Comparison of Random Forest and Support Vector Machine Regression for Prediction of BIM Labor Cost on Architectural Modeling and Plotting [隨機森林及支持向量機應用於結構BIM建模及出圖人力成本預測能力之比較分析]
Journal
Journal of the Chinese Institute of Civil and Hydraulic Engineering
Journal Volume
33
Journal Issue
5
Pages
389-398
Date Issued
2021
Author(s)
Abstract
In recent years, Building Information Modeling (BIM) has been widely used in the construction industry. The increased labor cost of executing related BIM uses also become an important issue when BIM is adopted into building projects. At present, the BIM labor cost estimation is mainly based on simple linear regression such as the percentage of the total construction cost or multiplying the total floor area by a coefficient. In order to evaluate whether machine learning technologies can more accurately estimate BIM labor costs, this research adopts the timesheets data of BIM tasks recorded in 21 projects executed by an engineering company in Taiwan to build two machine learning models, which are based on Random Forest (RF) and Support Vector Machine (SVM) respectively. The research results show that, based on leave-one-out cross-validation (LOOCV), by comparing the mean absolute error (MAE) and mean square error (MSE) of the two models, the RF model which the MSE is 8.693 and the MAE is 2.307 performs better than others for predicting the BIM labor cost on building structural BIM model. However, for the production of construction working drawing from the BIM model, the best model is the linear regression model based on effective floor area which its MSE is 2.186 and MAE is 1.118. The performance of both RF and SVM models have no significant advantage over the commonly used linear regression models. ? 2021, Chinese Institute of Civil and Hydraulic Engineering. All right reserved.
Subjects
Building information modeling
Labor cost prediction
Random forest
Support vector machine
Architectural design
Construction industry
Cost benefit analysis
Cost estimating
Decision trees
Employment
Floors
Information theory
Mean square error
Regression analysis
Support vector machines
Building Information Modelling
Cost prediction
Floor areas
Labor costs
Linear regression modelling
Mean absolute error
Means square errors
Random forests
Support vectors machine
Wages
Type
journal article