Advancing Engineering Geophysics through AI-Driven Field Data Acquisition: Developing Real-Time, High-Resolution Onsite Soil Assessment Methods to Prevent Telecommunications and Energy Tower Foundation Failure
Arcade Integrated Services Limited, Technical Department, Ikeja, Lagos State Nigeria.
Research Article
World Journal of Advanced Research and Reviews, 2022, 15(02), 891-906
Publication history:
Received on 22 July 2022; revised on 25 August 2022; accepted on 28 August 2022
Abstract:
Introduction: Structural and geotechnical engineering are rapidly evolving fields driven by the integration of Artificial Intelligence (AI) and advanced digital technologies. In structural engineering, AI enhances multiple domains including design optimization, structural analysis, material selection, seismic design, smart structure monitoring, project management, and education. The rapid expansion of telecommunication and energy infrastructure across diverse geological environments has heightened the need for reliable and sustainable foundation systems. Despite technological progress in geotechnical engineering, foundation failures in tower installations continue to occur, largely due to inadequate subsurface characterization and the limitations of conventional soil testing methods. Traditional techniques such as the Cone Penetration Test (CPT), Standard Penetration Test (SPT), and laboratory analyses are invasive, time-consuming, and restricted in spatial coverage, rendering them unsuitable for real-time decision-making during field operations.
Material and Method: To address these challenges, this study proposes the development of an AI-driven geophysical field data acquisition system designed for real-time, high-resolution onsite soil assessment. The system integrates Electrical Resistivity Tomography (ERT), Ground Penetrating Radar (GPR), and Seismic Refraction methods with advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNNs) and Random Forests, to enhance data interpretation accuracy and reduce human subjectivity. The proposed framework operates across three functional layers data acquisition, AI analytics, and decision support enabling autonomous noise filtering, pattern recognition, and predictive modeling of soil stability parameters.
Result: This comprehensive bibliometric review examines global advancements, challenges, and trends in post-disaster building damage assessment and reconnaissance methods, emphasizing the growing role of Artificial Intelligence (AI) and emerging technologies. Analysis of publications from major databases highlights the increasing global collaboration and interdisciplinary integration that are driving innovation in disaster research. Such cooperation enhances knowledge sharing, strengthens regional resilience, and improves the global capacity to respond to and recover from disasters.
Discussion: The study underscores the transformative impact of remote sensing technologies including satellite imagery, UAVs, LiDAR, and Synthetic Aperture Radar (SAR) in delivering rapid, high-resolution damage assessments. However, challenges persist in data fusion, real-time processing, and the harmonization of diverse data sources. Machine learning and deep learning models, particularly Convolutional Neural Networks (CNNs) and transfer learning, have significantly improved the accuracy and speed of damage detection and prediction.
Conclusion: In parallel, AI’s expanding role in structural and geotechnical engineering through design optimization, seismic assessment, and risk prediction demonstrates its potential to enhance infrastructure resilience. The findings also reveal emerging trends in earthen site protection, where digital and AI-assisted tools are increasingly applied for sustainable conservation.
Keywords:
AI-Driven Field Data Acquisition; Real-time Geophysical Soil Assessment Methods
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