An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment.

Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region's economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera's view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification ("presence/absence" detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating.

The rise of 3D Printing entangled with smart computer aided design during COVID-19 era.

During the current Pandemic, seven and a half million flights worldwide were canceled which disrupted the supply chain of all types of goods such as, personal protective gears, medical health devices, raw materials, food, and other essential equipments. The demand for health and medical related goods increased during this period globally, while the production using classical manufacturing techniques were effected because of the lockdowns and disruption in the transporation system. This created the need of geo scattered, small, and rapid manufacturing units along with a smart computer aided design (CAD) facility. The availability of 3D printing technologies and open source CAD design made it possible to overcome this need. In this article, we present an extensive review on the utilization of 3D printing technology in the days of pandamic. We observe that 3D printing together with smart CAD design show promise to overcome the disruption caused by the lockdown of classical manufacturing units specially for medical and testing equipment, and protective gears. We observe that there are several short communications, commentaries, correspondences, editorials and mini reviews compiled and published; however, a systematic state-of-the-art review is required to identify the significance of 3D printing, design for additive manufacturing (AM), and digital supply chain for handling emergency situations and in the post-COVID era. We present a review of various benefits of 3DP particularly in emergency situations such as a pandemic. Furthermore, some relevant iterative design and 3DP case studies are discussed systematically. Finally, this article highlights the areas that can help to control the emergency situation such as a pandemic, and critically discusses the research gaps that need further research in order to exploit the full potential of 3DP in pandemic and post-pandemic future era.