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.

Very few construction companies and consulting firms in Australia measure their costs of quality. Consequently, it is difficult for them to prove that systems for preventing quality failures are cost-effective. Although the direct costs of a quality system can be quantified with some accuracy (salaries, costs of documentation, audits, etc.), the corresponding benefits are far more difficult to assess. Indeed quality failures have become an endemic feature of the procurement process in construction and invariably lead to time and cost overruns in projects. Thus, in order to improve the performance of projects it is necessary to identify the causes and costs rework. The research presented in this paper quantifies the causes, magnitude and costs of rework experienced in two construction projects that were procured using different contractual arrangements. The causes and costs of rework projects are analysed and discussed. The findings reveal that the cost of rework for the case study projects was 3.15% and 2.40% of their project contract value. Changes initiated by the client and end-user together with errors and omissions in contract documentation were found to be the primary causes of rework. It is recommended that construction companies and consultant firms (particularly design consultants) implement quality management practices as well as place emphasis on coordinating project documentation during the design development process so that the amount of rework in projects can be reduced or even eliminated.