Authors:
P. Prasanth Anand , G. Jayanth, K. Sankara Rao, P. Deepika, Muhammad Faisal, Mohamad Mokdad
Addresses:
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India, prasanthanand2002@gmail.com, gg4307@srmist.edu.in, shankarlucky141@gmail.com, deepikap2@srmist.edu.in. Department of Computer Science, STMIK Profesional Makassar, Makassar, Indonesia, muhfaisal@stmikprofesional.ac.id. Department of International Relations, Intelligentsia Center for Research and Studies, Beirut, Lebanon, mokdad.mohamad@edu.ceds.fr.
This paper investigates the integration of progressed inconsistency discovery frameworks in geotechnical building, utilizing advanced machine learning methods to promote chance evaluation and decision-making forms. Through categorization models and dimensionality decrease estimates, the research indicates a rethink of the industry’s strategy for identifying and managing basic framework research risks. The categorization is shown with unusual execution measurements, including precision, exactness, review, and F1-score, after careful information curation and exhibition preparation. These tests confirm the model’s ability to precisely identify geotechnical framework discrepancies, equipping engineers to mitigate risks and maintain framework stability. The paper’s focus on a well-balanced dataset ensures an adequate representation of Tall Hazard and Moo Hazard scenarios, boosting the model’s versatility and reliability across various natural settings and real-world scenarios. Integrating advanced peculiarity location frameworks sends important safety and strength recommendations, engaging engineers to make informed decisions, execute focused support procedures, and optimize asset assignment, improving framework performance and reducing downtime. This paper advances irregularity location strategies, improving foundation security, maintainability, and social well-being.
Keywords: Hybrid Machine Learning; Anomalous Multivariate Time-Series; Geotechnical Engineerings; Improving Foundation Security; Maintainability and Social Well-Being; Tall Hazard and Moo Hazard Scenarios.
Received on: 24/08/2023, Revised on: 15/10/2023, Accepted on: 24/11/2023, Published on: 05/03/2024
AVE Trends in Intelligent Computing Systems, 2024 Vol. 1 No. 1, Pages: 32-41