Advancements in Structural Health Monitoring through Neural Networks and Data Mining
- Xuan Ying
This article explores the synergy of neural networks and data mining in the realm of Structural Health Monitoring (SHM). The integration of advanced artificial intelligence techniques, such as neural networks, with data mining methodologies promises transformative insights into the condition and performance of structures. The study navigates through a comprehensive literature review, delineating the evolution of SHM, the diverse applications of data mining in structural analysis, and the potential of neural networks in predictive modeling. The research methodology encompasses the fusion of neural network algorithms with data mining techniques applied to real-world structural datasets. Results showcase the efficacy of this integrative approach in enhancing the accuracy of structural health assessments. The article concludes by reflecting on the implications, challenges, and the promising future of SHM through the amalgamation of neural networks and data mining.