Big data in construction: current applications and future opportunities

Big data have become an integral part of various research fields due to the rapid advancements in the digital technologies available for dealing with data. The construction industry is no exception and has seen a spike in the data being generated due to the introduction of various digital disruptive technologies. However, despite the availability of data and the introduction of such technologies, the construction industry is lagging in harnessing big data. This paper critically explores literature published since 2010 to identify the data trends and how the construction industry can benefit from big data. The presence of tools such as computer-aided drawing (CAD) and building information modelling (BIM) provide a great opportunity for researchers in the construction industry to further improve how infrastructure can be developed, monitored, or improved in the future. The gaps in the existing research data have been explored and a detailed analysis was carried out to identify the different ways in which big data analysis and storage work in relevance to the construction industry. Big data engineering (BDE) and statistics are among the most crucial steps for integrating big data technology in construction. The results of this study suggest that while the existing research studies have set the stage for improving big data research, the integration of the associated digital technologies into the construction industry is not very clear. Among the future opportunities, big data research into construction safety, site management, heritage conservation, and project waste minimization and quality improvements are key areas

Civil infrastructure damage and corrosion detection: an application of machine learning

Automatic detection of corrosion and associated damages to civil infrastructures such as bridges, buildings, and roads, from aerial images captured by an Unmanned Aerial Vehicle (UAV), helps one to overcome the challenges and shortcomings (objectivity and reliability) associated with the manual inspection methods. Deep learning methods have been widely reported in the literature for civil infrastructure corrosion detection. Among them, convolutional neural networks (CNNs) display promising applicability for the automatic detection of image features less affected by image noises. Therefore, in the current study, we propose a modified version of deep hierarchical CNN architecture, based on 16 convolution layers and cycle generative adversarial network (CycleGAN), to predict pixel-wise segmentation in an end-to-end manner using the images of Bolte Bridge and sky rail areas in Victoria (Melbourne). The convolutedly designed model network proposed in the study is based on learning and aggregation of multi-scale and multilevel features while moving from the low convolutional layers to the high-level layers, thus reducing the consistency loss in images due to the inclusion of CycleGAN. The standard approaches only use the last convolutional layer, but our proposed architecture differs from these approaches and uses multiple layers. Moreover, we have used guided filtering and Conditional Random Fields (CRFs) methods to refine the prediction results. Additionally, the effectiveness of the proposed architecture was assessed using benchmarking data of 600 images of civil infrastructure. Overall, the results show that the deep hierarchical CNN architecture based on 16 convolution layers produced advanced performances when evaluated for different methods, including the baseline, PSPNet, DeepLab, and SegNet. Overall, the extended method displayed the Global Accuracy (GA); Class Average Accuracy (CAC); mean Intersection Of the Union (IOU); Precision (P); Recall (R); and F-score values of 0.989, 0.931, 0.878, 0.849, 0.818 and 0.833, respectively

Segmentation of Liver Tumor in CT Scan Using ResU-Net

Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver diseases. Although many automated techniques have been developed for liver and tumor segmentation; however, segmentation of the liver is still challenging due to the fuzzy & complex background of the liver position with other organs. As a result, creating a considerable automated liver and tumour division from CT scans is critical for identifying liver cancer. In this article, deeply dense-network ResU-Net architecture is implemented on CT scan using the 3D-IRCADb01 dataset. An essential feature of ResU-Net is the residual block and U-Net architecture, which extract additional information from the input data compared to the traditional U-Net network. Before being fed to the deep neural network, image pre-processing techniques are applied, including data augmentation, Hounsfield windowing unit, and histogram equalization. The ResU-Net network performance is evaluated using the dice similarity coefficient (DSC) metric. The ResU-Net system with residual connections outperformed state-of-the-art approaches for liver tumour identification, with a DSC value of 0.97% for organ recognition and 0.83% for segmentation methods

Predilation Ballooning in High Thrombus Laden STEMIs: An Independent Predictor of Slow Flow/No-Reflow in Patients Undergoing Emergent Percutaneous Coronary Revascularization

Background. Distal embolization due to microthrombus fragments formed during predilation ballooning is considered one of the possible mechanisms of slow flow/no-reflow (SF/NR). Therefore, this study aimed to compare the incidence of intraprocedure SF/NR during the primary percutaneous coronary intervention (PCI) in patients with high thrombus burden (≥4 grade) with and without predilation ballooning for culprit lesion preparation. Methodology. This prospective descriptive cross-sectional study included patients with a high thrombus burden (≥4 grades) who underwent primary PCI. Propensity-matched cohorts of patients with and without predilation ballooning in a 1 : 1 ratio were compared for the incidence of intraprocedure SF/NR. Results. A total of 765 patients with high thrombus burden undergoing primary PCI were included in this study. The mean age was 55.75 ± 11.54 years, and 78.6% (601) were males. Predilation ballooning was conducted in 346 (45.2%) patients. The incidence of intraprocedure SF/NR was significantly higher (41.3% vs. 27.4%; p<0.001) in patients with predilation ballooning than in those without preballooning, respectively. The incidence of intraprocedure SF/NR also remained significantly higher for the predilation ballooning cohort with an incidence rate of 41.3% as against 30.1% (p=0.002) for the propensity-matched cohort of patients without predilation ballooning with a relative risk of 1.64 (95% CI: 1.20 to 2.24). Moreover, the in-hospital mortality rate remained higher but insignificant, among patients with and without predilation ballooning (8.1% vs. 4.9%; p=0.090). Conclusion. In conclusion, predilation ballooning can be associated with an increased risk of incidence of intraprocedure SF/NR during primary PCI in patients with high thrombus burden