This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.

The construction industry is prone to major safety hazards owing to the complex and onsite nature of construction projects. Hence, construction researchers have been pursuing concepts, methods, and tools using advancing technologies to improve construction safety management. Subsequently, the rapid digitization of construction work practices is providing opportunities to improve construction safety. Research has revealed that construction safety management practices can benefit from the applications of virtual design construction technologies (VDC), such as building information modeling (BIM), virtual reality (VR), augmented reality (AR), geographic information systems (GIS), and gaming technology. This study comprehensively reviews 191 research articles, published between 2010 and 2019, focusing on providing an overview of the implementation and application of VDC technologies for improving construction safety. The objective of this review is to critically collect and analyze applications of VDC technologies and present a holistic view of the features and functions of VDC technologies that can impact jobsite safety improvement in the construction industry. The review showed that VDC technologies can substantially improve construction safety. Emerging digital technologies, such as BIM, VR, AR, GIS, and gaming technologies can transform the traditional document-oriented safety procedures into digitalized safety practices allowing safety managers to visualize and analysis construction sites virtually to devise proactive safety measures and effective safety trainings. This study also highlights challenges such as research gaps regarding these digital tools that are currently impeding their widespread use in construction safety.

The use of deep learning (DL) in civil inspection, especially in crack detection, has increased over the past years to ensure long-term structural safety and integrity. To achieve a better understanding of the research work on crack detection using DL approaches, this paper aims to provide a bibliometric analysis and review of the current literature on DL-based crack detection published between 2010 and 2022. The search from Web of Science (WoS) and Scopus, two widely accepted bibliographic databases, resulted in 165 articles published in top journals and conferences, showing the rapid increase in publications in this area since 2018. The evolution and state-of-the-art approaches to crack detection using deep learning are reviewed and analyzed based on datasets, network architecture, domain, and performance of each study. Overall, this review article stands as a reference for researchers working in the field of crack detection using deep learning techniques to achieve optimal precision and computational efficiency performance in light of electing the most effective combination of dataset characteristics and network architecture for each domain. Finally, the challenges, gaps, and future directions are provided to researchers to explore various solutions pertaining to (a) automatic recognition of crack type and severity, (b) dataset availability and suitability, (c) efficient data preprocessing techniques, (d) automatic labeling approaches for crack detection, (e) parameter tuning and optimization, (f) using 3D images and data fusion, (g) real-time crack detection, and (h) increasing segmentation accuracy at the pixel level.

The term “smart lab” refers to a system that provides a novel and flexible approach to automating and connecting current laboratory processes. In education, laboratory safety is an essential component of undergraduate laboratory classes. The institution provides formal training for the students working in the labs that involve potential exposure to a wide range of hazards, including chemical, biological, and physical agents. During the laboratory safety lessons, the instructor explains the lab safety protocols and the use of personal protective equipment (PPE) to prevent unwanted accidents. However, it is not always guaranteed that students follow safety procedures throughout all lab sessions. Currently, the lab supervisors monitor the use of PPE, which is time consuming, laborious, and impossible to see each student. Consequently, students may unintentionally commit unrecognizable unsafe acts, which can lead to unwanted situations. Therefore, the aim of the research article was to propose a real-time smart vision-based lab-safety monitoring system to verify the PPE compliance of students, i.e., whether the student is wearing a mask, gloves, lab coat, and goggles, from image/video in real time. The YOLOv5 (YOLOv5l, YOLOv5m, YOLOv5n, YOLOv5s, and YOLOv5x) and YOLOv7 models were trained using a self-created novel dataset named SLS (Students Lab Safety). The dataset comprises four classes, namely, gloves, helmets, masks, and goggles, and 481 images, having a resolution of 835 × 1000, acquired from various research laboratories of the United Arab Emirates University. The performance of the different YOLOv5 and YOLOv7 versions is compared based on instances’ size using evaluation metrics such as precision, F1 score, recall, and mAP (mean average precision). The experimental results demonstrated that all the models showed promising performance in detecting PPE in educational labs. The YOLOv5n approach achieved the highest mAP of 77.40% for small and large instances, followed by the YOLOv5m model having a mAP of 75.30%. A report detailing each student’s PPE compliance in the lab can be prepared based on data collected in real time and stored in the proposed system. Overall, the proposed approach can be utilized to make laboratories smarter by enhancing the efficacy of safety in research settings; this, in turn, will aid the students in establishing a health and safety culture among students.

Defects can have a significant impact on construction performance. A defect is not usually an outcome of a single cause, but rather occurs when multiple interrelated causes combine, forming a defect’s pathway. Because defects may occur through numerous pathways, the risks of causes can vary in terms of the number (i.e., frequency) of pathways that they take part in and the impact (i.e., magnitude) of the contribution they provide for the formation of these pathways. Several studies have strived to identify generic defect causes; however, research on the analysis of the risk of causes in order to develop further defect management efforts is lacking. To address this deficiency, this study aims to develop a methodology to identify and quantify the risk of defect pathways. Specifically, the study uses a fault-tree approach to construct the taxonomy of the defect causes and uses risk importance measures to assess the identified defect causes in terms of frequency and magnitude. Applying the developed m...