Advancements in nano-enhanced steel structures for earthquake resilience: Integrating metallic elements, AI, and sensor technology for engineering disasters mitigation in steel buildings

Xiaoping Zou , Gongxing Yan , Khidhair Jasim Mohammed , Meldi Suhatril , Mohamed Amine Khadimallah , Riadh Marzouki , Hamid Assilzadeh , José Escorcia-Gutierrez

Abstract

This study develops Titanium (Ti) and Magnesium (Mg)-based nano-alloys to enhance the earthquake resilience of steel structures using machine learning (SVM) and sensor technology. Embedding Ti and Mg into steel at the nanoscale creates a lightweight, durable, and flexible material capable of withstanding seismic forces. Ti enhances tensile strength and flexibility, while Mg reduces weight, lowering seismic loads on buildings. The performance of these nano-alloys was assessed through shake table tests, cyclic load testing, and dynamic response testing, showing that nano-alloy-enhanced steel structures experienced 60% less displacement and 40% lower acceleration than traditional steel, demonstrating superior energy absorption and stress distribution. Fatigue tests revealed that the nano-alloy could endure 20,000 loading cycles, outperforming the 8,000 cycles of conventional steel. Integrated sensor technology, including strain gauges and accelerometers, provided real-time stress and deformation data, confirming the material's effectiveness in stress distribution and vibration damping. The SVM model optimized alloy composition, achieving 94% prediction accuracy in assessing seismic performance, highlighting the nano-alloys' durability and resilience. This study suggests that Ti and Mg nano-alloys could greatly improve earthquake-resistant construction.

Key Words

earthquake-resilient steel structures; Machine Learning (SVM); predictive maintenance and disaster mitigation; seismic energy dissipation; sensor technology; titanium-magnesium nano-alloys

Address

Xiaoping Zou — Sichuan Jinghengxin Construction Engineering Testing Co., LtdLu, zhou 646000, China
Gongxing Yan — 1)School of Intelligent Construction, Luzhou vocational and technical college, Luzhou 646000, China 2)Luzhou Key Laboratory of Intelligent Construction and Low-carbon Technology, Luzhou 646000, China
Khidhair Jasim Mohammed — Air Conditioning and Refrigeration Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University, 51001 Hilla, Babylon, Iraq
Meldi Suhatril — Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
Mohamed Amine Khadimallah — Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
Riadh Marzouki — Department of Chemistry, College of Science, King Khalid University, P.O. Box 9004, 61413 Abha, Saudi Arabia
Hamid Assilzadeh — 1)Faculty of Architecture and Urbanism, UTE University, Calle Rumipamba S/N and Bourgeois, Quito, Ecuador 2)Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam 3)School of Engineering & Technology, Duy Tan University, Da Nang, Viet Nam 4)Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai 600077, India 5)University of Calgary, Schulich School of. Engineering, Department of Geomatics. Engineering. Calgary, Alberta, Canada
José Escorcia-Gutierrez — Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia

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