The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures. However, the current manual crack description method is time consuming and labor consuming. To improve the efficiency of crack inspection, advanced computer vision‐based techniques have been utilized to detect cracks automatically at image level and grid‐cell level. But existing crack detections are of (high specificity) low generality and inefficient, in terms that conventional approaches are unable to identify and measure diverse cracks concurrently at pixel level. Therefore, this research implements a novel deep learning technique named fully convolutional network (FCN) to address this problem. First, FCN is trained by feeding multiple types of cracks to semantically identify and segment pixel‐wise cracks at different scales. Then, the predicted crack segmentations are represented by single‐pixel width skeletons to quantitatively measure the morphological features of cracks, providing valuable crack indicators for assessment in practice, such as crack topology, crack length, max width, and mean width. To validate the prediction, the predicted segmentations are compared with recent advanced method for crack recognition and ground truth. For crack segmentation, the accuracy, precision, recall, and F1 score are 97.96%, 81.73%, 78.97%, and 79.95%, respectively. For crack length, the relative measurement error varies from −48.03% to 177.79%, meanwhile that ranges from −13.27% to 24.01% for crack width. The results show that FCN is feasible and sufficient for crack identification and measurement. Although the accuracy is not as high as CrackNet because of three types of errors, the prediction has been increased to pixel level and the training time has been dramatically decreased to several per cents of previous methods due to the novel end‐to‐end structure of FCN, which combines typical convolutional neural networks and deconvolutional layers.

Based on an investigation of 106 projects involving the use of building information modelling (BIM), this paper examines current BIM practices in China, and assesses how various practices alter their effectiveness. The results reveal that in current practice BIM is principally employed as a visualization tool, and how it is implemented is significantly associated with project characteristics. BIM use in the majority of the surveyed projects is seen to have positive outcomes, with the benefits of improved task effectiveness being more substantial than those related to efficiency improvement. The results also provide evidence that project characteristics significantly influence the success of BIM use; however, more substantial contributing factors to BIM effectiveness are the extent of integrated use and client/owner support. While indicating that current BIM practices involve both technological and organizational problems, the findings also provide insights into how the potential for BIM could be better exploited within the industry.

No abstract

No abstract

Virtual prototyping (VP) technology has been regarded as a cost-effective way of envisaging real circumstances that enhance effective communication of designs and ideas, without manufacturing physical samples. In the construction field, although a large number of digital technologies have been developed to visualize the innovative architectural design, few VP systems have been developed to facilitate integrated planning and visualization of construction plans of the building projects. This paper describes a virtual prototyping system, called the Construction Virtual Prototyping (CVP) system, which is developed for modeling, simulation, analysis and VP of construction processes from digital design. The CVP system allows project teams to check constructability, safety and to visualize 3D models of a facility before the commencement of construction works. The real-life case study presented in the study shows that the CVP system is effective in assessing the executability of a construction planning including site layout, temporary work design, as well as resource planning.