Comparative review of intelligent structural safety in building seismic risk mitigation utilizing an integrated artificial intelligence controller - Normaisharah Mamat; Rawad Abdulghafor; Sherzod Turaev; Fitri Yakub (Discover Applied Sciences)
Abstract Seismic events provide significant hazards to the safety and structural integrity of building structures, requiring efficient mitigation techniques. Conventional approaches to mitigating earthquake risks may lack the capacity for prompt adaptation and response. The integration of artificial intelligence (AI) controllers offers an attractive way to improve building safety in earthquake-prone regions. The fundamental unpredictability and complexity of seismic forces can undermine conventional safety measures, resulting in deficiencies in effectiveness and responses. A solution that can dynamically respond and proactively mitigate hazards must be developed to solve these concerns. This review attempts to analyze the possible impact of AI controllers in substantially mitigating seismic risks for structures. This study investigates the efficacy of intelligence structural security systems in enhancing resilience and reducing damage during seismic events through the analysis of AI-driven techniques, methodology, applications, and performance metrics. A systematic analysis of the literature is performed to identify and evaluate prior research on AI controllers employed to reduce seismic risk in structures. The research highlights the influence of integrated AI controllers on control systems, examining several AI controllers, including machine learning algorithms, neural networks, and evolutionary algorithms concerning structural safety. A case study is carried out on a conventional controller, specifically the sliding mode controller (SMC), fuzzy logic controller (FLC), and radial basis function neural network nonsingular terminal sliding mode controller (RBFNN-NTSMC). The findings reveal that the RBFNN-NTSMC effectively reduces building vibrations by up to 63% compared to an uncontrolled structure, significantly outperforming the FLC and SMC, which achieved reductions of 13% and 12%, respectively. This case study illustrates how AI-driven techniques enhance structural resilience and reduce seismic vulnerability. The integration of AI controllers has the potential to enhance the safety and durability of structures by employing advanced computational methods to limit hazards, facilitate real-time response, and optimize structural performance during earthquakes.