision support tool in the preliminary cost estimation stage of tall building projects.

KEYWORDS:

• Tall buildings
• cost estimation
• regression
• k nearest neighbour
• neural networks
• support vector machines

Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the authors.