Computational fluid dynamics (CFD) is being used in various research fields on the building environment. Target space of the CFD model is divided into a finite number of grids for numerical analysis. Therefore, an optimal grid design is required to obtain accurate results. The grid independence test is generally performed to design an optimal grid. However, given that there is no standardized procedure for gird independence test, most depend on the researcher’s experience and knowledge. In the conventional method, the subjective judgment of the researcher affected the selection of the grid conditions and the criteria for the optimal grid. It can lead to a decrease in the reliability of the simulation results by poor grid design. This study proposed a grid independence test method that applies the grid resolution to improve the conventional method. The grid resolution was calculated by applying the characteristic length. CV(RMSE) and R² were applied as the criteria for optimal grid. A case study was conducted to evaluate the adequacy of the proposed method. As the characteristic length increased, the optimal grid resolution increased. In particular, for a characteristic length of 0.7 or more, the optimal grid resolution was evaluated as 24. The grid convergence index (GCI) was calculated to verify the suitability of the proposed method. As a result, all of the optimal grid resolution derived from the proposed method was evaluated as the optimal condition.

Concrete structural component detection in color images is a key pre-process in various applications such as construction progress measurement, structural health monitoring, and three-dimensional as-built modeling. The goal of this research was to identify an automated color model–based concrete detection method that (by using a machine learning algorithm) can detect concrete structural components in color images with a high level of accuracy. A data set consisting of more than 87 million pixels was generated from 108 images of concrete surfaces with a variety of surfaces. Transformations from the RGB color space to non-RGB color spaces were performed to increase separability between concrete and background classes and to achieve robustness to changes in illumination. To find the optimal combination of color space and machine learning algorithm, the performance of three machine learning algorithms (e.g., a Gaussian mixture model, an artificial neural network model, and a support vector machine model) in two non-RGB color spaces (e.g., HSI and normalized RGB) was comparatively analyzed. The comparison showed that the combination of the support vector machine algorithm and the HSI color space is superior in detecting concrete structural components in color images, compared with the other five algorithm–color space combinations. Performance was validated by experiments run on various images of actual construction-site scenes.