Pavement condition assessment is an essential piece of modern pavement management systems as rehabilitation strategies are planned based upon its outcomes. For proper evaluation of existing pavements, they must be continuously and effectively monitored using practical means. Conventionally, truck-based pavement monitoring systems have been in-use in assessing the remaining life of in-service pavements. Although such systems produce accurate results, their use can be expensive and data processing can be time consuming, which make them infeasible considering the demand for quick pavement evaluation. To overcome such problems, Unmanned Aerial Vehicles (UAVs) can be used as an alternative as they are relatively cheaper and easier-to-use. In this study, we propose a UAV based pavement crack identification system for monitoring rigid pavements' existing conditions. The system consists of recently introduced image processing algorithms used together with conventional machine learning techniques, both of which are used to perform detection of cracks on rigid pavements' surface and their classification. Through image processing, the distinct features of labelled crack bodies are first obtained from the UAV based images and then used for training of a Support Vector Machine (SVM) model. The performance of the developed SVM model was assessed with a field study performed along a rigid pavement exposed to low traffic and serious temperature changes. Available cracks were classified using the UAV based system and obtained results indicate it ensures a good alternative solution for pavement monitoring applications.

The use of biological means for ground improvement have become popular, which generally works through the process called microbially-induced calcium carbonate precipitation (MICP). Many studies indicate successful application of MICP based improvement with multiple bacteria and on several soils. Given the proven performance of MICP, this study aims to examine the MICP process by comparing the calcium carbonate precipitation ability of widely studied bacteria, i.e., Sporosarcina pasteurii and relatively under-recognized bacteria, i.e., Bacillus licheniformis to outline the formation success. For this purpose, two different sands were tested for observing precipitation behavior using a series of syringe tests. Furthermore, the effect of concentration and inclusion of calcium chloride for nutrition of bacteria, saturation with water, and hybrid use of two bacteria were investigated in some tests for diversification. X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive x-ray spectroscopy (EDS) were used for the interpretation of results. Results indicated that Sporosarcina pasteurii had performed superior over Bacillus licheniformis when achieving calcium carbonate precipitation in tests for both sands. In addition, many intriguing SEM images contributed to the literature of MICP monitoring, highlighting the effects of the variables investigated.

This paper presents a comprehensive overview of the rapid damage assessment and reconnaissance efforts following the devastating earthquakes on February 6, 2023, in Türkiye. It specifically focuses on implementing the SiteEye Disaster Plugin, an additional component of SiteEye software developed by i4 Company engineers and Middle East Technical University researchers. This tool played a critical role in managing and analyzing a massive dataset comprising over 28,000 images and videos. The research highlights the plugin’s innovative features, such as offline data collection, georeferenced-based layering, and an integrated damage classification system, significantly improving earthquake impact assessments’ accuracy and efficiency. It also underscores the importance of interdisciplinary collaboration involving national and international teams and the role of open data in disaster management. The findings demonstrate how digital technologies can transform the field of disaster response, offering new approaches for rapid assessment and effective management in the aftermath of seismic events. This research contributes valuable insights into enhancing disaster preparedness and response strategies, particularly in earthquake-prone areas.