Mame Diarra Bousso Diouf

This paper investigates integrating and comparing digital technology in the architectural, engineering, and construction (AEC) industry on the world's six continents, concentrating on the adoption of designs, points of interest, and suggestions for AEC instruction. The study draws insights from current research and industry reports to underline the five most recent popular digital technologies—building Information Modeling (BIM), 3D Printing, the Internet of Things (IoT), Digital twins, and GIS—and their significance and the importance of aligning construction education with industry innovations. The subject utilizes an online survey, exhaustive online information search (using search engines), and choices of journals for the investigation. To begin with, the five biggest economies nations of each continent, but Antarctica was partially utilized for comparison in this subjective research to complete the seven continents of the world. The result appears that North America (US and Canada) and Europe (UK, France, and Germany) are the driving pioneers and early adopters of digital technology in architecture, engineering, and construction. Asia (China, Seoul) The AEC market is adopting this digital technology spontaneously. Oceania (except Australia) is behind Asia in the adoption rate; South America and Africa are the late adopters of this digital technology in the industry.

The integration of cutting-edge remote sensing technologies, biophysical principles, and advanced spatial statistics enables innovative landscape analysis across various spatial and temporal scales. Traditional approaches relied on classification methods and indices derived from multi-spectral imagery to assess landscape degradation. However, modern techniques can extract

Groundwater is vital to industry, agriculture, and drinking water production. A growing amount of groundwater needs to be managed effectively because of the effects of climate change and growing demand. Conventional methods frequently prove inadequate for managing groundwater and tackling these issues. This study investigates how to use machine learning, Internet of Things (IoT) technologies, and civil engineering to create a more reliable and effective groundwater management strategy and Infrastructure in our environments. Real-time monitoring capabilities offered by IoT technology allow for ongoing data collection on groundwater levels, quality, and usage. Machine learning algorithms can use this data to forecast future patterns and anomalies, providing an initiative-taking groundwater management tool. Civil engineering solutions like artificial recharge and sophisticated irrigation systems are crucial for sustainable usage and replenishment. This paper thoroughly analyzes current developments in various domains and suggests a synergistic framework to improve groundwater management by fusing machine learning, IoT, and civil engineering. According to our research, integrating these technologies can maximize groundwater resource utilization, raise aquifer sustainability, and increase the accuracy of groundwater monitoring and forecasting. The suggested framework offers a comprehensive and innovative technological solution to overcome the shortcomings of current groundwater management techniques. Future research should concentrate on improving integrated systems and investigating their applications across various geographical and climatic contexts to ensure the sustainable management of groundwater resources globally.