An overview of a leader journal in the field of transport: a bibliometric analysis of “Computer-Aided Civil and Infrastructure Engineering” from 2000 to 2019

Computer-Aided Civil And Infrastructure Engineering (CACAIE) is an international journal, and the first documents was published from 1980. This article is to make an overview based on bibliometric analysis to celebrate the 35th anniversary of CACAIE till 2019. At present, 1045 publications can be indexed in the Clarivate Analytics Web of Science (WoS) from 2000 to 2019, and we explore the characteristics of these publications by bibliometric methods and tools (VOSviewer and CiteSpace). First, the fundamental information of publications is given with the help of some bibliometric indicators, such as the number of citations and h-index. According to high-citing and high-cited publications, we analyse that who pays closer attention to the journal and what the journal most focuses on considering sources, countries/regions, institutions and authors. After that, the influential countries/regions and references are presented, and collaboration networks are given to show the relationship among countries/regions, institutions and authors. In order to understand the development trends and hot topics, co-occurrence analysis and timeline view of keywords are made to be visual. In addition, publications in four fields – Construction & Building Technology; Engineering, Civil; Transportation Science & Technology; Computer Science, Interdisciplinary Applications – that CACAIE refers are summarized, and further discussions are made for the journal and scholars. Finally, some main findings are concluded according to all analysis. This article provides a certain reference for scholars and journals to further research and promote the scientific-technological progress. First published online 6 January 202

Concrete Surface Crack Detection with Convolutional-based Deep Learning Models

Effective crack detection is pivotal for the structural health monitoring and inspection of buildings. This task presents a formidable challenge to computer vision techniques due to the inherently subtle nature of cracks, which often exhibit low-level features that can be easily confounded with background textures, foreign objects, or irregularities in construction. Furthermore, the presence of issues like non-uniform lighting and construction irregularities poses significant hurdles for autonomous crack detection during building inspection and monitoring. Convolutional neural networks (CNNs) have emerged as a promising framework for crack detection, offering high levels of accuracy and precision. Additionally, the ability to adapt pre-trained networks through transfer learning provides a valuable tool for users, eliminating the need for an in-depth understanding of algorithm intricacies. Nevertheless, it is imperative to acknowledge the limitations and considerations when deploying CNNs, particularly in contexts where the outcomes carry immense significance, such as crack detection in buildings. In this paper, our approach to surface crack detection involves the utilization of various deep-learning models. Specifically, we employ fine-tuning techniques on pre-trained deep learning architectures: VGG19, ResNet50, Inception V3, and EfficientNetV2. These models are chosen for their established performance and versatility in image analysis tasks. We compare deep learning models using precision, recall, and F1 scores.Comment: 11 pages, 3 figures, Journal pape

Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network

Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images. Our approach requires minimal feature engineering compared to other machine learning techniques. We propose a U-Net-based network architecture in which we replace the encoder with a pretrained ResNet-34 neural network. We use a "one-cycle" training schedule based on cyclical learning rates to speed up the convergence. Our method achieves an F1 score of 96% on the CFD dataset and 73% on the Crack500 dataset, outperforming other algorithms tested on these datasets. We perform ablation studies on various techniques that helped us get marginal performance boosts, i.e., the addition of spatial and channel squeeze and excitation (SCSE) modules, training with gradually increasing image sizes, and training various neural network layers with different learning rates.Comment: Accepted for publication in IEEE Acces