Abstract. New transport infrastructure construction can stimulate the growth of economy as well as improving the public citizen welfare. However, with the rising number of mega infrastructure projects, the low project performance, such as project delay and cost escalation, are challenging the traditional Architecture, Engineering, and Construction (AEC) industry. Traditional Construction progress monitoring methods rely on manual data collecting and paperwork reporting which can be labor-intensive, time-consuming and error-prone. Therefore, it is necessary for the involved stakeholders to introduce advanced technologies which facilitates assessing the construction performance automatically and ensures the projects to be delivered on time. The application of building information modeling (BIM) provides involved parties an accurate understandable single source of truth that can improve the interoperability of project information. Nevertheless, current ‘Scan-to-BIM’ workflow cannot support the demand for real-time data analysis and status reporting. This paper presents a semi-automatic construction progress monitoring framework that evaluating the project performance of the infrastructure in real-time. It introduces Hausdorff distance which transmits the 3-D geometrical information contained by as-built point cloud to virtual point cloud directly, to avoid the drawbacks of space partitioning algorithms. The Poisson surface reconstruction utilizing volume as criterion to improve the robustness of progress determination. In addition, the application of 2D polygon fitting provides a potentially feasible method to identify the installation of pre-cast components of the bridge construction. The results indicate that the proposed framework can effectively monitor the geometric increment of road bridge construction project.

Aging infrastructure has drawn increased attention globally, as its collapse would be destructive economically and socially. Precise quantification of minor defects is essential for identifying issues before structural failure occurs. Most studies measured the dimension of defects at image level, ignoring the third-dimensional information available from close-range photogrammetry. This paper aims to develop an efficient approach to accurately detecting and quantifying minor defects on complicated infrastructures. Pixel sizes of inspection images are estimated using spatial information generated from three-dimensional (3D) point cloud reconstruction. The key contribution of this research is to obtain the actual pixel size within the grided small sections by relating spatial information. To automate the process, deep learning technology is applied to detect and highlight the cracked area at the pixel level. The adopted convolutional neural network (CNN) achieves an F1 score of 0.613 for minor crack extraction. After that, the actual crack dimension can be derived by multiplying the pixel number with the pixel size. Compared with the traditional approach, defects distributed on a complex structure can be estimated with the proposed approach. A pilot case study was conducted on a concrete footpath with cracks distributed on a selected 1500 mm × 1500 mm concrete road section. Overall, 10 out of 88 images are selected for validation; average errors ranging from 0.26 mm to 0.71 mm were achieved for minor cracks under 5 mm, which demonstrates a promising result of the proposed study.

Abstract. The increasing number of aging infrastructures has drawn attention among the industry as the results caused by critical infrastructure failure could be destructive. It is essential to monitor the infrastructure assets and provide timely maintenance. However, one of the crucial problems is that the budget allocated to the maintenance stage is much less than that for the designing and construction stages. The cost of labor, equipment, and vehicles are significant. Therefore, it is impossible to perform a thorough inspection by human inspectors over each asset. A more efficient method will be needed to solve this problem. This paper aims to provide an automatic approach to detecting and measuring the dimensions of minor cracks that appear on concrete structures with a noisy background. This research also investigates the relationship between image pixel size, accuracy, detection rate of cracks, and shooting distance of images. The proposed method will be able to reduce the cost and increase accuracy. A case study was performed on a concrete sewer with cracks distributed on the surface in Sydney, New South Wales, Australia.