About this Research Topic
Seismic vulnerability assessment and control (SVAC) of civil structures is important for enhancing their protection in the occurrence of unexpected dynamical hazards, like earthquakes. SVAC is aimed to assess the vulnerability of the structures deeper and propose a methodology to mitigate any extreme response generated by earthquakes. Although numerous methods have so far been studied, additional apprehensions including their real usage remain to be studied. These involve obstacles as a result of numerical solutions or optimizations. In recent years, particle swarm optimization (PSO), harmony search algorithm (HAS), or genetic algorithm (GA) have been widely used for improvements when facing massive complex optimization problems of civil structures. Likewise, most recent inventions in artificial intelligence (AI), have invented practical techniques to use complicated models that were previously impossible to illustrate statistically. This Research Topic thus aims to compile research articles describing novel SVAC solutions and review papers on the latest advances in seismic vulnerability assessment and control (SVAC) of civil structures. Research manuscripts that offer novel theoretical findings along with numerical and experimental validations are welcome.
Possible topics involve but are not constrained to the following:
• Use of artificial intelligence (AI) to seismic vulnerability assessment and control (SVAC) of civil structures
• Use of optimization techniques and combining with AI to seismic vulnerability assessment and control (SVAC) of civil
• Novel algorithms and mathematical solutions to retrofitting of existing structures.
Keywords: Machine Learning, Control, Vulnerability Assessment, Seismic, Structures
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.