Translate 
Abstract
The primary objective of this study is to evaluate the impact of smart sensor networks on geotechnical data management, specifically enhancing accuracy, real-time monitoring, safety, and reliability. To achieve this, data was collected through a survey of 380 geotechnical professionals in Saudi Arabia, with 106 valid responses analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Principal Component Analysis (PCA) and Factor Analysis (FA) were employed to identify the key variables and underlying relationships among them. The findings demonstrate that smart sensor networks significantly improve the accuracy of geotechnical data (path coefficient = 0.662), real-time monitoring and early warning systems (path coefficient = 0.701), safety and risk management (path coefficient = 0.761), and data reliability (path coefficient = 0.410). This study introduces a novel framework integrating advanced statistical methods with smart sensor networks, offering a practical approach to optimizing geotechnical operations. The research highlights the importance of advanced data analytics in enhancing the full potential of smart sensors, presenting an innovative solution for improving decision-making and risk management in geotechnical engineering. These findings provide a significant contribution to sustainable and effective geotechnical practices. Doi: 10.28991/CEJ-2025-011-01-020 Full Text: PDF
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call